Exclude from dalia (455)#
.lif files misbehaving in fiji but fine in LASX#
Pamela Young
Published 2025-05-07
Licensed CC-BY-4.0
.lif files misbehaving in fiji but fine in LASX. This data opens fine in LASX but FIJI only likes some of the files. I think it was captured during a poweroutage so may have lived on a temp drive and been recovered when the power came back. LASX uses the .lifext but I don’t think FIJI does. I have included it however since it is part of the dataset output from the microscope.
Tags: Exclude From Dalia
10 frames of fluorescent particles#
Zach Marin, Maohan Su
Published 2024-12-05
Licensed CC-BY-4.0
10 frames of fluorescent particles. They don’t do much, but they are a DCIMG version 0x7 file example.
Tags: Exclude From Dalia
2020 BioImage Analysis Survey: Community experiences and needs for the future#
Nasim Jamali, Ellen T. A. Dobson, Kevin W. Eliceiri, Anne E. Carpenter, Beth A. Cimini
Published 2021
Licensed BSD-3-CLAUSE
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Publication
3D Ground Truth Annotations of Nuclei in 3D Microscopy Volumes#
Alain Chen, Liming Wu, Seth Winfree, Kenneth Dunn, Paul Salama, Edward Delp, Teresa Zulueta-Coarasa
Published 2024-12-20
Licensed CC-BY-4.0
This submission contains a set of 3D microscopy volumes of cell nuclei from different species and tissues that have been manually segmented. We also provide synthetically generated 3D microscopy volumes that can be used for training segmentation methods.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1518/
3D HL60 Cell line (synthetic data)#
David Svoboda, Michal Kozubkek, Stanislav Stejskal
Published 2009-06-01
Licensed CC-BY-3.0
One of the principal challenges in counting or segmenting nuclei is dealing with clustered nuclei. To help assess algorithms performance in this regard, this synthetic image set consists of four subsets with increasing degree of clustering. Each subset is also provided in two diferent levels of quality: high SNR and low SNR.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
3D Nuclei annotations and StarDist 3D model(s) (rat brain)#
Romain Guiet
Published 2022-06-15
Licensed CC-BY-4.0
Name: 3D Nuclei annotations and StarDist3D model(s) (rat brain)
Images: From a large tiling acquisition ( https://doi.org/10.5281/zenodo.6646128 ) individual Tile (xyz : 1024x1024x62) were downsampled and cropped (128x128x62). Four crops, from different tiles (./annotations_BIOP/images/) were manually annotated with ITK-SNAP (./annotations_BIOP/masks/)
These four images, and their corresponding masks, were cropped into four quadrants (./crops_BIOP_v1/) in order to get 16 different images (64x64x62).
Conda environment: A conda environment was created using the yml file stardist0.8_TF1.15.yml
Training : Training was performed using the jupyter notebook 1-Training_notebook.ipynb. Three different trainings (with the same random seed, same anisotropy, patch size and grid) were performed and produced three different models (./models/)
Validation images (from the random seed used) were exported to ease the visual inspection of the results(./val_rdm42/).
Validation: To save metrics in a csv file and compare predictions to the annotations the jupyter notebook 2-QC_notebook.ipynb can be used on the validation folder.
Large images: To test the model on larger images one can use Whole_ds441.tif (or Crop_ds441.tif ) These images were obtained using the plugin BigSticher on the raw data ( https://doi.org/10.5281/zenodo.6646128 ), resaved as h5 and exported the downsample by 4 version.
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3D cell shape of Drosophila Wing Disc#
Giulia Paci, Ines Fernandez Mosquera, Pablo Vicente Munuera, Yanlan Mao
Published 2023-08-14
Licensed CC0-1.0
Segmentation masks of individual cells in Drosophila wing discs
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD843-ai.html
3D light-sheet microscopy data for SELMA3D 2024 challenge - Training subset with annotations#
Ying Chen, Johannes C. Paetzold, Ali Erturk, Doris Kaltenecker, Mihail Todorov, Harsharan Singh Bhatia, Shan Zhao, Luciano Höher
Published 2024-06-05
Licensed CC-BY-4.0
This dataset is the training set with annotations for the SELMA3D challenge. The SELMA3D challenge focuses on self-supervised learning for 3D light-sheet microscopy image segmentation. Its objective is to encourage the development of self-supervised learning methods for general segmentation of various structures in 3D light-sheet microscopy images. The dataset comtains 3D image patches of different labeled biological structures in the brain, including blood vessels, c-Fos labeled brain cells involved in neural activity, cell nuclei, and Alzheimers disease plaques. Each patch includes corresponding pixel-wise annotations for the labeled structures.
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Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1196/
3D nuclei instance segmentation dataset of fluorescence microscopy volumes of C. elegans#
Fuhui Long, Hanchuan Peng, Xiao Liu, Stuart K Kim, Eugene Myers, Dagmar Kainmüller, Martin Weigert
Published 2022-02-01
Licensed CC-BY-4.0
The dataset consists of 28 confocal microscopy volumes of C. elegans worms at the L1 stage and corresponding stacks of densely annotated nuclei instance segmentation masks.
28 raw images and corresponding masks of average dimension (xyz) 1050 x 140 x 140
Pixelsize (xyz): 0.116 x 0.116 x 0.122μm
Microscope: Leica confocal microscopy, 63x oil objective
The original raw data and preliminary annotations were part of the following publication (please cite if you use the dataset): Long, F., Peng, H., Liu, X., Kim, S. K., & Myers, E. (2009). A 3D digital atlas of C. elegans and its application to single-cell analyses. Nature methods, 6(9), 667-672.
The nuclei annotation masks were further manually curated by Dagmar Kainmueller (MDC Berlin) for the following publication:
Hirsch, P., & Kainmueller, D. (2020). An auxiliary task for learning nuclei segmentation in 3d microscopy images. In Medical Imaging with Deep Learning (pp. 304-321). PMLR.
We provide the dataset already structured into the train/validation/test split as used by the above as well as the following publications:
Weigert, M., Schmidt, U., Haase, R., Sugawara, K., & Myers, G. (2020). Star-convex polyhedra for 3d object detection and segmentation in microscopy. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 3666-3673).
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
A Cloud-Optimized Storage for Interactive Access of Large Arrays#
Josh Moore, Susanne Kunis
Licensed CC-BY-4.0
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Content type: Publication, Conference Abstract
A Glimpse of the Open-Source FLIM Analysis Software Tools FLIMfit, FLUTE and napari-flim-phasor-plotter#
Anca Margineanu, Chiara Stringari, Marcelo Zoccoler, Cornelia Wetzker
Published 2024-03-27
Licensed CC-BY-4.0
The presentations introduce open-source software to read in, visualize and analyse fluorescence lifetime imaging microscopy (FLIM) raw data developed for life scientists. The slides were presented at German Bioimaging (GerBI) FLIM Workshop held February 26 to 29 2024 at the Biomedical Center of LMU München by Anca Margineanu, Chiara Stringari and Conni Wetzker.
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A call for public archives for biological image data#
Jan Ellenberg, Jason R. Swedlow, Mary Barlow, Charles E. Cook, Ugis Sarkans, Ardan Patwardhan, Alvis Brazma, Ewan Birney
Tags: Research Data Management, Exclude From Dalia
Content type: Publication
A deep learning approach to quantify auditory hair cells#
Maurizio Cortada, Loïc Sauteur, Michael Lanz, Soledad Levano, Daniel Bodmer
Published 2021-03-09
Licensed CC-BY-4.0
StarDist 2D deep learning model and training dataset.
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Content type: Data
A mihc mrxs example#
Wang
Published 2025-08-27
Licensed CC-BY-4.0
Tags: Exclude From Dalia
A study on long-term reproducibility of image analysis results on ImageJ and Fiji#
Robert Haase, Deborah Schmidt, Wayne Rasband, Curtis Rueden, Florian Jug, Pavel Tomancak, Eugene W. Myers
Tags: Imagej, Exclude From Dalia
Content type: Publication, Poster
https://figshare.com/articles/poster/I2K_Poster_Haase_V6_ImageJ_repro_pdf/7409525
ABIC - Intermediate Fiji Image Analysis Course 2024#
Rensu Petrus Theart
Licensed CC-BY-4.0
A structured beginner to intermediate-level course in image analysis using Fiji, developed for ABIC 2024. It includes a video lecture playlist, course documentation, and participant image files.
Tags: Bioimage Analysis, Image Processing, Teaching Resource, Imagej, Exclude From Dalia
Content type: Workshop, Video, Document
https://www.youtube.com/playlist?list=PL0RrV4sTNwh2S9Lb7d1TzJWPGgdw_YVnb
https://docs.google.com/document/d/1h-3oJDR7gd_y3tfgN3clPwt2O4g34QRPp_FJ4v2Q7kA/edit?usp=sharing
AI4Life teams up with Galaxy Training Network (GTN) to enhance training resources#
Caterina Fuster-Barceló
Licensed UNKNOWN
Tags: Artificial Intelligence, Workflow Engine, Bioimage Analysis, Exclude From Dalia
Content type: Documentation
Abdominal Imaging Window (AIW) for Intravital Imaging#
Michael Gerlach
Published 2024-11-15
Licensed CC-BY-4.0
This upload features a simple model for the creation (Manufacturing/Prototyping) of an abdominal imaging window (AIW) for use in mice intravital microscopy. Manufacture in titanium for chronic implantation. Measures in mm.
Tags: Exclude From Dalia
Aberrated Bead Stack#
Zach Marin
Published 2024-12-03
Licensed CC-BY-4.0
Bead stack taken on lower path of a 4Pi without deformable mirror corrections. DCIMG examples, not for other purposes.
Tags: Exclude From Dalia
Abstract - NFDI Basic Service for Data Management Plans#
Licensed CC-BY-4.0
The NFDI Basic Service DMP4NFDI supports consortia in developing and providing data management plans (DMP) services for their community.
Tags: Research Data Management, Exclude From Dalia
Content type: Document
Adding a Workflow to BIAFLOWS#
Sébastien Tosi, Volker Baecker, Benjamin Pavie
Licensed BSD-2-CLAUSE
Tags: Neubias, Bioimage Analysis, Exclude From Dalia
Content type: Slides
An annotated fluorescence image dataset for training nuclear segmentation methods#
Sabine Taschner-Mandl, Inge M. Ambros, Peter F. Ambros, Klaus Beiske, Allan Hanbury, Wolfgang Doerr, Tamara Weiss, Maria Berneder, Magdalena Ambros, Eva Bozsaky, Florian Kromp, Teresa Zulueta-Coarasa
Published 2023-03-07
Licensed CC0-1.0
Ground-truth annotated fluorescence image dataset for training nuclear segmentation methods
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Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD634-ai.html
An annotated high-content fluorescence microscopy dataset with Hoechst 33342-stained nuclei and manually labelled outlines#
Malou Arvidsson, Salma Kazemi Rashed, Sonja Aits
Published 2022-06-17
Licensed CC-BY-4.0
Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert.
The dataset contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. It is pre-split into training, development and test set, each in a zip file. The dataset should be referred to as Aitslab_bioimaging1. A brief article describing the dataset is also available (Arvidsson M, Kazemi Rashed S, Aits S. 10.1016/j.dib.2022.108769 )
Dataset description:
Fluorescence microscopy images: original .C01 files and files converted to 8-bit .png format (Grayscale)
Annotations: 24-bit .png format (RGB)
Script used to convert C01 to png images: C01_to_png.py file with python code and readme.md file with instructions to run it
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
An image-based data-driven analysis of cellular architecture in a developing tissue#
Jonas Hartmann, Mie Wong, Elisa Gallo, Darren Gilmour
Published 2022-12-13
Licensed CC-BY-4.0
3D zebrafish embryo images with single-cell segmentation and point cloud-based morphometry
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD599-ai.html
Andor Dragonfly confocal image of BPAE cells stained for actin, IMS file format#
Hoku West-Foyle
Published 2025-01-16
Licensed CC0-1.0
Tags: Exclude From Dalia
Angebote der NFDI für die Forschung im Bereich Zoologie#
Birgitta König-Ries, Robert Haase, Daniel Nüst, Konrad Förstner, Judith Sophie Engel
Published 2024-12-04
Licensed CC-BY-4.0
In diesem Slidedeck geben wir einen Einblick in Angebote und Dienste der Nationalen Forschungsdaten Infrastruktur (NFDI), die Relevant für die Zoologie und angrenzende Disziplinen relevant sein könnten.
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Annotated Research Context (ARC#
Kevin Frey, Kevin Schneider, Lukas Weil, Dominik Brilhaus, Timo Mühlhaus, Manuel Feser, Hannah-Doerpholz
Licensed UNKNOWN
The ARC is a framework for organizing and documenting research data, as well as a container that continuously supports collaboration, data exchange, and adherence to FAIR principles among various researchers. The ARC can be checked for completeness and quality at any time and converted into a citable data publication without interrupting the research or documentation process. It is built on widely accepted research data standards such as RO-Crate, ISA, and abstract CWL.
Tags: Research Data Management, Fair, Exclude From Dalia
Content type: Framework
Annotated high-throughput microscopy image sets for validation#
Vebjorn Ljosa, Katherine L Sokolnicki, Anne E Carpenter
Broad Bioimage Benchmark Collection (BBBC)
Tags: Exclude From Dalia
Content type: Collection, Data
Artificial Blobs and Labels image#
Romain
Published 2023-05-10
Licensed CC-BY-4.0
A groovy script to use in Fiji to generate artificial images and labels, with example images.
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Assessment of Residual Breast Cancer Cellularity after Neoadjuvant Chemotherapy using Digital Pathology#
Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne L. Martel
Published 2017-10-04
Licensed CC-BY-3.0
Breast cancer (BC) is the second most commonly diagnosed cancer in the U.S. with more than 250,000 new cases of invasive breast cancers reported in 2017. The majority of women with locally advanced and a subset of patients with operable breast cancer will undergo systemic therapy prior to their surgery (neoadjuvant therapy/ NAT) to reduce the size of tumor(s) and possibly further undergo breast conserving surgery. The Post-NAT-BRCA dataset is a collection of representative sections from breast resections in patients with residual invasive BC following NAT. Histologic sections were prepared and digitized to produce high resolution, microscopic images of treated BC tumors. Also included, are clinical features and expert pathology annotations of tumor cellularity and cell types. The Residual Cancer Burden Index (RCBi), is a clinically validated tool for assessment of response to NAT associated with prognosis. Tumor cellularity is one of the parameters used for calculating the RCBi. In this dataset, tumor cellularity refers to a measure of residual disease after NAT, in the form of proportion of malignant tumor inside the tumor bed region; also annotated. (See MD Anderson RCB Calculator for a detailed description of tumor cellularity.) Malignant, healthy, lymphocyte and other labels were also provided for individual cells to aid development of cell segmentation algorithms.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
https://www.cancerimagingarchive.net/collection/post-nat-brca/
Astigmatic 4Pi bead stack#
Zach Marin, Maohan Su
Published 2024-12-06
Licensed CC-BY-4.0
Bead stack taken on a 4Pi. DCIMG 0x1000000 file with a 4-pixel correction requirement.
Tags: Exclude From Dalia
Automatic labelling of HeLa “Kyoto” cells using Deep Learning tools#
Romain Guiet
Published 2022-02-25
Licensed CC-BY-4.0
Name: Automatic labelling of HeLa “Kyoto” cells using Deep Learning tools
Data type: Microscopy images from the dataset “HeLa “Kyoto” cells under the scope”, Brightfield (BF), Digital Phase Contrast (DPC, either “raw” or “square-rooted”), Tubulin and H2B fluorescent channel, paired with their corresponding nuclei or cell/cyto label images.
Labels images: Labels images were generated using the script “prepare_trainingDataset_cellpose.ijm”.
Briefly, for 5 defined time-points (1,10,50,100,150), channels of interest were duplicated, resaved and :
- nuclei label images were obtained using StarDist on H2B channel
- cell label images were obtained using Cellpose on Tubulin and H2B channels
A quick visual inspection of the resulting label images concluded that they were satisfying enough, despite certainly not being perfect.
Notes :
- This labelling strategy:
o will not produce 100% accurate labels, but they might be more reproducible than labels generated by humans and are (definitely) much faster to obtain.
o is NOT a recommended way of generating labels images, but for educational purposes.
- The fluorescent channels are part of the dataset to ease the process of review of the labels and are NOT used for training. We generated the labels from the fluorescent channels to later predict labels from the BF or DPC channels only. As such, the fluorescent channels should not be “reused” with our labels during training.
File format: .tif (16-bit)
Image size: 540x540 (Pixel size: 0.299 nm)
NOTE: This dataset uses the “HeLa “Kyoto” cells under the scope” dataset (https://doi.org/10.5281/zenodo.6139958) to automatically generate annotations
NOTE: This dataset was used to train cellpose models in the following Zenodo entry https://doi.org/10.5281/zenodo.6140111
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
Axioscan 7 fluorescent channels not displaying in QuPath#
j
Published 2024-06-25
Hi @ome team,Please find the .czi file attached. When loaded into QuPath using BioFormats, the fluorescence channels populate the brightness/contrast panel but do not show up in the viewer. Re-exporting as OME.Tiff from Zen and loading into QuPath does not help either - the channels do not populate the brightness/contrast panel in this case, and it shows as a RGB image.Please let me know if any further info is needed from me to troubleshoot! Best,J
Tags: Exclude From Dalia
BCCD Dataset#
Shenggan Gan, Nicolas Chen
Published 2017-12-07
Licensed MIT
BCCD Dataset is a small-scale dataset for blood cells detection.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
BHG2023-OMERO-ARC#
Andrea Schrader, Michele Bortolomeazzi, Niraj Kandpal, Torsten Stöter, Kevin Schneider, Peter Zentis, Josh Moore, Jeam-Marie Burel, Tom Boissonnet
Licensed CC-BY-4.0
Repository for documentation during the 2nd de.NBI BioHackathon Germany - 11.-15.12.2023 - OMERO-ARC project (in short: BHG2023-OMERO-ARC)
Tags: Nfdi4Bioimage, Bioinformatics, OMERO, Exclude From Dalia
Content type: Github Repository
BIA Seminar Series#
GloBIAS
Licensed UNKNOWN
The primary goal of this seminar series is to provide a dynamic platform for bioimage analysts, enabling the community to stay up to date with the latest developments in the field and foster community interactions. The seminars are designed to cater to intermediate and advanced analysts, focusing on practical, high-level content that extends beyond basic instruction.
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection
BIDS-lecture-2024#
Robert Haase
Licensed CC-BY-4.0
Training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.
Tags: Bioimage Analysis, Artificial Intelligence, Python, Exclude From Dalia
Content type: Github Repository
BIOMERO - A scalable and extensible image analysis framework#
Torec T. Luik, Rodrigo Rosas-Bertolini, Eric A.J. Reits, Ron A. Hoebe, Przemek M. Krawczyk
Published None
Licensed CC-BY-4.0
The authors introduce BIOMERO (bioimage analysis in OMERO), a bridge connecting OMERO, a renowned bioimaging data management platform, FAIR workflows, and high-performance computing (HPC) environments.
Tags: OMERO, Workflow, Bioimage Analysis, Exclude From Dalia
Content type: Publication
Beads imaged over time#
Zach Marin
Published 2025-04-04
Licensed CC-BY-4.0
DCIMG 0x1000000 images of beads over time (30 seconds, 0.03 s exposure).
Tags: Exclude From Dalia
BiaPy: Bioimage analysis pipelines in Python#
Daniel Franco-Barranco, et al.
BiaPy is an open source Python library for building bioimage analysis pipelines, also called workflows.
Tags: Workflow Engine, Python, Exclude From Dalia
Content type: Documentation
BigDataProcessor2: A free and open-source Fiji plugin for inspection and processing of TB sized image data#
Christian Tischer, Ashis Ravindran, Sabine Reither, Nicolas Chiaruttini, Rainer Pepperkok, Nils Norlin
Licensed CC-BY-4.0
Tags: Research Data Management, Bioimage Analysis, Exclude From Dalia
Content type: Publication
Bio Image Analysis#
Christian Tischer
Licensed UNKNOWN
Tags: Exclude From Dalia
Content type: Slides
Bio Image Analysis Lecture 2020#
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Video
https://www.youtube.com/watch?v=e-2DbkUwKk4&list=PL5ESQNfM5lc7SAMstEu082ivW4BDMvd0U
Bio-image Analysis ICOB 2023#
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Workshop, Collection
Bio-image Data Science Lectures 2025 @ Uni Leipzig / ScaDS.AI#
Robert Haase
Published 2025-07-10
Licensed CC-BY-4.0
These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material will develop here and in the corresponding github repository between April and July 2025.
Tags: Nfdi4Bioimage, Bioimage Analysis, Artificial Intelligence, Exclude From Dalia
Bio-image Data Science Lectures @ Uni Leipzig / ScaDS.AI#
Robert Haase
Licensed CC-BY-4.0
These are the PPTx training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python. The material developed here between April and July 2024.
Tags: Bioimage Analysis, Artificial Intelligence, Python, Exclude From Dalia
Content type: Slides
Bio.tools database#
Licensed CC-BY-4.0
Tags: Bioinformatics, Exclude From Dalia
Content type: Collection
BioEngine#
Jeremy Metz, Beatriz Serrano-Solano, Wei Ouyang
Licensed UNKNOWN
BioEngine is a cloud infrastructure to run BioImage model zoo based workflows in the cloud.
Tags: Artificial Intelligence, Workflow Engine, Exclude From Dalia
Content type: Publication
BioEngine Documentation#
Wei Ouyang, Nanguage, Jeremy Metz, Craig Russell
Licensed MIT
BioEngine, a Python package designed for flexible deployment and execution of bioimage analysis models and workflows using AI, accessible via HTTP API and RPC.
Tags: Workflow Engine, Artificial Intelligence, Python, Exclude From Dalia
Content type: Documentation
BioFormats Command line (CLI) tools#
Published 2024-10-24
Licensed CC-BY-4.0
Bio-Formats is a standalone Java library for reading and writing life sciences image file formats. There are several scripts for using Bio-Formats on the command line, which are listed here.
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Content type: Documentation
https://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html
BioImage Archive AI Gallery#
Licensed CC0-1.0
Tags: Bioimage Analysis, Artificial Intelligence, Exclude From Dalia
Content type: Collection, Data
BioImage Archive Visual Gallery#
Licensed CC0-1.0
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Data
https://www.ebi.ac.uk/bioimage-archive/galleries/visualisation.html
BioImage Archive Volume EM Gallery#
Licensed CC0-1.0
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Data
BioImage Informatics Index Training Materials#
Licensed ODC-BY-1.0
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection
Bioimage Archive#
Tags: Exclude From Dalia
Content type: Collection, Data, Publication
https://www.ebi.ac.uk/bioimage-archive/
https://www.sciencedirect.com/science/article/abs/pii/S0022283622000791
Bioimage Model Zoo#
Licensed UNKNOWN
Tags: Bioimage Analysis, Artificial Intelligence, Exclude From Dalia
Content type: Collection
Bioimaging workflow based on OMERO, eLabFTW, and Adamant for integrating images with multimodal metadata#
Mohsen Ahmadi, Robert Wagner, Sander Bekeschus, Becker, Markus M.
Published 2025-07-29
Licensed CC-BY-4.0
This research data management workflow for bioimaging is designed to bridge the gap between image metadata and experimental / process metadata. By linking images and microscopy-related metadata with broader experimental records, the workflow particularly supports the adoption of the FAIR (Findable, Accessible, Interoperable, Reusable) data principles in interdisciplinary fields where bioimaging is used to analyse treated samples requiring multimodal metadata. A Jupyter Notebook guides the user through the metadata level, data handling level, and data processing level and connects various software components in a modular manner. On the metadata level, microscope-specific metadata are captured using the Micro-Meta App and stored as reusable digital assets. Adamant provides a user interface to collect and process schema-based metadata related to the experiment / treatment procedure. Structured imaging and process metadata are attached to the complete experiment description in eLabFTW. On the data handling level, OMERO serves as the central platform for storing and managing microscopy images together with their metadata retrieved from eLabFTW (workflow with ELN) or directly from JSON files (workflow without ELN). On the data processing level, OMERO supports both automated and manual image analysis either directly via the Jupyter Notebook or FIJI. Due to the modularity of the workflow, the integrated tools can be substituted with equivalent systems based on institutional / user requirements. Whether in teaching or research settings, this workflow supports high-throughput, reproducible imaging workflows, ensuring that data, metadata, and analysis steps remain transparent, interoperable, and reusable across diverse bioimage analysis platforms.
Tags: Nfdi4Bioimage, Exclude From Dalia
Biologists, stop putting UMAP plots in your papers#
Rafael Irizarry
Published 2024-12-23
Licensed UNKNOWN
UMAP is a powerful tool for exploratory data analysis, but without a clear understanding of how it works, it can easily lead to confusion and misinterpretation.
Tags: Biology, Data Analysis, Umap, Exclude From Dalia
Content type: Blog Post
https://simplystatistics.org/posts/2024-12-23-biologists-stop-including-umap-plots-in-your-papers/
Biomero#
Torec Luik, Johannes Soltwedel
Published 2024-07-24
Licensed APACHE-2.0
The BIOMERO framework, for BioImage analysis in OMERO, allows you to run (FAIR) bioimage analysis workflows directly from OMERO on a high-performance compute (HPC) cluster, remotely through SSH.
Tags: OMERO, Github, Exclude From Dalia
Content type: Github Repository
Breast Cancer Nuclei images for DL Training + ZeroCostDL4Mic StarDist Model#
Ofra Golani, Vishnu Mohan, Tamar Geiger
Published 2024-05-21
Licensed CC-BY-4.0
Training dataset:Paired microscopy images (fluorescence) and corresponding masks Microscopy data type: Fluorescence microscopy and masks obtained via manual correction of automatic segmentation with pre-trained StarDist model (see qupath/models) Cells were imaged using a 20x objective with a 1x camera adapter was used in conjunction with a pco.edge 4.2 4MP camera on Pannoramic SCAN 150 scanner. Cell type: FFPE tissue sections were sliced from all cancer-containing paraffin blocks File format: .tif (8-bit for fluorescence and 16-bit for the masks) StarDist Model:The StarDist model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). This custom StarDist model was trained for 100 epochs using 80 manually annotated paired images (image dimensions: (257, 257)) with a batch size of 2, an augmentation factor of 10 and a mae loss function. The StarDist “Versatile fluorescent nuclei” model was used as a training starting point. Key python packages used include TensorFlow (v 2.2.0), Keras (v 1.1.2), CSBdeep (v 0.7.2), NumPy (v 1.21.6), Cuda (v 11..1.105). The training was accelerated using a Tesla P100GPU.The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook or in the StarDist Fiji plugin. a QuPath-compatible model is also provided.
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Content type: Data
Breast Cancer Semantic Segmentation (BCSS) dataset#
Mohamed Amgad, Habiba Elfandy, Hagar Hussein, Lamees A Atteya, Mai A T Elsebaie, Lamia S Abo Elnasr, Rokia A Sakr, Hazem S E Salem, Ahmed F Ismail, Anas M Saad, Joumana Ahmed, Maha A T Elsebaie, Mustafijur Rahman, Inas A Ruhban, Nada M Elgazar, Yahya Alagha, Mohamed H Osman, Ahmed M Alhusseiny, Mariam M Khalaf, Abo-Alela F Younes, Ali Abdulkarim, Duaa M Younes, Ahmed M Gadallah, Ahmad M Elkashash, Salma Y Fala, Basma M Zaki, Jonathan Beezley, Deepak R Chittajallu, David Manthey, David A Gutman, Lee A D Cooper
Published 2019-11-09
Licensed CC0-1.0
This repo contains the necessary information and download instructions to download the dataset associated with the paper: Amgad M, Elfandy H, …, Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics. 2019. doi: 10.1093/bioinformatics/btz083. This data can be visualized in a public instance of the Digital Slide Archive at this link. If you click the “eye” image icon in the Annotations panel on the right side of the screen, you will see the results of a collaborative annotation.
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Content type: Data
Bridging Imaging Users to Imaging Analysis - A community survey#
Suganya Sivagurunathan, Stefania Marcotti, Carl J Nelson, Martin L Jones, David J Barry, Thomas J A Slater, Kevin W Eliceiri, Beth A Cimini
Published 2023
Licensed BSD-3-CLAUSE
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Publication, Preprint
Bring your own data workshops#
Tags: Bioimage Analysis, Research Data Management, Exclude From Dalia
Content type: Workshop
Building FAIR image analysis pipelines for high-content-screening (HCS) data using Galaxy#
Riccardo Massei, Matthias Berndt, Beatriz Serrano-Solano, Wibke Busch, Stefan Scholz, Hannes Bohring, Jo Nyffeler, Luise Reger, Jan Bumberger, Lucille Lopez-Delisle
Published 2024-11-06
Licensed CC-BY-4.0
Imaging is crucial across various scientific disciplines, particularly in life sciences, where it plays a key role in studies ranging from single molecules to whole organisms. However, the complexity and sheer volume of image data, especially from high-content screening (HCS) experiments involving cell lines or other organisms, present significant challenges. Managing and analysing this data efficiently requires well-defined image processing tools and analysis pipelines that align with the FAIR principles—ensuring they are findable, accessible, interoperable, and reusable across different domains. In the frame of NFDI4BioImaging (the National Research Data Infrastructure focusing on bioimaging in Germany), we want to find viable solutions for storing, processing, analysing, and sharing HCS data. In particular, we want to develop solutions to make findable and machine-readable metadata using (semi)automatic analysis pipelines. In scientific research, such pipelines are crucial for maintaining data integrity, supporting reproducibility, and enabling interdisciplinary collaboration. These tools can be used by different users to retrieve images based on specific attributes as well as support quality control by identifying appropriate metadata. Galaxy, an open-source, web-based platform for data-intensive research, offers a solution by enabling the construction of reproducible pipelines for image analysis. By integrating popular analysis software like CellProfiler and connecting with cloud services such as OMERO and IDR, Galaxy facilitates the seamless access and management of image data. This capability is particularly valuable in bioimaging, where automated pipelines can streamline the handling of complex metadata, ensuring data integrity and fostering interdisciplinary collaboration. This approach not only increases the efficiency of HCS bioimaging but also contributes to the broader scientific community’s efforts to embrace FAIR principles, ultimately advancing scientific discovery and innovation. In the present study, we proposed an automated analysis pipeline for storing, processing, analysing, and sharing HCS bioimaging data. The (semi)automatic workflow was developed by taking as a case study a dataset of zebrafish larvae and cell lines images previously obtained from an automated imaging system generating data in an HCS fashion. In our workflows, images are automatically enriched with metadata (i.e. key-value pairs, tags, raw data, regions of interest) and uploaded to the UFZ-OME Remote Objects (OMERO) server using a novel OMERO tool suite developed with GALAXY. Workflows give the possibility to the user to intuitively fetch images from the local server and perform image analysis (i.e. annotation) or even more complex toxicological analyses (dose response modelling). Furthermore, we want to improve the FAIRness of the protocol by adding a direct upload link to the Image Data Resource (IDR) repository to automatically prepare the data for publication and sharing.
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https://zenodo.org/records/14044640
https://doi.org/10.5281/zenodo.14044640
https://galaxyproject.org/news/2024-11-08-galaxy-imaging-fair-pipelines/
Building a FAIR image data ecosystem for microscopy communities#
Isabel Kemmer, Antje Keppler, Beatriz Serrano-Solano, Arina Rybina, Buğra Özdemir, Johanna Bischof, Ayoub El Ghadraoui, John E. Eriksson, Aastha Mathur
Tags: Research Data Management, Exclude From Dalia
Content type: Publication
https://link.springer.com/article/10.1007/s00418-023-02203-7
CLIJ: GPU-accelerated image processing for everyone#
Robert Haase, Loic Royer, et al.
Published 2020
Licensed ALL RIGHTS RESERVED
CLIJ is a collection of image processing functions that use graphics processing units for accelerated processing.
Tags: Imagej, Bioimage Analysis, Exclude From Dalia
Content type: Publication
COBA-NIH/2024_Bridging_Imaging_Users_to_Imaging_Analysis_Survey: Survey data with preliminary exploration#
Erin Weisbart
Published 2025-09-15
Licensed BSD-3-CLAUSE
Contains the survey data collected through the 2024 Bridging Imaging Users to Imaging Analysis Survey and figures/code from preliminary data exploration of the survey results.
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COBA: Center for Open Bioimage Analysis YouTube Channel#
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Video
CT Physics#
Radiology Tutorials
Published 2025-01-01
Licensed UNKNOWN
This is a playlist of videos about how CT works
Tags: Imaging, Exclude From Dalia
Content type: Video
https://youtube.com/playlist?list=PLWfaNqiSdtzW_muHrCkwJm9FyovAue-jN&si=-zIXh87DMkAEuJVd
CZI (Carl Zeiss Image) dataset with artificial test camera images with various dimension for testing libraries reading#
Sebastian Rhode
Published 2022-08-22
Licensed CC-BY-4.0
Set of CZI test images created by using a simulated microscope with a test grayscale camera (no LSM or AiryScan or RGB). The filename indicates the used dimension(s) for the acquisition experiment. The files can be used to test the basic functionality of libraries reading CZI files.
Examples:
S=2_T=3_CH=1.czi = 2 Scenes, 3 TimePoints and 1 Channel
Z-Stack was not activated inside acquisition experiment
S=2_T=3_Z=5_CH=2.czi = 2 Scenes, 3 TimePoints, 5-Z-Planes and 1 Channels
Z-Stack was activated inside acquisition experiment
The test files (so far) contain not any data with more “advanced” dimensions like AiryScan rawdata, illumination angles etc. Also no CZI files with pixel type RGB are included yet.
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CZI file examples#
Nicolas Chiaruttini
Published 2023-08-18
Licensed CC-BY-4.0
A set of public CZI files. These can be used for testing CZI readers.
Demo LISH 4x8 15pct 647.czi: A cleared mouse brain acquired with a Zeiss LightSheet Z1 with 32 tiles. Courtesy of the Carl Petersen lab LSENS (https://www.epfl.ch/labs/lsens). Sampled prepared by Yanqi Liu an imaged by Olivier Burri.
test_gray.czi: a synthetically generated CZI file without metadata, made by Sebastian Rhode
Image_1_2023_08_18__14_32_31_964.czi: an example multi-part CZI file, containing only camera noise
a xt scan, xz scan, xzt scan
a set of multi angle, multi illumination, mutli tile acquisition, taken on the LightSheet Z1 microscope of the PTBIOP by Lorenzo Talà
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CZI: Open Science Program Collection#
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Content type: Collection
Cell Tracking Challenge - 2D Datasets#
Licensed UNKNOWN
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Data
Cell Tracking Challenge - 3D Datasets#
Licensed UNKNOWN
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Data
CellBinDB: A Large-Scale Multimodal Annotated Dataset#
Can Shi, Jinghong Fan, Zhonghan Deng, Huanlin Liu, Qiang Kang, Yumei Li, Jing Guo, Jingwen Wang, Jinjiang Gong, Sha Liao, Ao Chen, Ying Zhang, Mei Li
Published 2024-11-20
Licensed CC-ZERO
CellBinDB is a large-scale, multimodal annotated dataset for cell segmentation. It contains 1,044 annotated microscope images and 109,083 cell annotations, covering four staining types: DAPI, ssDNA, H&E, and mIF. CellBinDB contains samples from two species, human and mouse, covering more than 30 histologically different tissue types, including disease-related tissues. The images in CellBinDB come from two sources: 844 mouse images from internal experiments and 200 human images from the open access platform 10x Genomics. We annotated all images in CellBinDB and provide two types of image annotations: semantic and instance masks. A xlsx file is attached to record the detailed information of each image. In addition, we provide the images and annotations of nine other widely used publicly available cell segmentation datasets downloaded from their original sources, retaining their original formats for ease of use. The file ‘mixed_licenses.txt’ contains the original accessions of the public datasets used in our project and their associated licenses. Please refer to these links for more information about each dataset and its licensing terms, and use it according to the specifications.
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Content type: Data
CellTrackColab#
Guillaume Jacquemet
Licensed MIT
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Content type: Notebook, Collection
Cellpose model for Digital Phase Contrast images#
Laura Capolupo, Olivier Burri, Romain Guiet
Published 2022-02-09
Licensed CC-BY-4.0
Name: Cellpose model for Digital Phase Contrast images
Data type: Cellpose model, trained via transfer learning from ‘cyto’ model.
Training Dataset: Light microscopy (Digital Phase Contrast) and Manual annotations (10.5281/zenodo.5996883)
Training Procedure: Model was trained using a Cellpose version 0.6.5 with GPU support (NVIDIA GeForce RTX 2080) using default settings as per the Cellpose documentation
python -m cellpose –train –dir TRAINING/DATASET/PATH/train –test_dir TRAINING/DATASET/PATH/test –pretrained_model cyto –chan 0 –chan2 0
The model file (MODEL NAME) in this repository is the result of this training.
Prediction Procedure: Using this model, a label image can be obtained from new unseen images in a given folder with
python -m cellpose –dir NEW/DATASET/PATH –pretrained_model FULL_MODEL_PATH –chan 0 –chan2 0 –save_tif –no_npy
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Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images#
Romain Guiet, Olivier Burri
Published 2022-02-25
Licensed CC-BY-4.0
Name: Cellpose models for Brightfield and Digital Phase Contrast images
Data type: Cellpose models trained via transfer learning from the ‘nuclei’ and ‘cyto2’ pretrained model with additional Training Dataset . Includes corresponding csv files with ‘Quality Control’ metrics(§) (model.zip).
Training Dataset: Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (https://doi.org/10.5281/zenodo.6140064)
Training Procedure: The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the Cellpose documentation . Training was done using a Renku environment (renku template).
Command Line Execution for the different trained models
nuclei_from_bf:
cellpose –train –dir ‘data/train/’ –test_dir ‘data/test/’ –pretrained_model nuclei –img_filter _bf –mask_filter _nuclei –chan 0 –chan2 0 –use_gpu –verbose
cyto_from_bf:
cellpose –train –dir ‘data/train/’ –test_dir ‘data/test/’ –pretrained_model cyto2 –img_filter _bf –mask_filter _cyto –chan 0 –chan2 0 –use_gpu –verbose
nuclei_from_dpc:
cellpose –train –dir ‘data/train/’ –test_dir ‘data/test/’ –pretrained_model nuclei –img_filter _dpc –mask_filter _nuclei –chan 0 –chan2 0 –use_gpu –verbose
cyto_from_dpc:
cellpose –train –dir ‘data/train/’ –test_dir ‘data/test/’ –pretrained_model cyto2 –img_filter _dpc –mask_filter _cyto –chan 0 –chan2 0 –use_gpu –verbose
nuclei_from_sqrdpc:
cellpose –train –dir ‘data/train/’ –test_dir ‘data/test/’ –pretrained_model nuclei –img_filter _sqrdpc –mask_filter _nuclei –chan 0 –chan2 0 –use_gpu –verbose
cyto_from_sqrdpc:
cellpose –train –dir ‘data/train/’ –test_dir ‘data/test/’ –pretrained_model cyto2 –img_filter _sqrdpc –mask_filter _cyto –chan 0 –chan2 0 –use_gpu –verbose
NOTE (§): We provide a notebook for Quality Control, which is an adaptation of the “Cellpose (2D and 3D)” notebook from ZeroCostDL4Mic .
NOTE: This dataset used a training dataset from the Zenodo entry(https://doi.org/10.5281/zenodo.6140064) generated from the “HeLa “Kyoto” cells under the scope” dataset Zenodo entry(https://doi.org/10.5281/zenodo.6139958) in order to automatically generate the label images.
NOTE: Make sure that you delete the “_flow” images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results.
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Cellpose training data and scripts from “Machine learning for histological annotation and quantification of cortical layers”#
Jean Jacquemier, Julie Meystre, Olivier Burri
Published 2024-07-04
Licensed CC-BY-4.0
This Workflow contains all the material necessary to reproduce the cells detection, thanks to the QuPath performed in the paper “Machine learning for histological annotation and quantification of cortical layers” Inside this workflow and dataset, you will find the following folders
QuPath Training Project: A QuPath 0.5.0 project containing all the manual annotations (ground truths) used to train the cellpose model, as well as the script to start the training Training Images and Demo Images: The raw whole slide scanner images needed by the above QuPath project Model: The fodler containing the trained cellpose model cellpose-training Folder: The exported raw and ground truth images that the above cellpose model was trained on Scripts: The QuPath scripts, also located in their respective QuPath projects, that were created for this whole workflow QC: A Jupyter notebook, based on ZeroCostDL4Mic that computes quality metrics in order to assess the performance of the trained cellpose model. The folder also contains the resulting metrics.
Installation and Use If you are going to use the QuPath projects, you need a local QuPath Installation https://qupath.github.io/ that is configured to run the QuPath Cellpose Extension BIOP/qupath-extension-cellpose as well as a working Cellpose installation MouseLand/cellpose Instructions for installation are available from the links above. After that, you should be able to open the QuPath project, navigate to the “Automate > Project scripts” menu and locate the script you wish to run.
train a cell segmentation algorithm in the context of the rat brain Layer Boundaries project
trigger cell segmentation from a QuPath project in a semi-automated pipeline
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Content type: Data
Checklists for publishing images and image analysis#
Christopher Schmied
Published 2023-09-14
Licensed CC0-1.0
In this paper we introduce two sets of checklists. One is concerned with the publication of images. The other one gives instructions for the publication of image analysis.
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Forum Post
https://forum.image.sc/t/checklists-for-publishing-images-and-image-analysis/86304
Chinese Hamster Ovary Cells#
Krisztian Koos, József Molnár, Lóránd Kelemen, Gábor Tamás, Peter Horvath
Published 2016-07-29
Licensed CC-BY-3.0
The image set consists of 60 Differential Interference Contrast (DIC) images of Chinese Hamster Ovary (CHO) cells. The images are taken on an Olympus Cell-R microscope with a 20x lens at the time when the cell initiated their attachment to the bottom of the dish.
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Content type: Data
Choose an open source license#
GitHub
Licensed CC-BY-3.0 UNPORTED
Ressource that helps to choose an open source license for your project.
Tags: Open Source, Licensing, Exclude From Dalia
Content type: Website
Chris Halvin YouTube channel#
Licensed UNKNOWN
Tags: Napari, Python, Bioimage Analysis, Exclude From Dalia
Content type: Collection, Video
https://www.youtube.com/@chrishavlin
https://www.youtube.com/playlist?list=PLqbhAmYZU5KxuAcnNBIxyBkivUEiKswq1
Cloud-Based Virtual Desktops for Reproducible Research#
Yi Sun, Christian Tischer, Kelleher, Harry Alexander, Jean-Karim Heriche
Published 2025-09-10
Licensed CC-BY-4.0
Reproducing computing environments become increasingly challenging in research, especially when compute-intensive scientific workflows require specialised software stacks, specialized hardware (e.g. GPUs), and interactive analysis tools. While traditional high-performance computing (HPC) systems offer scalable resources for batch processing, they don’t easily support interactive workflows. On the other hand, workstations have fixed resources and face workflow deployment challenges because conflicts can occur when multiple tools and dependencies are deployed into the same environment. To address these limitations, we present cloud-based virtual desktop platforms, built on the desktop-as-a-service (DaaS) model, using a containerised, cloud-native approach. Our platforms offer on-demand, customized desktop environments accessible from any web browser, with dynamic allocation of CPU, memory, and GPU resources for efficient utilization of resources. We introduce two types of virtual desktops: BAND, built on top of a Slurm scheduler and BARD, using Kubernetes. In both cases, containerization ensures consistent and reproducible environments across sessions and pre-installed software improves accessibility for researchers. Deployment and system administration are also simplified through the use of orchestration and automation tools. Our virtual desktop platforms are particularly valuable for bioimage analysis, which requires complex workflows involving high interactivity, multiple software and GPU acceleration. By combining containerization and cloud-native services, BAND and BARD offer a scalable and sustainable model for delivering interactive, reproducible research environments.
Tags: Nfdi4Bioimage, Exclude From Dalia
Collaborative working and Version Control with Git[Hub]#
Robert Haase
Published 2025-05-10
Licensed CC-BY-4.0
Working together on the internet presents us with new challenges: Who uploaded a file and when? Who contributed to the project when and why? How can content be merged when multiple team members make changes at the same time? The version control tool Git offers a comprehensive solution to these questions. The online platform GitHub.com provides a Git-driven platform that enables effective collaboration. Attendees of this hands-on tutorial will learn:
Introduction to version control with Git[Hub]
Working with Git: Pull requests
Resolving merge conflicts
Artificial intelligence that can respond to GitHub issues
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Combining StarDist and TrackMate example 1 - Breast cancer cell dataset#
Guillaume Jacquemet
Published 2020-09-17
Licensed CC-BY-4.0
Description: Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic (see HenriquesLab/ZeroCostDL4Mic)
Training dataset: 72 Paired microscopy images (fluorescence) and corresponding masks
Microscopy data type: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see HenriquesLab/ZeroCostDL4Mic for details about the segmentation)
Microscope: Spinning disk confocal microscope with a 20x 0.8 NA objective
Cell type: DCIS.COM Lifeact-RFP cells
File format: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)
Image size: 1024x1024 (Pixel size: 634 nm)
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Content type: Data
Combining StarDist and TrackMate example 2 - T cell dataset#
Nathan H. Roy, Guillaume Jacquemet
Published 2020-09-17
Licensed CC-BY-4.0
Description: Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic (see HenriquesLab/ZeroCostDL4Mic)
Training dataset: 209 Paired microscopy images (brightfield) and corresponding masks
Microscopy data type: brightfield microscopy and masks obtained via manual segmentation (see HenriquesLab/ZeroCostDL4Mic for details about the segmentation)
Microscope: Imaging was done using a 10x phase contrast objective at 37°C on a Zeiss Axiovert 200M microscope equipped with an automated X-Y stage and a Roper EMCCD camera. Time-lapse images were collected every 30 sec for 10 min using SlideBook 6 software (Intelligent Imaging Innovations).
File format: .tif (16-bit for brightfield images and 8 and 16-bit for the masks)
Image size: 1024x1024 (Pixel size: 645 nm)
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Content type: Data
Combining StarDist and TrackMate example 3 - Flow chamber dataset#
Gautier Follain, Guillaume Jacquemet
Published 2020-09-17
Licensed CC-BY-4.0
Description: Contains a StarDist example training dataset, a test dataset, and the StarDist model generated using ZeroCostDL4Mic (see HenriquesLab/ZeroCostDL4Mic)
Training dataset: Paired microscopy images (brightfield) and corresponding masks
Microscopy data type: brightfield microscopy and masks obtained via manual segmentation (see HenriquesLab/ZeroCostDL4Mic for details about the segmentation)
Microscope: Images were acquired with a brightfield microscope (Zeiss Laser-TIRF 3 Imaging System, Carl Zeiss) and a 10X objective.
File format: .tif (8-bit for brightfield images and 8 and 16-bit for the masks)
Image size: 1024x1024 (Pixel size: 650 nm)
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Content type: Data
Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization#
Torsten Stöter, Tobias Gottschall, Andrea Schrader, Peter Zentis, Monica Valencia-Schneider, Niraj Kandpal, Werner Zuschratter, Astrid Schauss, Timo Dickscheid, Timo Mühlhaus, Dirk von Suchodoletz
Licensed CC-BY-4.0
Interdisciplinary collaboration and integrating large, diverse datasets are crucial for answering complex research questions, requiring multimodal data analysis and adherence to FAIR principles. To address challenges in capturing the full research cycle and contextualizing data, DataPLANT developed the Annotated Research Context (ARC), while the neuroimaging community extended the Brain Imaging Data Structure (BIDS) for microscopic image data, both providing standardized, file system-based storage structures for organizing and sharing data with metadata.
Tags: Research Data Management, FAIR-Principles, Exclude From Dalia
Content type: Poster
Conference Slides - 4th Day of Intravital Microscopy#
Dr. Hellen Ishikawa-Ankerhold
Published 2024-11-13
Licensed CC-BY-4.0
Conference Slides for the presentation of GerBI e.V. at the 4th Day of Intravital Microscopy in Leuven, Belgium. Features Structure, activities and Links to join GerBI e.V.
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CryoNuSeg#
Amirreza Mahbod, Benjamin Bancher, Isabella Ellinger, Deyun Zhang, Syed Nauyan Rashid
Published 2019-12-31
Licensed CC-BY-NC-SA-4.0
A Dataset for Nuclei Segmentation of Cryosectioned H&E-Stained Histologic Images
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Content type: Data
https://www.kaggle.com/datasets/ipateam/segmentation-of-nuclei-in-cryosectioned-he-images
DCIMG dense beads taken in chunks over time#
Zach Marin
Published 2025-08-14
Licensed CC-BY-4.0
Two 2000-frame chunks acquired at different times (~40 minutes apart) on a 4Pi widefield, showing some slow sample drift.
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DEEP NAPARI : Napari as a tool for deep learning project management#
Herearii Metuarea, David Rousseau, Pejman Rasti, Valentin Gilet
Licensed UNKNOWN
Tags: Artificial Intelligence, Bioimage Analysis, Exclude From Dalia
Content type: Notebook
DL4MicEverywhere#
Iván Hidalgo, et al.
Licensed CC-BY-4.0
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Notebook, Collection
DL4Proteins-notebooks#
Michael Chungyoun, Courtney Thomas, Britnie Carpentier, GabeAu79, puv-sreev, Jeffrey Gray
Published 2024-09-04T12:24:24+00:00
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
Tags: Bioinformatics, Exclude From Dalia
Content type: Github Repository, Collection
DL@MBL 2021 Exercises#
Jan Funke, Constantin Pape, Morgan Schwartz, Xiaoyan
Licensed UNKNOWN
Tags: Artificial Intelligence, Bioimage Analysis, Exclude From Dalia
Content type: Slides, Notebook
DNG in BioFormat opens in wrong resolution#
Michael
Published 2025-07-15
Licensed CC-BY-4.0
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Dask Course#
Guillaume Witz
Licensed UNKNOWN
Tags: Python, Bioimage Analysis, Big Data, Exclude From Dalia
Content type: Notebook
Data Stewardship Wizard#
Licensed UNKOWN
Leading open-source platform for collaborative and living data management plans.
Tags: Data Stewardship, Open Source, Research Data Management, FAIR-Principles, Exclude From Dalia
Content type: Website, Online Tutorial
Data life cycle#
ELIXIR (2021) Research Data Management Kit
Licensed CC-BY-4.0
In this section, information is organised according to the stages of the research data life cycle.
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Content type: Collection, Website, Online Tutorial
Dataset from InCell 2200 microscope misread as a plate#
Fabien Kuttler, Rémy Dornier
Published 2025-01-30
Licensed CC-BY-4.0
Two dummy datasets are provided in this repository :
Dataset_Ok : 96 wells, 9 fields of view per well, 4 different channels (DAPI, Cy3, FITC, Brightfield), no Z, no T. The .xcde file of this dataset is correctly read by BioFormats, as the dataset is recognized as a plate, and can be imported on OMERO Dataset_fail: 20 wells, 4 fields of view per well, 5 channels, with one duplicate (DAPI, FITC, Cy3, Cy5 wix 4 , Cy5 wix 5), no Z, no T. The .xcde file of this dataset is not correctly read by BioFormats and no images are imported on OMERO.
BioFormats version: 8.0.1 A discussion thread has been open on this topic.
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Deconvolution Test Dataset#
Romain Guiet
Published 2021-07-14
Licensed CC-BY-4.0
This a test dataset, HeLa cells stained for action using Phalloidin-488 acquired on confocal Zeiss LSM710, which contains
Ph488.czi (contains all raw metadata)
Raw_large.tif ( is the tif version of Ph488.czi, provided for conveninence as tif doesn’t need Bio-Formats to be open in Fiji )
Raw.tif , is a crop of the large image
- PSFHuygens_confocal_Theopsf.tif , is a theoretical PSF generated with HuygensPro
- PSFgen_WF_WBpsf.tif , is a theoretical PSF generated with PSF generator
PSFgen_WFsquare_WBpsf.tif, is the result of the square operation on PSFgen_WF_WBpsf.tif , to approximate a confocal PSF
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Deep learning segmentation projects of FIB-SEM dataset of U2-OS cell#
Belevich Ilya, Eija Jokitalo
Published 2023-10-26
Licensed CC-BY-4.0
This submission includes ground truth datasets that were used to segment the nuclear envelope (NE), mitochondria, endoplasmic reticulum (ER) and Golgi from a human bone osteosarcoma epithelial cell (U2-OS) imaged using focused-ion beam scanning electron microscopy (FIB-SEM).The full FIB-SEM dataset is deposited to EMPIAR (https://www.ebi.ac.uk/empiar, EMPIAR-11746).
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Content type: Data
Deep learning training data (JOVE)#
Jessica Heebner, Carson Purnell, Ryan Hylton, Mike Marsh, Michael Grillo, Matt Swulius
Published 2022-11-18
Licensed CC-ZERO
Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. However, the technique has several limitations that make analyzing the data it generates time-intensive and difficult. Hand-segmenting a single tomogram can take hours to days of human effort, but the microscope can easily generate 50 or more tomograms a day. Current deep learning segmentation programs for cryo-ET do exist but are limited to segmenting one structure at a time. Here multi-slice U-Net convolutional neural networks are trained and applied to automatically segment multiple structures simultaneously within cryo-tomograms. With proper preprocessing, these networks can be robustly inferred to many tomograms without the need for training individual networks for each tomogram. This workflow dramatically improves the speed with which cryo-electron tomograms can be analyzed by cutting segmentation time down to under 30 min in most cases. Further, segmentations can be used to improve the accuracy of filament tracing within a cellular context and to rapidly extract coordinates for subtomogram averaging.
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Content type: Data
DeepBacs – Bacillus subtilis fluorescence segmentation dataset#
Séamus Holden, Mia Conduit
Published 2021-10-05
Licensed CC-BY-4.0
Training and test images of live B. subtilis cells expressing FtsZ-GFP for the task of segmentation.
Additional information can be found on this github wiki.
The example shows the fluorescence widefield image of live B. subtilis cells expressing FtsZ-GFP and the manually annotated segmentation mask.
Data type: Paired fluorescence and segmented mask images
Microscopy data type: 2D widefield images (fluorescence)
Microscope: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics)
Cell type: B. subtilis strain SH130 grown under agarose pads
File format: .tiff (8-bit) or .png (8-bit)
For segmented masks, binary masks are used for training of CARE/U-Net models, 8-bit .tif ROI maps for training of StarDist models and .png images for training of pix2pix models
Image size: 1024 x 1024 px² (Pixel size: 65 nm)
Image preprocessing: Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin
Author(s): Mia Conduit1,2, Séamus Holden1,3
Contact email: Seamus.Holden@newcastle.ac.uk
Affiliation:
Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK
ORCID: 0000-0002-7169-907X
Associated publications: Whitley et al., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
DeepBacs – Escherichia coli bright field segmentation dataset#
Christoph Spahn, Mike Heilemann
Published 2021-10-05
Licensed CC-BY-4.0
Training and test images of live E. coli cells imaged under bright field for the task of segmentation.
Additional information can be found on this github wiki.
The example shows a bright field image of live E. coli cells and the manually annotated segmentation mask.
Data type: Paired bright field and segmented mask images
Microscopy data type: 2D bright field images recorded at 1 min interval
Microscope: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective
Cell type: E. coli MG1655 wild type strain (CGSC #6300).
File format: .tif (8-bit)
Image size: 1024 x 1024 px² (79 nm / pixel), 19/15 individual frames (training/test dataset)
1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series)
Image preprocessing: Raw images were recorded in 16-bit mode (image size 512 x 512 px² @ 158 nm/px). Images were upscaled with a factor of 2 (no interpolation) to enable generation of higher-quality segmentation masks. Two sets of mask images are provided: RoiMaps for instance segmentation using e.g. StarDist or binary images for CARE or U-Net.
Author(s): Christoph Spahn1,2, Mike Heilemann1,3
Contact email: christoph.spahn@mpi-marburg.mpg.de
Affiliation(s):
Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany
ORCID: 0000-0001-9886-2263
ORCID: 0000-0002-9821-3578
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
DeepBacs – Mixed segmentation dataset and StarDist model#
Christoph Spahn, Mike Heilemann, Séamus Holden, Mia Conduit, Pereira, Pedro Matos, Mariana Pinho
Published 2021-10-05
Licensed CC-BY-4.0
Mixed training and test images of S. aureus, E. coli and B. subtilis for cell segmentation using StarDist, as well as the trained StarDist model.
Additional information can be found on this github wiki.
Data type: Paired bright field / fluorescence and segmented mask images
Microscopy data type: 2D widefield images; DIC and fluorescence for S. aureus, bright field images for E. coli, and fluorescence images for B. subtilis
Microscopes:
S. aureus:
GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence)
E.coli:
Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective
B. subtilis:
Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics)
Cell types: S. aureus strain JE2, E. coli MG1655 (CGSC #6300) and B. subtilis strain SH130; all grown under agarose pads
File format: .tif (8-bit and 16-bit)
Image size: 512 x 512 px² @ 80 nm pixel size (S. aureus); 1024 x 1024 px² @ 79 nm pixel size (E. coli); 1024 x 1024 px² @ 65 nm pixel size (B. subtilis)
Image preprocessing:
S. aureus:
Raw images were manually annotated by drawing ellipses in the NR fluorescence image and segmented images were created using the LOCI plugin (“ROI Map”). For training, images and masks were quartered into four 256 x 256 px² patches.
E. coli:
Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). Images were upscaled with a factor of 2 (no interpolation) to enable generation of higher-quality segmentation masks.
B. subtilis:
Images were denoised using PureDenoise and resulting 32-bit images were converted into 8-bit images after normalizing to 1% and 99.98% percentiles. Images were manually annotated using the Labkit Fiji plugin
StarDist model:
The StarDist 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 200 epochs (120 steps/epoch) on 155 paired image patches (image dimensions: (1024, 1024), patch size: (256,256)) with a batch size of 4, 10% validation data, 64 rays on grid 2, a learning rate of 0.0003 and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1.12.2). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), csbdeep (v 0.6.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla P100GPU. The dataset was augmented by a factor of 3.
The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook, the StarDist Fiji plugin or the TrackMate Fiji plugin (v7+).
Author(s): Christoph Spahn1,2, Mike Heilemann1,3, Mia Conduit4, Séamus Holden4,5, Pedro Matos Pereira6,7, Mariana Pinho6,8
Contact email: christoph.spahn@mpi-marburg.mpg.de, Seamus.Holden@newcastle.ac.uk, pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt
Affiliation(s):
Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany
ORCID: 0000-0001-9886-2263
ORCID: 0000-0002-9821-3578
Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK
ORCID: 0000-0002-7169-907X
Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
ORCID: 0000-0002-1426-9540
ORCID: 0000-0002-7132-8842
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
DeepBacs – Staphylococcus aureus widefield segmentation dataset#
Pereira, Pedro Matos, Mariana Pinho
Published 2021-10-05
Licensed CC-BY-4.0
Training and test images of live S. aureus cells for the task of cell segmentation.
Additional information can be found in the github wiki.
The example shows the bright field and Nile Red fluorescence image of live S. aureus cells, as well as the manually annotated segmentation mask.
Data type: Paired DIC/fluorescence and segmented mask images
Microscopy data type: 2D widefield images (DIC and fluorescence)
Microscope: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence)
Cell type: S. aureus strain JE2 grown under agarose pads
File format: .tif (16-bit)
Image size: 512 x 512 px² (80 nm/px) Image preprocessing: Raw images were manually annotated by drawing ellipses in the NR fluorescence image and segmented images were created using the LOCI plugin (“ROI Map”). For training, images and masks were quartered into four 256 x 256 px² patches.
Author(s): Pedro Matos Pereira1,2, Mariana Pinho1,3
Contact email: pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt
Affiliation:
Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
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Content type: Data
Democratizing knowledge representation with BioCypher#
Sebastian Lobentanzer, Patrick Aloy, Jan Baumbach, Balazs Bohar, Vincent Carey, Pornpimol Charoentong, Katharina Danhauser, Tunca Doğan, Johann Dreo, Ian Dunham, Elias Farr, Adrià Fernandez-Torras, Benjamin Gyori, Michael Hartung, Charles Tapley Hoyt, Christoph Klein, Tamas Korcsmaros, Andreas Maier, Matthias Mann, David Ochoa, Elena Pareja-Lorente, Ferdinand Popp, Martin Preusse, Niklas Probul, Benno Schwikowski, Bünyamin Sen, Maximilian Strauss, Denes Turei, Erva Ulusoy, Dagmar Waltemath, Judith Wodke, Julio Saez-Rodriguez
Published 2023-06-19
Licensed UNKNOWN
BioCypher is a framework to support users in creating KGs
Tags: Knowledge Graph, Bioinformatics, Exclude From Dalia
Content type: Publication
Developing (semi)automatic analysis pipelines and technological solutions for metadata annotation and management in high-content screening (HCS) bioimaging#
Riccardo Massei, Stefan Scholz, Wibke Busch, Thomas Schnike, Hannes Bohring, Jan Bumberger
Licensed CC-BY-4.0
High-content screening (HCS) bioimaging automates the imaging and analysis of numerous biological samples, generating extensive metadata that is crucial for effective image management and interpretation. Efficiently handling this complex data is essential to implementing FAIR principles and realizing HCS’s full potential for scientific discoveries.
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Poster
Development FAIR image analysis workflows and RDM pipelines in Galaxy#
Riccardo Massei, Beatriz Serrano-Solano, Anne Fouilloux, Björg Gruening, Yi Sun, Diana Chiang, Matthias Bernt, Leonid Kostrykin
Published 2025-09-10
Licensed CC-BY-4.0
Imaging is crucial across various scientific disciplines, particularly in life sciences, where it plays a key role in studies ranging from single molecules to whole organisms. However, the complexity and sheer volume of image data present significant challenges. Managing and analyzing this data efficiently requires well-defined image processing tools and analysis pipelines that align with the FAIR principles—ensuring they are findable, accessible, interoperable, and reusable across different domains. In the frame of NFDI4BIOIMAGE1 (the National Research Data Infrastructure focusing on bioimaging in Germany), we want to find viable solutions for storing, processing, analyzing, and sharing bioimaging data. In particular, we want to develop solutions to make findable and machine-readable metadata developing analysis pipelines. In scientific research, such pipelines are crucial for maintaining data integrity, supporting reproducibility, and enabling interdisciplinary collaboration. These tools can be used by different users to retrieve images based on specific attributes as well as support quality control by identifying appropriate metadata. Galaxy, an open-source, web-based platform for data-intensive research, offers a solution by enabling the construction of reproducible pipelines for image analysis2. By integrating popular analysis software like CellProfiler and connecting with cloud services such as OMERO and IDR, Galaxy facilitates the seamless access and management of image data. This capability is particularly valuable in bioimaging, where automated pipelines can streamline the handling of complex metadata, ensuring data integrity and fostering interdisciplinary collaboration. This approach not only increases the efficiency of RDM processes in bioimaging but also contributes to the broader scientific community’s efforts to embrace FAIR principles, ultimately advancing scientific discovery and innovation. In the present poster, we showed how to integrate RDM processes and tools in Galaxy. We will showcase how Images can be enriched with metadata (i.e. key-value pairs, tags, raw data, regions of interest) and uploaded to a target OME Remote Objects (OMERO) server using a novel set of OMERO tools developed with Galaxy3. Workflows give the possibility to the user to intuitively fetch images from the local server and perform image analysis (i.e. annotation). Furthermore, we will show the potential integration of eletronic lab books such as eLabFTW4, cloud storage systems (i.e. OneData)5 and interactive norebooks (Jupyter Notebooks) 6 in the Galaxy pipeline.
Tags: Nfdi4Bioimage, Exclude From Dalia
Diffusion Models for Image Restoration - An Introduction#
Anirban Ray
Licensed UNKNOWN
Presentation given at the EMBO-DL4MIA 2024, Advanced Topic Seminar, May-11-2024
Tags: Bioimage Analysis, Diffusion Models, Tu Dresden, Exclude From Dalia
Content type: Presentation
https://drive.google.com/file/d/1pPVUUMi5w2Ojw_SaBzSQVaXUuIKtQ7Ma/view
Digital Phase Contrast on Primary Dermal Human Fibroblasts cells#
Laura Capolupo
Published 2022-02-09
Licensed CC-BY-4.0
Name: Digital Phase Contrast on Primary Dermal Human Fibroblasts cells
Data type: Paired microscopy images (Digital Phase Contrast, square rooted) and corresponding labels/masks used for cellpose training (the corresponding Brightfield images are also present), organized as recommended by cellpose documentation.
Microscopy data type: Light microscopy (Digital Phase Contrast and Brighfield )
Manual annotations: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects (Dust, fused cells) were left unannotated, so that the cellpose model (10.5281/zenodo.6023317) may mimic the same user bias during prediction. This was particularly necessary due to the accumulation of floating debris in the center of the well.
Microscope: Perkin Elmer Operetta microscope with a 10x 0.35 NA objective
Cell type: Primary Dermal Human Fibroblasts cells
File format: .tif (16-bit for DPC and 16-bit for the masks)
Image size: 1024x1024 (Pixel size: 634 nm)
NOTE : This dataset was used to train cellpose model ( 10.5281/zenodo.6023317 )
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DigitalSreeni YouTube Channel#
Sreeni Bhattiprolu
A collection tutorial videos for using Python in general and for processing images using Python, machine learning and deep learning
Tags: Python, Exclude From Dalia
Content type: Collection, Video
Dokumentation und Anleitung zum elektronischen Laborbuch (eLabFTW)#
Lienhard Wegewitz, F. Strauß
Published 2020-03-23
Licensed AGPL-3.0
Documentation for eLabFTW. With eLabFTW you get a secure, modern and compliant system to track your experiments efficiently but also manage your lab with a powerful and versatile database.
Tags: Research Data Management, Exclude From Dalia
Content type: Documentation, Document, Tutorial
https://www.fdm.tu-clausthal.de/fileadmin/FDM/documents/Manual_eLab_v0.3_20200323.pdf
Drosophila Kc167 cells#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
Drosophila melanogaster Kc167 cells were stained for DNA (to label nuclei) and actin (a cytoskeletal protein, to show the cell body). Automatic cytometry requires that cells be segmented, i.e., that the pixels belonging to each cell be identified. Because segmenting nuclei and distinguishing foreground from background is comparatively easy for these images, the focus here is on finding the boundaries between adjacent cells.
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Content type: Data
EDAM-bioimaging - The ontology of bioimage informatics operations, topics, data, and formats#
Matúš Kalaš et al.
Licensed CC-BY-4.0
EDAM-bioimaging is an extension of the EDAM ontology, dedicated to bioimage analysis, bioimage informatics, and bioimaging.
Tags: Ontology, Bioimage Analysis, Exclude From Dalia
Content type: Poster
EMBL-EBI material collection#
EMBL-EBI
Licensed CC0 (MOSTLY, BUT CAN DIFFER DEPENDING ON RESOURCE)
Online tutorial and webinar library, designed and delivered by EMBL-EBI experts
Tags: Bioinformatics, Exclude From Dalia
Content type: Collection
https://www.ebi.ac.uk/training/on-demand?facets=type:Course%20materials&query=
EMBO Practical Course Advanced methods in bioimage analysis#
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Event
https://www.embl.org/about/info/course-and-conference-office/events/bia23-01/
EPFLx: Image Processing and Analysis for Life Scientists#
Licensed ALL RIGHTS RESERVED
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Online Tutorial
Effect of local topography on cell division of Staphylococci sp.#
Sorzabal Bellido, Ioritz, Luca Barbieri, Beckett, Alison J., Prior, Ian A., Arturo Susarrey-Arce, Tiggelaar, Roald M., Jo Forthergill, Rasmita Raval, Diaz Fernandez, Yuri A.
Published 2021-05-16
Licensed CC-BY-4.0
Dataset.zip
This dataset includes the raw and annotated images used to train a Stardist 2D deep learning model for segmentation of surface attached S.aureus as described in Effect of local topography on cell division of Staphylococci sp.
Stardist2d_Model.zip
Stardist 2D deep learning model for segmentation of surface attached S.aureus, obtained using the StarDist 2D ZeroCostDL4Mic notebook (v 1.12.3).
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Content type: Data
EmbedSeg Repository#
Manan Lalit, Joran Deschamps, Florian Jug, Ajinkya Kulkarni
Licensed CC-BY-NC-4.0
Code Implementation for EmbedSeg, an Instance Segmentation Method for Microscopy Images
Tags: Bioimage Analysis, Instance Segmentation, Exclude From Dalia
Content type: Github Repository
Embryonic mice ultrasound volumes with body and brain volume segmentation masks#
Ziming Qiu, Matthew Hartley
Published 2023-05-10
Licensed CC0-1.0
Ultrasound images of mouse embryos with body and brain volume segmentation masks
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Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD686-ai.html
Enabling Global Image Data Sharing in the Life Sciences#
Peter Bajcsy, Sreenivas Bhattiprolu, Katy Boerner, Beth A Cimini, Lucy Collinson, Jan Ellenberg, Reto Fiolka, Maryellen Giger, Wojtek Goscinski, Matthew Hartley, Nathan Hotaling, Rick Horwitz, Florian Jug, Anna Kreshuk, Emma Lundberg, Aastha Mathur, Kedar Narayan, Shuichi Onami, Anne L. Plant, Fred Prior, Jason Swedlow,, Adam Taylor, Antje Keppler
Published 2024-01-23
Licensed CC-BY-NC-SA-4.0 INTERNATIONAL
Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects).
Tags: Research Data Management, Exclude From Dalia
Content type: Publication
Erick Martins Ratamero - Expanding the OME ecosystem for imaging data management | SciPy 2024#
SciPy, Erick Martins Ratamero
Published 2024-08-19
Licensed YOUTUBE STANDARD LICENSE
Tags: OMERO, Bioimage Analysis, Exclude From Dalia
Content type: Video, Presentation
Euro-BioImaging Scientific Ambassadors Program#
Beatriz Serrano-Solano
Published 2023-07-25
Licensed CC-BY-4.0
Graduation presentation for the 7th cohort of the Open Seeds mentoring & training program for Open Science ambassadors. The project presented is called “Euro-BioImaging Scientific Ambassadors Program”.
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Euro-BioImaging Annual Report 2020#
Euro-BioImaging ERIC
Published 2025-07-23
Licensed CC-BY-4.0
Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2020.
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Euro-BioImaging Annual Report 2021#
Euro-BioImaging ERIC
Published 2022-06-30
Licensed CC-BY-4.0
Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2021.
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Euro-BioImaging Annual Report 2023#
Euro-BioImaging ERIC
Published 2024-06-30
Licensed CC-BY-4.0
Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2023.
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Euro-BioImaging Annual Report 2024#
Euro-BioImaging ERIC
Published 2025-06-30
Licensed CC-BY-4.0
Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2024.
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Euro-BioImaging Communication YouTube Channel#
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Content type: Collection, Video
Euro-BioImaging ERIC Annual Report 2022#
Euro-BioImaging ERIC
Published 2023-07-14
Licensed CC-BY-4.0
Euro-BioImaging ERIC is the European landmark research infrastructure for biological and biomedical imaging as recognized by the European Strategy Forum on Research Infrastructures (ESFRI). Euro-BioImaging is the gateway to world-class imaging facilities across Europe. This document is the Euro-BioImaging Annual Report for the year 2022.
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Euro-BioImaging’s Template for Research Data Management Plans#
Isabel Kemmer, Euro-BioImaging ERIC
Published 2024-06-04
Licensed CC-BY-4.0
Euro-BioImaging has developed a Data Management Plan (DMP) template with questions tailored to bioimaging research projects. Outlining data management practices in this way ensures traceability of project data, allowing for a continuous and unambiguous flow of information throughout the research project. This template can be used to satisfy the requirement to submit a DMP to certain funders. Regardless of the funder, Euro-BioImaging users are encouraged to provide a DMP and can use this template accordingly. This DMP template is available as a fillable PDF with further instructions and sample responses available by hovering over the fillable fields.
Tags: Bioimage Analysis, FAIR-Principles, Research Data Management, Exclude From Dalia
Content type: Collection, Tutorial
Euro-BioImaging/BatchConvert: v0.0.4#
bugraoezdemir
Published 2024-02-19
Licensed CC-BY-4.0
Changes implemented since v0.0.3
Support provided for file paths with spaces. Support provided for globbing filenames from s3 for one-to-one conversion (parse_s3_filenames.py modified). Support provided for single file import from s3 (parse_s3_filenames.py modified). run_conversion.py replaces batchconvert_cli.sh and construct_cli.py, uniting them. Error handling updated for each process
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Evident OIR sample files tiles + stitched image - FV 4000#
Nicolas Chiaruttini
Published 2024-09-04
Licensed CC-BY-4.0
The files contained in this repository are confocal images taken with the Evident FV 4000 of a sample containing DAPI and mCherry stains, excited with a 405 nm laser and a 561 nm laser
individual tiles are named tiling-sample-brain-section_A01_G001_{i}.oir
The stiched image is named Stitch_A01_G001 and contains an extra file Stitch_A01_G001_00001
Some metadata like the tiles positions are stored in the extra files (omp2info)
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Evident OIR sample files with lambda scan - FV 4000#
Nicolas Chiaruttini
Published 2024-07-18
Licensed CC-BY-4.0
The files contained in this repository are confocal images taken with the Evident FV 4000 of a sample containing DAPI and mCherry stains, excited with the 405 nm laser and images for different emission windows (lambda scan). They are public sample files which goal is to help test edge cases of the bio-formats library (https://www.openmicroscopy.org/bio-formats/), in particular for the proper handling of lambda scans.
DAPI_mCherry_22Lambda-420-630-w10nm-s10nm.oir : 22 planes, each plane is an emission window, starting from 420 nm up to 630 nm by steps of 10 nm DAPI_mCherry_4T_5Lambda-420-630-w10nm-s50nm.oir : 20 planes, 5 lambdas from 420 to 630 nm by steps of 50 nm, 4 timepoints DAPI_mCherry_4Z_5Lambda-420-630-w10nm-s50nm.oir : 20 planes, 5 lambdas from 420 to 630 nm by steps of 50 nm, 4 slices DAPI-mCherry_3T_4Z_5Lambda-420-630-w10nm-s50nm.oir : 60 planes, 5 lambdas from 420 to 630 nm by steps of 50 nm, 4 slices, 3 timepoints
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Example Imaris ims datasets.#
Marco Stucchi
Published 2024-11-28
Licensed CC-BY-4.0
The files contained in this repository are example Imaris ims images. Initially related to ome/bioformats#4249
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Example Microscopy Metadata JSON files produced using Micro-Meta App to document example microscopy experiments performed at individual core facilities#
Alessandro Rigano, Ulrike Boehm, Claire M. Brown, Joel Ryan, James J. Chambers, Robert A. Coleman, Orestis Faklaris, Thomas Guilbert, Michelle S. Itano, Judith Lacoste, Alex Laude, Marco Marcello, Paula Montero-Llopis, Glyn Nelson, Roland Nitschke, Jaime A. Pimentel, Stefanie Weidtkamp-Peters, Caterina Strambio-De-Castillia
Published 2022-01-15
Licensed CC-BY-4.0
Example Microscopy Metadata (Microscope.JSON and Settings.JSON) files produced using Micro-Meta App to document the Hardware Specifications of example Microscopes and the Image Acquisition Settings utilized to acquire example images as listed in the table below.
For each facility, the dataset contains two JSON files:
Microscope.JSON file (e.g., 01_marcello_uliverpool_cci_zeiss_axioobserz1_lsm710.json)
Settings.JSON file (indicated with the name of the image and with the _AS suffix)
Micro-Meta App was developed as part of a global community initiative including the 4D Nucleome (4DN) Imaging Working Group, BioImaging North America (BINA) Quality Control and Data Management Working Group, and QUAlity and REProducibility for Instrument and Images in Light Microscopy (QUAREP-LiMi), to extend the Open Microscopy Environment (OME) data model.
The works of this global community effort resulted in multiple publications featured on a recent Nature Methods FOCUS ISSUE dedicated to Reporting and reproducibility in microscopy.
Learn More! For a thorough description of Micro-Meta App consult our recent Nature Methods and BioRxiv.org publications!
Nr.
Manufacturer
Model
Tier
Εxperiment Type
Facility Name
Department and Institution
URL
References
1
Carl Zeiss Microscopy
Axio Observer Z1 (with LSM 710 scan head)
1
3D visualization of superhydrophobic polymer-nanoparticles
Centre for Cell Imaging (CCI)
University of Liverpool
https://cci.liv.ac.uk/equipment_710.html
Upton et al., 2020
2
Carl Zeiss Microscopy
Axio Observer (Axiovert 200M)
2
Μeasurement of illumination stability on Chinese Hamster Ovary cells expressing Paxillin-EGFP
Advanced BioImaging Facility (ABIF).
McGill University
https://www.mcgill.ca/abif/equipment/axiovert-1
Kiepas et al., 2020
3
Carl Zeiss Microscopy
Axio Observer Z1 (with Spinning Disk)
2
Immunofluorescence imaging of cryosection of Mouse kidney
Imagerie Cellulaire; Quality Control managed by Miacellavie (https://miacellavie.com/)
Centre de recherche du Centre Hospitalier Université de Montréal (CR CHUM), University of Montreal
https://www.chumontreal.qc.ca/crchum/plateformes-et-services (the web site is for all core facilities, not specifically for the core facility hosting this microscope)
Pilliod et al., 2020
4
Carl Zeiss Microscopy
Axio Imager Z2 (with Apotome)
2
Immunofluorescence imaging of mitotic division in Hela cells using
Bioimaging Unit
Newcastle University
https://www.ncl.ac.uk/bioimaging/
Watson et al., 2020
5
Carl Zeiss Microscopy
Axio Observer Z1
2
Fluorescence microscopy of human skin fibroblasts from Glycogen Storage Disease patients.
Life Imaging Center (LIC)
Centre for Integrative Signalling Analysis (CISA), University of Freiburg
https://miap.eu/equipments/sd-i-abl/
Hannibal et al., 2020
6
Leica Microsystems
DMI6000B
2
3D immunofluorescence imaging rhinovirus infected macrophages
IMAG'IC Confocal Microscopy Facility
Institut Cochin, CNRS, INSERM, Université de Paris
https://www.institutcochin.fr/core_facilities/confocal-microscopy/cochin-imaging-photonic-microscopy/organigram_team/10054/view
Jubrail et al., 2020
7
Leica Microsystems
DM5500B
2
Immunofluorescence analysis of the colocalization of PML bodies with DNA double-strand breaks
Bioimaging Unit
Edwardson Building on the Campus for Ageing and Vitality, Newcastle University
https://www.ncl.ac.uk/bioimaging/equipment/leica-dm5500/#overview
da Silva et al., 2019; Nelson et al., 2012
8
Leica Microsystems
DMI8-CS (with TCS SP8 STED 3X)
2
Live-cell imaging of N. benthamiana leaves cells-derived protoplasts
Center for Advanced Imaging (CAi)
School of Mathematics/Natural Sciences, Heinrich-Heine-Universität Düsseldorf
https://www.cai.hhu.de/en/equipment/super-resolution-microscopy/leica-tcs-sp8-sted-3x
Singer et al., 2017; Hänsch et al., 2020
9
Nikon Instruments
Eclipse Ti
2
Immunofluorescence analysis of the cytoskeleton structure in COS cells
Advanced Imaging Center (AIC)
Janelia Research Campus, Howard Hughes Medical Institute
https://www.janelia.org/support-team/light-microscopy/equipment
Abdelfattah et al., 2019; Qian et al., 2019; Grimm et al., 2020
10
Nikon Instruments
Eclipse Ti-E (HCA)
2
Τime-lapse analysis of the bursting behavior of amine-functionalized vesicular assemblies
Light Microscopy Facility (IALS-LIF)
Institute for Applied Life Sciences, University of Massachusetts at Amherst
https://www.umass.edu/ials/light-microscopy
Fernandez et al., 2020
11
Nikon Instruments/Coleman laboratory (customized)
TIRF HILO Epifluorescence light Microscope (THEM)/ Eclipse Ti
2
Single-particle tracking of Halo-tagged PCNA in Lox cells
Coleman laboratory
Anatomy and Structural Biology Department, The Albert Einstein College of Medicine
https://einsteinmed.org/faculty/12252/robert-coleman/
Drosopoulos et al., 2020
12
Nikon Instruments
Eclipse Ti (with Andor Dragon Fly Spinning Disk)
2
Investigation of the 3D structure of cerebral organoids
Montpellier Resources Imagerie
Centre de Recherche de Biologie cellulaire de Montpellier (MRI-CRBM), CNRS, Univerity of Montpellier
https://www.mri.cnrs.fr/en/optical-imaging/our-facilities/mri-crbm.html
Ayala-Nunez et al., 2019
13
Nikon Instruments
Eclipse Ti2
2
Ιmmunofluorescence imaging of cryosections of mouse hearth myocardium
Neuroscience Center Microscopy Core
Neuroscience Center, University of North Carolina
https://www.med.unc.edu/neuroscience/core-facilities/neuro-microscopy/
Aghajanian et al., 2021
14
Nikon Instruments
Eclipse Ti2
2
Live-cell imaging of bacterial cells expressing GFP-PopZ
Microscopy Resources on the North Quad (MicRoN)
Harvard Medical School
https://micron.hms.harvard.edu/
Lim and Bernhardt 2019; Lim et al., 2019
15
Olympus/Biomedical Imaging Group (customized)
TIRF Epifluorescence Structured light Microscope (TESM)/IX71
3
3D distribution of HIV-1 in the nucleus of human cells
Biomedical Imaging Group
Program in Molecular Medicine, University of Massachusetts Medical School
https://trello.com/b/BQ8zCcQC/tirf-epi-fluorescence-structured-light-microscope
Navaroli et al., 2012
16
Olympus/Computer Vision Laboratory (customized)
3D BrightField Scanner/IX71
3
Transmitted light brightfield visualization of swimming spermatocytes
Laboratorio Nacional de Microscopia Avanzada (LNMA) and Computer Vision Laboratory of the Institute of Biotechnology
Universidad Nacional Autonoma de Mexico (UNAM)
https://lnma.unam.mx/wp/
Pimentel et al., 2012; Silva-Villalobos et al., 2014
Getting started
Use these videos to get started with using Micro-Meta App after installation into OMERO and downloading the example data files:
Video 1
Video 2
More information
For full information on how to use Micro-Meta App please utilize the following resources:
Micro-Meta App website
Full documentation
Installation instructions
Step-by-Step Instructions
Tutorial Videos
Background
If you want to learn more about the importance of metadata and quality control to ensure full reproducibility, quality and scientific value in light microscopy, please take a look at our recent publications describing the development of community-driven light 4DN-BINA-OME Microscopy Metadata specifications Nature Methods and BioRxiv.org and our overview manuscript entitled A perspective on Microscopy Metadata: data provenance and quality control.
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Example Operetta Dataset#
Nicolas Chiaruttini
Published 2023-07-17
Licensed CC-BY-4.0
This is a microscopy image dataset generated by the Perkin Elmer Operetta HCS microscope by of the user of the PTBIOP EPFL facility. As of the 17th of July 2023, opening this file in ImageJ/Fiji using the BioFormats 6.14 library, this dataset generates a Null Pointer Exception.
A post on forum.image.sc is linked to this issue:
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Example Pipeline Tutorial#
Tim Monko
Published 2024-10-28
Licensed BSD-3-CLAUSE
Napari-ndev is a collection of widgets intended to serve any person seeking to process microscopy images from start to finish. The goal of this example pipeline is to get the user familiar with working with napari-ndev for batch processing and reproducibility (view Image Utilities and Workflow Widget).
Tags: Napari, Bioimage Analysis, Exclude From Dalia
Content type: Documentation, Github Repository, Tutorial
https://timmonko.github.io/napari-ndev/tutorial/01_example_pipeline/
Excel template for adding Key-Value Pairs to images#
Thomas Zobel, Jens Wendt
Published 2024-10-30
Licensed CC-BY-4.0
This Excel Workbook contains some simple Macros to help with the generation of a .csv in the necessary format for Key-Value pair annotations of images in OMERO. The format is tailored for the OMERO.web script “KeyVal_from_csv.py” (from the version <=5.8.3 of the core omero-scripts). Attached is also a video of Thomas Zobel, the head of the imaging core facility Uni Münster, showcasing the use of the Excel workbook.The video uses a slightly older version of the workbook and OMERO, but the core functionality remains unchanged. Please keep in mind, that the OMERO.web script(s) to handle Key-Value Pairs from/to .csv files will undergo a major change very soon.This might break the compatibility with the format used now for the generated .csv from the workbook.
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FAIR BioImage Data#
Licensed CC-BY-4.0
Tags: Research Data Management, Fair, Bioimage Analysis, Exclude From Dalia
Content type: Collection, Video
https://www.youtube.com/watch?v=8zd4KTy-oYI&list=PLW-oxncaXRqU4XqduJzwFHvWLF06PvdVm
FAIR High Content Screening in Bioimaging#
Rohola Hosseini, Matthijs Vlasveld, Joost Willemse, Bob van de Water, Sylvia E. Le Dévédec, Katherine J. Wolstencroft
Published 2023-07-17
Licensed CC-BY-4.0
The authors show the utility of Minimum Information for High Content Screening Microscopy Experiments (MIHCSME) for High Content Screening (HCS) data using multiple examples from the Leiden FAIR Cell Observatory, a Euro-Bioimaging flagship node for high content screening and the pilot node for implementing FAIR bioimaging data throughout the Netherlands Bioimaging network.
Tags: FAIR-Principles, Metadata, Research Data Management, Exclude From Dalia
Content type: Publication
Fiber and vessel dataset for segmentation and characterization#
Saqib Qamar, Baba, Abu Imran, Stèphane Verger, Magnus Andersson
Published 2024-05-03
Licensed CC-BY-4.0
This repository hosts a comprehensive collection of datasets used to develop an innovative deep learning model designed to enhance the segmentation and characterization of macerated fibers and vessel forms in microscopy images. Included in the deposit are raw images, alongside meticulously prepared training and validation datasets. We present an automated segmentation approach that utilizes the one-stage YOLOv8 model, which has been specifically adapted to process high-resolution microscopy images up to 32640 x 25920 pixels. Our model excels in cell detection and segmentation, demonstrating exceptional proficiency.
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Content type: Data
Fiji#
Licensed BSD-2-CLAUSE
Fiji is a popular free open-source image processing package based on ImageJ.
Tags: Imagej, OMERO, Exclude From Dalia
Content type: Online Tutorial
https://omero-guides.readthedocs.io/en/latest/fiji/docs/index.html
Fiji Is Just ImageJ Tutorials#
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Video
https://www.youtube.com/playlist?list=PL5Edc1v41fyCLFZbBCLo41zFO-_cXBfAb
Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging#
Starts Oct 16, 2024, 9:00 AM, Ends Oct 17, 2024, 5:00 PM
Tags: Bioimage Analysis, OMERO, Research Data Management, Exclude From Dalia
Content type: Workshop
Forschungsdatenmanagement zukunftsfest gestalten – Impulse für die Strukturevaluation der Nationalen Forschungsdateninfrastruktur (NFDI)#
Steuerungsgremium Allianz-Schwerpunkt, Alexander von Humboldt Foundation, Deutsche Forschungsgemeinschaft, Fraunhofer Society, German Rectors’ Conference, Leibniz Association, German National Academy of Sciences Leopoldina, German Academic Exchange Service, Helmholtz Association of German Research Centres, Max Planck Society
Published 2024-11-04
Licensed CC-BY-4.0
Arbeitspapier des Steuerungsgremiums des Allianz-Schwerpunkts “Digitalität in der Wissenschaft”
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Fractal Documentation#
Fractal is a framework to process high-content imaging data at scale and prepare it for interactive visualization.
Tags: Workflow Engine, Python, Exclude From Dalia
Content type: Documentation
Galaxy Documentation#
Galaxy is an open source, web-based platform for data intensive biomedical research.
Tags: Workflow Engine, Exclude From Dalia
Content type: Documentation
Galaxy Imaging#
Galaxy Team
Licensed [‘ACADEMIC FREE LICENSE VERSION 3.0’, ‘CREATIVE COMMONS ATTRIBUTION 3.0 (CC BY 3.0) LICENSE’]
Galaxy is an open source tool that offers a filtered set of tools that you can assemble into workflows to manage image data, and perform image analysis and processing.
Tags: Galaxy, Bioinformatics, Exclude From Dalia
Content type: Tool
Galaxy Training#
Published None
Licensed CC-BY-4.0
Collection of tutorials developed and maintained by the worldwide Galaxy community.
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Content type: Collection, Tutorial
Galaxy Training Material#
Licensed MIT
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Content type: Slides, Tutorial
GalaxyProject YouTube Channel#
Galaxy Team
Published 2020-06-16
Licensed UNKNOWN
Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational research.
Tags: Galaxy, Bioinformatics, Exclude From Dalia
Content type: Youtube Channel
GerBI Teaching Resources Link List#
Licensed UNKNOWN
List of training materials by the German BioImaging community provided by facilities, the scientific community, and companies.
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Content type: Collection
GerBI-Chat: Teil 1 - Vom Bedarf bis zum Großgeräteantrag-Schreiben#
Financial & Legal Framework of Core Facilities, Elmar Endl, Jana Hedrich, Juliane Hoth, Julia Nagy, Astrid Schauss, Nina Schulze, Silke Tulok
Published 2024-09-11
Licensed CC-BY-4.0
Die GermanBioImaging (GerBI-GMB) - Deutsche Gesellschaft für Mikroskopie und Bildanalyse e.V. bietet über regelmäßig stattfindende Treffen (GerBI-Chats) die Möglichkeit zum aktiven Austausch der Mitglieder untereinander. Das GerBI-GMB Team “Legal und Finacial Framwork”, welches sich mit administrativen Aufgaben rund um das Core Facility Management beschäftigt, nutzt diese Möglichkeit zum aktiven Austausch innerhalb des Netzwerkes und darüber hinaus. Der Beschaffungsprozess von Forschungsgroßgeräten ist komplex und je nach Institution unterschiedlich geregelt. Aus unserer Sicht lässt sich dieser Prozess grob in drei Stufen aufteilen:
Bedarfsanmeldung Antragsvorbereitung und -fertigstellung Antragsbewilligung und Nutzung
Dieser hier enthaltene Beitrag ist der Initialvortrag des GerBi-Chats zum Teil 1 - Von der Bedarfsanmeldung bis zum Beginn der Antragststellung. Die weiteren Stufen der Großgerätebeschaffung werden in nachfolgenden Beiträgen behandelt.
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GerBI-Chat: Teil 2 - Wie schreibe ich am besten einen Großegräteantrag#
Financial & Legal Framework of Core Facilities, Elmar Endl, Jana Hedrich, Juliane Hoth, Julia Nagy, Astrid Schauss, Nina Schulze, Silke Tulok
Published 2024-10-02
Licensed CC-BY-4.0
Die GermanBioImaging (GerBI-GMB) - Deutsche Gesellschaft für Mikroskopie und Bildanalyse e.V. bietet über regelmäßig stattfindende Treffen (GerBI-Chats) die Möglichkeit zum aktiven Austausch der Mitglieder untereinander. Das GerBI-GMB Team “Legal und Finacial Framwork”, welches sich mit administrativen Aufgaben rund um das Core Facility Management beschäftigt, nutzt diese Möglichkeit zum aktiven Austausch innerhalb des Netzwerkes und darüber hinaus. Der Beschaffungsprozess von Forschungsgroßgeräten ist komplex und je nach Institution unterschiedlich geregelt. Aus unserer Sicht lässt sich dieser Prozess grob in drei Stufen aufteilen:
Bedarfsanmeldung Antragsvorbereitung und -fertigstellung Antragsbewilligung und Nutzung
Nach dem Initialvortrag der GerBI-Chat Reihe, in dem das Thema Bedarfsanmeldung im Fokus stand, geht es im hier enthaltenen zweiten Teil „Antragsvorbereitung und -fertigstellung: Wie schreibe ich am besten einen Großgeräteantrag?“ um die Beantragung von Forschungsgroßgeräten nach Art. 91b GG.
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Get started accessing the de.NBI cloud.#
de.NBI
Licensed UNKNOWN
Tutorial for accessing de.NBI cloud
Tags: Bioinformatics, Cloud Computing, Exclude From Dalia
Content type: Tutorial
Ghent University Research Data Management (RDM) - policy and support#
University of Ghent
Licensed UNKNOWN
The website provides resources and guidelines for managing research data efficiently and responsibly. Its focus is to ensure that data are properly organized, stored, documented, and shared throughout a research project, and even beyond, in a way that aligns with Open Science principles.
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Content type: Website
Glencoe Software Webinars#
Chris Allan, Emil Rozbicki
Licensed UNKNOWN
Example Workflows / usage of the Glencoe Software.
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Content type: Video, Tutorial, Collection
GloBIAS#
GloBIAS
Published 2024-07-17
Licensed UNKNOWN
This is the YouTube channel of GloBIAS, the Global BioImage Analysts Society. GloBIAS is a non-profit association officially constituted in October 2024.
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Content type: Youtube Channel
GloBIAS in-person workshop 2024#
Christa Walther
Published 2025-04-07
Licensed CC-BY-4.0
This document reports on the first in-person workshop supported by GloBIAS. Each session has its own chapter provided by the people chairing the sessions, summarising the outputs achieved.
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Global BioImaging Training Database#
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Event
Global BioImaging YouTube channel#
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Content type: Collection, Video
Go-Nuclear. A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context#
Kay Schneitz, Athul Vijayan, Tejasvinee Mody
Published 2024-06-29
Licensed CC0-1.0
We present computational tools that allow versatile and accurate 3D nuclear segmentation in plant organs, enable the analysis of cell-nucleus geometric relationships, and improve the accuracy of 3D cell segmentation. This biostudies submission includes Arabidopsis ovule model training dataset used in the study. The training dataset is composed of strong and weak nuclei image channels, corresponding ground truth segmentation, cell wall image and associated cell segmentation mentioned in the study. Trained models from the study, a total of 47 trained models are made available from this study. This included 15 initial models, 30 gold models, and 2 platinum models. Models were trained using PlantSeg, Stardist and Cellpose. All image datasets and its segmentation as part of the figures in this study is also available as separate zip files. This includes image dataset from different species and organs as listed below.
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Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1026/
Ground-truth cell body segmentation used for Starfinity training#
Yuhan Wang, Martin Weigert, Uwe Schmidt, Stephan Saalfeld, Eugene W. Myers, Tim Wang, Karel Svoboda, Mark Eddison, Greg Fleishman, Shengjin Xu, Fredrick E. Henry, Andrew L. Lemire, Hui Yang, Konrad Rokicki, Cristian Goina, Eugene W Myers, Wyatt Korff, Scott M. Sternson, Paul W. Tillberg
Published 2021-03-05
Licensed CC-BY-4.0
Accurate segmentation of volumetric fluorescence image data has been a long-standing challenge and it can considerably degrade the accuracy of multiplexed fluorescence in situ hybridization (FISH) analysis. To overcome this challenge, we developed a deep learning-based automatic 3D segmentation algorithm, called Starfinity. It first predicts its cell center probability and its radial distances to the nearest cell borders for each pixel. It then aggregates pixel affinity maps from the densely predicted distances and applies a watershed segmentation on the affinity maps using the thresholded center probability as seeds.
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Content type: Data
Gut Analysis Toolbox#
Luke Sorensen, Ayame Saito, Sabrina Poon, Noe Han, Myat, Ryan Hamnett, Peter Neckel, Adam Humenick, Keith Mutunduwe, Christie Glennan, Narges Mahdavian, JH Brookes, Simon, M McQuade, Rachel, PP Foong, Jaime, Estibaliz Gómez-de-Mariscal, Muñoz Barrutia, Arrate, Kaltschmidt, Julia A., King, Sebastian K., Robert Haase, Simona Carbone, A. Veldhuis, Nicholas, P. Poole, Daniel, Pradeep Rajasekhar
Published 2025-07-24
Licensed BSD-3-CLAUSE
Reverted to StarDist for neuron segmentation. Used this bugfix for stardist plugin issue. protobuf-java-3.23.4.jar is being shipped as part of GAT update site. Added StarDist models back and removed deepimageJ models for neuron segmentation Updated documentation website to use a stable Fiji download: https://gut-analysis-toolbox.gitbook.io/docs#installation-and-configuration
Full Changelog: https://github.com/pr4deepr/GutAnalysisToolbox/compare/v1.0…v1.1
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Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia#
Luke Sorensen, Ayame Saito, Sabrina Poon, Myat Noe Han, Adam Humenick, Peter Neckel, Keith Mutunduwe, Christie Glennan, Narges Mahdavian, Simon JH Brookes, Rachel M McQuade, Jaime PP Foong, Sebastian K. King, Estibaliz Gómez-de-Mariscal, Arrate Muñoz-Barrutia, Robert Haase, Simona Carbone, Nicholas A. Veldhuis, Daniel P. Poole, Pradeep Rajasekhar
Published 2025-05-01
Licensed CC-BY-4.0
This upload is associated with the software, Gut Analysis Toolbox (GAT). If you use it please cite: Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: https://doi.org/10.1242/jcs.261950 The upload contains StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and FPN+ResNet101 model for enteric ganglia in 2D in gut wholemount tissue. GAT is implemented in Fiji, but the models can be used in any software that supports StarDist and the use of 2D UNet models. The files here also consist of Python notebooks (Google Colab), training and test data as well as reports on model performance. Note: The enteric ganglia model is has been updated to v3 which uses pytorch and is a different architecture (FPN+ResNet101). The model files are located in the respective folders as zip files. The folders have also been zipped:
Neuron (Hu; StarDist model):
Main folder: 2D_enteric_neuron_model_QA.zip StarDist Model File:2D_enteric_neuron_v4_1.zip DeepImageJ compatible model: 2D_enteric_neuron.bioimage.io.model.zip (used currently in GAT)
Neuronal subtype (StarDist model):
Main folder: 2D_enteric_neuron_subtype_model_QA.zip Model File: 2D_enteric_neuron_subtype_v4.zip DeepImageJ compatible model: 2D_enteric_neuron_subtype.bioimage.io.model.zip (used currently in GAT)
Enteric ganglia (2D FPN_ResNet101; Use in FIJI with deepImageJ)
Main folder: 2D_enteric_ganglia_v3_training.zip Model File: 2D_Ganglia_RGB_v3.bioimage.io.model.zip (used currently in GAT)
For the all models, files included are:
Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet. Training and Test datasets used for training. Google Colab notebooks used for training and quality assurance (ZeroCost DL4Mic notebooks). Python notebook and code for training ganglia model with QA. Quality assurance reports generated from above notebooks. StarDist model exported for use in QuPath.
The model files can be used within can be used within the software, StarDist. They are intended to be used within FIJI or QuPath, but can be used in any software that supports the implementation of StarDist in 2D. Data: All the images were collected from 4 different research labs and a public database (SPARC database) to account for variations in image acquisition, sample preparation and immunolabelling. For enteric neurons the pan-neuronal marker, Hu has been used and the 2D wholemounts images from mouse, rat and human tissue. For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used.. 25 images were used from the following entries in the SPARC database:
Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. Graham, K. D., Huerta-Lopez, S., Sengupta, R., Shenoy, A., Schneider, S., Wright, C. M., Feldman, M., Furth, E., Lemke, A., Wilkins, B. J., Naji, A., Doolin, E., Howard, M., & Heuckeroth, R. (2020). Robust 3-Dimensional visualization of human colon enteric nervous system without tissue sectioning (Version 1) [Data set]. SPARC Consortium. Wang, L., Yuan, P.-Q., Gould, T. and Tache, Y. (2021). Antibodies Tested in theColon – Mouse (Version 1) [Data set]. SPARC Consortium. doi:10.26275/i7dl-58h
Additional images for new ganglia model:
Hamnett, R., Dershowitz, L. B., Sampathkumar, V., Wang, Z., Gomez-Frittelli, J., De Andrade, V., Kasthuri, N., Druckmann, S. and Kaltschmidt, J. A. (2022b). Regional cytoarchitecture of the adult and developing mouse enteric nervous system. Curr. Biol. 32, 4483-4492.e5.
The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using:
Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3)
Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88)
Zeiss Axio Imager M2 (20X HC PL APO NA 0.3)
Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45)
Human tissue images were acquired using:
IX71 Olympus microscope (10X HC PL APO NA 0.3)
For more information, visit the Documentation website. NOTE: The images for enteric neurons and neuronal subtypes have been rescaled to 0.568 µm/pixel for mouse and rat. For human neurons, it has been rescaled to 0.9 µm/pixel . This is to ensure the neuronal cell bodies have similar pixel area across images. The area of cells in pixels can vary based on resolution of image, magnification of objective used, animal species (larger animals -> larger neurons) and potentially how the tissue is stretched during wholemount preparation Average neuron area for neuronal model: 701.2 ± 195.9 pixel2 (Mean ± SD, 6267 cells) Average neuron area for neuronal subtype model: 880.9 ± 316 pixel2 (Mean ± SD, 924 cells) Software References: Stardist Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 265-273). Springer, Cham. deepImageJ Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia, A. and Sage, D., 2021. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nature Methods, 18(10), pp.1192-1195. ZeroCost DL4Mic von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature communications, 12(1), pp.1-18.
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HPA Nucleus Segmentation (DPNUnet)#
Hao Xu, Wei Ouyang
Published 2023-03-02
Licensed CC-BY-4.0
Download RDF Package
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Content type: Data
HT1080WT cells embedded in 3D collagen type I matrices - manual annotations for cell instance segmentation and tracking#
Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Denis Wirtz, Arrate Muñoz-Barrutia
Published 2021-12-13
Licensed CC-BY-4.0
Human fibrosarcoma HT1080WT (ATCC) cells at low cell densities embedded in 3D collagen type I matrices [1]. The time-lapse videos were recorded every 2 minutes for 16.7 hours and covered a field of view of 1002 pixels × 1004 pixels with a pixel size of 0.802 μm/pixel The videos were pre-processed to correct frame-to-frame drift artifacts, resulting in a final size of 983 pixels × 985 pixels pixels.
Hasini Jayatilaka, Anjil Giri, Michelle Karl, Ivie Aifuwa, Nicholaus J Trenton, Jude M Phillip, Shyam Khatau, and Denis Wirtz. EB1 and cytoplasmic dynein mediate protrusion dynamics for efficient 3-dimensional cell migration. FASEB J., 32(3):1207–1221, 2018. ISSN 0892-6638. doi: 10.1096/fj.201700444RR.
Further information about how to use this data is given in esgomezm/microscopy-dl-suite-tf
This dataset is provided together with the following preprint and if you use it, we would like to kindly ask you to cite it properly:
Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek, Thomas Brox, Denis Wirtz, Arrate Muñoz-Barrutia, Search for temporal cell segmentation robustness in phase-contrast microscopy videos, arXiv 2021 (arXiv:2112.08817)
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Content type: Data
Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy#
Riccardo Massei
Published 2024-03-07
Licensed CC-BY-4.0
Results of the project “Conversion of KNIME image analysis workflows to Galaxy” during the Hackathon “Image Analysis in Galaxy” (Freiburg 26 Feb - 01 Mar 2024)
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Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data#
Nikki Bialy, Frank Alber, Brenda Andrews, Michael Angelo, Brian Beliveau, Lacramioara Bintu, Alistair Boettiger, Ulrike Boehm, Claire M. Brown, Mahmoud Bukar Maina, James J. Chambers, Beth A. Cimini, Kevin Eliceiri, Rachel Errington, Orestis Faklaris, Nathalie Gaudreault, Ronald N. Germain, Wojtek Goscinski, David Grunwald, Michael Halter, Dorit Hanein, John W. Hickey, Judith Lacoste, Alex Laude, Emma Lundberg, Jian Ma, Leonel Malacrida, Josh Moore, Glyn Nelson, Elizabeth Kathleen Neumann, Roland Nitschke, Shuichi Onami, Jaime A. Pimentel, Anne L. Plant, Andrea J. Radtke, Bikash Sabata, Denis Schapiro, Johannes Schöneberg, Jeffrey M. Spraggins, Damir Sudar, Wouter-Michiel Adrien Maria Vierdag, Niels Volkmann, Carolina Wählby, Siyuan (Steven)Wang, Ziv Yaniv, Caterina Strambio-De-Castillia
Published 2024-08-30
Licensed CC-BY-NC-SA-4.0 INTERNATIONAL
Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated.
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Content type: Publication
HeLa “Kyoto” cells under the scope#
Romain Guiet
Published 2022-02-25
Licensed CC-BY-4.0
Name: HeLa “Kyoto” cells under the scope
Microscope: Perkin Elmer Operetta microscope with a 20x N.A. 0.8 objective and an Andor Zyla 5.5 camera.
Microscopy data type: The time-lapse datasets were acquired every 15 minutes, for 60 hours. From the individual plan images (channels, time-points, field of view exported by the PerkinElmer software Harmony) multi-dimension images were generated using the Operetta_Importer-0.1.21 with a downscaling of 4.
Channel 1 : Low Contrast DPC (Digital Phase Contrast)
Channel 2 : High Contrast DPC
Channel 3 : Brightfield
Channel 4 : EGFP-α-tubulin
Channel 5 : mCherry-H2B
File format: .tif (16-bit)
Image size: 540x540 (Pixel size: 0.299 nm), 5c, 1z , 240t
Cell type: HeLa “Kyoto” cells, expressing EGFP-α-tubulin and mCherry-H2B ( Schmitz et al, 2010 )
Protocol: Cells were resuspended in Imaging media and were seeded in a microscopy grade 96 wells plate ( CellCarrier Ultra 96, Perkin Elmer). The day after seeding, and for 60 hours, images were acquired in 3 wells, in 25 different fields of view, every 15 minutes.
Imaging media: DMEM red-phenol-free media (FluoroBrite™ DMEM, Gibco) complemented with Fetal Calf Serum and Glutamax.
NOTE: This dataset was used to automatically generate label images in the following Zenodo entry: https://doi.org/10.5281/zenodo.6140064
NOTE: This dataset was used to train the cellpose models in the following Zenodo entry: https://doi.org/10.5281/zenodo.6140111
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Hitchhiking through a diverse Bio-image Analysis Software Universe#
Robert Haase
Published 2022-07-22
Licensed CC-BY-4.0
Overview about decision making and how to influence decisions in the bio-image analysis software context.
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Slides, Presentation
How open-source software could finally get the world’s microscopes speaking the same language#
Michael Brooks
Published 2023-10-02
Licensed UNKNOWN
A plethora of standards mean shareable and verifiable microscopy data often get lost in translation. Biologists are working on a solution.
Tags: Research Data Management, Microscopy, Exclude From Dalia
Content type: Blog Post
How to get started with Jupyter and Colab#
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Content type: Video
How to make cartographic projections using ImSAnE#
Vellutini, Bruno C.
Published 2022-03-29
Licensed GPL-2.0
This tutorial shows how to make cartographic projections of fly embryos using the ImSAnE Toolbox (Heemskerk and Streichan 2015).
Instructions: download and open the imsane-tutorial.html file on your browser.
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Human DAB staining Axioscan BF 20x#
Mario Garcia
Published 2024-05-21
Licensed CC-BY-4.0
Human brain tissue with DAB immunostaining. Image acquired by BF microscopy in Zeiss Axioscan at 20x.
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Human HT29 colon-cancer cells#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC-BY-NC-SA-3.0
These images are of human HT29 colon cancer cells, a cell line that has been widely used for the study of many normal and neoplastic processes. A set of about 43,000 such images was used by Moffat et al. (Cell, 2006) to screen for mitotic regulators. The analysis followed the common pattern of identifying and counting cells with a phenotype of interest (in this case, cells that were in mitosis), then normalizing the count by dividing by the total number of cells. Such experiments present two image analysis problems. First, identifying the cells that have the phenotype of interest requires that the nuclei and cells be segmented. Second, normalizing requires an accurate cell count.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
Human Hepatocyte and Murine Fibroblast cells Co-culture experiment#
David J. Logan, Jing Shan, Sangeeta N. Bhatia, Anne E. Carpenter
Published 2016-03-01
Licensed CC-BY-3.0
This 384-well plate has images of co-cultured hepatocytes and fibroblasts. Every other well is populated (A01, A03, …, C01, C03, …) such that 96 wells comprise the data. Each well has 9 sites and thus 9 images associated, totaling 864 images.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
Human Lung Tissue Microscopy (DIC, Fluorescence, Cell and Nuclei Semantic Instance Annotations)#
Melanie Dohmen, Mirja Mittermaier, Andreas Hocke
Published 2024-02-22
The zip file contains 3 folders (annotations, images and training_splits).The annotation folder contains 3 folders (cell_instances, nuclei_instances and semantic). Cell and nuclei instance annotations are long int tif images, containing numbered instance ids and 0 in the background. Semantic annotations are 8-bit int png files containing the class ids (0: background, 1: normal tissue, 2: erythrocytes, 3: alveolar epithelial type 2 cells, 4: alveolar macrophages, 5: other nuclei, 6: alveolar epithelial type 2 cell nuclei, 7: alveolar macrophage nuclei, 8: cell debris). The image folder contains 4 folders (CD68, DAPI, DIC, proSPC), where DIC contains float valued background-corrected differential interference contrast images, the others contain normalized float-valued fluorescence channels of a multi-plex staining with CD-68 (whole alveolar macrophages), DAPI (any cell nuclei), proSPC (cytoplasm of alveolar epithelial type 2 cell). All images are in tif format. The training split folder contains 3 text files, with the image prefix (compared to images and annotations without ending, i.e. e.g. without “_DIC.tif”) of all cases in the respective subset. With a total of 68 cases, there are 51 cases in the train set, 7 cases in the validation set and 10 cases in the test set.The lung tissue origins from lung surgery of patients, but does not include resected tumors. Please see reference [1]. The images were acquired with a laser scanning microscope with 40x magnification and 1024 x 1024 pixels per image.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
Human U2OS cells (out of focus)#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
Since robust foreground/background separation and segmentation of cellular objects (i.e., identification of which pixels below to which objects) strongly depends on image quality, focus artifacts are detrimental to data quality. This image set provides examples of in- and out-of-focus HCS images which can be used for validation of focus metrics.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
I3D bio – Information Infrastructure for BioImage Data - Bioimage Metadata#
Christian Schmidt
Licensed UNKNOWN
A Microscopy Research Data Management Resource.
Tags: Metadata, I3Dbio, Research Data Management, Exclude From Dalia
Content type: Collection
https://gerbi-gmb.de/i3dbio/i3dbio-rdm/i3dbio-bioimage-metadata/
I3D:bio list of online training material#
Licensed UNKNOWN
List of links to training materials by the I3D:bio community.
Tags: Research Data Management, Exclude From Dalia
Content type: Collection
https://gerbi-gmb.de/i3dbio/i3dbio-teaching/train-mat/bioimagelist/
ICS/IDS stitched file#
IMCF
Published 2024-06-13
Licensed CC-BY-4.0
Hi @ome team ! We usually use ICS/IDS file formats as an output to our stitching pipeline as the reading and writing is pretty fast. However, it seems that since Bio-Formats 7.x opening the files is not working anymore. I tried with a Fiji with Bio-Formats 6.10.1 and the files open, but more recent versions give an issue. java.lang.NullPointerException at loci.formats.in.ICSReader.initFile(ICSReader.java:1481) at loci.formats.FormatReader.setId(FormatReader.java:1480) at loci.plugins.in.ImportProcess.initializeFile(ImportProcess.java:498) at loci.plugins.in.ImportProcess.execute(ImportProcess.java:141) at loci.plugins.in.Importer.showDialogs(Importer.java:156) at loci.plugins.in.Importer.run(Importer.java:77) at loci.plugins.LociImporter.run(LociImporter.java:78) at ij.IJ.runUserPlugIn(IJ.java:244) at ij.IJ.runPlugIn(IJ.java:210) at ij.Executer.runCommand(Executer.java:152) at ij.Executer.run(Executer.java:70) at ij.IJ.run(IJ.java:326) at ij.IJ.run(IJ.java:337) at ij.macro.Functions.doRun(Functions.java:703) at ij.macro.Functions.doFunction(Functions.java:99) at ij.macro.Interpreter.doStatement(Interpreter.java:281) at ij.macro.Interpreter.doStatements(Interpreter.java:267) at ij.macro.Interpreter.run(Interpreter.java:163) at ij.macro.Interpreter.run(Interpreter.java:93) at ij.macro.MacroRunner.run(MacroRunner.java:146) at java.lang.Thread.run(Thread.java:750)
You can find one example file at this link 1. Thanks for your help !Best,Laurent
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ITKElastix Examples#
Licensed APACHE-2.0
Tags: Bioimage Analysis, Exclude From Dalia
Ibiology. Bioimage Analysis Course. The Life Cycle of an Image Data Set#
Licensed CC BY-NC-ND 3.0 DEED
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Video
https://www.ibiology.org/online-biology-courses/bioimage-analysis-course/
Image Data Resources#
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Content type: Collection, Data, Publication
Image Data Services at Euro-BioImaging: Community efforts towards FAIR Image Data and Analysis Services#
Aastha Mathur
Licensed UNKNOWN
Tags: Exclude From Dalia
Content type: Slides
https://docs.google.com/presentation/d/1henPIDTpHT3bc1Y26AltItAHJ2C5xCOl/edit#slide=id.p1
Image analysis in Galaxy#
Beatriz Serrano-Solano, Björn Grüning
Licensed UNKNOWN
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Slides
https://docs.google.com/presentation/d/1WG_4307XmKsGfWT3taxMvX2rZiG1k0SM1E7SAENJQkI/edit#slide=id.p
ImageJ Bioformats 8.3.0 Importer Incorrectly Reading ND2 Metadata#
Snyder, Erika, Erika Thomas, Erika T.
Published 2025-08-21
Licensed CC-BY-4.0
Hi all,I was referred to this community from the Image.sc Forum original post: https://forum.image.sc/t/imagej-bioformats-importer-incorrectly-reading-metadata/115943 I have an ND2 file, 3 color channels, 2 positions in the well, and 81 timepoints. However, when I open this as I normally would in ImageJ as a hyperstack, the stack interpretation is totally incorrect. It is including my Z-positions as frames in the timelapse. Even when I open the series for the positions independently, images from the other series will appear within it. I am running Bioformats 8.3.0. I have tried swapping dimensions. That did not work. I have tried creating substacks to parse out one series from the other, this also did not work. The only thing I can think of that is different from before is that I was previously aquiring z-stacks with our MCL nanodrive Piezo, and we had to have that serviced so in the meantime I used the Ti2 eclipse camera drive for z-stack aquisiton. I have opened the metadata to compare aquisitions between the two, and the stack order appears exactly the same, although Bioformats has no problem reading the metadata for aquisitions with the Piezo. I have also opened this file in NIS elements viewer, and all the information for the stacks appears correctly, so I dont think aquisitions is the issue. I have also tried opening this file on multiple computers with multiple versions of imageJ, and the issue persists. Any advice would be greatly appreciated I am panicking a bit because this is a few months worth of data I am suddenly not able to analyze. Please let me know if there’s anything else needed to help figure this out.
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ImageJ tool for percentage estimation of pneumonia in lungs#
Martin Schätz, Olga Rubešová, Jan Mareš, Alan Spark
Published 2025-07-07
Licensed CC-BY-4.0
The software tool is developed on demand of Radiological Department of Faculty Hospital of Královské Vinohrady, with the aim to provide a tool to estimate the percentage of pneumonia (or COVID-19 presence) in lungs. Paper Estimation of Covid-19 lungs damage based on computer tomography images analysis presenting the tool is available on F1000reserach DOI: 10.12688/f1000research.109020.1. The underlying dataset is published in Zenodo (DOI:10.5281/zenodo.5805939). One of the challenges was to design a tool that would be available without complicated install procedures and would process data in a reasonable time even on office computers. For this reason, 8-bit and 16-bit version of the tool exists. The FIJI software (or ImageJ with Bio-Formats plugin installed) was selected as the best candidate. Examples of use and tutorials are available at GitHub. The third version includes an intra-variabilty analysis, containing evaluation both for percentage and score metrics. Underlying data:DOI:10.5281/zenodo.5805939The first five datasets are analyzed using this tool, with results and parameters to repeat the analysis in results_csv.csv or results.xlsx. Contributions:Martin SCHÄTZ: Coding, tool testing, data curation, data set analysisOlga RUBEŠOVÁ: Code review, tutorial preparation, tool testing, data set analysisJan MAREŠ: Tool testing, data set analysis, funding acquisitionAlan SPARK: Tool testing The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001.
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Images acquired with Zeiss Sigma 300 - Images with low magnification are corrently not handeled correctly#
Johannes Preußner
Published 2025-08-07
Licensed CC-BY-4.0
When using bioformats the images are not scaled correctly. The problem arises with low magnifications where the lengths in the metadata are given in µm (not in nm). Attached are two pictures. Only with the picture with the ending “Correct_scale_bar” the import is working correctly. One issue might be that the metadata information of the images are stored in iso-8859-1
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Imaris Tutorials#
Licensed ALL RIGHTS RESERVED
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Video
Implantation of abdominal imaging windows on the mouse kidney#
Michael Gerlach
Published 2024-09-04
Licensed CC-BY-ND-4.0
This video describes the surgical process of implanting an abdominal imaging window (AIW) on the kidney of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. This upload contains the full version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.
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Implantation of abdominal imaging windows on the mouse kidney - short version#
Michael Gerlach
Published 2024-09-09
Licensed CC-BY-ND-4.0
This video describes the surgical process of implanting an abdominal imaging window (AIW) on the kidney of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. This upload contains the shortened version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.
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Implantation of abdominal imaging windows on the mouse liver#
Michael Gerlach
Published 2024-09-04
Licensed CC-BY-ND-4.0
This video describes the surgical process of implanting an abdominal imaging window (AIW) on the liver of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. This upload contains the full version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.
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Implantation of abdominal imaging windows on the mouse liver - short version#
Michael Gerlach
Published 2024-09-09
Licensed CC-BY-ND-4.0
This video describes the surgical process of implanting an abdominal imaging window (AIW) on the liver of mice. This window can be used for acute or longitudinal imaging. All experiments have been reviewed and approved by the local authorities (Landesdirektion Sachsen). Implantation of chronic abdominal windows allows for microscopical investigation of highly dynamic processes in physiological and pathological circumstances and is generally tolerated well by experimental animals. It enables insights which otherwise could only be obtained using high numbers of experimental animals. The method can be regarded as reduction approach in terms of 3R implementation. This upload contains the short version and is distributed under CC BY-ND 4.0 license to inhibit decontextualized misuse. Please check license terms for usage, especially for remixing/transforming! If you want to remix the material, get in contact with the author.
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InCell datasets with mix of 2D and 3D failed to be read#
Fabien Kuttler, Rémy Dornier
Published 2025-01-31
Licensed CC-BY-4.0
The provided dataset contains 2 wells, 4 fields of view, 4 channels, no T but different number of Z according to the channel
Cy3 : 1 Z DAPI : 16 Z FITC : 1 Z Brightfield : 1 Z
The mix 2D/3D is not correctly supported and the .xcde file cannot be read. A discussion thread is already open on that topic. Bio-Formats version : 8.0.1
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Ink in a dish#
Cavanagh
Published 2024-09-03
Licensed CC0-1.0
A test data set for troublshooting. no scientific meaning.
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Insights and Impact From Five Cycles of Essential Open Source Software for Science#
Kate Hertweck, Carly Strasser, Dario Taraborelli
Licensed CC-BY-4.0
Open source software (OSS) is essential for advancing scientific discovery, particularly in biomedical research, yet funding to support these vital tools has been limited. The Chan Zuckerberg Initiative’s Essential Open Source Software for Science (EOSS) program has significantly contributed to this field by providing $51.8 million in funding over five years to support the maintenance, growth, and community engagement of critical OSS tools. The program has impacted scientific OSS projects by improving their technical outputs, community building, and sustainability practices, and fostering collaborations within the OSS community. Additionally, EOSS funding has enhanced diversity, equity, and inclusion within the OSS community, although changes in principal investigator demographics were not observed. The funded projects have had a substantial impact on biomedical research by improving the usability and accessibility of scientific software, which has led to increased adoption and advancements in various biomedical fields.
Tags: Open Source Software, Funding, Sustainability, Exclude From Dalia
Content type: Publication
Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development#
Stefan Dvoretskii
Published 2024-04-10
Licensed CC-BY-4.0
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Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development#
Stefan Dvoretskii
Published 2024-04-10
Licensed CC-BY-4.0
Tags: Exclude From Dalia
Institutionalization and Collaboration as a Way of Addressing the Challenges Open Science Presents to Libraries: The University of Konstanz as a National Pioneer#
Sophie Habinger, Maximilian Heber, Sonja Kralj, Emilia Mikautsch
Published 2024-07-09
Licensed CC-BY-4.0
The rise of Open Science (OS) and the academic community’s needs that come with it bring about a range of challenges for academic libraries. To face these challenges, the University of Konstanz has created a competence unit called Team Open Science in the Communication, Information, Media Center (KIM) - a joint unit of library and IT infrastructure. The Team creates synergies within itself and across the library. In December 2023, it involved 12 staff members specialising in open access (OA), research data management (RDM), open educational resources (OER) and virtual research environments (VRE). It collaborates closely with other KIM departments. This submission shall serve as a best practice example for the impact of OS on research libraries and, beyond that, the impact of research libraries on universities. To enhance and foster OS, the Team provides individual consultations, services and office hours for researchers. Here, it collaborates closely with other librarians like subject specialists and the Team University Publications. Along similar lines, the KIM offers institutional repositories for publications (KOPS) and research data (KonDATA). Beyond that, the Team provides solutions to host OA journals and analyses researchers’ VRE needs to decide on implementation options. In sum, the Team is the central OS contact point for the entire university, underlining the major role the library holds in making institutional impact. Furthermore, the Team had the leading role in creating the University of Konstanz’ OS Policy, one of the first ones passed by a German university. This policy stands out because it encompasses various OS domains. It demands, among other things, that text publications be made OA and that research data be managed according to relevant subject-specific standards. If permissible and reasonable, it demands that research data should be made publicly available at the earliest possible time. Along these lines, the policy has a large impact on how the library handles closed access books and subscription-based journals. As a consequence, OA is pursued wherever possible, leading to the highest OA quota of all German universities. In that sense, the Team is a crucial driving force of OS in the University of Konstanz, which ties in with the library’s major role of open research transformation. Beyond the University of Konstanz, the Team is involved in a range of national and international projects collaborating with other libraries. On a national level, they lead the project open.access-network which provides an information platform for researchers and librarians and connects the German-speaking OA community through events like bar camps. The project KOALA-AV supports libraries in establishing consortial solutions for financing Diamond OA publications. Moreover, the Team is involved in the federal state initiative for RDM in Baden-Württemberg (bwFDM). Here, the Team is in charge of forschungsdaten.info, the German-speaking countries’ leading RDM information platform, which will be offered in English within the next years. Internationally, the Team cooperates with librarians and other OS professionals from the European Reform University Alliance (ERUA) and the European University for Well-Being (EUniWell), establishing formats for best practice exchange, such as monthly OS Meet-Ups.
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Integration of Bioimage and *Omics data resources#
Carsten Fortmann-Grote, Mariana Meireles
Published 2025-02-03
This Poster was presented at the 2025 All Hands Meeting of the NFDI4BIOIMAGE Consortium. It presents the current state of data integration activities at the MPI for Evolutionary Biology. Various data and metadata resources such as the internal image data repository OMERO and the Electronic Lab Notebook System OpenBIS are converted into a RDF Knowledge Graph utilizing a R2RML mapping scheme based on the Ontop-VKG framework. The materialized Knowledge Graph is then served via the QLever SPARQL endpoint and user interface. A graphical query editor (SPARNatural) assists users with no SPARQL knowledge in constructing their queries by selecting triple elements from dropdown menus and other widgets. We also present a benchmark comparison of query response times on 10 selected SPARQL queries run against three different endpoint/triplestore implementations.
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Intravital microscopy contrasting agents for application - Database#
Michael Gerlach
Published 2024-06-19
Licensed CC-BY-4.0
This is a set of databases containing published use of substances which can be applied to rodents in order to contrast specific structures for optical intravital microscopy. The first dataset contains applied final dosages, calculated for 25g-mice, as well as the orignally published amounts, concentrations and application routes of agents directly applied into the target organism. The second dataset contains dosages and cell numbers for the external contrastation and subsequent application of cells into the target organism. Filtering possible for organ system and contrasted structure/cell type in both datasets, substance class and fluorescent detection windows can be filtered in the dataset for direct agent application. Source publications are listed by DOI.
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Introducing OMERO-vitessce: an OMERO.web plugin for multi-modal data#
Michele Bortolomeazzi, Christian Schmidt, Jan-Philipp Mallm
Published 2025-02-07
Licensed CC-BY-4.0
omero-vitessce: an OMERO.web plugin for multi-modal data viewing. OMERO is the most used research data management system (RDM) in the bioimaging domain, and has been adopted as a centralized RDM solution by several academic and research institutions. A main reason for this is the ability to directly view and annotate images from a web-based interface. However, this feature of OMERO is currently underpowered for the visualization of very large or multimodal datasets. These datasets, are becoming a more and more common foundation for biological and biomedical studies, due to the recent developments in imaging, and sequencing technologies which enabled their application to spatial-omics. In order to begin to provide this multimodal-data capability to OMERO, we developed omero-vitessce (NFDI4BIOIMAGE/omero-vitessce), a new OMERO.web plugin for viewing data stored in OMERO with the Vitessce (http://vitessce.io/) multimodal data viewer. omero-vitessce can be installed as an OMERO.web plugin with PiPy (https://pypi.org/project/omero-vitessce/), and allows users to set up interactive visualizations of their images of cells and tissues through interactive plots which are directly linked to the image. This enables the visual exploration of bioimage-analysis results and of multimodal data such as those generated through spatial-omics experiments. The data visualization is highly customizable and can be configured not only through custom configuration files, but also with the graphical interface provided by the plugin, thus making omero-vitessce a highly user-friendly solution for multimodal data viewing. most biological datasets. We plan to extend the interoperability of omero-vitessce with the OME-NGFF and SpatialData file formats to leverage the efficiency of these cloud optimized formats. The three files in this Zenodo Record are all the same poster saved in different format all with high resolution images.
Tags: Nfdi4Bioimage, Exclude From Dalia
Introduction to light-microscopy / Widefield microscopy#
Thomas Laurent
Published 2022-05-10
Licensed OTHER-AT
This is a short introduction to light-microscopy, illustrated with widefield microscopy.
It introduces :
upright and inverted widefield microscopes
the transmitted and fluorescent light-path
- contrasting methods (optical and at the sample level)
the molecular principle of fluorescence (Perrin-Jablonski)
objective, resolution and limitations of the method (diffraction, diffusion/scattering)
In addition to the PPT (with few animations), a lighter PDF version is provided for preview in Zenodo.
Illustrations are mostly extracted from the ThermoFisher Molecular Probes School of Fluorescence educator packet and from the course material from Micron Facility in Oxford.
As stated in the presentation, illustrations are copyrighted but can be reproduced provided the original attribution is conserved.
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JIPipe: visual batch processing for ImageJ#
Ruman Gerst, Zoltán Cseresnyés, Marc Thilo Figge
JIPipe is an open-source visual programming language for easy-access pipeline development
Tags: Workflow Engine, Imagej, Exclude From Dalia
Content type: Publication, Documentation
Jupyter for interactive cloud computing#
Guillaume Witz
Licensed UNKNOWN
Tags: Neubias, Bioimage Analysis, Exclude From Dalia
Content type: Slides
KNIME Image Processing#
None
Licensed GPL-3.0
The KNIME Image Processing Extension allows you to read in more than 140 different kinds of images and to apply well known methods on images, like preprocessing. segmentation, feature extraction, tracking and classification in KNIME.
Tags: Imagej, OMERO, Workflow, Exclude From Dalia
Content type: Tutorial, Online Tutorial, Documentation
Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)#
Silke Tulok, Anja Nobst, Anett Jannasch, Tom Boissonnet, Gunar Fabig
Published 2024-06-28
Licensed CC-BY-4.0
This Key-Value pair template is used for the data documentation during imaging experiments and the later data annotation in OMERO. It is tailored for the usage and image acquisition at the slide scanning system Zeiss AxioScan 7 in the Core Facility Cellular Imaging (CFCI). It contains important metadata of the imaging experiment, which are not saved in the corresponding imaging files. All users of the Core Facility Cellular Imaging are trained to use that file to document their imaging parameters directly during the data acquisition with the possibility for a later upload to OMERO. Furthermore, there is a corresponding public example image used in the publication “Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users” and is available here: https://omero.med.tu-dresden.de/webclient/?show=image-33248 This template was developed by the CFCI staff during the setup and usage of the AxioScan 7 and is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015). With this template it is possible to create a csv-file, that can be used to annotate an image or dataset in OMERO using the annotation script (ome/omero-scripts). How to use:
fill the template sheet with your metadata select and copy the data range containing the Keys and Values open a new excel sheet and paste transpose in cell A1 Important: cell A1 contains always the name ‘dataset’ and cell A2 contains the exact name of the image/dataset, which should be annotated in OMERO save the new excel sheet in csv-file (comma separated values) format
An example can be seen in sheet 3 ‘csv_AxioScan’. Important note: The code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might be not able to decode by the annotation script. We encountered this issue with old Microsoft-Office versions (MS Office 2016). Note: By filling the values in the excel sheet, avoid the usage of comma as decimal delimiter. See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Mueller-Reichert 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Key-Value pair template for annotation of datasets in OMERO (PERIKLES study)#
Anett Jannasch, Silke Tulok, Vanessa Aphaia Fiona Fuchs, Tom Boissonnet, Christian Schmidt, Michele Bortolomeazzi, Gunar Fabig, Chukwuebuka Okafornta
Published 2024-06-26
Licensed CC-BY-4.0
This is a Key-Value pair template used for the annotation of datasets in OMERO. It is tailored for a research study (PERIKLES project) on the biocompatibility of newly designed biomaterials out of pericardial tissue for cardiovascular substitutes (https://doi.org/10.1063/5.0182672) conducted in the research department of Cardiac Surgery at the Faculty of Medicine Carl Gustav Carus at the Technische Universität Dresden . A corresponding public example dataset is used in the publication “Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users” and is available here (https://omero.med.tu-dresden.de/webclient/?show=dataset-1557). The template is based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015) and it was developed during the PoL-Bio-Image Analysis Symposium in Dresden Aug 28th- Sept 1th 2023. With this template it is possible to create a csv-file, that can be used to annotate a dataset in OMERO using the annotation script (ome/omero-scripts). How to use: select and copy the data range containing Keys and Values open a new excel sheet and paste transpose in column B1 type in A1 ‘dataset’ insert in A2 the exact name of the dataset, which should be annotated in OMERO save the new excel sheet in csv- (comma seperated values) file format
Example can be seen in sheet 1 ‘csv import’. Important note; the code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might not be able to decode by the annotation script. We encountered this issue with old Microsoft Office versions (e.g. MS Office 2016). Note: By filling the values in the excel sheet, avoid the usage of decimal delimiter. See cross reference: 10.5281/zenodo.12547566 Key-Value pair template for annotation of datasets in OMERO (light- and electron microscopy data within the research group of Prof. Mueller-Reichert) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Key-Value pair template for annotation of datasets in OMERO for light- and electron microscopy data within the research group of Prof. Müller-Reichert#
Gunar Fabig, Anett Jannasch, Chukwuebuka Okafornta, Tom Boissonnet, Christian Schmidt, Michele Bortolomeazzi, Vanessa Aphaia Fiona Fuchs, Maria Koeckert, Aayush Poddar, Martin Vogel, Hanna-Margareta Schwarzbach, Andy Vogelsang, Michael Gerlach, Anja Nobst, Thomas Müller-Reichert, Silke Tulok
Published 2024-06-26
Licensed CC-BY-4.0
This are a two Key-Value pair templates used for the annotation of datasets in OMERO. They are tailored for light- and electron microcopy data for all research projects of the research group of Prof. T. Mueller-Reichert. All members of the Core Facility Cellular Imaging agreed for using these templates to annotate data in OMERO. Furthermore, there are a corresponding public example datasets used in the publication “Setting up an institutional OMERO environment for bioimage data: perspectives from both facility staff and users” and are available here: https://omero.med.tu-dresden.de/webclient/?show=dataset-1552 –> for lattice-light sheet microscopy https://omero.med.tu-dresden.de/webclient/?show=dataset-1555–> for electron microscopy data That templates are based on the REMBI recommendations (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015) and were developed during the PoL-Bio-Image Analysis Symposium in Dresden Aug 28th- Sept 1st in 2023 and further adapeted during the usage of OMERO. With every template it is possible to create a csv-file, that can be used to annotate a dataset in OMERO using the annotation script (ome/omero-scripts). How to use:
fill the template with metadata select and copy the data range containing the Keys and Values open a new excel sheet and paste transpose in cell A1 Important: cell A1 contains always the name ‘dataset’ and cell A2 contains the exact name of the dataset, which should be annotated in OMERO save the new excel sheet in csv-file (comma separated values) format
Examples can be seen in sheet 3 ‘csv_TOMO’ and sheet 5 csv_TEM’. Important note: The code has to be 8-Bit UCS transformation format (UTF-8) otherwise several characters (for example µ, %,°) might be not able to decode by the annotation script. We encountered this issue with old Microsoft-Office versions (MS Office 2016). Note: By filling the values in the excel sheet, avoid the usage of comma as decimal delimiter. See cross reference: 10.5281/zenodo.12546808 Key-Value pair template for annotation of datasets in OMERO (PERIKLES study) 10.5281/zenodo.12578084 Key-Value pair template for annotation in OMERO for light microscopy data acquired with AxioScan7 - Core Facility Cellular Imaging (CFCI)
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Key-Value pairs scripts#
Licensed UNKNOWN
The key-value pairs are annotations in OMERO useful to describe thoroughly the data and can be added & edited via the OMERO.web interface.
Tags: OMERO, Exclude From Dalia
Content type: Documentation, Collection
LEO#
Rodrigo Escobar Diaz Guerrero
Published 2025-01-08T10:20:30+00:00
Licensed MIT
Linking Electronic Lab Notebooks and other sources with OMERO objects
Tags: OMERO, Research Data Management, Electronic Lab Notebooks, Exclude From Dalia
Content type: Github Repository
LEO: Linking ELN with OMERO#
Escobar Diaz Guerrero, Rodrigo
Published 2024-05-08
Licensed CC-BY-4.0
First updates of LEO (Linking ELN with OMERO)
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LMRG Image Analysis Study - FISH datasets#
Kristopoher Kubow, Thomas Pengo
Published 2022-05-18
Licensed CC-BY-4.0
Original image files, label (ground truth) files, and PSF files used in the ABRF Light Microscopy Research Group (LMRG) image analysis study. Simulated 3D confocal fluorescence images of sub-diffraction punctate staining (fluorescence in situ hybridization (FISH) in C. elegans).
See ABRFLMRG/image-analysis-study for more details.
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Content type: Data
LMRG Image Analysis Study - nuclei datasets#
Kristopher Kubow, Thomas Pengo
Published 2022-05-18
Licensed CC-BY-4.0
Original image files, label (ground truth) files, and PSF files used in the ABRF Light Microscopy Research Group (LMRG) image analysis study. Simulated 3D widefield fluorescence images of nuclei.
See ABRFLMRG/image-analysis-study for more details.
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Content type: Data
LSM example J. Dubrulle#
Salama Lab Fred Hutchinson Cancer Center
Published 2024-12-17
Licensed CC-BY-4.0
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LZ4-compressed Imaris ims example datasets.#
Marco Stucchi
Published 2024-11-21
Licensed CC-BY-4.0
The files contained in this repository are cropped versions of Imaris demo images compressed with LZ4.
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Large tiling confocal acquisition (rat brain)#
Julie Meystre
Published 2022-06-15
Licensed CC-BY-4.0
Name: Large tiling confocal acquisition (rat brain)
Microscope: Zeiss LSM700
Microscopy data type: 108 tiles, each with 62 z-slices and 2 channels : Channel 1: DAPI Channel 2: cck staining
File format: .lsm (16-bit)
Image size: 1024x1024x62 (Pixel size: 0.152 x 0.152 x 1 micron), 2 channels.
NOTE : Some tiles were annotated and used to train a StarDist3D model (https://doi.org/10.5281/zenodo.6645978 )
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Laser perturbation imaging data for: Patterned invagination prevents mechanical instability during gastrulation#
Vellutini, Bruno C., Cuenca, Marina B., Abhijeet Krishna, Alicja Szałapak, Modes, Carl D., Pavel Tomančák
Published 2025-07-14
Licensed CC-BY-4.0
This repository contains the imaging data for the laser perturbation experiments of the manuscript: Vellutini BC, Cuenca MB, Krishna A, Szałapak A, Modes CD, Tomančák P. Patterned embryonic invagination evolved in response to mechanical instability. bioRxiv (2023) doi:10.1101/2023.03.30.534554 Please refer to the main repository for more information: https://doi.org/10.5281/zenodo.7781947
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LauLauThom/MaskFromRois-Fiji: Masks from ROIs plugins for Fiji - initial release#
Laurent Thomas, Pierre Trehin
Published 2021-07-22
Licensed MIT
Fiji plugins for the creation of binary and semantic masks from ROIs in the RoiManager. Works with stacks too.
Installation in Fiji: activate the Rois from masks update site in Fiji.
See GitHub readme for the documentation.
Latest tested with Fiji 2.1.0/ImageJ 1.53j
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LauLauThom/MaskFromRois-Fiji: v1.0.1 - better handle “cancel”#
Laurent Thomas, Pierre Trehin
Published 2025-02-24
Licensed MIT
Also re-uploaded the compiled FilenameGetter.py$class to the update site, to fix LauLauThom/MaskFromRois-Fiji#7
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Lecture-materials of the DeepLife course#
Carl Herrmann, annavonbachmann, David Hoksza, Martin Schätz, Dario Malchiodi, jnguyenvan, Britta Velten, Elodie Laine, JanaBraunger, barwil
Published 2023-12-06
Licensed UNKNOWN
Tags: Bioinformatics, Exclude From Dalia
Content type: Github Repository, Slides, Notebook
Leica (.lif) file with errors in channel order when imported with Bio-formats#
Areli Rodriguez
Published 2025-02-26
The blue and red channels get swapped when imported with Bio-formats. Happens consistently with .lif imports in QuPath and ImageJ.
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Leitlinie? Grundsätze? Policy? Richtlinie? – Forschungsdaten-Policies an deutschen Universitäten#
Bea Hiemenz, Monika Kuberek
Published 2018-07-13
Licensed CC-BY-4.0
As a methodological approach, research data policies of German universities are collected and evaluated, and compared to international recommendations on research data policies.
Tags: Research Data Management, FAIR-Principles, Exclude From Dalia
Content type: Publication
Life Science Competence Centres: Open by Design#
Romain David, Nektarios Liaskos, Arina Rybina, Christos Arvanitidis, Anne-Sophie Bage, Carvajal-Vallejos, Patricia K., Sudeep Das, De Pascalis, Francesca, Dorothea Dörr, Katrina Exter, Petr Holub, Gurwitz, Kim Tamara, Fabio Liberante, Philippe Lieutaud, Allyson Lister, Joaquin Lopez, Bénédicte Madon, Marzia Massimi, Rafaele Matteoni, Maria Mîrza, Sarah Morgan, Bugra Oezdemir, Maria Panagiotopoulou, Christina Pavloudi, P. Melo, Ana M., Susanna-Assunta Sansone, Harald Schwalbe, Beatriz Serrano-Solano, Sorzano, Carlos Oscar, Emilio Urbinati, Jing Tang, Jonathan Tedds, Gary Saunders, Jonathan Ewbank
Published 2025-07
Licensed CC-BY-4.0
Preprint in submission process to GigaScience journal Abstract: European Life Science Research Infrastructures (LS-RIs), one of the five major RI Science Clusters in Europe, were established to provide access to cutting-edge technologies to the scientific community. Individually, and collectively as the LS-RI cluster, they contribute to the development of the European Open Science Cloud (EOSC), under the aegis of the EOSC Federation. They are actively involved in the design and implementation of Competence Centres (CCs). These aim to increase the accessibility of domain-specific knowledge and tools, enhance interoperability, facilitate sharing and harmonisation of procedures, and promote Open Science and FAIR (Findable, Accessible, Interoperable, Reusable) practices. In this paper, we report a landscape mapping of the existing resources that formed the basis for the construction of CCs. We describe the possible design of CCs and their articulation with the LS-RIs. We focus on community-based ideas and recommendations to increase the potential of CCs to address long-standing challenges in sustainability, governance, scalability, and interoperability of Open Science within EOSC and the European Research Area (ERA) more generally.This paper provides a description of the nascent LS CCs, built following a survey of needs and services of existing LS-RI communities. When fully implemented, the LS CCs will serve as dynamic hubs to foster innovation, contribute to the EOSC’s future FAIR web of data, and support ongoing developments of the EOSC Federation. They will act as drivers of collaborative and impactful LS research in Europe and beyond. We explore the underlying challenges, and propose solutions, to ensure that the establishment of CCs will add value to the LS RI community, and to the EOSC, in a sustainable way.
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Lightsheet and in situ imaging data for: Patterned invagination prevents mechanical instability during gastrulation#
Vellutini, Bruno C., Cuenca, Marina B., Abhijeet Krishna, Alicja Szałapak, Modes, Carl D., Pavel Tomančák
Published 2025-07-14
Licensed CC-BY-4.0
This repository contains the lightsheet and in situ hybridization imaging data for the manuscript: Vellutini BC, Cuenca MB, Krishna A, Szałapak A, Modes CD, Tomančák P. Patterned embryonic invagination evolved in response to mechanical instability. bioRxiv (2023) doi:10.1101/2023.03.30.534554 Please refer to the main repository for more information: https://doi.org/10.5281/zenodo.7781947
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LimeSeg Test Datasets#
Sarah Machado, Vincent Mercier, Nicolas Chiaruttini
Published 2018-10-27
Licensed CC-BY-4.0
Image datasets from the publication : LimeSeg: A coarse-grained lipid membrane simulation for 3D image segmentation
Vesicles.tif: spinning-disc confocal images of giant unilamellar vesicles
HelaCell-FIBSEM.tif: a 3D Electron Microscopy (EM) dataset of nearly isotropic sections of a Hela cell, acquired with a focused ion beam scanning electron microscope (FIB-SEM). Sections are aligned with TrackEm2 (doi: ), without additional preprocessing.
DrosophilaEggChamber.tif: point scanning confocal images of a Drosophila egg chamber. Channel 1: cell nuclei stained with DAPI. Channel 2: cell membranes visualized with fused membrane proteins Nrg::GFP and Bsg::GFP.
Image metadata contains extra information including voxel sizes.
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Linked (Open) Data for Microbial Population Biology#
Carsten Fortmann-Grote
Published 2024-03-12
Licensed CC-BY-4.0
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Linking of Research (Meta-)data in OMERO to Foster FAIR Data in Plasma Science#
Robert Wagner, Mohsen Ahmadi, Dagmar Waltemath, Kristina Yordanova, Becker, Markus M.
Published 2025-09-10
Licensed CC-BY-4.0
Applied plasma research involves several disciplines such as physics, medicine and biology to solve application-oriented problems, often generating large and heterogeneous experimental data sets. The descriptions and metadata describing these interdisciplinary scientific investiga-tions is stored in distributed systems (e.g., physical laboratory notebooks or electronic labora-tory notebooks (ELN) like eLabFTW [1]), and the experimental data are either stored locally within the laboratories or on centralized institutional storage systems. As a result, the collected information often has to be tediously assembled for processing into publications. The workflow represented in Figure 1 addresses this suboptimal situation and promotes the combination of the image database OMERO [2], the ELN system eLabFTW, the research data management tool Adamant [3] and Python scripts for handling microscopy images in plasma life science and plasma medicine [4]. This workflow highlights how the developments from the NFDI4BIOIMAGE consortium can be brought into practical applications by addressing the specific demands of plasma science, where domain-specific metadata is essential for effective data interpretation and reuse. It showcases the benefits of FAIR [5] metadata combining do-main-specific requirements with method-specific solutions. Similar to most imaging workflows, image analysis in plasma research requires metadata from several sections of the experiment. Moreover, the plasma-related metadata are essential for the experimental context and must be included in the analysis, e.g. to describe the influence of plasma on the treated sample. Therefore, the metadata schema Plasma-MDS [6] is adapted to collect plasma-related metadata, such as information on the plasma species having a major impact on the treated samples. Alongside Plasma-MDS, the Recommended Metadata for Bio-logical Images (REMBI) standard [7] is used for the biological metadata such as the sample preparation and treatment procedures. The collection of these metadata is realized using Adamant, which enables the beginner-friendly collection of structured metadata. The tool presents JSON schemas in easy-to-read and easy-to-fill HTML forms, enabling metadata validation. Once completed and validated, the metadata are uploaded directly to eLabFTW using Adamant’s workflow functionalities. The images from the treated samples are uploaded to OMERO by OMERO.insight and afterwards automatically annotated via Python scripts. These scripts take previously collected metadata from the related eLabFTW experiments and the microscope description metadata collected by the Micro Meta App [8], which are also stored in eLabFTW. The metadata is categorized and annotated according to the various data organizational levels within OMERO, specifically fo-cusing on project and dataset hierarchies, as well as screens that are composed of plates, which in turn contain wells. Screens resemble microwell plates, commonly used in a variety of biological experiments. The hieraic organization of metadata significantly enhances the ease of reusing images and associated metadata for subsequent processing and analysis. By efficiently distributing and reducing large metadata sets to an acceptable level, while simultaneously eliminating redun-dancies, this approach facilitates straightforward analyses with tools like ImageJ [9] and FIJI [10], thanks to the close association of metadata with the images themselves. In summary, one of the application-specific developments within the NFDI4BIOIMAGE consor-tium is presented, which contributes to the adoption of the FAIR principles in laboratory envi-ronments. Further work will address the integration of ontologies for the semantic description of data and metadata.
Tags: Nfdi4Bioimage, Bioimage Analysis, Exclude From Dalia
Lund Declaration on Maximising the Benefits of Research Data#
Tags: Research Data Management, Exclude From Dalia
Content type: Document
LyNSeC: Lymphoma Nuclear Segmentation and Classification#
Naji Hussein, Büttner Reinhard, Simon Adrian, Eich Marie-Lisa, Lohneis Philipp, Bozek Katarzyna
Published 2023-06-21
Licensed CC-BY-4.0
Over the last years, there has been large progress in automated segmentation and classification methods in histological whole slide images (WSIs) stained with hematoxylin and eosin (H&E). Current state-of-the-art techniques are based on diverse datasets of H&E-stained WSIs of different types of predominantly solid cancer. However, there is a lack of publicly available annotated datasets of lymphoma, which is why we generated a labeled diffuse large B-cell lymphoma dataset and denoted it LyNSeC (lymphoma nuclear segmentation and classification). LyNSeC comprises three subsets: LyNSeC 1 consists of 379 IHC images of size 512 x 512 pixels at 40x magnification. In the images, we annotated the contours of each cell nuclei and the cell class: marker-positive or marker-negative.
In total, LyNSeC 1 contains 87,316 annotated cell nuclei of four different cases, with 48,171 of them assigned the class negative and 39,145 positive. We included three markers in this dataset showing visually different staining patterns: cluster of differentiation 3 (CD3), Ki67 as a marker of proliferation, and erythroblast transformation-specific (EST)-related gene (ERG).
LyNSeC 2 and 3 contain H&E-stained images of 70 different patients. LyNSeC 2 consists of 280 images and LyNSeC 3 of 40 images of size 512 x 512 pixels at 40x magnification. 65,479 and 8,452 nuclei were annotated in LyNSeC 2 and 3, respectively. In LyNSeC 3, the nuclei were also assigned a class label (tumor and non-tumor). 3,747 nuclei were identified as tumors and 4,705 as non-tumors.
In the annotation procedure, the contours of the H&E images (LyNSeC 2 and LyNSeC 3) were annotated by two pathologists and by two students (trained by the pathologists). Annotation of the cell classes in LyNSeC 3 was done by the pathologists only. LyNSeC 1 was annotated by the two students who were additionally trained to annotate the contours and to distinguish marker-positive and marker-negative cells. The pathologists inspected and (if necessary) adjusted the LyNSeC 3 annotations.
The files are uploaded in ‘.npy’ format. The files of LyNSeC 1 (x_l1.npy) and LyNSeC 3 (x_l3.npy) contain five channels, respectively: the first three are the RGB channels of the images, channel 4 contains the instance maps, and channel 5 the class type maps (for LyNSeC 1 a pixel value of 1 corresponds to the class negative and 2 to the class positive, whereas in LyNSeC 3 1 corresponds to the class non-tumor and 2 to the class tumor). The files of LyNSeC 2 (x_l2.npy) have 4 channels (without the class type map).
Additionally, we also make our HoVer-Net-based pre-trained nuclei segmentation and classification models available (he.tar for H&E images and ihc.tar for IHC images).
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Content type: Data
MDEmic: a metadata annotation tool to facilitate management of FAIR image data in the bioimaging community#
Susanne Kunis, Sebastian Hänsch, Christian Schmidt, Frances Wong, Caterina Strambio-De-Castillia, Stefanie Weidtkamp-Peters
Licensed ALL RIGHTS RESERVED
Tags: Research Data Management, Metadata, Exclude From Dalia
Content type: Publication
MIDOG 2021#
Marc Aubreville, Frauke Wilm
Published 2021-03-16
Licensed UNLICENSED
Mitosis domain generation. Here you can find code of our own evaluations and a dockered reference algorithm for mitotic figures to use as a template.
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Content type: Data
MRI Physics#
Radiology Tutorials
Published 2025-01-01
Licensed UNKNOWN
This is a playlist of videos about how MRI works
Tags: Imaging, Exclude From Dalia
Content type: Video
https://youtube.com/playlist?list=PLWfaNqiSdtzVkfJW2gO-unAYjcDji7-9i&si=U5gvYtUYvmLHxi0z
Machine and Deep Learning on the cloud: Segmentation#
Ignacio Arganda-Carreras
Licensed UNKNOWN
Tags: Neubias, Artificial Intelligence, Bioimage Analysis, Exclude From Dalia
Content type: Slides
Masterclasses from the Euro-Bioimaging EVOLVE Mentoring programme 2025#
Euro-BioImaging ERIC
Published 2025-06-26
Licensed CC-BY-4.0
EVOLVE Mentoring Masterclasses Description:This series captures the class guides of the 2025 masterclasses from Euro‑BioImaging’s EVOLVE Mentoring Program. Included Masterclasses:
Peter O’Toole – “Entrepreneurship & Leadership in Imaging Core Facilities” Peter O’Toole, President of the Royal Microscopical Society and Director of the Bioscience Technology Facility (University of York), kicks off the series with a deep dive into entrepreneurial leadership. He highlights how to balance science, business, and technology, emphasizing stakeholder engagement, staff investment, cross-training, and using social media to boost visibility and unlock funding. Ilaria Testa – “Interdisciplinary Science, SMART Microscopy & Team Building” Professor Ilaria Testa (SciLifeLab & KTH) reflects on her transition from physics to super-resolution microscopy and team leadership. Her session underscores the power of crossing disciplinary boundaries, mentorship, and innovation in live-cell imaging . Daphna Link‑Sourani – “Leadership, Facility Management & Work‑Life Balance” Dr. Daphna Link‑Sourani (Technion Human MRI Research Center) challenges hierarchical notions of leadership, advocating instead for integrity, empathy, and strategic vision. She draws on her experience establishing an MRI facility to discuss crisis management, user engagement, and balancing career demands. Muriel Mari – “Women in Science: Normalizing, Supporting & Leading” Dr. Muriel Mari (Aarhus University) leads a powerful reflection on gender equity in science. Her masterclass goes beyond barriers—focusing on cultural shifts, inclusive leadership, and redefining success. She encourages institutions and individuals alike to move from tokenism to transformative support, and to recognize the diverse paths women take in STEM. Sylvia E. Le Dévédec – “Image Data Management & FAIR Core Facilities” Dr. Sylvia Le Dévédec (Leiden University) discusses how to integrate FAIR data principles in imaging core facilities. Drawing on her experience with high-content imaging and Open Science advocacy, she outlines actionable steps toward sustainable, reusable, and accessible data workflows.
Why Archive These Sessions?These masterclasses offer invaluable insights for core facility managers, imaging scientists, and team leaders in life sciences. They blend hands-on leadership strategies, technical facility growth advice, and real-world experience—making them essential viewing for professionals and institutions aiming to build sustainable, people-centred imaging infrastructures.
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Materials for EMBL Coding Club Mini-Tutorials#
Jonas Hartmann, et al.
Licensed UNKNOWN
Tags: Python, Exclude From Dalia
Content type: Code, Notebook
Measuring reporter activity domain in EPI aggregates and Gastruloids.ijm#
Romain Guiet, Olivier Burri, Mehmet Girgin, Matthias Lutolf
Published 2022-12-07
Licensed CC-BY-4.0
This imagej macro analyses the reporter intensity activity and expression domain in EPI aggregates and Gastruloids.
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Melanoma Histopathology Dataset with Tissue and Nuclei Annotations#
Mark Schuiveling
Published 2025-03-19
Licensed CC-ZERO
Description: This dataset is designed for development of deep learning models for segmentation of nuclei and tissue in melanoma H&E stained histopathology. Existing nuclei segmentation models that are trained on non-melanoma specific datasets have low performance due to the ability of melanocytes to mimic other cell types, whereas existing melanoma specific models utilize older, sub-optimal techniques. Moreover, these models do not provide tissue annotations necessary for determining the localization of tumor-infiltrating lymphocytes, which may hold value for predictive and prognostic tasks. To address this, we created a melanoma specific dataset with nuclei and tissue annotations. Methodology: Sample Collection: Regions of interest (ROIs) were sampled from H&E stained slides of 103 primary melanoma specimens and 102 metastatic melanoma specimens, scanned using a Hamamatsu scanner at 40× magnification (0.23 μm per pixel). All slides were obtained from regular diagnostic procedures.From each specimen, a 40× magnified ROI of 1024×1024 pixels was selected for annotation. Additionally, a context ROI of 5120×5120 pixels was sampled to provide information about the broader context for the annotation process. Selection was performed by a trained medical expert (M.S.) and subsequently verified by a dermatopathologist (W.B.). Manual ROI selection ensured the inclusion of diverse tissue and nuclei types. Annotation Process:
Nuclei segmentationNuclei segmentations were generated using Hover-Net pretrained on the PanNuke dataset. Manual annotation adjustments were performed by author M.S. using QuPath, with the following nuclei categories: tumor, stroma, vascular endothelium, histiocyte, melanophage, lymphocyte, plasma cell, neutrophil, apoptotic cell, and epithelium. All annotations were reviewed and corrected, where needed, by a dermatopathologist (W.B.). Tissue segmentationTissue segmentations were created manually using QuPath by M.S., with the following categories: tumor, stroma, epidermis, necrosis, blood vessel, and background. Annotations were reviewed and corrected, where needed, by a dermatopathologist (W.B.).
Quality Control: To assess the reliability of the annotations, intra- and interobserver agreement (by pathologist G.B.) were determined on 12 randomly selected ROIs.
Nuclei segmentationThe intraobserver overall precision was 84.89%, with a recall of 86.45%, and an F1 score of 85.66%. Interobserver overall precision was 80.34%, with a recall of 80.62%, and an F1 score of 80.20%. These results are based on the sum of all true positive, false positive, and false negative counts for the 12 ROIs. Tissue segmentationThe DICE score was determined on the same 12 randomly selected ROIs. The average intraobserver DICE score was 0.90, and the interobserver DICE score was also 0.90.
Version 3:Removed sample “training_set_metastatic_roi_103” due to inconsistencies in annotation file. Version 4:Sample training_set_metastatic_roi_088 missed one color annotation for a nuclei_apoptosis in the geojson file rendering it qupath uncompatible. This is fixed in the new version. Version 5:Addition of correct sample of training_set_metastatic_roi_103” after deadline of panoptic segmentation of nuclei and tissue in advanced melanoma challenge test phase.
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Content type: Data
Melbourne Advanced Microscopy Facility#
Collection of tutorial videos for Fiji users
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Video
MemBrain-seg training data#
Lorenz Lamm
Published 2023-03-16
Licensed CC-BY-4.0
This dataset contains training data for segmenting membranes in cryo-electron tomograms.
More details will follow.
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Content type: Data
Memorandum of Understanding of NFDI consortia from Earth-, Chemical and Life Sciences to support a network called the Geo-Chem-Life Science Helpdesk Cluster#
Lars Bernard, Maike Brück, Christian Busse, Judith Engel, Jan Eufinger, Frank Ewert, Juliane Fluck, Konrad Förstner, Julia Fürst, Holger Gauza, Klaus Getzlaff, Glöckner, Frank Oliver, Johannes Hunold, Oliver Koepler, Ksenia Krooß, Birte Lindstädt, McHardy, Alice C., Hela Mehrtens, Elena Rey-Mazon, Marcus Schmidt, Isabel Schober, Annett Schröter, Oliver Stegle, Christoph Steinbeck, Feray Steinhart, von Suchodoletz, Dirk, Stefanie Weidtkamp-Peters, Jens Wendt, Conni Wetzker
Published 2025-04-02
Licensed CC-BY-4.0
In a Memorandum of Understanding, the undersigned consortia agree to work together to enhance their support capabilities (helpdesks) to meet the needs of interdisciplinary research in Earth-, Chemical and Life Sciences.
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Metadata Annotation Workflow for OMERO with Tabbles#
Wendt Jens
Published 2023-09-04
Licensed CC-BY-4.0
Short presentation given at at PoL BioImage Analysis Symposium Dresden 2023
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Metadata in Bioimaging#
Josh Moore, Susanne Kunis
Published 2025-03-25
Licensed CC-BY-4.0
Presentation given to the Search & Harvesting workgroup of the Metadata section of NFDI on March 25th, 2025
Tags: Nfdi4Bioimage, Exclude From Dalia
MethodsJ2: a software tool to capture metadata and generate comprehensive microscopy methods text#
Joel Ryan, Thomas Pengo, Alex Rigano, Paula Montero Llopis, Michelle S. Itano, Lisa A. Cameron, Guillermo Marqués, Caterina Strambio-De-Castillia, Mark A. Sanders, Claire M. Brown
Tags: Metadata, Exclude From Dalia
Content type: Publication
Metrics Reloaded - A framework for trustworthy image analysis validation#
Licensed UNKNOWN
The mission of Metrics Reloaded is to guide researchers in the selection of appropriate performance metrics for biomedical image analysis problems, as well as provide a comprehensive online resource for metric-related information and pitfalls
Tags: Bioimage Analysis, Quality Control, Exclude From Dalia
Content type: Website, Collection
MiToBo - A Toolbox for Image Processing and Analysis#
Birgit Möller, Markus Glaß, Danny Misiak, Stefan Posch
The Microscope Image Analysis Toolbox is a toolbox with a collection of algorithms for processing and analyzing digital images.
Tags: Workflow Engine, Imagej, Exclude From Dalia
Content type: Publication, Documentation
https://openresearchsoftware.metajnl.com/articles/10.5334/jors.103
Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications#
Alessandro Rigano, et al.
Tags: Metadata, Exclude From Dalia
Content type: Publication
MicroSam-Talks#
Constantin Pape
Published 2024-05-23
Licensed CC-BY-4.0
Talks about Segment Anything for Microscopy: computational-cell-analytics/micro-sam. Currently contains slides for two talks:
Overview of Segment Anythign for Microscopy given at the SWISSBIAS online meeting in April 2024 Talk about vision foundation models and Segment Anything for Microscopy given at Human Technopole as part of the EMBO Deep Learning Course in May 2024
Tags: Bioimage Analysis, Artificial Intelligence, Exclude From Dalia
Content type: Slides
Microscopy-BIDS - An Extension to the Brain Imaging Data Structure for Microscopy Data#
Marie-Hélène Bourget, Lee Kamentsky, Satrajit S. Ghosh, Giacomo Mazzamuto, Alberto Lazari, et al.
Published 2022-04-19
Licensed CC-BY-4.0
The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way.
Tags: Research Data Management, Exclude From Dalia
Content type: Publication
https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full
MicroscopyDB#
Licensed ALL RIGHTS RESERVED
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Content type: Collection
MoNuSeg Dataset#
Neeraj Kumar, Ruchika Verma, Sanuj Sharma, Surabhi Bhargava, Abhishek Vahadane, Amit Sethi
Published 2017-07-01
Licensed CC-BY-NC-SA-4.0
The dataset for this challenge was obtained by carefully annotating tissue images of several patients with tumors of different organs and who were diagnosed at multiple hospitals. This dataset was created by downloading H&E stained tissue images captured at 40x magnification from TCGA archive. H&E staining is a routine protocol to enhance the contrast of a tissue section and is commonly used for tumor assessment (grading, staging, etc.). Given the diversity of nuclei appearances across multiple organs and patients, and the richness of staining protocols adopted at multiple hospitals, the training datatset will enable the development of robust and generalizable nuclei segmentation techniques that will work right out of the box.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
Model and simulations for: Patterned invagination prevents mechanical instability during gastrulation#
Abhijeet Krishna, Alicja Szałapak, Vellutini, Bruno C., Cuenca, Marina B., Pavel Tomančák, Modes, Carl D.
Published 2025-07-14
Licensed CC-BY-4.0
This repository contains the code and simulations for the manuscript: Vellutini BC, Cuenca MB, Krishna A, Szałapak A, Modes CD, Tomančák P. Patterned embryonic invagination evolved in response to mechanical instability. bioRxiv (2023) doi:10.1101/2023.03.30.534554 Please refer to the main repository for more information: https://doi.org/10.5281/zenodo.7781947
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Modeling community standards for metadata as templates makes data FAIR#
Mark A Musen, Martin J O’Connor, Erik Schultes, Marcos Martínez-Romero, Josef Hardi, John Graybeal
Published 2022-11-12
Licensed CC-BY-4.0
The authors have developed a model for scientific metadata, and they have made that model usable by both CEDAR and FAIRware. The approach shows that a formal metadata model can standardize reporting guidelines and that it can enable separate software systems to assist (1) in the authoring of standards-adherent metadata and (2) in the evaluation of existing metadata.
Tags: Data Stewardship, FAIR-Principles, Metadata, Exclude From Dalia
Content type: Publication
Models and Applications for BioImage.IO#
Wei Ouyang, et al.
Licensed UNKNOWN
Tags: Exclude From Dalia
Modular training resources for bioimage analysis#
Christian Tischer, Antonio Politi, Tim-Oliver Buchholz, Elnaz Fazeli, Nicola Gritti, Aliaksandr Halavatyi, Gonzalez Tirado, Sebastian, Julian Hennies, Toby Hodges, Arif Khan, Dominik Kutra, Stefania Marcotti, Bugra Oezdemir, Felix Schneider, Martin Schorb, Anniek Stokkermans, Yi Sun, Nima Vakili
Published 2025-01-21
Licensed CC-BY-4.0
The newly developed image data formats course was taught for the first time: NEUBIAS/training-resources
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ModularImageAnalysis (MIA): Assembly of modularisedimage and object analysis workflows in ImageJ#
Stephen J. Cross, Jordan D. J. R. Fisher, Mark A. Jepson
ModularImageAnalysis is a Fiji plugin providing a modular framework for assembling image and object analysis workflows
Tags: Workflow Engine, Imagej, Exclude From Dalia
Content type: Publication, Documentation
MonuSAC 2020#
Ruchika Verma, Neeraj Kumar, Abhijeet Patil, Nikhil Cherian Kurian, Swapnil Rane, Simon Graham
Published 2021-06-04
Licensed CC-BY-NC-SA-4.0
H&E staining of human tissue sections is a routine and most common protocol used by pathologists to enhance the contrast of tissue sections for tumor assessment (grading, staging, etc.) at multiple microscopic resolutions. Hence, we will provide the annotated dataset of H&E stained digitized tissue images of several patients acquired at multiple hospitals using one of the most common 40x scanner magnification. The annotations will be done with the help of expert pathologists.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
MorphoLibJ documentation#
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Document
Mouse embryo blastocyst cells#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
Segmenting nuclei in 3D images can be challenging especially when nuclei are clustered not only in XY plane but also in XZ and YZ planes. Manually annotated ground truth provides a reference for image analysis software testing purposes. These images of mouse embryo blastocyst cells also have changing nuclei intensity in Z plane which makes finding the right threshold for successful segmentation a difficult task. This image set also contains GAPDH transcripts that can be quantified in each cell.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
Multimodal large language models for bioimage analysis#
Shanghang Zhang, Gaole Dai, Tiejun Huang, Jianxu Chen
Licensed CC-BY-NC-SA
Multimodal large language models have been recognized as a historical milestone in the field of artificial intelligence and have demonstrated revolutionary potentials not only in commercial applications, but also for many scientific fields. Here we give a brief overview of multimodal large language models through the lens of bioimage analysis and discuss how we could build these models as a community to facilitate biology research
Tags: Bioimage Analysis, FAIR-Principles, Workflow, Exclude From Dalia
Content type: Publication
Multiplexed histology of COVID-19 post-mortem lung samples - CONTROL CASE 1 FOV1#
Anna Pascual Reguant, Ronja Mothes, Helena Radbruch, Anja E. Hauser
Published 2022-12-16
Licensed CC-BY-4.0
Image-based data set of a post-mortem lung sample from a non-COVID-related pneumonia donor (CONTROL CASE 1, FOV1)
Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.
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NEUBIAS YouTube Channel#
A collection of bio-image analysis webinars where commonly authors of open-source bio-image analysis software explain how to use their tools.
Tags: Neubias, Exclude From Dalia
Content type: Collection, Video
NFDI - Daten als gemeinsames Gut für exzellente Forschung, organisiert durch die Wissenschaft in Deutschland.#
Licensed UNKNOWN
Schritt für Schritt verbessern wir die Nutzungsmöglichkeiten von Daten für Wissenschaft und Gesellschaft. Durch unser Zusammenwirken im NFDI-Verein entsteht eine Dachorganisation für das Forschungsdatenmanagement in allen Wissenschaftszweigen.
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Content type: Website
NFDI4BIOIMAGE#
Carsten Fortmann-Grote
Published 2024-04-22
Licensed CC-BY-4.0
This presentation was given at the 2nd MPG-NFDI Workshop on April 18th.
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NFDI4BIOIMAGE - An Initiative for a National Research Data Infrastructure for Microscopy Data#
Christian Schmidt, Elisa Ferrando-May
Published 2021-04-29
Licensed CCY-BY-SA-4.0
Align existing and establish novel services & solutions for data management tasks throughout the bioimage data lifecycle.
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Content type: Conference Abstract, Slides
NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis [conference talk: The Pelagic Imaging Consortium meets Helmholtz Imaging, 5.10.2023, Hamburg]#
Riccardo Massei
Licensed CC-BY-4.0
NFDI4BIOIMAGE is a consortium within the framework of the National Research Data Infrastructure (NFDI) in Germany. In this talk, the consortium and the contribution to the work programme by the Helmholtz Centre for Environmental Research (UFZ) in Leipzig are outlined.
Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage, Exclude From Dalia
Content type: Slides
NFDI4BIOIMAGE - a consortium of the National Research Data Infrastructure#
Nfdi4Bioimage
Licensed UNKNOWN
Tags: Bioimage Analysis, Research Data Management, Nfdi4Bioimage, Exclude From Dalia
Content type: Collection
NFDI4BIOIMAGE Calendar April 2025#
Martin Zurowietz, Nattkemper, Tim W.
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar April 2025. This image shows the BIIGLE image and video annotation tool, which is a web-based software for collaborative research on large imaging datasets [1, 2]. It offers tools for manual and computer-assisted annotation, quality control and the collaboration on custom taxonomies to describe objects. BIIGLE is freely available and can be installed in cloud environments, a local network or on mobile platforms during research expeditions. A public instance can be found at biigle.de. The annotated image shows the coastline of Fernandina Island, Galapagos, which is the habitat of the Galapagos Marine Iguana (Amblyrhynchus cristatus). The image is a large mosaic that was stitched together from many individual images captured by a drone. The green annotations marking the iguanas were machine-generated as part of a feasibility study for the automatic analysis of the data in the project Iguanas from Above [3, 4]. [1] Langenkämper, D., Zurowietz, M., Schoening, T., & Nattkemper, T. W. (2017). BIIGLE 2.0-browsing and annotating large marine image collections. Frontiers in Marine Science, 4, 83. https://doi.org/10.3389/fmars.2017.00083 [2] Zurowietz, M., & Nattkemper, T. W. (2021). Current trends and future directions of large scale image and video annotation: Observations from four years of BIIGLE 2.0. Frontiers in Marine Science, 8, 760036. https://doi.org/10.3389/fmars.2021.760036 [3] Varela-Jaramillo, A., Rivas-Torres, G., Guayasamin, J. M., Steinfartz, S., & MacLeod, A. (2023). A pilot study to estimate the population size of endangered Galápagos marine iguanas using drones. Frontiers in Zoology, 20(1), 4. https://doi.org/10.1186/s12983-022-00478-5 [4] https://iguanasfromabove.com
Project
Iguanas from Above
Location
Fernandina Island, Galapagos
Organism
Amblyrhynchus cristatus
Drone model
DJI Mavic 2 Pro
Camera
Hasselblad L1D-20c
Size
26,545 × 20,894 px
Mosaic algorithm
Agisoft Metashape Professional v.1.6
Submitted via NFDI4Biodiversity
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NFDI4BIOIMAGE Calendar August 2025#
Haowen Jiang, Claire Chalopin
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar August 2025. This image illustrates tissue oxygen saturation in the hand, calculated using various computer-assisted methods and based on hyperspectral and multispectral imaging. The purpose of this image is to compare the perfusion parameters (3 and 4) obtained with multispectral cameras delivering relatively less spectral information but capable of real-time imaging against the perfusion parameters (2) obtained with a hyperspectral medical system delivering large spectral information but not capable of real-time imaging. The picture shows that deep learning methods (4) perform better than classical methods (3) that are not based on artificial intelligence. It lays the groundwork for future real-time quantitative assessment of perfusion during organ transplantation surgeries. Image Metadata (using REMBI template):
Study
Study description
Quantification of tissue reperfusion using real-time spectral imaging and deep learning
Study type
Study on volunteers
Study Component
Imaging method
(1) RGB imaging (2) Hyperspectral imaging (3) and (4) Multispectral imaging
Image component description
(1) RGB image of the hand under normal perfusion. (2) Perfusion parameter map computed based on hyperspectral imaging (100 spectral bands between 500 and 1000 nm). High perfusion values are represented in red, low perfusion values in blue. (3) Perfusion parameter map computed based on multispectral imaging (31 spectral bands between 460 and 850 nm) and using the spectral bands that are available but that are less than in (2). Therefore, the result in (3) looks very different from the result in (2). (4) Perfusion parameter map computed based on multispectral imaging and using a deep neural network. The result in (4) looks similar to the result in (2).
Biosample
Biological entity
Hand
Organism
Homo sapiens
Image data
Image resolution hyperspectral imaging
Spatial Resolution: 480*640 pixels Spectral Resolution: 500 nm-1000 nm, 100 bands, 5 nm
Image resolution multispectral imaging
Spatial Resolution: 1088*2048 pixels Spectral Resolution: 16 bands in 460-600 nm; 15 bands in 600-850 nm
Image mode
Reflectance
Submitted via NFDI4BIOIMAGE
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NFDI4BIOIMAGE Calendar Cover 2025#
Anne Rademacher, Alik Huseynov, Michele Bortolomeazzi, Wille, Sina Jasmin, Sabrina Schumacher, Pooja Sant, Denise Keitel, Konstantin Okonechnikov, Ghasemi, David R., Pajtler, Kristian W., Jan-Philipp Mallm, Karsten Rippe
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar Cover 2025. The image is a visualization showing the integration of multimodal data including a spinning disk confocal image and gene expression data from a spatial transcriptomic experiment on a human medulloblastoma sample. The microscopy image of the tissue with the nuclei in white has been overlayed with the result of the cell segmentation colored according to the assigned cell type (immune cells: red, stromal cells: violet, brain cells: cyan/blue, tumor cells: green). A subset of transcripts for three genes whose expression varies across the different cell types in the tissue have been represented as colored dots (CD4 (immune cells): red, PTCH1 (tumor cells): green, AQP4 (brain cells): blue). Image Metadata (using REMBI template):
Study
Study description
Comparison of spatial transcriptomics technologies for medulloblastoma cryosection
Study type
Spatial Transcriptomics (Xenium) on medulloblastoma cryosections
Study Component
Imaging method
Xenium and Spinning disk confocal microscopy
Study component description
Datasets with raw and processed data from the study “Comparison of spatial transcriptomics technologies for medulloblastoma cryosections” including Xenium and spinning disk confocal microscopy data
Biosample
Identity
MB266
Biological entity
Human cerebellum from a patient with Medulloblastoma with extensive nodularity
Organism
Homo sapiens
Specimen
Experimental status
Patient sample
Preparation method
10 µm cryosections were acquired using the cryostar NX50 with a cutting temperature of -15 °C. Tissues were section in 10 µm slices and four samples were placed on one Xenium slide. Subsequently, the tissue was fixed with PFA according to the manufacture´s protocol. Tissues were permeabilized with SDS, incubated in 70% ice cold methanol and washed with PBS. Hybridization of the human generic brain panel with 70 add-on genes (Supplementary Dataset 1) was performed at 50°C in a Bio-Rad C1000 touch cycler for 20 hours. Washing, ligation and amplification steps were carried out according to the manufacturer’s instructions. ROIs were selected according to the tissue area excluding non-tissue covered tiles. Each transcript was imaged in a bright state five times across 60 cycle-channels (15 cycles x 4 channels). After the run on the Xenium analyzer slides were removed and buffer exchanged with PBS-T for further storage at 4°C.
Signal/contrast mechanism
Fluorescence
Channel 1 – content
DAPI
Channel 1 – biological entity
Nuclei (DNA)
Image acquisition
Instrument attributes
Imaging of RNAscope samples and reimaging of Xenium slides by SDCM was conducted on an Andor Dragonfly 505 spinning disk confocal system equipped with a Nikon Ti2-E inverted microscope and a CFI P-Fluor 40X/1.30 oil objective or a Plan Apo 60x/1.40 oil objective. Multicolor images were acquired with the following laser lines 405 nm (DAPI), 488 nm (Alexa 488, eosin), 561 nm (Atto 550), 637 nm (Atto 647) 730nm (Alexa 750).
Image acquisition parameters
Images were recorded at 16-bit depth and with 1024x1024 pixels dimensions (pixel size: 0.217 µm) using an iXon Ultra 888 EM-CCD camera. The region of interest was selected based on the DAPI signal and 50 z-slices were acquired with a step size of 0.4 µm (20 µm z-range) per field of view (FOV). Tiles were imaged with a 10% overlap to ensure accurate stitching.
Image data
Type
Figure
Format & compression
PNG
Size description
8800x8788+0+0 pixels (Primary image)
Pixel/voxel size description
0.217 µm (Primary image)
Channel information
RGB
Image processing method
Tiles were imaged with a 10% overlap to ensure accurate stitching. Subsequently, a flatfield-correction was conducted based on the DAPI channel and stitching and registration of the tiles was conducted with Fiji. First, SDCM image stacks were subjected to a maximum intensity projection, followed by flat field and chromatic aberration correction using a custom script. Next, image tiles were stitched using the “Grid/Collection Stitching” plugin. DAPI images from SDCM were registered to MC or Xenium widefield images using “Register Virtual Stack Slices” with Affine feature extraction model and the Elastic bUnwarpJ splines registration model. In case of further staining, images were transformed via Transform Virtual Stack slices employing the transformation file of the DAPI registration.
Image Correlation
Spatial and temporal alignment
The region of interest was selected based on the DAPI signal and 50 z-slices were acquired with a step size of 0.4 µm (20 µm z-range) per field of view (FOV). Tiles were imaged with a 10% overlap to ensure accurate stitching. Subsequently, a flatfield-correction was conducted based on the DAPI channel and stitching and registration of the tiles was conducted with Fiji (RippeLab/MBEN) (RippeLab/MBEN).
Related images and relationship
MB266-morphology_mip.ome.tif at https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1093
Analysed data
Analysis result type
Figure
Data used for analysis
MB266-transcripts.csv.gz, MB266-transcripts.csv.gz at https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1093
Analysis method and details
Most of the analysis and visualization (including tidyverse, data.table, ggridges R packages) was done in R 4.2.2. Raw data were processed using technology-specific corporate pipelines (custom pipeline was used for MC). For each technology Seurat objects of the sample data and analysis results were created using the Seurat (v. 4.3.0) R package (scOpenLab/spatial_analysis)
Submitted via NFDI4BIOIMAGE
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NFDI4BIOIMAGE Calendar December 2025#
Kira Müntjes, Kerstin Schipper
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar December 2025. The microscopic image shows yeast cells of the fungal model Ustilago maydis that produce single cell oil at nitrogen-starvation conditions. The genetically engineered cells are packed with oil droplets that were visualized by BODIPY staining. The study was conducted in the framework of the BioSC project “NextVegOil”. Image Metadata (using REMBI template):
Study
Study type
Visualisation of microbial oil in the fungus Ustilago maydis
Study Component
Imaging method
Wide field whole organism microscopy
Biosample
Biological entity
Ustilago maydis
Organism
Yeast cells (sporidia)
Identity
Ustilago maydis MB215 cyp1Δemt1Δ (published in https://doi.org/10.1128/AEM.71.6.3033-3040.2005)
Intrinsic variable
Glycolipid production has been ablated by genetic engineering
Extrinsic variable
BODIPY (4,4-Difluoro-1,3,5,7,8-Pentamethyl-4-Bora-3a,4a-Diaza-s-Indacene 493/503) staining
Experimental variables
Cultivation time
Specimen
Location within biosample
Overview image with yeast cells from liquid culture at nitrogen-starvation condition
Preparation method
Living cells attached to agarose mounts
Signal/contrast mechanism
Differential interference contrast and fluorescence
Channel 1 - content
DIC
Channel 1 - biological entity
Intact yeast cells
Channel 2 - content
BODIPY 493/503
Channel 2 - biological entity
Intracellular lipid droplets
Image acquisition
Instrument attributes
Zeiss Axio Observer.Z1; Prime BSI express; solid-state laser 488 nm
Submitted via NFDI4BIOIMAGE
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NFDI4BIOIMAGE Calendar February 2025#
Oleg Kutskiy
Published 2025-09-15
Licensed CC-BY-SA-4.0
Image from the NFDI4BIOIMAGE Calendar February 2025. “The Way of the Cross: Christ collapses under the weight of the cross” is a recording from the partially destroyed Transfiguration Cathedral in Odessa, Ukraine. The image was taken in September 2023 and impressively shows the urgency of photographic documentation of cultural heritage. It was created as part of the “Documenting Ukrainian Cultural Heritage Project – Photographic Documentation of War-Threatened Buildings in Ukraine”.
Project
Documenting Ukrainian Cultural Heritage – Photographic Documentation of War-Threatened Buildings in Ukraine
Recording date
2023-09-01
Location
The Savior Transfiguration Cathedral, south side choir Ploshcha Soborna 3, Odessa, Ukraine
Dating
1999/2005
Factual term
Mural
Genus
Wall painting
Status
Partially destroyed 2023-07-23
Image file number
fmd10034507
Topic
Iconography: 73D4113 * the third fall (Christ carrying the cross)
Dataset from
Bildarchiv Foto Marburg
Acquisition parameter
Color, born digital
Submitted via NFDI4Culture
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NFDI4BIOIMAGE Calendar January 2025#
Lea Miebach, Sander Bekeschus
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar January 2025. A Heart for Redox Biology: The image of primary bone mesenchymal stromal/stem cells (hBM-MSCs) was captured in a study evaluating the cellular effects of therapeutic oxidation in the context of regenerative medicine. The cells were isolated from an arthroplasty patient cohort in a joint research project between the Center for Orthopaedics at University Medical Center and the group ZIK plasmatis at the Leibniz Institute for Plasma Science and Technology (INP) in Greifswald. You can appreciate the characteristic morphology and complex actin cytoskeleton that is crucial for the cellular function of hBM-MSCs. Can you spot the heart that is formed by the prominent actin protrusions of interconnected cells? Image Metadata (using REMBI template):
Study Component
Imaging method
Spinning-disc confocal mode, epifluorescence
Biosample
Biological entity
Bone marrow-mesenchymal stem cells (BM-MSCs)
Organism
Homo sapiens
Specimen
Preparation method
Fixation (4% PFA)
Signal/contrast mechanism
Fluorescence
Channel 1 – content
4’,6-Diamidin-2-phenylindol (DAPI; Thermo Fisher, USA), blue
Channel 1 – biological entity
Nuclei
Channel 2 – content
MitoSpy Green (Biolegend, USA), green
Channel 2 – biological entity
Mitochondria
Channel 3 – content
Flash Phalloidin Red (Biolegend, USA), orange
Channel 3 – biological entity
Actin
Image acquisition
Microscope model
Operetta CLS (PerkinElmer, USA)
Image data
Type
Raw and processed image in comparison
Magnification
20x air objective (NA = 0.8)
Excitation
Channel 1: 365 nm; Channel 2: 475 nm; Channel 3: 550 nm
Detection
Channel 1: 465 nm; Channel 2: 525 nm; Channel 3: 610 nm
Analysed data
Image processing method
Algorithm-based, unsupervised image segmentation with Harmony 4.9 analysis software (PerkinElmer, USA)
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NFDI4BIOIMAGE Calendar July 2025#
Pilar Lörzing, Denis Iliasov, Michael Schlierf, Thorsten Mascher
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar July 2025. The sample was provided through a collaboration with the group of Thorsten Mascher at TU Dresden. Aim of this project is to explore the cellular autofluorescence patterns in Streptomyces using advanced imaging techniques. Streptomyces coelicolor are multicellular, mycelial bacteria that grow as vegetative hyphae. The use of confocal microscopy in this project was crucial for optically sectioning these filamentous cells, enabling the resolution of their cellular autofluorescence patterns with a high signal-to-noise ratio, which allowed us to visualize the 3D arrangement of the hyphae. Image Metadata (using REMBI template):
Study
Study type
Characterization of the intrinsic autofluorescence in filamentous actinobacteria
Study Component
Imaging method
Spinning Disk Confocal Microscopy
Biosample
Biological entity
Hyphae
Organism
Streptomyces coelicolor M600
Intrinsic variable
Plasmid free derivative of the wild type strain
Experimental variables
Live-Cell imaging
Specimen
Preparation method
S. coelicolor was grown in maltose-yeast extract-malt extract (MYM) medium with tap and deionized water (1:1) and supplemented with 0.2 mL R2 trace element solution per 100 mL. Cultures were inoculated from spore suspension and grown for 18 hours at 28 °C. 2 µl cell suspension was immobilized on 1% agarose pads and covered with a cleaned coverslip (1.5H).
Channel 1 - content
Cellular autofluorescence
Channel 1 - biological entity
S. coelicolor hyphae
Image acquisition
Instrument attributes
Imaging was performed using a Nikon Ti-E Spinning Disk microscope with 100x objective and 1.5x tube lens. Fluorescence was excited with a 488 nm laser and emission light was filtered using a dual band filter 433/530 HC. An Andor Ixon Ultra 888 EMCCD camera was used for detection.
Image acquisition parameter
Z-stacks of confocal images with 0.2 µm step size
Image data
Type
Maximum intensity projection of individual z-stacks
Format & compression
TIFF
Dimension extents
x: 1024 y: 1024 z: 28 px
Size description
x: 63.12 y: 63.12 z: 5.6 µm
Pixel/voxel size description
x: 86 y: 86 z: 200 nm
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NFDI4BIOIMAGE Calendar June 2025#
Kevin Warstat
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar June 2025. This illustration compares two orthomosaics generated from UAV imagery. On the left, a true-color RGB orthomosaic is displayed, accompanied by three smaller orthomosaic images above it, each representing the red, green, and blue bands, vividly colored to highlight their significance. On the right, a corresponding NDVI orthomosaic of the same field is shown, with two images above it illustrating the red and near-infrared bands used as input. All images are processed products from structure from motion modelling.
Title
Crop spectra
Research project
PhenoRob (EXC 2070)
Recording date
2023-07-11
Location
Campus Klein-Altendorf, 53359 Rheinbach, Germany
Sensor platform
DJI Matrice 600 Pro
Sensors
Sony alpha 7 mark IV RGB MicaSense RedEdge-MX Dual multispectral camera
Submitted via FAIRagro
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NFDI4BIOIMAGE Calendar March 2025#
Michael Schwarz
Published 2025-09-11
Licensed CC-BY-4.0
Raw microscopy image from the NFDI4Bioimage calendar March 2025. The image shows 125x magnified microscopic details of a biofilm formed by Pseudomonas fluorescence on the surface of a liquid culture medium. The culture was inoculated with a cellulose-overexpressing and surface-colonizing mScarlet-tagged wild type and a GFP-tagged mutant that is unable to colonize the surface. The biofilm can collapse over time due to its own mass, so that new strategies have to be developed and thus a life cycle emerges. Image Metadata (using REMBI template):
Study
Study description Biofilm formation
Study Component
Imaging method Stereo microscopy
Biosample
Biological entity Bacteria
Organism Pseudomonas fluorescence
Specimen
Signal/contrast mechanism Relief, fluorescence
Channel 1 - content Relief, grey
Channel 1 - biological entity Details of the biofilm in transmitted light
Channel 2 - content mScarlet, red
Channel 2 - biological entity WT over-expressing cellulose and colonizing the surface
Channel 3 - content GFP, green
Channel 3 - biological entity ∆wss mutant unable to colonize the surface
Image Acquisition
Microscope model Zeiss Axio Zoom V16
Image Data
Magnification 125x
Objective PlanNeoFluar Z 1.0x
Dimension extents x: 2752, y: 2208
Pixel size description 0.91 µm x 0.91 µm
Image area 2500µm x 2500µm
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NFDI4BIOIMAGE Calendar May 2025#
Stefanie Lück
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar May 2025. The microscopy image captures the interaction between the barley cv. Golden Promise and the barley powdery mildew fungus Blumeria graminis f.sp. hordei, observed 48 hours post-inoculation. The fungus was stained with Coomassie dye, enhancing its visibility against the barley leaves. The leaves were prepared and fixed onto slides, followed by scanning with a Zeiss Axio Scan Z.1 microscope scanner using a 5x objective lens. The upper section of the image displays the hyphal colonies, which were automatically segmented, highlighting the fungal structures (black) against the plant tissue (white). The lower section presents a machine learning-based analysis where a Convolutional Neural Network (CNN) was employed to predict the fungal structures. Here, the red bounding boxes show the outer boundaries of detected objects, while the green contours precisely trace the segmented hyphae, illustrating the effectiveness of the segmentation and prediction processes. Image Metadata (using MIAPPE template):
Investigation information
Investigation Title
Analysis of Hordeum vulgare cv. Golden promise infected with Blumeria graminis f. sp. hordei (causative for barley powdery mildew)
Objective
To study the interaction between Hordeum vulgare and Blumeria graminis f. sp. hordei using advanced imaging techniques and automated image analysis.
Study information
Study Title
Microscopy imaging and analysis of barley powdery mildew infection on Hordeum vulgare cv. Golden promise
Study Type
Microscopy-based phenotyping experiment
Study Description
The study involves imaging barley leaves inoculated with Blumeria graminis f. sp. hordei, followed by automated segmentation and CNN-based prediction of fungal structures.
Plant material
Plant Species
Hordeum vulgare
Cultivar
Golden promise
Experimental design
Experiment Type
Fungal inoculation and microscopy imaging
Inoculation Details
Barley leaves were inoculated with Blumeria graminis f. sp. hordei.
Time post-inoculation
48 hours
Imaging information
Microscopy type
Brightfield microscopy
Staining method
Coomassie stain for fungal structures
Microscope
Zeiss Axio Scan Z.1
Objective Lens
5x
Image Format
Zeiss CZI file
Image analysis information
Segmentation method
Automated segmentation of hyphal colonies
Image analysis software
BluVision Micro software
Prediction method
Convolutional Neural Network (CNN) for fungal structure detection
Upper image
Shows binary image with hyphal colonies (black) and background (white).
Lower image
Displays CNN predictions with red bounding boxes marking detected objects and green contours outlining segmented hyphae.
Submitted via NFDI4Biodiversity
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NFDI4BIOIMAGE Calendar November 2025#
Jadranka Macas
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar November 2025. The image shows a perivascular accumulation of perivascular B cells, T cells and plasma cells in a human brain tumor. These structures, also known as tertiary lymphoid structures, are sites of lymphocyte clonal expansion and plasma cell formation. The study aims to determine the clinical relevance and immunological function of tertiary lymphoid structures in human primary brain tumors. Image Metadata (using REMBI template):
Study
Study type
Immunomonitoring study in human oncology
Study Component
Imaging method
COMET™ highplex seq-IF staining and scanning system, HORIZON™ Viewer (Lunaphore Technologies, SA)
Biosample
Biological entity
Tertiary lymphoid structure in glioblastoma
Organism
Homo sapiens
Specimen
Location within biosample
Tumor (glioblastoma)
Preparation method
FFPE sample, automatic sequential-IF using COMET™ (Lunaphore Technologies, SA)
Signal/contrast mechanism
HORIZON™ Viewer (Lunaphore Technologies, SA)
Channel 1 - content
Alexa Fluor Plus 555, red
Channel 1 - biological entity
CD20 - B-cells
Channel 2 - content
Alexa Fluor Plus 647, green
Channel 2 - biological entity
CD3 - T-cells
Channel 3 - content
Alexa Fluor Plus 555, white
Channel 3 - biological entity
CD163 - anti-inflammatory macrophages (M2-like)
Channel 4 - content
Alexa Fluor Plus 647, magenta
Channel 4 - biological entity
MZB-1 - Marginal zone B and B1 cell-specific protein, MEDA-7 - plasma cells, memory B-cells
Channel 5 - content
Alexa Fluor Plus 647, orange
Channel 5 - biological entity
NF- Neurofilament - intermediate filaments around the axons
Channel 6- content
Alexa Fluor Plus 555, cyan
Channel 6 - biological entity
GAP43 - Neuromodulin, neuronal growth-associated protein 43 - neurons
Channel 7 - content
Alexa Fluor Plus 555, blue
Channel 7 - biological entity
vWF - von-Willebrand-Factor - endothelial cells
Image acquisition
Instrument attributes
COMET™ highplex seq-IF staining and scanning system v.1.1.1.0 (Lunaphore Technologies, SA)
Image acquisition parameters
COMET™ acquisition software
Image data
Pixel size
0.23 µm/pixel
Image size
Width 11986 pixels - 2.76 mmHeight 11514 pixels - 2.65 mm
Pixel bit depth
16-bit
Channel information
Displayed are 7 markers out of the highplex IF-panel; number of channels 43 (including autofluorescence)
Submitted via NFDI4Immuno
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NFDI4BIOIMAGE Calendar October 2025#
Vivien Joisten-Rosenthal
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar October 2025. As part of the MibiNet SFB 1535 project (https://www.sfb1535.hhu.de), this lichen was collected in the Northern Eifel region, between Blankenheim and Schmidtheim in Germany. Lichens are among the most successful examples of complex mutualistic symbiosis, where a fungus (mycobiont) forms an association with one or more photosynthetic organisms (photobionts), including green algae and/or cyanobacteria. Based on ITS analysis, the lichen shown has been identified as Peltigera neckeri. Lichens of the genus Peltigera are classified as cyanolichens due to their symbiotic association with a cyanobacterial photobiont of the genus Nostoc. The image shows the lichen’s blue-gray thallus when wet, after its collection on a mossy stone.
Research project
MibiNet SFB 1535 Project B02
Recording date; time
2023-10-28; 12:21 CEST
Location
Northern Eifel, between Blankenheim and Schmidtheim, Germany
Environmental conditions
Cloudy, slightly rainy
Temperature
14°C
Organism
Peltigera neckeri
Organism attribute
Cyanolichen, foliose
Mycobiont
Peltigera
Photobiont
Nostoc
Substrate
Moss
Camera
Apple iPhone 12
Objective
iPhone 12 back dual wide camera 4.2mm f/1.6
Size
4,032 x 3,024 px
Submitted via NFDI4BIOIMAGE
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NFDI4BIOIMAGE Calendar September 2025#
Heidi Faber-Zuschratter, Torsten Stöter, Werner Zuschratter, Roland Hartig, Markus Wilke
Published 2025-09-15
Licensed CC-BY-4.0
Image from the NFDI4BIOIMAGE Calendar September 2025. The scanning electron micrograph shows the approach of T-lymphocytes (Jurkat cells; cyan) to an antigen-presenting B cell (Raji cell; yellow) in the center. The image was taken as part of the research work of the CRC 854, which focused on molecular processes that regulate inter- and intracellular communication within the immune system. Image Metadata (using REMBI template):
Study
Study description
Ultrastructure of the immune synapse
Study type
Research project within DFG CRC 854 (Molecular organisation of cellular communication within the immune system)
Study Component
Imaging method
Scanning Electron Microscopy
Biosample
Biological entity
Jurkat cell line E6.1 and Raji B cell lymphoma cell line
Organism
Homo sapiens
Identity
Z21_A1
Specimen
Preparation method
Cell lines were maintained in RPMI 1640 medium supplemented with 10% fetal calf serum (FCS; PAN Biotech), stable L-glutamine, penicillin (50 U/ml), and streptomycin (50 mg/ml) (Biochrom) in humidified 5% CO2 at 37°C. E6.1 cells were mixed at a 1:1 ratio with Raji B cells that had been pulsed with SEE (bacterial SAG staphylococcal enterotoxin E). After 10 min cells were plated on poly-L-lysine–covered slides at room temperature for 5 min and fixed for 10 min in PBS (pH 7.4) containing 1.5% PFA and 0.025% glutaraldehyde. Cryo-drying by critical point dryer (Leica EM CPD300) followed by sputtering with gold.
Signal/contrast mechanism
Detected secondary electrons
Channel 1 - content
Jurkat cell line E6.1 (artificial color table, cyan)
Channel 1 - biological entity
Surface of Jurkat cells
Channel 2 - content
Raji B cell lymphoma cell line (artificial color table, yellow)
Channel 2 - biological entity
Surface of a Raji B cell
Image acquisition
Instrument attributes
FEI XL30 FEG ESEM
Image acquisition parameters
10 keV, Magnification 6500 x, Scale bar: 2 µm
Submitted via NFDI4BIOIMAGE
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NFDI4BIOIMAGE: Perspective for a national bioimaging standard#
Josh Moore, Susanne Kunis
Licensed CC-BY-4.0
Tags: Nfdi4Bioimage, Exclude From Dalia
Content type: Publication
NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon)#
Mohamed M. Abdrabbou, Mehrnaz Babaki, Tom Boissonnet, Michele Bortolomeazzi, Eik Dahms, Vanessa A. F. Fuchs, Moritz Hoevels, Niraj Kandpal, Christoph Möhl, Joshua A. Moore, Astrid Schauss, Andrea Schrader, Torsten Stöter, Julia Thönnißen, Monica Valencia-S., H. Lukas Weil, Jens Wendt and Peter Zentis
Licensed CC-BY-4.0
Tags: Arc, Dataplant, Hackathon, Nfdi4Bioimage, OMERO, Python, Research Data Management, Exclude From Dalia
Content type: Event, Publication, Documentation
NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne-Hackathon-2023, GitHub repository)#
Mohamed Abdrabbou, Mehrnaz Babaki, Tom Boissonnet, Michele Bortolomeazzi, Eik Dahms, Vanessa Fuchs, A. F. Moritz Hoevels, Niraj Kandpal, Christoph Möhl, Joshua A. Moore, Astrid Schauss, Andrea Schrader, Torsten Stöter, Julia Thönnißen, Monica Valencia-S., H. Lukas Weil, Jens Wendt, Peter Zentis
Licensed CC-BY-4.0
This repository documents the first NFDI4Bioimage - TA3-Hackathon - UoC-2023 (Cologne Hackathon), where topics like ‘Interoperability’, ‘REMBI / Mapping’, and ‘Neuroglancer (OMERO / zarr)’ were explored through collaborative discussions and workflow sessions, culminating in reports that bridge NFDI4Bioimage to DataPLANT. Funded by various DFG initiatives, this event emphasized documentation and use cases, contributing preparatory work for future interoperability projects at the 2nd de.NBI BioHackathon in Bielefeld.
Tags: Research Data Management, FAIR-Principles, Bioimage Analysis, Nfdi4Bioimage, Exclude From Dalia
Content type: Github Repository
NFDI4Bioimage Calendar 2024 October; original image#
Christian Jüngst, Peter Zentis
Published 2024-09-25
Licensed CC-BY-4.0
Raw microscopy image from the NFDI4Bioimage calendar October 2024
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
NFDI4Bioimage Calendar 2025 March; original image#
Sonja Schimmler, Reinhard Altenhöner, Lars Bernard, Juliane Fluck, Axel Klinger, Sören Lorenz, Brigitte Mathiak, Bernhard Miller, Raphael Ritz, Thomas Schörner-Sadenius, Alexander Sczyrba, Regine Stein
Published 2025-02-27
Licensed CC-BY-4.0
Raw microscopy image from the NFDI4Bioimage calendar March 2025. The image shows 125x magnified microscopic details of a biofilm formed by Pseudomonas fluorescence on the surface of a liquid culture medium. The culture was inoculated with a cellulose-overexpressing and surface-colonizing mScarlet-tagged wild type and a GFP-tagged mutant that is unable to colonize the surface. The biofilm can collapse over time due to its own mass, so that new strategies have to be developed and thus a life cycle emerges. Image Metadata (using REMBI template):
Study
Study description Biofilm formation
Study Component
Imaging method Stereo microscopy
Biosample
Biological entity Bacteria
Organism Pseudomonas fluorescence
Specimen
Signal/contrast mechanism Relief, fluorescence
Channel 1 - content Relief, grey
Channel 1 - biological entity Details of the biofilm in transmitted light
Channel 2 - content mScarlet, red
Channel 2 - biological entity WT over-expressing cellulose and colonizing the surface
Channel 3 - content GFP, green
Channel 3 - biological entity ∆wss mutant unable to colonize the surface
Image Acquisition
Microscope model Zeiss Axio Zoom V16
Image Data
Magnification 125x
Objective PlanNeoFluar Z 1.0x
Dimension extents x: 2752, y: 2208
Pixel size description 0.91 µm x 0.91 µm
Image area 2500µm x 2500µm
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NGFF Converter#
Licensed GPL-2.0
An easy to use and open source converter for bioimaging data. NGFF-Converter is a GUI application for conversion of bioimage formats into OME-NGFF (Next-Generation File Format) or OME-TIFF.
Tags: Open Source Software, Exclude From Dalia
Content type: Application
Nd2 does not open in Fiji Bio_formats 8.1.1#
Jaramillo Carlos
Published 2025-06-02
Licensed CC-BY-4.0
this file is a .nd2 image of a pollen grain taken with a Nikon 80i. It is in RGB and it is a stack of hundreds of Z layers
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Nd2 does not open in Fiji Bio_formats 8.1.1 (additional files)#
Jonatan Bustos
Published 2025-05-23
Licensed CC-BY-4.0
This dataset contains 4 .nd2 image files of pollen grains captured using a Nikon 80i microscope. The files include both the original full-frame images and cropped Regions of Interest (ROIs) extracted from them. All images are in RGB format and include multiple Z-stack layers.
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Nd2 does not open in Fiji Bio_formats 8.1.1 (on Windows)#
Loïc Sauteur
Published 2025-07-31
Licensed CC-BY-4.0
Related to github issue: ome/bioformats#3517
Tags: Exclude From Dalia
NeurIPS 2022 Cell Segmentation Competition Dataset#
Jun Ma, Ronald Xie, Shamini Ayyadhury, Cheng Ge, Anubha Gupta, Ritu Gupta, Song Gu, Yao Zhang, Gihun Lee, Joonkee Kim, Wei Lou, Haofeng Li, Eric Upschulte, Timo Dickscheid, de Almeida, José Guilherme, Yixin Wang, Lin Han, Xin Yang, Marco Labagnara, Vojislav Gligorovski, Maxime Scheder, Rahi, Sahand Jamal, Carly Kempster, Alice Pollitt, Leon Espinosa, Tam Mignot, Middeke, Jan Moritz, Jan-Niklas Eckardt, Wangkai Li, Zhaoyang Li, Xiaochen Cai, Bizhe Bai, Greenwald, Noah F., Van Valen, David, Erin Weisbart, Cimini, Beth A, Trevor Cheung, Oscar Brück, Bader, Gary D., Bo Wang
Published 2024-02-27
Licensed CC-BY-NC-ND-4.0
The official data set for the NeurIPS 2022 competition: cell segmentation in multi-modality microscopy images. https://neurips22-cellseg.grand-challenge.org/ Please cite the following paper if this dataset is used in your research. @article{NeurIPS-CellSeg, title = {The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions}, author = {Jun Ma and Ronald Xie and Shamini Ayyadhury and Cheng Ge and Anubha Gupta and Ritu Gupta and Song Gu and Yao Zhang and Gihun Lee and Joonkee Kim and Wei Lou and Haofeng Li and Eric Upschulte and Timo Dickscheid and José Guilherme de Almeida and Yixin Wang and Lin Han and Xin Yang and Marco Labagnara and Vojislav Gligorovski and Maxime Scheder and Sahand Jamal Rahi and Carly Kempster and Alice Pollitt and Leon Espinosa and Tâm Mignot and Jan Moritz Middeke and Jan-Niklas Eckardt and Wangkai Li and Zhaoyang Li and Xiaochen Cai and Bizhe Bai and Noah F. Greenwald and David Van Valen and Erin Weisbart and Beth A. Cimini and Trevor Cheung and Oscar Brück and Gary D. Bader and Bo Wang}, journal = {Nature Methods}, volume={21}, pages={1103–1113}, year = {2024}, doi = {https://doi.org/10.1038/s41592-024-02233-6} } This is an instance segmentation task where each cell has an individual label under the same category (cells). The training set contains both labeled images and unlabeled images. You can only use the labeled images to develop your model but we encourage participants to try to explore the unlabeled images through weakly supervised learning, semi-supervised learning, and self-supervised learning. The images are provided with original formats, including tiff, tif, png, jpg, bmp… The original formats contain the most amount of information for competitors and you have free choice over different normalization methods. For the ground truth, we standardize them as tiff formats. We aim to maintain this challenge as a sustainable benchmark platform. If you find the top algorithms (https://neurips22-cellseg.grand-challenge.org/awards/) don’t perform well on your images, welcome to send us the dataset (neurips.cellseg@gmail.com)! We will include them in the new testing set and credit your contributions on the challenge website! Dataset License: CC-BY-NC-ND
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
Neural Networks and Deep Learning#
Michael Nielsen
Published 2019-12-01
Licensed CC-BY-NC-3.0 UNPORTED
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.
Tags: Deep Learning, Neural Networks, Machine Learning, Exclude From Dalia
Content type: Book
New Kid on the (NFDI) Block: NFDI4BIOIMAGE - A National Initiative for FAIR Data Management in Bioimaging and Bioimage Analysis#
Cornelia Wetzker
Published 2024-10-29
Licensed CC-BY-4.0
The poster introduces the consortium NFDI4BIOIMAGE with its central objectives, provides an overview of challenges in bioimage data handling, sharing and analysis and lists support options by the consortium through its data stewardship team. It is part of the work of the German consortium NFDI4BIOIMAGE funded by the Deutsche Forschungsgemeinschaft (DFG grant number NFDI 46/1, project number 501864659) and has been presented at the conference FDM@Campus held in Göttingen September 23-25, 2024.
Tags: Exclude From Dalia
New report highlights the scientific impact of open source software#
UNKNOWN
Published UNKNOWN
Licensed UNKNOWN
Tags: Open Source, Alphafold, Exclude From Dalia
Content type: Report, Blog Post
NextFlow core#
nf-core is a community effort to collect a curated set of analysis pipelines built using Nextflow
Tags: Python, Exclude From Dalia
Content type: Collection
NextFlow documentation#
Nextflow enables scalable and reproducible scientific workflows using software containers.
Tags: Workflow Engine, Exclude From Dalia
Content type: Documentation
Nextflow: Scalable and reproducible scientific workflows#
Floden Evan, Di Tommaso Paolo
Published 2020-12-17
Licensed CC-BY-4.0
Nextflow is an open-source workflow management system that prioritizes portability and reproducibility. It enables users to develop and seamlessly scale genomics workflows locally, on HPC clusters, or in major cloud providers’ infrastructures. Developed since 2014 and backed by a fast-growing community, the Nextflow ecosystem is made up of users and developers across academia, government and industry. It counts over 1M downloads and over 10K users worldwide.
Tags: Workflow Engine, Exclude From Dalia
Content type: Slides
NuInsSeg#
Amirreza Mahbod, Christine Polak, Katharina Feldmann, Rumsha Khan, Katharina Gelles, Georg Dorffner, Ramona Woitek, Sepideh Hatamikia, Isabella Ellinger
Published 2024-05-14
Licensed CC-BY-4.0
A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Images
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
Nuclei of U2OS cells in a chemical screen#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
This image set is part of a high-throughput chemical screen on U2OS cells, with examples of 200 bioactive compounds. The effect of the treatments was originally imaged using the Cell Painting assay (fluorescence microscopy). This data set only includes the DNA channel of a single field of view per compound. These images present a variety of nuclear phenotypes, representative of high-throughput chemical perturbations. The main use of this data set is the study of segmentation algorithms that can separate individual nucleus instances in an accurate way, regardless of their shape and cell density. The collection has around 23,000 single nuclei manually annotated to establish a ground truth collection for segmentation evaluation.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
Nuclei of mouse embryonic cells#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
Cell dynamics during the early mouse embryogenesis change spatiotemporally. For understanding the mechanism of this developmental process, imaging cell dynamics by live-cell imaging of fluorescently labeled nuclei and performing nuclei segmentation of these images by image processing are essential. This dataset contains the fluorescence images and Ground Truth used when performing nuclei segmentation using deep learning. Fluorescence images are time-series images from fertilization to blastocyst formation. Ground Truth is supervised data of the cell nuclear region.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology#
Jeongun Ryu, Aaron Valero Puche, JaeWoong Shin, Seonwook Park, Biagio Brattoli, Mohammad Mostafavi, Jinhee Lee, Sérgio Pereira, Wonkyung Jung, Soo Ick Cho, Chan-Young Ock, Kyunghyun Paeng, Donggeun Yoo
Published 2023-03-23
The OCELOT dataset is a histopathology dataset designed to facilitate the development of methods that utilize cell and tissue relationships. The dataset comprises both small and large field-of-view (FoV) patches extracted from digitally scanned whole slide images (WSIs), with overlapping regions. The small and large FoV patches are accompanied by annotations of cells and tissues, respectively. The WSIs are sourced from the publicly available TCGA database and were stained using the H&E method before being scanned with an Aperio scanner.
For more details, please check https://lunit-io.github.io/research/ocelot_dataset/.
Before downloading the dataset, please make sure to carefully read and agree to the Terms and Conditions at (https://lunit-io.github.io/research/ocelot_tc/).
Also, please provide 1. name, 2. e-mail address, 3. organization/company name.
Release note.
In version 1.0.1, we exclude four test cases (586, 589, 609, 615) due to under-annotated issue. In version 1.0.0, we include images and annotations of validation and test splits. In version 0.1.2, we modified the coordinates of cell labels to range from 0 to 1023 (-1 from the previous coordinates). In version 0.1.1, we removed non-H&E stained patches from the dataset.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
OME Event Database#
Tags: OMERO, Research Data Management, Exclude From Dalia
Content type: Collection, Event
OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies#
Josh Moore, Chris Allan, Sébastien Besson, Jean-Marie Burel, Erin Diel, David Gault, Kevin Kozlowski, Dominik Lindner, Melissa Linkert, Trevor Manz, Will Moore, Constantin Pape, Christian Tischer, Jason R. Swedlow
Licensed CC-BY-4.0
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Content type: Publication
OME-Zarr course#
Bugra Oezdemir, Christian Tischer
Licensed UNKNOWN
Tags: Exclude From Dalia
Content type: Tutorial
OME2024 NGFF Challenge Results#
Josh Moore
Published 2024-11-01
Licensed CC-BY-4.0
Presented at the 2024 FoundingGIDE event in Okazaki, Japan: https://founding-gide.eurobioimaging.eu/event/foundinggide-community-event-2024/ Note: much of the presentation was a demonstration of the OME2024-NGFF-Challenge – https://ome.github.io/ome2024-ngff-challenge/ especially of querying an extraction of the metadata (ome/ome2024-ngff-challenge-metadata)
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
OMERO - QuPath#
Rémy Jean Daniel Dornier
Licensed CC-BY-NC-SA-4.0
OMERO-RAW extension for QuPath allows to directly access to the raw pixels of images. All types of images (RGB, fluorescence, …) are supported with this extension.
Tags: Bioimage Analysis, OMERO, Exclude From Dalia
Content type: Online Tutorial
OMERO for microscopy research data management#
Thomas Zobel, Sarah Weischner, Jens Wendt
Licensed ALL RIGHTS RESERVED
A use case example from the Münster Imaging Network
Tags: Nfdi4Bioimage, OMERO, Research Data Management, Exclude From Dalia
Content type: Publication
https://analyticalscience.wiley.com/do/10.1002/was.0004000267/
OMExcavator: a tool for exporting and connecting domain-specific metadata in a wider knowledge graph#
Stefan Dvoretskii, Michele Bortolomeazzi, Marco Nolden, Christian Schmidt, Klaus Maier-Hein, Josh Moore
Published 2025-02-21
Licensed CC-BY-4.0
Tags: Nfdi4Bioimage, Exclude From Dalia
Omero-tools#
Johannes Soltwedel
Licensed CC-BY-4.0
This repository contains a collection of tools for working with OMERO. Such tools can be working with the OMERO command line interface to transfer datasets between repositories, etc.
Tags: OMERO, Bioimage Analysis, Exclude From Dalia
Content type: Github Repository
Open Micoscropy Environment (OME) Youtube Channel#
Published None
Licensed CC-BY-4.0
OME develops open-source software and data format standards for the storage and manipulation of biological microscopy data
Tags: Open Source Software, Exclude From Dalia
Content type: Video, Collection
Open Microscopy Environment YouTube channel#
YouTube channel collecting videos and webinar recordings about the Open Microscopy Environment (OME), the Next Generation File Format OME-NGFF, the Image Data Resource (IDR), the Omero platform and Omero plugins.
Tags: OMERO, Exclude From Dalia
Content type: Collection, Video
Open Source Platform for Scalable Multi-Purpose Virtual Desktop Infrastructures#
Michael Scherle, Rafael Gieschke, Isabela Mocanu, Björn Grüning, von Suchodoletz, Dirk
Published 2025-09-12
Licensed CC-BY-4.0
Data and access to it are central to each NFDI consortium. However, moving data around is often impractical—it may be too large, sensitive, or restricted by agreements with, e.g., the funding provider, and copying introduces duplication, versioning issues, and loss of provenance. Rather than bringing data to the researcher, a Desktop-as-a-Service (DaaS) approach can offer researchers interactive, high-performance access in a secure and efficient manner. Driven by the need for seamless workflows and efficient data handling in NFDI4BIOIMAGE, we present a DaaS approach that is broadly applicable across NFDI. It supports diverse use cases, such as standardized virtual training environments for distributed participants (like required in DataPLANT), remote visualization of large-scale HPC datasets, and secure access to sensitive data (BERD)—all without the overhead of local machine setup and maintenance.
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Open microscopy in the life sciences: quo vadis?#
Johannes Hohlbein, Benedict Diederich, Barbora Marsikova, Emmanuel G. Reynaud, Séamus Holden, Wiebke Jahr, Robert Haase, Kirti Prakash
Published 2022
Licensed ALL RIGHTS RESERVED
This comment article outlines the current state of the art in open hardware publishing in the context of microscopy.
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Content type: Publication
Optimisation and Validation of a Swarm Intelligence based Segmentation Algorithm for low Contrast Positron Emission Tomography#
Robert Haase
Published 2014-04-01
Licensed CC-BY-4.0
In the field of radiooncological research, individualised therapy is one of the hot topics at the moment. As a key aspect biologically-adapted therapy is discussed. Therapy adaption based on biological parameters may include tomographic imaging to determine biological properties of the tumour. One often invoked imaging modality is positron emission tomography (PET) using the tracer [18F]-fluoromisonidazole (FMISO) for hypoxia imaging. Hypoxia imaging is of interest, because hypoxic tumours are known to be radiorestistant. Even further, patients with hypoxic tumours have worse prognosis compared to patients with normoxic tumours. Thus, hypoxia imaging appears promising for radiotherapy treatment adaption. For example, volumetric analysis of FMISO PET could deliver additional hypoxia target volumes, which may be irradiated with higher radiation doses to improve the therapeutic effect. However, limited contrast between target volume and background in FMISO PET images interferes image analysis.Established methods for target volume delineation in PET do not allow determination of reliable contours in FMISO PET. To tackle this aspect, this thesis focusses on an earlier developed swarm intelligence based segmentation algorithm for FMISO PET and rather, its optimisation and validation in a clinically relevant setting. In this setting, clinical FMISO PET images were used which were acquired as part of a clinical trial performed at the Clinic and Policlinic for Radiation Therapy and Radiooncology of the University Hospital Carl Gustav Carus Dresden. The segmentation algorithm was applied to these imaging data sets and optimised using a cross-validation approach incorporating reference contours from experienced observers who outlined FMISO PET positive volumes manually. Afterwards, the performance of the algorithm and the properties of the resulting contours were studied in more detail. The algorithm was shown to deliver contours which were similar to manually-created contours to a degree like manually-created contours were similar to each other. Thus, the application of the algorithm in clinical research is recommended to eliminate inter-observer-variabilities. Finally, it was shown that repeated FMISO PET imaging before and shortly after the beginning of combined radiochemotherapy lead to manually-created contours with significantly higher variations than the variations of automatically-created contours using the proposed algorithm. Increased contour similarity in subsequently acquired imaging data highlights the observer-independence of the algorithm. While several observers outline different volumes, in identical data sets as well as in subsequent imaging data sets, the algorithm outlines more stable volumes in both cases. Thus, increased contour reproducibility is reached by automation of the delineation process by the proposed algorithm.
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Optimized cranial window implantation for subcellular and functional imaging in vivo#
Ben Vermaercke
Published 2025-01-13
Licensed CC-BY-4.0
Intravital workshop 15/11/2024
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Parallel_Fiji_Visualizer#
Matthew Mueller, Aaron, Advanced Bioimaging Center
Published 2024-05-15T06:14:24+00:00
Licensed BSD-3-CLAUSE
Tags: Fiji, Exclude From Dalia
Content type: Github Repository
Parhyale 3D segmentation dataset#
Frederike Alwes, Ko Sugawara, Michalis Averof
Published 2023-08-11
Licensed CC-BY-4.0
The Parhyale 3D Segmentation dataset consists of 50 timepoints (TP01-TP50) of 3D images (512x512x34), where the manual annotations can be found at discrete 6 timepoints (at TP01, TP11, TP21, TP31, TP41 and TP50).
For further details, see README file.
This version fixes the duplicated label IDs found in the previous version of label files. This version ensures that each instance has a unique ID. Thanks to Jackson Borchardt for reporting that error.
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Content type: Data
Platynereis EM training data#
Constantin Pape
Published 2020-02-19
Licensed CC-BY-4.0
Training data for Convolutional Neural Networks used in the publication Whole-body integration of gene expression and single-cell morphology. We provide training data for segmenting structures in the SerialBlockface Electron Microscopy data-set containing a complete 6 day old Platynereis dumerilii larva, in particular for:
cell membranes: 9 training blocks @ resolution 20x20x25 nm. Based on initial training data provided by https://ariadne.ai/.
cilia: 3 training and 2 validation blocks @ resolution 20x20x25 nm.
cuticle: 5 training blocks @ resolution 40x40x50 nm.
nuclei: 12 training blocks @ resolution 80x80x100 nm. Based on initial training data provided by https://ariadne.ai/.
For details on how to use this data for training, see platybrowser/platybrowser-backend.
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Content type: Data
Platynereis dumerilii full-length transcriptome of developmental stages#
Vellutini, Bruno C., Mette Handberg-Thorsager, Pavel Tomancak
Published 2024-11-29
Licensed CC-BY-4.0
To generate a high-quality full-length transcriptome for the annelid Platynereis dumerilii, we collected samples from representative developmental stages, from unfertilized eggs to 5 days post-fertilization. Each sample consisted of a bulk mix from 1 to 5 batches of embryos fertilized by different parents. We incubated all batches at 18 degrees Celsius until the desired time point, then collected the embryos into a clean tube and snap-froze them in liquid nitrogen with as little seawater as possible. The samples were stored at -80 degrees Celsius until RNA extraction. We extracted total RNA from the samples using a Trizol protocol. After measuring the RNA concentration with NanoDrop, we created a bulk RNA mix by combining 1 µL from each sample into a new tube. We gave the sample to the Sequencing and Genotyping facility of the Max Planck Institute of Molecular Cell Biology and Genetics, who ran aliquots of this bulk mix through a Bioanalyzer and gel electrophoresis. They found no evidence of RNA degradation. From this sample, they prepared PacBio Iso-Seq libraries using the Express Template Prep Kit 2.0 and sequenced full-length transcripts on a SMRT 8M Cell for 30 hours using a PacBio Sequel II System. They processed the raw movie subreads with SMRT Analysis software, following the Iso-Seq v3 workflow to generate representative circular consensus sequences, demultiplex and remove primers, trim poly(A) tails, and remove concatemers. After transcript clustering and merging, the resulting dataset contained 176,122 polished high-quality isoforms. Using Cogent, we removed redundant isoforms and obtained a dataset with 117,524 transcripts. From this, we generated a dataset containing only the longest isoform for each gene, with 70,003 sequences in total. We calculated descriptive metrics using Transrate. To estimate their completeness, we used BUSCO for metazoa and obtained a score of 85%. Finally, we annotated the longest-isoform dataset using EnTAP. About 85% of the transcripts have a coding sequence. We obtained annotations for 67% of the sequences, while 33% have remained unannotated. Datasets
file name file size (zipped) sequences description
0-Pdum_workflow.zip (folder) 3.40 GB#
entire pipeline with notebook entries and analyses
1-Pdum_hq_isoforms.zip (fasta) 180.30 MB 176,122 polished high-quality isoforms from CCS
2-Pdum_co_isoforms.zip (fasta) 70.68 MB 117,524 non-redundant polished high-quality isoforms
3-Pdum_co_longest.zip (fasta) 54.85 MB 70,003 longest of non-redundant polished high-quality isoforms
4-Pdum_co_longest_annotations.zip (tsv) 34.37 MB 70,003 (46,635 annotated) annotations for longest-isoform dataset
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Plugin “omero-batch-plugin”#
Licensed GPL-2.0
An ImageJ plugin to run a script or macro on a batch of images from/to OMERO.
Tags: OMERO, Imagej, Imagej Macro, Github, Exclude From Dalia
Content type: Github Repository
Plugin “omero-cli-transfer”#
Erick Martins Ratamero, Jean-Marie Burel, Will Moore, Guillaume Gay, Christoph Moehl, et al.
Published 2024-09-14
Licensed GPL-2.0
An OMERO CLI plugin for creating and using transfer packets between OMERO servers.
Tags: OMERO, Exclude From Dalia
Content type: Github Repository
Plugin “simple-omero-client”#
Pierre Pouchin, Rdornier, kekunn, Jean-Marie Burel
Licensed GPL-2.0
A wrapper library which can be called from scripts in Fiji, but can mostly be used in Maven projects to wrap calls to the underlying OMERO Java Gateway.
Tags: OMERO, Github, Fiji, Exclude From Dalia
Content type: Github Repository
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides#
Wenqi Tang, MIC Group
Published 2021-12-12
Licensed UNLICENSED
This repo is the official implementation of our paper “Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides”.
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Content type: Data
Preprint: “Be Sustainable”, Recommendations for FAIR Resources in Life Sciences research: EOSC-Life’s Lessons#
Romain David, Arina Rybina, Jean-Marie Burel, Jean-Karim Heriche, Pauline Audergon, Jan-Willem Boiten, Frederik Coppens, Sara Crockett, Exter Katrina, Sven Fahrener, Maddalena Fratelli, Carole Goble, Philipp Gormanns, Tobias Grantner, Bjorn Gruning, Kim Tamara Gurwitz, John Hancock, Henriette Harmse, Petr Holub, Nick Juty, Geoffrey Karnbach, Emma Karoune, Antje Keppler, Jessica Klemeier, Carla Lancelotti, Jean-Luc Legras, L. Allyson Lister, Dario Livio Longo, Rebecca Ludwig, Benedicte Madon, Marzia Massimi, Vera Matser, Rafaele Matteoni, Mayrhofer Michaela Th., Christian Ohmann, Maria Panagiotopoulou, Helen Parkinson, Isabelle Perseil, Claudia Pfander, Roland Pieruschka, Michael Raess, Andreas Rauber, Audrey S. Richard, Paolo Romano, Antonio Rosato, Alex Sanchez-Pla, Susanna-Assunta Sansone, Ugis Sarkans, Beatriz Serrano-Solano, Jing Tang, Ziaurrehman Tanoli, Jonathan Tedds, Harald Wagener, Martin Weise, Hans V. Westerhoff, Rudolf Wittner, Jonathan Ewbank, Niklas Blomberg, Philip Gribbon
Published 2023-09-12
Licensed CC-BY-4.0
“Be SURE - Be SUstainable REcommendations”The main goals and challenges for the Life Science (LS) communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable LS resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European LS Research Infrastructures (RIs), it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable FAIR data management, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.IN PRESS EMBO Journal: https://www.embopress.org/journal/14602075 AVAILABLE SOON at : https://doi.org/10.15252/embj.2023115008
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ProdgerLab-StarDist-HIV Target Cell Training Set#
Zhongtian Shao
Published 2023-06-28
Licensed CC-BY-4.0
40 annotated immunofluorescence microscopy images (600 microns x 600 microns) of foreskin tissue stained for CD3/CD4/CCR5/Nuclei. These images were used to train StarDist models used for the identification of HIV Target Cells in foreskin tissue section scans.
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Content type: Data
RDF as a bridge to domain-platforms like OMERO, or There and back again.#
Josh Moore, Andra Waagmeester, Kristina Hettne, Katherine Wolstencroft, Susanne Kunis
Licensed CC-BY-4.0
In 2005, the first version of OMERO stored RDF natively. However, just a year after the 1.0 release of RDF, performance considerations led to the development of a more traditional SQL approach for OMERO. A binary protocol makes it possible to query and retrieve metadata but the resulting information cannot immediately be combined with other sources. This is the adventure of rediscovering the benefit of RDF triples as a – if not the – common exchange mechanism.
Tags: Research Data Management, FAIR-Principles, Bioimage Analysis, Exclude From Dalia
Content type: Slides
RDM4Mic Presentations#
Licensed CC-BY-4.0
Tags: Research Data Management, Exclude From Dalia
Content type: Collection
RDM4mic#
Licensed UNKNOWN
Tags: Research Data Management, OMERO, Exclude From Dalia
Content type: Collection, Video
RDMBites BioImage metadata#
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Content type: Collection, Video
https://www.youtube.com/watch?v=aRHNHk07t3Q&list=PLyCNTVs-UBvuJF7WausQ5q7v7pI1vEpI1
RDMO - Research Data Management Organiser#
Licensed UNKNOWN
Der Research Data Management Organiser (RDMO) unterstützt Forschungsprojekte bei der Planung, Umsetzung und Verwaltung aller Aufgaben des Forschungsdatenmanagements.
Tags: Research Data Management, Open Source Software, Exclude From Dalia
Content type: Website, Online Tutorial
RDM_system_connector#
SaibotMagd
Licensed UNKNOWN
This tool is intended to link different research data management platforms with each other.
Tags: Research Data Management, Exclude From Dalia
Content type: Github Repository
REMBI - Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology#
Ugis Sarkans, Wah Chiu, Lucy Collinson, Michele C. Darrow, Jan Ellenberg, David Grunwald, et al.
Published 2021-05-21
Licensed UNKNOWN
Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. The authors propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy.
Tags: Metadata, Research Data Management, Exclude From Dalia
Content type: Publication
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606015/
RESEARCH DATA MANAGEMENT on Campus and in NFDI4BIOIMAGE#
Cornelia Wetzker, Michael Schlierf
Published 2024-08-29
Licensed CC-BY-4.0
The poster is part of the work of the German consortium NFDI4BIOIMAGE funded by the Deutsche Forschungsgemeinschaft (DFG grant number NFDI 46/1, project number 501864659).
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Reconstructed images of a 2DSIM multiposition acquisition.#
Louis Romette
Published 2025-02-19
Licensed CC-BY-4.0
Acquired with an Nikon SIM, in 2D-SIM mode at 488nm of excitation with 30% laser power and 200ms of exposition. Fluorescence is a knocked-in mStayGold-β2Spectrin. Cells are rat hippocampal neurons à DIV 3. The file is a reconstructed multiposition acquisition (10 positions). Uploaded to show a probable issue with Bio-Formats in Fiji, where SIM reconstrcuted multipositions files open like static noise.
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Reference Collection to push back against “Common Statistical Myths”#
Andrew Althouse
Published 2023-06-29
Licensed UNKNOWN
Reference Collection to understand how to deal with common statistical myths.
Tags: Statistics, Exclude From Dalia
Content type: Wiki
Reporting and reproducibility in microscopy#
Published 2021-12-03
Licensed UNKNOWN
This Focus issue features a series of papers offering guidelines and tools for improving the tracking and reporting of microscopy metadata with an emphasis on reproducibility and data re-use.
Tags: Reproducibility, Metadata, Exclude From Dalia
Content type: Collection
Repository for: Combinatorial Wnt signaling landscape during brachiopod anteroposterior patterning#
Vellutini, Bruno C., Martín-Durán, José M., Aina Børve, Andreas Hejnol
Published 2024-08-16
Licensed CC-BY-4.0
This repository contains the data and analyses for the manuscript: Vellutini, B. C., Martín-Durán, J. M., Børve, A. & Hejnol, A. Combinatorial Wnt signaling landscape during brachiopod anteroposterior patterning. BMC Biol. 22, 1–23 (2024). https://doi.org/10.1186/s12915-024-01988-w The source is maintained at bruvellu/terebratalia-wnts.
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Research Data Reusability - Conceptual Foundations, Barriers and Enabling Technologies#
Costantino Thanos
Published 2017-01-09
Licensed CC-BY-4.0
This article discusses various aspects of data reusability in the context of scientific research, including technological, legal, and policy frameworks.
Tags: Research Data Management, Open Science, Data Protection, Exclude From Dalia
Content type: Publication
Research data - what are the key issues to consider when publishing this kind of material?#
Licensed UNKNOWN
The website offers detailed advice on publishing research data, focusing on key issues like data management, FAIR data principles, legal considerations, and repository selection.
Tags: Research Data Management, FAIR-Principles, Licensing, Exclude From Dalia
Content type: Tutorial
https://www.publisso.de/en/advice/publishing-advice-faqs/research-data
Research data management for bioimaging - the 2021 NFDI4BIOIMAGE community survey#
Christian Schmidt, Janina Hanne, Josh Moore, Christian Meesters, Elisa Ferrando-May, et al.
Published 2022-09-20
Licensed CC-BY-4.0
As an initiative within Germany’s National Research Data Infrastructure, the authors conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.
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Content type: Publication
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey#
Christian Schmidt, Janina Hanne, Josh Moore, Christian Meesters, Elisa Ferrando-May, Stefanie Weidtkamp-Peters, members of the NFDI4BIOIMAGE initiative
Licensed CC-BY-4.0
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Content type: Publication
Root tissue segmentation dataset#
Julian Wanner, Kuhn Cuellar, Luis, Friederike Wanke
Published 2022-01-12
Licensed CC-BY-4.0
The PHDFM dataset is composed of fluorescence microscopy images of root tissue samples from A. thaliana, using the ratiometric fluorescent indicator 8‐hydroxypyrene‐1,3,6‐trisulfonic acid trisodium salt (HPTS). This semantic segmentation training dataset consists of 2D microscopy images (the brightfield channel for excitation at 405 nm), each containing a segmentation mask as an additional image channel (manually annotated by plant biologists). The segmentation masks classify pixels into the following 5 labels with the corresponding IDs: background (0), root tissue (1), early elongation zone (2), late elongation zone (3), and meristematic zone (4).
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Content type: Data
Round Table Workshop 1 - Sample Stabilization in intravital Imaging#
Michael Gerlach, Hans-Ulrich Fried, Christiane Peuckert
Published 2024-11-14
Licensed CC-BY-4.0
Notes from a round table workshop on the 4th Day of Intravital Microscopy in Leuven, Belgium. Contains hands-on tips, tricks and schemes to improve sample stability during various models of Intravital Miroscopy.
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Round Table Workshop 2 - Correction of Drift and Movement#
Dr. Hellen Ishikawa-Ankerhold, Max Nobis
Published 2024-11-14
Licensed CC-BY-4.0
Session 2 of a round table workshop. Features description of image processing methods useful in intravital imaging to compensate for the motion of living tissue.
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STEDYCON OBF dataset with simulated intensity and complex stacks for bioformats MR #4362#
Nils Gladitz
Published 2025-09-02
Licensed CC-BY-4.0
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Sample data for PR#4284 (ome/bioformats#4284)#
Jürgen Bohl
Published 2025-03-04
Licensed CC-BY-4.0
With this file the problem addressed in PR#4284 can be reproduced: when runningbfconvert -series 4 -channel 0 2025_01_27__0007_offline_Zen_3_9_5.czi output.png the result is garbled.
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SciAugment#
Martin Schätz
Published 2022-07-29
Licensed OTHER-OPEN
SciAugment v0.2.0 has pip installable version, channel-wise augmentation was added, and an option for all augmentations or no augmentation. Examples of how to use the tool are in README and in Google Colab notebooks. Practical examples of how to use results with YOLOv5 on scientific data can be found in the SciCount project.
SciAugment aims to provide an option to create an augmented image set with similar changes in data as the imaging sensor and technique would do.
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Scripts_FilopodyanR - a case study for the NEUBIAS TS7 in Szeged#
Marion Louveaux
Licensed UNKNOWN
Tags: Neubias, Bioimage Analysis, Exclude From Dalia
Content type: Code
Segmentation of Nuclei in Histopathology Images by deep regression of the distance map#
Naylor Peter Jack, Walter Thomas, Laé Marick, Reyal Fabien
Published 2018-02-16
Licensed CC-BY-4.0
This dataset has been annonced in our accepted paper “Segmentation of Nuclei in Histopathology Images by deep regression of the distance map” in Transcation on Medical Imaging on the 13th of August. This dataset consists of 50 annotated images, divided into 11 patients.
v1.1 (27/02/19): Small corrections to a few pixel that were labelled nuclei but weren’t.
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Content type: Data
Segmenting cells in a spheroid in 3D using 2D StarDist within TrackMate#
Jean-Yves Tinevez, Joanna W. Pylvänäinen, Guillaume Jacquemet
Published 2021-08-19
Licensed CC-BY-4.0
3D image of cells in a spheroid, imaged on a confocal microscope, used in a tutorial to demonstrate how to hack TrackMate to segment cells in 3D using the 2D segmentation algorithms it ships.
Image by Guillaume Jacquemet.
For more details see https://imagej.net/plugins/trackmate/trackmate-stardist#generation-of-3d-labels-by-tracking-2d-labels-using-trackmate
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Content type: Data
Setting up an institutional OMERO environment for bioimage data: Perspectives from both facility staff and users#
Anett Jannasch, Silke Tulok, Chukwuebuka William Okafornta, Thomas Kugel, Michele Bortolomeazzi, Tom Boissonnet, Christian Schmidt, Andy Vogelsang
Published 2024-09-14
Licensed CC-BY-4.0
Modern bioimaging core facilities at research institutions are essential for managing and maintaining high-end instruments, providing training and support for researchers in experimental design, image acquisition and data analysis.
Tags: Nfdi4Bioimage, OMERO, Bioimage Analysis, Exclude From Dalia
Content type: Publication
Simulated HL60 cells (from the Cell Tracking Challenge)#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC0-1.0
These are synthetic images from the Cell Tracking Challenge. The images depict simulated nuclei of HL60 cells stained with Hoescht (training datasets). These synthetic images of HL60 cells provide an opportunity to test image analysis software by comparing segmentation results to the available ground truth for each time point. The number of clustered nuclei increases with time adding more complexity to the problem. This time-laps dataset can be used for simple segmentation or for nuclei tracking.
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Content type: Data
Single-cell approach dissecting agr quorum sensing dynamics in Staphylococcus aureus#
Julian Bär
Published 2024-02-28
Licensed CC-BY-4.0
Training data for the two StarDist2D models and the DeLTA 2.0 2D tracking model used in the publication on bioarxiv. The trained stardist models are included in the respective zip files of the training data. mm: mother-machine; cc: connected chamber. Each of them contains two folders, img and seg_label. They contain matching pairs of phasecontrast images (img) and label images (seg_label). tracking_set_subset.zip contains the training data for the DeLTA tracking model following the default folder structure. We used custom weight functions to create the training weight maps in the folder wei. The folder wei_bck contains weights generated with the original function. The unet_pads_tracking.hdf5 is the retrained tracking model used in the associated publication. See associated GitHub repository for example code on how to use the models for segmentation and tracking. The four numbered zip files contain the data used to create all figures displaying image analysis output. Abstract: Staphylococcus aureus both colonizes humans and causes severe virulent infections. Virulence is regulated by the agr quorum sensing system and its autoinducing peptide (AIP), with dynamics at the single-cell level across four agr-types – each defined by distinct AIP sequences and capable of cross-inhibition – remaining elusive. Employing microfluidics, time-lapse microscopy, and deep-learning image analysis, we uncovered significant differences in AIP sensitivity among agr-types. We observed bimodal agr activation, attributed to intergenerational phenotypic stability and influenced by AIP concentration. Upon AIP stimulation, agr‑III showed AIP insensitivity, while agr‑II exhibited increased sensitivity and prolonged generation time. Beyond expected cross-inhibition of agr‑I by heterologous AIP‑II and ‑III, the presumably cross-activating AIP‑IV also inhibited agr‑I. Community interactions across different agr-type pairings revealed four main patterns: stable or switched dominance, and delayed or stable dual activation, influenced by community characteristics. These insights underscore the potential of personalized treatment strategies considering virulence and genetic diversity.
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Content type: Data
Snakemake Documentation#
The Snakemake workflow management system is a tool to create reproducible and scalable data analyses.
Tags: Workflow Engine, Python, Exclude From Dalia
Content type: Documentation
https://snakemake.readthedocs.io/en/stable/
https://academic.oup.com/bioinformatics/article/28/19/2520/290322
SpatialData: an open and universal data framework for spatial omics#
Luca Marconato, Giovanni Palla, Kevin A Yamauchi, Isaac Virshup, Elyas Heidari, Tim Treis, Marcella Toth, Rahul Shrestha, Harald Vöhringer, Wolfgang Huber, Moritz Gerstung, Josh Moore, Fabian J Theis, Oliver Stegle
Licensed CC-BY-4.0
Tags: Python, Exclude From Dalia
Content type: Publication, Preprint
https://www.biorxiv.org/content/10.1101/2023.05.05.539647v1.abstract
Stackview sliceplot example data#
Robert Haase
Published 2024-11-03
Licensed CC-BY-4.0
This is a dataset of PNG images of Bio-Image Data Science teaching slides. The png_umap.yml file contains a list of all images and a dimensionality reduced embedding (Uniform Manifold Approximation Projection, UMAP) made using OpenAI’s text-embedding-ada-002 model. A notebook for visualizing this data is published here: haesleinhuepf/stackview
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Standard and Super-Resolution Bioimaging Data Analysis: A Primer#
Ann Wheeler (Editor), Ricardo Henriques (Editor)
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Content type: Book
StarDist Adipocyte Segmentation Training data, Training Notebook and Model#
Sarkis Rita, Naveiras Olaia, Burri Olivier, Weigert Martin, De Leval Laurence
Published 2022-08-17
Licensed CC-BY-4.0
Data from H&E human bone marrow whole slide scanner images used in the paper: “MarrowQuant 2.0: a digital pathology workflow assisting bone marrow evaluation in clinical and experimental hematology” (https://doi.org/10.21203/rs.3.rs-1860140/v1)
292 image patches
Ground truth were manually annotated using QuPath and split into 263 images for training and 29 for validation.
Training in StarDist was done on a Windows 10 PC with an RTX 2080 GPU. The requirements file for installing a Python 3.7 environment to run the attached notebooks is provided (stardist-val.txt).
The StarDist model configuration can be found in the Jupyter Notebook :
Adipocyte Training.ipynb
Model validation and metrics can be performed by running the notebook after finishing the Adipocyte Training notebook.
Quality Control.ipynb
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Content type: Data
StarDist model and data for the segmentation of Yersinia enterocolitica cells in widefield images#
Christoph Spahn, Andreas Diepold, Francesca Ermoli
Published 2024-05-02
Licensed CC-BY-4.0
Dataset and StarDist model for the segmentation of Yersinia enterocolitica cells This dataset and StarDist model are part of the publication “Active downregulation of the type III secretion system at higher local cell densities promotes Yersinia replication and dissemination”. It contains the dataset that was used for training the provided StarDist model using ZeroCostDL4Mic. Data: Yersinia enterocolitica cells were spotted on an agarose pad (1.5% low melting agarose (Sigma-Aldrich) in minimal medium, 1% Casamino acids, 5 mM EGTA, glass depression slides (Marienfeld)). For imaging, a Deltavision Elite Optical Sectioning Microscope equipped with a UPlanSApo 100×/1.40 oil objective (Olympus) and an EDGE sCMOS_5.5 camera (Photometrics) was used. Z-stacks with 9 slices (∆z = 0.15 µm) per fluorescence channel were acquired and 5 slices were selected for network training. Images were annotated in Fiji using the Freehand selection tool, and brightlight and mask images were quartered to obtain the final dataset of 300 paired images. 260 images were used for training, while 40 images were used to test model performance. Model: The StarDist 2D model was trained from scratch for 100 epochs on 300 paired image patches (image dimensions: (480 x 480 px²), patch size: (480 x 480 px²)) with a batch size of 4 and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1) (von Chamier & Laine et al., 2020). Grid parameter was set to 2 and the number of rays to 120. The model was trained with an initial learning rate of 0.0003 using a 80/20 train/test split. The dataset was augmented 4-fold by flipping and rotation. Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.7.2), numpy (v 1.21.6), cuda (v 11.1.105Build cuda_11.1.TC455_06.29190527_0). The training was accelerated using a Tesla T4 GPU.
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Content type: Data
StarDist_AsPC1_Lifeact#
Gautier Follain, Sujan Ghimire, Joanna Pylvänäinen, Johanna Ivaska, Guillaume Jacquemet
Published 2024-08-29
Licensed CC-BY-4.0
This repository includes a StarDist deep learning model designed for segmenting AsPC1 cells labeled with Lifeact from fluorescence microscopy images. The model distinguishes individual AsPC1 cells within clusters and separates them from the background. The model was trained on a small dataset and achieved an Intersection over Union (IoU) score of 0.884 and an F1 Score of 0.967, indicating high accuracy in cell segmentation. Specifications
Model: StarDist for segmenting AsPC1 cells in fluorescence microscopy images
Training Dataset:
Number of Images: 10 paired fluorescence microscopy images and label masks
Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8
Data Type: Fluorescence microscopy images of the AsPC1 Lifeact channel with manually segmented masks
File Format: TIFF (.tif)
Fluorescence Images: 16-bit
Masks: 8-bit
Image Size: 1024 x 1024 pixels (Pixel size: 0.6337 x 0.6337 µm²)
Model Capabilities:
Segment AsPC1 Cells: Detects individual AsPC1 cells from a cluster and separates them from the background
Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity under individual cells in respective channels
Performance:
Average IoU: 0.884
Average F1 Score: 0.967
Model Training: Conducted using ZeroCostDL4Mic (HenriquesLab/ZeroCostDL4Mic)
Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers
Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
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Content type: Data
StarDist_BF_Monocytes_dataset#
Gautier Follain, Sujan Ghimire, Joanna Pylvänäinen, Johanna Ivaska, Guillaume Jacquemet
Published 2024-01-26
Licensed CC-BY-4.0
This repository includes a StarDist deep learning model and its training and validation datasets for detecting mononucleated cells perfused over an endothelial cell monolayer. The model was trained on 27 manually annotated images and achieved an average F1 Score of 0.941. The dataset and model are helpful for biomedical research, especially in studying interactions between mononucleated and endothelial cells. Specifications
Model: StarDist for mononucleated cell detection on endothelial cells
Training Dataset:
Number of Images: 27 paired brightfield microscopy images and label masks
Microscope: Nikon Eclipse Ti2-E, 20x objective
Data Type: Brightfield microscopy images with manually segmented masks
File Format: TIFF (.tif)
Brightfield Images: 16-bit
Masks: 8-bit
Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)
Training Parameters:
Epochs: 400
Patch Size: 992 x 992 pixels
Batch Size: 2
Performance:
Average F1 Score: 0.941
Average IoU: 0.831
Model Training: Conducted using ZeroCostDL4Mic (HenriquesLab/ZeroCostDL4Mic)
Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
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Content type: Data
StarDist_BF_Neutrophil_dataset#
Gautier Follain, Sujan Ghimire, Joanna Pylvänäinen, Johanna Ivaska, Guillaume Jacquemet
Published 2024-01-26
Licensed CC-BY-4.0
This repository includes a StarDist deep learning model and its training and validation datasets for detecting neutrophils perfused over an endothelial cell monolayer. The model was trained on 36 manually annotated images, achieving an average F1 Score of 0.969. The dataset and model are intended for use in biomedical research, particularly for analyzing interactions between neutrophils and endothelial cells. Specifications
Model: StarDist for neutrophil detection on endothelial cells
Training Dataset:
Number of Images: 36 paired brightfield microscopy images and label masks
Microscope: Nikon Eclipse Ti2-E, 20x objective
Data Type: Brightfield microscopy images with manually segmented masks
File Format: TIFF (.tif)
Brightfield Images: 16-bit
Masks: 8-bit
Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)
Training Parameters:
Epochs: 400
Patch Size: 992 x 992 pixels
Batch Size: 2
Performance:
Average F1 Score: 0.969
Average IoU: 0.914
Model Training: Conducted using ZeroCostDL4Mic (HenriquesLab/ZeroCostDL4Mic)
Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers
Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
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Content type: Data
StarDist_BF_cancer_cell_dataset_10x#
Gautier Follain, Sujan Ghimire, Joanna Pylvänäinen, Johanna Ivaska, Guillaume Jacquemet
Published 2024-08-12
Licensed CC-BY-4.0
This repository includes a StarDist deep learning model and its training dataset designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 10x magnification. The model was trained on 77 manually annotated images, with the dataset being computationally augmented during training by a factor of 8. The model was trained for 500 epochs and achieved an average F1 Score of 0.968, indicating high accuracy in segmenting cancer cells on endothelial cells. Specifications
Model: StarDist for cancer cell segmentation on endothelial cells (10x magnification)
Training Dataset:
Number of Images: 77 paired brightfield microscopy images and label masks
Augmented Dataset: Computational augmentation by a factor of 8 during training
Microscope: Nikon Eclipse Ti2-E, 10x objective
Data Type: Brightfield microscopy images with manually segmented masks
File Format: TIFF (.tif)
Brightfield Images: 16-bit
Masks: 8-bit or 16-bit
Image Size: 1024 x 1022 pixels (pixel size: 1.3148 μm)
Training Parameters:
Epochs: 500
Patch Size: 992 x 992 pixels
Batch Size: 2
Performance:
Average F1 Score: 0.968
Average IoU: 0.882
Model Training: Conducted using ZeroCostDL4Mic (HenriquesLab/ZeroCostDL4Mic)
Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers
Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
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Content type: Data
StarDist_BF_cancer_cell_dataset_20x#
Gautier Follain, Sujan Ghimire, Joanna Pylvänäinen, Johanna Ivaska, Guillaume Jacquemet
Published 2024-01-26
Licensed CC-BY-4.0
This repository contains a StarDist deep learning model and its training and validation datasets designed for segmenting cancer cells perfused over an endothelial cell monolayer captured at 20x magnification. Using computational methods, the initial dataset of 20 manually annotated images was augmented to 160 paired images. The model was trained over 400 epochs and achieved an average F1 Score of 0.921, demonstrating high accuracy in cell segmentation tasks. Specifications
Model: StarDist for cancer cell segmentation on endothelial cells (20x magnification)
Training Dataset:
Number of Original Images: 20 paired brightfield microscopy images and label masks
Microscope: Nikon Eclipse Ti2-E, 20x objective
Data Type: Brightfield microscopy images with manually segmented masks
File Format: TIFF (.tif)
Brightfield Images: 16-bit
Masks: 8-bit
Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)
Training Parameters:
Epochs: 400
Patch Size: 992 x 992 pixels
Batch Size: 2
Performance:
Average F1 Score: 0.921
Average IoU: 0.793
Model Training: Conducted using ZeroCostDL4Mic (HenriquesLab/ZeroCostDL4Mic)
Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers
Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
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Content type: Data
StarDist_Fluorescent_cells#
Gautier Follain, Sujan Ghimire, Joanna Pylvänäinen, Johanna Ivaska, Guillaume Jacquemet
Published 2024-01-26
Licensed CC-BY-4.0
This repository includes a StarDist deep learning model and its training and validation datasets for detecting fluorescently labeled cancer cells perfused over an endothelial cell monolayer. The model was trained on 66 images labeled with CellTrace and demonstrated high accuracy, achieving an average F1 Score of 0.877. The dataset and the trained model can be used for biomedical image analysis, particularly in cancer research. Specifications
Model: StarDist for cancer cell detection
Training Dataset:
Number of Images: 66 paired fluorescent microscopy images and label masks
Microscope: Nikon Eclipse Ti2-E, 10x objective
Data Type: Fluorescent microscopy images with manually segmented masks
File Format: TIFF (.tif)
Brightfield Images: 16-bit
Masks: 8-bit
Image Size: 1024 x 1024 pixels (Pixel size: 1.3205 μm)
Training Parameters:
Epochs: 200
Patch Size: 1024 x 1024 pixels
Batch Size: 2
Performance:
Average F1 Score: 0.877
Average IoU: 0.646
Model Training: Conducted using ZeroCostDL4Mic (HenriquesLab/ZeroCostDL4Mic)
Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers
Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
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Content type: Data
StarDist_HUVEC_nuclei_dataset#
Gautier Follain, Sujan Ghimire, Joanna Pylvänäinen, Johanna Ivaska, Guillaume Jacquemet
Published 2024-02-05
Licensed CC-BY-4.0
This repository contains a StarDist deep learning model and its training and validation datasets for segmenting endothelial nuclei while ignoring cancer cells. The cancer cells were perfused over an endothelial cell monolayer. The initial dataset consisted of 17 images, where cancer cell nuclei were manually removed after segmentation with the StarDist Versatile Nuclei model. This dataset was augmented to 68 paired images using computational techniques like rotation and flipping. The model was trained for 200 epochs, achieving an average F1 Score of 0.976, demonstrating high accuracy in segmenting endothelial nuclei while excluding cancer cells. Specifications
Model: StarDist for segmenting endothelial nuclei while ignoring cancer cells
Training Dataset:
Number of Original Images: 17 paired predictions of nuclei and label images
Augmented Dataset: Expanded to 68 paired images using rotation and flipping
Source Image Generation: Generated using a pix2pix model trained to predict nuclei from brightfield images of cancer cells on top of an endothelium (DOI: 10.5281/zenodo.10617532)
Target Image Generation: Masks obtained via manual segmentation
File Format: TIFF (.tif)
Brightfield Images: 8-bit
Masks: 8-bit
Image Size: 1024 x 1022 pixels (uncalibrated)
Training Parameters:
Epochs: 200
Patch Size: 1024 x 1024 pixels
Batch Size: 2
Performance:
Average F1 Score: 0.976
Average IoU: 0.927
Model Training: Conducted using ZeroCostDL4Mic (HenriquesLab/ZeroCostDL4Mic)
Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers
Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
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Content type: Data
StarDist_TumorCell_nuclei#
Gautier Follain, Sujan Ghimire, Joanna Pylvänäinen, Johanna Ivaska, Guillaume Jacquemet
Published 2024-08-29
Licensed CC-BY-4.0
This repository contains a StarDist deep learning model designed for segmenting tumor cell nuclei from the DAPI channel in fluorescence microscopy images while excluding HUVEC nuclei. The model was trained to accurately detect individual tumor cell nuclei for subsequent measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under the nuclei. The model achieved an Intersection over Union (IoU) score of 0.558 and an F1 Score of 0.793, reflecting its capability to distinguish tumor cell nuclei from HUVEC nuclei. Specifications
Model: StarDist for segmenting tumor cell nuclei from the DAPI fluorescence channel
Training Dataset:
Number of Images: 48 paired fluorescence microscopy images and label masks
Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8
Data Type: Fluorescence microscopy images of the DAPI channel with manually segmented masks
File Format: TIFF (.tif)
Fluorescence Images: 16-bit
Masks: 8-bit
Image Size: 920 x 920 pixels (Pixel size: 0.6337 x 0.6337 µm²)
Model Capabilities:
Segment Tumor Cell Nuclei: Detects individual tumor cell nuclei in the DAPI channel while distinguishing them from HUVEC nuclei
Measure Intensity: Enables measurement of CD44, ICAM1, ICAM2, or Fibronectin intensity around or under tumor cell nuclei in respective channels
Performance:
Average IoU: 0.558
Average F1 Score: 0.793
Model Training: Conducted using ZeroCostDL4Mic (HenriquesLab/ZeroCostDL4Mic)
Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers
Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
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Content type: Data
Stardist model and training dataset for automated tracking of MDA-MB-231 and BT20 cells#
Hussein Al-Akhrass, Johanna Ivaska, Guillaume Jacquemet
Published 2021-05-26
Licensed CC-BY-4.0
StarDist Model: The StarDist model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). This custom StarDist model was trained for 300 epochs using 46 manually annotated paired images (image dimensions: (1024, 1024)) with a batch size of 2, an augmentation factor of 4 and a mae loss function. The StarDist “Versatile fluorescent nuclei” model was used as a training starting point. Key python packages used include TensorFlow (v 0.1.12), Keras (v 2.3.1), CSBdeep (v 0.6.1), NumPy (v 1.19.5), Cuda (v 11.0.221). The training was accelerated using a Tesla P100GPU. The model weights can be used in the ZeroCostDL4Mic StarDist 2D notebook or in the StarDist Fiji plugin.
StarDist Training dataset: Paired microscopy images (fluorescence) and corresponding masks
Microscopy data type: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see HenriquesLab/ZeroCostDL4Mic for details about the segmentation)
Cells were imaged using a 20x Nikon CFI Plan Apo Lambda objective (NA 0.75) one frame every 10 minutes for 16h.
Cell type: MDA-MB-231 cells and BT20 cells
File format: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)
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Content type: Data
Stardist_MiaPaCa2_from_CD44#
Gautier Follain, Sujan Ghimire, Joanna Pylvänäinen, Johanna Ivaska, Guillaume Jacquemet
Published 2024-08-29
Licensed CC-BY-4.0
This repository contains a StarDist deep learning model designed for segmenting MiaPaCa2 cells from the CD44 channel in fluorescence microscopy images. The model is capable of accurately segmenting individual MiaPaCa2 cells while excluding HUVECs. Trained on a small dataset, the model achieved an Intersection over Union (IoU) score of 0.884 and an F1 Score of 0.950, indicating high precision in cell segmentation. Specifications
Model: StarDist for segmenting MiaPaCa2 cells from the CD44 fluorescence channel
Training Dataset:
Number of Images: 8 paired fluorescence microscopy images and label masks
Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8
Data Type: Fluorescence microscopy images of the CD44 channel, obtained after immunofluorescence staining with primary and secondary antibodies and manually segmented masks
File Format: TIFF (.tif)
Fluorescence Images: 16-bit
Masks: 8-bit
Image Size: 920 x 920 pixels (Pixel size: 0.6337 x 0.6337 µm²)
Model Capabilities:
Segment MiaPaCa2 Cells: Accurately detects individual MiaPaCa2 cells while ignoring HUVECs
Measure CD44 Intensity: Allows for the measurement of CD44 intensity around MiaPaCa2 cells, specifically from the CD44 channel
Performance:
Average IoU: 0.884
Average F1 Score: 0.950
Model Training: Conducted using ZeroCostDL4Mic (HenriquesLab/ZeroCostDL4Mic)
Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers
Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
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Content type: Data
Statistical Rethinking#
Richard McElreath
Published 2024-03-01
Licensed CC0-1.0
This course teaches data analysis, but it focuses on scientific models. The unfortunate truth about data is that nothing much can be done with it, until we say what caused it. We will prioritize conceptual, causal models and precise questions about those models. We will use Bayesian data analysis to connect scientific models to evidence. And we will learn powerful computational tools for coping with high-dimension, imperfect data of the kind that biologists and social scientists face.
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Content type: Github Repository
Studentsourcing - aggregating and re-using data from a practical cell biology course#
Joachim Goedhart
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Content type: Preprint
Submitting data to the BioImage Archive#
Licensed CC0-1.0
To submit, you’ll need to register an account, organise and upload your data, prepare a file list, and then submit using our web submission form. These steps are explained here.
Tags: Research Data Management, Exclude From Dalia
Content type: Tutorial, Video
SynapseNet Training Data#
Constantin Pape
Published 2024-12-01
Licensed CC-BY-4.0
This dataset contains room-temperature single-axis TEM tomograms from Schaffer collateral and mossy fiber synapses in organotypic hippocampal slices. The tomograms were published in the two studies [1, 2]. The data was re-used for training deep neural networks to segment different synaptic structures in electron micrographs in [3]. For the tomograms, organotypic slices were prepared from the hippocampi of neonatal mice according to the interface protocol55 and vitrified after 28 days in vitro in culture medium supplemented with 20% (w/v) bovine serum albumin using an HPM100 (Leica) high-pressure freezing device. The dataset also contains 23 tomograms resulting from chemically-fixed material, which were also published in (Maus et al., 2020). For these tomograms, wild-type animals at postnatal day 28 were transcardially perfused under deep anesthesia, first with 0.9% sodium chloride solution, and then one of two fixatives (Fixative 1: Ice-cold 4% paraformaldehyde, 2.5% glutaraldehyde in 0.1 M phosphate buffer16; Fixative 2: 37° C 2% paraformaldehyde, 2.5% glutaraldehyde, 2 mM CaCl2, in 0.1 M cacodylate buffer56). Brains were rinsed and sectioned coronally through the dorsal hippocampus in an ice-cold 0.1 M phosphate buffer using a VT 1200S vibratome (Leica) (step size 100 µm; amplitude 1.5 mm, speed 0.1 mm/sec). Hippocampal CA3 subregions were excised using a 1.5 mm diameter biopsy punch and high-pressure frozen on the same day in 20% (w/v) bovine serum albumin using an HPM100 (Leica) high-pressure freezing device. For both sample preparations, automated freeze-substitution was performed. Tomograms were collected using a 200 kV JEM-2100 (JEOL) transmission electron microscope equipped with an 11 MP Orius SC1000 CCD camera (Gatan). Tilt-series (tilt range +/- 60°; 1° angular increments) were acquired at 30 000x magnification using SerialEM58. Tomographic reconstructions were generated using weighted back-projection with etomo.The data is organized into two different subfolders for data with annotations for “vesicles” and “active_zones”. Each of these subfolders is further subdivided into “train” and “test” folders, which containtomograms for the two different sample preparations in “chemical_fixation” and “single_axis_tem”.Each tomogram and the corresponding annotation is stored as a hdf5 file, containing the following internal datasets:- raw: The tomogram data.- labels/vesicles: Annotations for the synaptic vesicles, annotated with IMOD, further postprocessed and then exported to instance masks. (for tomograms in “vesicles”)- labels/AZ: Annotations for the active zone, annotated with IMOD and exported to binary masks. [1] Imig et al., The Morphological and Molecular Nature of Synaptic Vesicle Priming at Presynaptic Active Zones, Neuron, 2014, DOI:10.1016/j.neuron.2014.10.009[2] Maus et al., Ultrastructural Correlates of Presynaptic Functional Heterogeneity in Hippocampal Synapses, Cell Reports, 2020, DOI: 10.1016/j.celrep.2020.02.083[3] Muth, Moschref et al., 2024, Preprint to be published
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Content type: Data
Synthetic cells#
Vebjorn Ljosa, Katherine L. Sokolnicki, Anne E. Carpenter
Published 2012-06-28
Licensed CC-BY-NC-SA-3.0
One of the principal challenges in counting or segmenting nuclei is dealing with clustered nuclei. To help assess algorithms performance in this regard, this synthetic image set consists of five subsets with increasing degree of clustering.
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Content type: Data
Synthetic images and segmentation masks simulating HL-60 cell nucleus in 3D#
David Svoboda, Michal Kozubek, Stanislav Stejskal, Teresa Zulueta-Coarasa
Published 2024-11-26
Licensed CC-BY-3.0
One of the principal challenges in counting or segmenting nuclei is dealing with clustered nuclei. To help assess algorithms performance in this regard, this synthetic image set consists of four subsets with increasing degree of clustering. Each subset is also provided in two different levels of quality: high SNR and low SNR.
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Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1492/
TESS Event database#
Tags: Bioinformatics, Exclude From Dalia
Content type: Collection, Event
TESS training materials#
Licensed UNKNOWN
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Content type: Collection
TNBC#
Naylor Peter Jack, Walter Thomas, Laé Marick, Reyal Fabien
Published 2018-02-16
Licensed CC-BY-4.0
Involves an annotated large number of cells, including normal epithelial and myoepithelial breast cells (localized in ducts and lobules), invasive carcinomatous cells, fibroblasts, endothelial cells, adipocytes, macrophages and inflammatory cells (lymphocytes and plasmocytes). In total, our data set consists of 50 images with a total of 4022 annotated cells, the maximum number of cells in one sample is 293 and the minimum number of cells in one sample is 5, with an average of 80 cells per sample and a high standard deviation of 58. The annotation was performed by three experts: an expert pathologist and two trained research fellows. Each sample was annotated by one of the annotators, checked by another one and in case of disagreement, a consensus was established by discussion among the 3 experts.
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Content type: Data
Terminology service for research data management and knowledge discovery in low-temperature plasma physics#
Markus M. Becker, Ihda Chaerony Siffa, Roman Baum
Published 2024-12-11
Licensed CC-BY-4.0
Abstract: Terminology services (TS) [1,2] play a pivotal role in achieving structured metadata by providing controlled vocabularies and ontologies that standardize the description of data. This is a crucial aspect of research data management (RDM) in all scientific disciplines. In addition, TS facilitate the use of a common vocabulary within a scientific community also in a more general context, e.g. to annotate scientific papers, patents or other content for better discoverability, as envisaged by the Open Research Knowledge Graph (ORKG) [3] or the Patents4Science project [4]. To make use of these opportunities, terminologies, ontologies and knowledge graphs must be developed and made available as TS where they do not yet exist. This step is currently being taken by the research community in low-temperature plasma (LTP) physics. LTP physics explores partially ionized gases and its technological applications. This vibrant field offers innovative solutions for societal challenges, ranging from developing efficient lighting and solar cells to revolutionizing healthcare through plasma medicine. Various activities and projects have been started in the past years to support the RDM in LTP research and development and to facilitate the application of data-driven research methods. These activities are supported in parts by the NFDI4BIOIMAGE consortium, active work in the NFDI section “(Meta)data, Terminologies, Provenance”, and the basic service Terminology Services 4 NFDI (TS4NFDI) funded by Base4NFDI. Recently, the ontology Plasma-O [5–7] for LTP physics has been developed at INP in collaboration with FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, providing a framework for structuring metadata and building a knowledge graph for scientific information within the field. The present contribution will show how a TS utilizing this resource can support different aspects of RDM and knowledge discovery using concrete examples. The application cases include (i) standardizing data annotation: By providing researchers with a controlled vocabulary of LTP-specific terms and their relationships, ensuring consistent and unambiguous data descriptions; (ii) enabling semantic search: Moving beyond keyword-based searches, TS allow for complex queries based on the relationships between concepts, significantly improving data discoverability; (iii) facilitating data integration: By mapping data from different sources to a common ontology, TS enable seamless integration and analysis of heterogeneous datasets, which is crucial for data-driven research and development. The TS Suite of TS4NFDI with the provided widgets [8] fits perfectly to the requirements of these three application cases and will support the harmonization of metadata in LTP physics. The implementation of a public TS is required to provide the domain-specific metadata in a standardized format and will be instrumental in unlocking the full potential of the TS widgets for RDM and knowledge discovery by LTP researchers. Furthermore, the results can provide insights to other domains on how to apply TS to their specific needs. The work was supported in parts by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – [NFDI46/1] – 501864659 and project number 496963457 as well as by the Federal Ministry of Education and Research (BMBF), project number 16KOA013A. References:
[1]
S. Jupp, T. Burdett, C. Leroy, H. Parkinson, “A new Ontology Lookup Service at EMBL-EBI”, Workshop on Semantic Web Applications and Tools for Life Sciences (2015), https://ceur-ws.org/Vol-1546/paper_29.pdf (accessed: 2024-09-20).
[2]
P. L. Whetzel, N. F. Noy, N. H. Shah, P. R. Alexander, C. Nyulas, T. Tudorache, M. A. Musen, “BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications”, Nucleic Acids Res. 39 (2011) W541–W545, https://doi.org/10.1093/nar/gkr469.
[3]
Open Research Knowledge Graph, https://orkg.org/ (accessed: 2024-09-20).
[4]
Patents4Science – Establishing an Information Infrastructure for the Use of Patent Knowledge in Science, https://www.patents4science.org/ (accessed: 2024-09-20).
[5]
H. Sack, F. Hoppe, “Verbundprojekt: Qualitätssicherung und Vernetzung von Forschungsdaten in der Plasmatechnologie - QPTDat; Teilvorhaben: Wissensgraph und Ontologieentwicklung zur Vernetzung von Metadaten : Schlussbericht des Teilvorhabens”, 2023, https://doi.org/10.2314/KXP:1883436974.
[6]
I. Chaerony Siffa, R. Wagner, L. Vilardell Scholten, M. M. Becker, “Semantic Information Management in Low-Temperature Plasma Science and Technology with VIVO”, 2024, preprint, https://doi.org/10.48550/arXiv.2409.11065.
[7]
I. Chaerony Siffa, R. Wagner, L. Vilardell Scholten, M. M. Becker, “Plasma Ontology and Knowledge Graph Initial Release v0.5.0”, 2024, Zenodo, https://doi.org/10.5281/zenodo.13325226.
[8]
J. Sasse, V. Kneip, R. Baum, P. Zimmermann, J. Darms, J. Schneider, V. Clemens, P. Oladazimi, L. Kühnel, “ts4nfdi/terminology-service-suite: v2.6.0”, 2024, Zenodo, https://doi.org/10.5281/zenodo.13692297.
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Tess Search for Data Life Cycle#
Licensed UNKNOWN
Tags: Data Life Cycle, Research Data Management, Exclude From Dalia
Content type: Collection
https://tess.elixir-europe.org/search?q=data+life+cycle#materials
Test Dataset for Whole Slide Image Registration#
Romain Guiet, Nicolas Chiaruttini
Published 2021-04-12
Licensed CC-BY-4.0
Mouse duodenum fixed in 4% PFA overnight at 4°C, processed for paraffin infiltration using a standard histology procedure and cut at 4 microns were dewaxed, rehydrated, permeabilized with 0.5% Triton X-100 in PBS 1x and stained with Azide - Alexa Fluor 555 (Thermo Fisher) to detect EdU and DAPI for nuclei. The images were taken using a Leica DM5500 microscope with a 40X N.A.1 objective (black&white camera: DFC350FXR2, pixel dimension: 0.161 microns). Next, the slide was unmounted and stained using the fully automated Ventana Discovery xT autostainer (Roche Diagnostics, Rotkreuz, Switzerland). All steps were performed on automate with Ventana solutions. Sections were pretreated with heat using the CC1 solution under mild conditions. The primary rat anti BrDU (clone: BU1/75 (ICR1), Serotec, diluted 1:300) was incubated 1 hour at 37°C. After incubation with a donkey anti rat biotin diluted 1:200 (Jackson ImmunoResearch Laboratories), chromogenic revelation was performed with DabMap kit. The section was counterstained with Harris hematoxylin (J.T. Baker) before a second round of imaging on DM5500 PL Fluotar 40X N.A.1.0 oil (color camera: DFC 320 R2, pixel dimension: 0.1725 microns). Before acquisition, a white-balance as well as a shading correction is performed according to Leica LAS software wizard. The fluorescence and DAB images were converted in ome.tiff multiresolution file with the kheops Fiji Plugin.
Sampled prepared in the EPFL histology core facility by Nathalie Müller and Gian-Filippo Mancini.
Associated documents:
https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/dab-intensity/
https://imagej.net/plugins/bdv/warpy/warpy
This document contains a full QuPath project with an example of registered image.
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The BioImage Archive – Building a Home for Life-Sciences Microscopy Data#
Matthew Hartley, Gerard J. Kleywegt, Ardan Patwardhan, Ugis Sarkans, Jason R. Swedlow, Alvis Brazma
Published 2022-06-22
Licensed UNKNOWN
The BioImage Archive is a new archival data resource at the European Bioinformatics Institute (EMBL-EBI).
Tags: Research Data Management, Exclude From Dalia
Content type: Publication
https://www.sciencedirect.com/science/article/pii/S0022283622000791?via%3Dihub
The FAIR Guiding Principles for scientific data management and stewardship#
Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, et. al
Published 2016-03-15
Licensed CC-BY-4.0
This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
Tags: FAIR-Principles, Research Data Management, Exclude From Dalia
Content type: Publication
The FAIR guiding principles for data stewardship - fair enough?#
Martin Boeckhout, Gerhard A. Zielhuis, Annelien L. Bredenoord
Published 2018-05-17
Licensed CC-BY-4.0
The FAIR guiding principles for research data stewardship (findability, accessibility, interoperability, and reusability) look set to become a cornerstone of research in the life sciences. A critical appraisal of these principles in light of ongoing discussions and developments about data sharing is in order.
Tags: FAIR-Principles, Data Stewardship, Sharing, Exclude From Dalia
Content type: Publication
The Fiji Updater#
Robert Haase
Licensed ALL RIGHTS RESERVED
Article about the Fiji Updater explaining how it works in the background.
Tags: Imagej, Exclude From Dalia
Content type: Publication
https://analyticalscience.wiley.com/do/10.1002/was.0004000112/
The Information Infrastructure for BioImage Data (I3D:bio) project to advance FAIR microscopy data management for the community#
Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Julia Dohle, Tobias Wernet, Janina Hanne, Roland Nitschke, Susanne Kunis, Karen Bernhardt, Stefanie Weidtkamp-Peters, Elisa Ferrando-May
Published 2024-03-04
Licensed CC-BY-4.0
Research data management (RDM) in microscopy and image analysis is a challenging task. Large files in proprietary formats, complex N-dimensional array structures, and various metadata models and formats can make image data handling inconvenient and difficult. For data organization, annotation, and sharing, researchers need solutions that fit everyday practice and comply with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. International community-based efforts have begun creating open data models (OME), an open file format and translation library (OME-TIFF, Bio-Formats), data management software platforms, and microscopy metadata recommendations and annotation tools. Bringing these developments into practice requires support and training. Iterative feedback and tool improvement is needed to foster practical adoption by the scientific community. The Information Infrastructure for BioImage Data (I3D:bio) project works on guidelines, training resources, and practical assistance for FAIR microscopy RDM adoption with a focus on the management platform OMERO and metadata annotations.
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
The Open Microscopy Environment (OME) Data Model and XML file - open tools for informatics and quantitative analysis in biological imaging#
Ilya G. Goldberg, Chris Allan, Jean-Marie Burel, Doug Creager, Andrea Falconi, et. al
Published 2005-05-03
Licensed CC-BY-4.0
The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results.
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Publication
https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47
The role of Helmholtz Centers in NFDI4BIOIMAGE - A national consortium enhancing FAIR data management for microscopy and bioimage analysis#
Riccardo Massei, Christian Schmidt, Michele Bortolomeazzi, Julia Thoennissen, Jan Bumberger, Timo Dickscheid, Jan-Philipp Mallm, Elisa Ferrando-May
Published 2024-06-06
Licensed CC-BY-4.0
Germany’s National Research Data Infrastructure (NFDI) aims to establish a sustained, cross-disciplinary research data management (RDM) infrastructure that enables researchers to handle FAIR (findable, accessible, interoperable, reusable) data. While FAIR principles have been adopted by funders, policymakers, and publishers, their practical implementation remains an ongoing effort. In the field of bio-imaging, harmonization of data formats, metadata ontologies, and open data repositories is necessary to achieve FAIR data. The NFDI4BIOIMAGE was established to address these issues and develop tools and best practices to facilitate FAIR microscopy and image analysis data in alignment with international community activities. The consortium operates through its Data Stewards team to provide expertise and direct support to help overcome RDM challenges. The three Helmholtz Centers in NFDI4BIOIMAGE aim to collaborate closely with other centers and initiatives, such as HMC, Helmholtz AI, and HIP. Here we present NFDI4BIOIMAGE’s work and its significance for research in Helmholtz and beyond
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Towards FAIR Data Workflows for Multidisciplinary Science: Ongoing Endeavors and Future Perspectives in Plasma Technology#
Robert Wagner, Dagmar Waltemath, Kristina Yordanova, Markus Becker
Licensed CC-BY-NC-ND-4.0
This paper focuses on the ongoing process of establishing a FAIR (Findable, Accessible, Interoperable and Reusable) data workflow for multidisciplinary research and development in applied plasma science. The presented workflow aims to support researchers in handling their project data while also fulfilling the requirements of modern digital research data management.
Tags: Research Data Management, Exclude From Dalia
Content type: Publication
https://www.scitepress.org/Link.aspx?doi=10.5220/0012808000003756
Towards Preservation of Life Science Data with NFDI4BIOIMAGE#
Robert Haase
Published 2024-09-03
Licensed CC-BY-4.0
This talk will present the initiatives of the NFDI4BioImage consortium aimed at the long-term preservation of life science data. We will discuss our efforts to establish metadata standards, which are crucial for ensuring data reusability and integrity. The development of sustainable infrastructure is another key focus, enabling seamless data integration and analysis in the cloud. We will take a look at how we manage training materials and communicate with our community. Through these actions, NFDI4BioImage seeks to enable FAIR bioimage data management for German researchers, across disciplines and embedded in the international framework.
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Towards Transparency and Knowledge Exchange in AI-assisted Data Analysis Code Generation#
Robert Haase
Published 2024-10-14
Licensed CC-BY-4.0
The integration of Large Language Models (LLMs) in scientific research presents both opportunities and challenges for life scientists. Key challenges include ensuring transparency in AI-generated content and facilitating efficient knowledge exchange among researchers. These issues arise from the in-transparent nature of AI-driven code generation and the informal sharing of AI insights, which may hinder reproducibility and collaboration. This paper introduces git-bob, an innovative AI-assistant designed to address these challenges by fostering an interactive and transparent collaboration platform within GitHub. By enabling seamless dialogue between humans and AI, git-bob ensures that AI contributions are transparent and reproducible. Moreover, it supports collaborative knowledge exchange, enhancing the interdisciplinary dialogue necessary for cutting-edge life sciences research. The open-source nature of git-bob further promotes accessibility and customization, positioning it as a vital tool in employing LLMs responsibly and effectively within scientific communities.
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Towards community-driven metadata standards for light microscopy - tiered specifications extending the OME model#
Mathias Hammer, Maximiliaan Huisman, Alessandro Rigano, Ulrike Boehm, James J. Chambers, et al.
Published 2022-07-10
Licensed UNKNOWN
Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata specifications that extend the OME data model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.
Tags: Reproducibility, Bioimage Analysis, Metadata, Exclude From Dalia
Content type: Publication
Training set of microscopy images for Dietler et al. Nature Communications 2020#
Nicola Dietler, Matthias Minder, Vojislav Gligorovski, Economou, Augoustina Maria, Joly, Denis Alain Henri Lucien, Ahmad Sadeghi, Chan, Chun Hei Michael, Mateusz Kozinski, Martin Weigert, Anne-Florence Bitbol, Rahi, Sahand Jamal
Published 2021-12-07
Licensed CC-BY-4.0
Training set of microscopy images for Dietler et al. Nature Communications 2020
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
TrendsInMicroscopy2025#
Marcelo Zoccoler, Johannes Soltwedel
Published 2025-03-10T13:42:57+00:00
Licensed CC-BY-4.0
Course contents for biapol course at Trends in Microscopy conference 2025
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Github Repository
Upcoming Image Analysis Events#
Curtis Rueden, Albane de la Villegeorges, Simon F. Nørrelykke, Romain Guiet, Olivier Burri, et al.
Licensed UNKNOWN
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Event, Forum Post, Workshop
https://forum.image.sc/t/upcoming-image-analysis-events/60018/67
Using Glittr.org to find, compare and re-use online training materials#
Geert van Geest, Yann Haefliger, Monique Zahn-Zabal, Patricia M. Palagi
Licensed CC-BY-4.0
Glittr.org is a platform that aggregates and indexes training materials on computational life sciences from public git repositories, making it easier for users to find, compare, and analyze these resources based on various metrics. By providing insights into the availability of materials, collaboration patterns, and licensing practices, Glittr.org supports adherence to the FAIR principles, benefiting the broader life sciences community.
Tags: Bioimage Analysis, Research Data Management, Exclude From Dalia
Content type: Publication, Preprint
V4SDB_Winter_School_2025#
Joanna Pylvänäinen
Published 2025-01-13T08:29:22+00:00
Training materials for V4SDB Student Winter School, 28th-31st January 2025 at ELTE Eötvös Loránd University in Budapest, Hungary
Tags: Cell Tracking, Bioimage Analysis, Exclude From Dalia
Content type: Github Repository, Collection
Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset#
Martyna Mazur, Wojciech Krauze
Published 2023-06-16
Licensed CC-BY-4.0
This dataset includes 4 files with segmentation results for 4 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain:
3D binary masks of biological cells obtained through Cellpose [1] and ODT-SAS;
3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS.
All files are .*mat files.
The files REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat and REC_SH-SY5Y_3.mat consist of 7 variables:
RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell; n_imm – refractive index of object immersion medium; dx – object space sample size in XY [(\mu m)]; rayXY – xy-coordinates of illumination vectors;
maskManual – table with manually determined 3D binary masks of organelles; maskCellpose – 3D binary mask of biological cell obtained through Cellpose; maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS.
File REC_SH-SY5Y_4.mat includes masks for the ODT-SAS and Cellpose segmentation of three closely packed cells and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.
Access a particular 3D binary mask from ‘maskManual’ and ‘maskODTSAS’ tables, using the following names: ‘Cell’, ‘Nucleoli’, ‘LS’. For example:
cellMask = maskODTSAS.Cell{1};
[1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
WebAtlas pipeline for integrated single cell and spatial transcriptomic data#
Tong Li, David Horsfall, Daniela Basurto-Lozada
Published 2023-04-28
Licensed None
Single cell and spatial transcriptomics illuminate complementary features of tissues. However, the online dissemination and exploration of multi-modal datasets is challenging. We introduce the WebAtlas pipeline for user-friendly sharing and interactive navigation of integrated datasets. WebAtlas unifies commonly used atlassing technologies into the cloud-optimised Zarr format and builds on Vitessce to enable remote data navigation. We showcase WebAtlas on the developing human lower limb to cross-query cell types and genes across single cell, sequencing- and imaging-based spatial transcriptomic data.
Tags: Spatial Transcriptomics, Single Cell, Bioimage Analysis, Exclude From Dalia
Content type: Collection, Atlas
Welcome to BioImage Town#
Josh Moore
Licensed CC-BY-4.0
Welcome at NFDI4BIOIMAGE All-Hands Meeting in Düsseldorf, Germany, October 16, 2023
Tags: OMERO, Bioimage Analysis, Nfdi4Bioimage, Exclude From Dalia
Content type: Slides
What is Bioimage Analysis? An Introduction#
Kota Miura
Licensed UNKNOWN
Tags: Neubias, Bioimage Analysis, Exclude From Dalia
Content type: Slides
https://www.dropbox.com/s/5abw3cvxrhpobg4/20220923_DefragmentationTS.pdf?dl=0
Who you gonna call? - Data Stewards to the rescue#
Vanessa Aphaia Fiona Fuchs, Jens Wendt, Maximilian Müller, Mohsen Ahmadi, Riccardo Massei, Cornelia Wetzker
Published 2024-03-01
Licensed CC-BY-4.0
The Data Steward Team of the NFDI4BIOIMAGE consortium presents themselves and the services (including the Helpdesk) that we offer.
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WorkflowHub#
Stian Soiland-Reyes, Finn Bacall, Bert Droesbeke, Alan R Williams, Johan Gustafsson, et al.
Licensed BSD-3-CLAUSE
A registry for describing, sharing and publishing scientific computational workflows.
Tags: Bioinformatics, Workflow, Workflow Engine, Python, R, Exclude From Dalia
Content type: Collection
Working Group Charter. RDM Helpdesk Network#
Judith Engel, Patrick Helling, Robert Herrenbrück, MarinaLemaire, Hela Mehrtens, Marcus Schmidt, Martha Stellmacher, Lukas Weimer, Cord Wiljes, Wolf Zinke
Published 2024-11-04
Licensed CC-BY-4.0
Support is an essential component of an efficient infrastructure for research data management (RDM). Helpdesks guide researchers through this complex landscape and provide reliable support about all questions regarding research data management, including support for technical services, best practices, requirements of funding organizations and legal topics. In NFDI, most consortia have already established or are planning to establish helpdesks to support their specific communities. On a local level, many institutions have set up RDM helpdesks that provide support for the researchers of their own institution. Additional RDM support services are offered by RDM federal state initiatives, by research data centers, by specialist libraries, by the EOSC, and by providers of RDM-relevant tools. Helpdesks cover a wide range of institutions, disciplines, topics, methodologies and target audiences. However, the individual helpdesks are not yet interconnected and therefore cannot complement one another in an efficient way: Given the wide and constantly increasing complexity of RDM, no single helpdesk can provide the expertise for all potential support requests. Therefore, we see great potential in combining the efforts and resources of the existing RDM helpdesks into an efficient and comprehensive national RDM support network in order to provide optimal and tailored RDM support to all researchers and research-related institutions in Germany and in an international context.
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Workshop-June2024-Madrid#
Licensed MIT
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Workshop, Collection
Zarr - A Cloud-Optimized Storage for Interactive Access of Large Arrays#
Josh Moore, Susanne Kunis
Published 2023-09-07
Licensed CC-BY-4.0
For decades, the sharing of large N-dimensional datasets has posed issues across multiple domains. Interactively accessing terabyte-scale data has previously required significant server resources to properly prepare cropped or down-sampled representations on the fly. Now, a cloud-native chunked format easing this burden has been adopted in the bioimaging domain for standardization. The format — Zarr — is potentially of interest for other consortia and sections of NFDI.
Tags: Nfdi4Bioimage, Bioimage Analysis, Exclude From Dalia
Content type: Publication
Zeiss AxioZoom Stage Adapter#
Michael Gerlach
Published 2024-06-20
Licensed CC-BY-4.0
A 3D- printable microscope stage adapter for the reproducible accomodation of samples at a Zeiss AxioZoom stereomicroscope. 4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors.
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Zeiss AxioZoom Stage Adapter - 12/6Well Plate#
Michael Gerlach
Published 2024-06-20
Licensed CC-BY-4.0
A 3D- printable microscope stage adapter for the reproducible accomodation of 6 or 12-well plates at a Zeiss AxioZoom microscope. 4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors and acommodates a standard Greiner 6- or 12-well plate.
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Zeiss AxioZoom Stage Adapter - EM block holder#
Michael Gerlach
Published 2024-06-20
Licensed CC-BY-4.0
A 3D- printable microscope stage adapter for the reproducible accomodation of EM Blocks at a Zeiss AxioZoom microscope.
4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors and acommodates 70 standard resin EM blocks.
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Zeiss AxioZoom Stage Adapter - Microscope slides#
Michael Gerlach
Published 2024-06-21
Licensed CC-BY-4.0
A 3D- printable microscope stage adapter for the reproducible accomodation of microscopic slides at a Zeiss AxioZoom microscope. 4 cylindrical anchors are fixed to the glass plate of the stage. The stage adapter is reversibly placed on these anchors and acommodates 4 standard glass slides.
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ZeroCostDL4Mic - Stardist 2D example training and test dataset (light)#
Johanna Jukkala, Guillaume Jacquemet
Published 2023-05-19
Licensed CC-BY-4.0
Name: ZeroCostDL4Mic - Stardist 2D example training and test dataset (light)
(see our Wiki for details)
Data type: Paired microscopy images (fluorescence) and corresponding masks
Microscopy data type: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist for details about the segmentation)
Microscope: Spinning disk confocal microscope with a 20x 0.8 NA objective
Cell type: DCIS.COM LifeAct-RFP cells
File format: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)
Image size: 1024x1024 (Pixel size: 634 nm)
Author(s): Johanna Jukkala1,2 and Guillaume Jacquemet1,2
Contact email: guillaume.jacquemet@abo.fi
Affiliation :
Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland
Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
Funding bodies: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
ZeroCostDL4Mic - Stardist example training and test dataset#
Johanna Jukkala, Guillaume Jacquemet
Published 2020-03-17
Licensed CC-BY-4.0
Name: ZeroCostDL4Mic - Stardist example training and test dataset
(see our Wiki for details)
Data type: Paired microscopy images (fluorescence) and corresponding masks
Microscopy data type: Fluorescence microscopy (SiR-DNA) and masks obtained via manual segmentation (see HenriquesLab/ZeroCostDL4Mic for details about the segmentation)
Microscope: Spinning disk confocal microscope with a 20x 0.8 NA objective
Cell type: DCIS.COM LifeAct-RFP cells
File format: .tif (16-bit for fluorescence and 8 and 16-bit for the masks)
Image size: 1024x1024 (Pixel size: 634 nm)
Author(s): Johanna Jukkala1,2 and Guillaume Jacquemet1,2
Contact email: guillaume.jacquemet@abo.fi
Affiliation :
Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland
Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
Associated publications: Unpublished
Funding bodies: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
ZeroCostDL4Mic: exploiting Google Colab to develop a free and open-source toolbox for Deep-Learning in microscopy#
Lucas von Chamier, Romain F. Laine, Johanna Jukkala, Christoph Spahn, Daniel Krentzel, Elias Nehme, Martina Lerche, Sara Hernández-pérez, Pieta Mattila, Eleni Karinou, Séamus Holden, Ahmet Can Solak, Alexander Krull, Tim-Oliver Buchholz, Martin L Jones, Loic Alain Royer, Christophe Leterrier, Yoav Shechtman, Florian Jug, Mike Heilemann, Guillaume Jacquemet, Ricardo Henriques
Licensed MIT
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Notebook, Collection
[BINA CC] Scalable strategies for a next-generation of FAIR bioimaging#
Josh Moore
Published 2024-09-24
Licensed CC-BY-4.0
Presented at https://www.bioimagingnorthamerica.org/events/bina-2024-community-congress/
Tags: Exclude From Dalia
[CORDI 2023] Zarr: A Cloud-Optimized Storage for Interactive Access of Large Arrays#
Josh Moore
Licensed CC-BY-4.0
For decades, the sharing of large N-dimensional datasets has posed issues across multiple domains. Interactively accessing terabyte-scale data has previously required significant server resources to properly prepare cropped or down-sampled representations on the fly. Now, a cloud-native chunked format easing this burden has been adopted in the bioimaging domain for standardization. The format — Zarr — is potentially of interest for other consortia and sections of NFDI.
Tags: Research Data Management, Bioimage Analysis, Data Science, Exclude From Dalia
Content type: Poster
[Community Meeting 2024] Overview Team Image Data Analysis and Management#
Susanne Kunis, Thomas Zobel
Published 2024-03-08
Licensed CC-BY-4.0
Overview of Activities of the Team Image Data Analysis and Management of German BioImaging e.V.
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
[Community Meeting 2024] Supporting and financing RDM projects within GerBI#
Stefanie Weidtkamp-Peters, Josh Moore, Christian Schmidt, Roland Nitschke, Susanne Kunis, Thomas Zobel
Published 2024-03-28
Licensed CC-BY-4.0
Overview of GerBI RDM projects: why and how?
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[GBI EoE IX] NFDI4BIOIMAGE#
National Research Data Infrastructure for Microscopy and BioImage Analysis
Josh Moore
Published 2024-10-29
Licensed CC-BY-4.0
Presented at https://globalbioimaging.org/exchange-of-experience/exchange-of-experience-ix in Okazaki, Japan.
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[N4BI AHM] Welcome to BioImage Town#
Josh Moore
Published 2023-10-16
Licensed CC-BY-4.0
Keynote at the NFDI4BIOIMAGE All-Hands Meeting in Düsseldorf, Germany, October 16, 2023.
Tags: Nfdi4Bioimage, Exclude From Dalia
[Short Talk] NFDI4BIOIMAGE - A consortium in the National Research Data Infrastructure#
Christian Schmidt
Published 2024-04-10
Licensed CC-BY-4.0
Short Talk about the NFDI4BIOIMAGE consortium presented at the RDM in (Bio-)Medicine Information Event on April 10th, 2024, organized C³RDM & ZB MED.
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[Solved] Sample fluorescence .qptiff file not rendered correctly by QuPath v.0.6.0, correctly by Qupath v.0.5.1#
VP
Published 2025-07-29
Licensed CDLA-SHARING-1.0
Solution: https://forum.image.sc/t/weird-representation-of-qptiff-of-fluorescent-sample-in-qupath-v-0-6-0/115165/6?u=zuksmp3 Image in qptiff file format of an immunofluorescence sample acquired on a KF-400-FL slide scanner. Image with three channels: blue, red, green. Weirdly rendered by QuPath v.0.6.0, but correctly displayed in v.0.5.1 and Fiji.
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[Workshop Material] Fit for OMERO - How imaging facilities and IT departments work together to enable RDM for bioimaging, October 16-17, 2024, Heidelberg#
Tom Boissonnet, Bettina Hagen, Susanne Kunis, Christian Schmidt, Stefanie Weidtkamp-Peters
Published 2024-11-18
Licensed CC-BY-4.0
Fit for OMERO: How imaging facilities and IT departments work together to enable RDM for bioimaging Description: Research data management (RDM) in bioimaging is challenging because of large file sizes, heterogeneous file formats and the variability of imaging methods. The image data management system OMERO (OME Remote Objects) allows for centralized and secure storage, organization, annotation, and interrogation of microscopy data by researchers. It is an internationally well-supported open-source software tool that has become one of the best-known image data management tools among bioimaging scientists. Nevertheless, the de novo setup of OMERO at an institute is a multi-stakeholder process that demands time, funds, organization and iterative implementation. In this workshop, participants learn how to begin setting up OMERO-based image data management at their institution. The topics include:
Stakeholder identification at the university / research institute Process management, time line expectations, and resources planning Learning about each other‘s perspectives on chances and challenges for RDM Funding opportunities and strategies for IT and imaging core facilities Hands-on: Setting up an OMERO server in a virtual machine environment
Target audience: This workshop was directed at universities and research institutions who consider or plan to implement OMERO, or are in an early phase of implementation. This workshop was intended for teams from IT departments and imaging facilities to participate together with one person from the IT department, and one person from the imaging core facility at the same institution. The trainers:
Prof. Dr. Stefanie Weidtkamp-Peters (Imaging Core Facility Head, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) Dr. Susanne Kunis (Software architect, OMERO administrator, metadata specialist, University of Osnabrück) Dr. Tom Boissonnet (OMERO admin and image metadata specialist, Center for Advanced Imaging, Heinrich Heine University of Düsseldorf) Dr. Bettina Hagen (IT Administration and service specialist, Max Planck Institute for the Biology of Ageing, Cologne) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center (DKFZ), Heidelberg)
Time and place The format was a two-day, in-person workshop (October 16-17, 2024). Location: Heidelberg, Germany Workshop learning goals
Learn the steps to establish a local RDM environment fit for bioimaging data Create a network of IT experts and bioimaging specialists for bioimage RDM across institutions Establish a stakeholder process management for installing OMERO-based RDM Learn from each other, leverage different expertise Learn how to train users, establish sustainability strategies, and foster FAIR RDM for bioimaging at your institution
Tags: Nfdi4Bioimage, Research Data Management, Exclude From Dalia
arivis Vision4D Tutorials#
Licensed ALL RIGHTS RESERVED
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Collection, Video
https://www.youtube.com/playlist?list=PLRc9dt9lEZh_SVRC4G5moOvgHuvjPwmv0
bioformats2raw Converter#
Melissa Linkert, Chris Allan, Josh Moore, Sébastien Besson, David Gault, et al.
Licensed GPL-2.0
Java application to convert image file formats, including .mrxs, to an intermediate Zarr structure compatible with the OME-NGFF specification.
Tags: Open Source Software, Exclude From Dalia
Content type: Application, Github Repository
bioimageio-chatbot#
Wei Ouyang, Wanlu Lei, Caterina Fuster-Barceló, Gabe Reder, arratemunoz, Weize, Curtis Rueden, Matt McCormick
Published 2023-10-10T16:05:49+00:00
Licensed MIT
Your Personal Assistant in Computational Bioimaging.
Tags: Artificial Intelligence, Bioimage Analysis, Exclude From Dalia
Content type: Github Repository
cba-support-template#
Arif Khan, Christian Tischer, Sebastian Gonzalez, Dominik Kutra, Felix Schneider, et al.
Published 2021-12-01
Licensed MIT
Tags: Workflow, Research Data Management, Exclude From Dalia
Content type: Tutorial
cellpose training data#
Carsen Stringer, Tim Wang, Michalis Michaelos, Marius Pachitariu
Published 2020-12-14
Licensed CUSTOM LICENSE
This is a cellpose training dataset. Cellpose is a generalist deep learning model for cell segmentation.
Tags: Ai-Ready, Exclude From Dalia
Content type: Data
datenbiene#
Torsten Stöter
Published 2025-02-02T18:50:20+00:00
Licensed GNU GENERAL PUBLIC LICENSE V3.0
Tags: Research Data Management, Exclude From Dalia
Content type: Github Repository, Software
de.NBI YouTube Channel#
de.NBI
Published 2015-08-28
Licensed UNKNOWN
The de.NBI (German Network for Bioformatics Infrastructure) Youtube channel (http://www.denbi.de/)
Tags: Bioinformatics, Exclude From Dalia
Content type: Youtube Channel
de.NBI cloud access registration guide#
de.NBI
Published 2024-11-04
Licensed UNKNOWN
Tutorial for registering for de.NBI cloud access
Tags: Bioinformatics, Cloud Computing, Exclude From Dalia
Content type: Tutorial
deNBI Online Training Media Library#
de.NBI
Licensed UNKNOWN
The de.NBI (German Network for Bioformatics Infrastructure) Online Training & Media Library provides a collection of training materials for bioinformatics and computational biology.
Tags: Bioinformatics, Galaxy, Exclude From Dalia
Content type: Website
dmtxSampleCreator#
SaibotMagd
Published 2023-06-06T11:52:14+00:00
Licensed APACHE-2.0
firefox extension: reads datamatrix code from camera and create a sample in an inventory to link it into an ELN.
Tags: Nfdi4Bioimage, Exclude From Dalia
Content type: Github Repository
embo-bia-2025#
Kevin Yamauchi
Published 2025-08-18T09:32:20+00:00
Licensed BSD-3-CLAUSE
Tags: Exclude From Dalia
Content type: Github Repository
i2k-2020-s3-zarr-workshop#
Constantin Pape, Christian Tischer
Licensed UNKNOWN
Tags: Python, Big Data, Exclude From Dalia
Content type: Github Repository
ilastik: interactive machine learning for (bio)image analysis#
Anna Kreshuk, Dominik Kutra
Licensed CC-BY-4.0
Tags: Artificial Intelligence, Bioimage Analysis, Exclude From Dalia
Content type: Slides
imaris file not read by bfGetReader()#
Julien Dubrulle
Published 2025-03-10
Licensed CC-BY-4.0
This file cannot be read by bfGetReader() v8.1.1 on a Windows operating workstation.
Tags: Exclude From Dalia
martinschatz-cz/SciCount: v1.0.0 with reusable example notebooks#
Martin Schätz, Lukáš Mrazík, Karolina Máhlerova
Published 2022-08-02
Licensed OTHER-OPEN
The first version contains an example of augmentation of scientific data and object detection with YOLO_v5 on colony counting (2 classes), object counting in blood smears (can be used as semisupervised learning for faster annotation), and wildlife detection from night records with a camera trap.
The project is available on GitHub.
Tags: Exclude From Dalia
microlist#
Licensed ALL RIGHTS RESERVED
A searchable database of resources for light and electron microscopists
Tags: Exclude From Dalia
Content type: Collection
ome-ngff-validator#
Will Moore, Josh Moore, Yaroslav Halchenko, Sébastien Besson
Published 2022-09-29
Licensed BSD-2-CLAUSE
Web page for validating OME-NGFF files.
Tags: Exclude From Dalia
Content type: Github Repository, Application
ome2024-ngff-challenge#
Will Moore, Josh Moore, sherwoodf, Jean-Marie Burel, Norman Rzepka, dependabot[bot], JensWendt, Joost de Folter, Torsten St\xF6ter, AybukeKY, Eric Perlman, Tom Boissonnet
Published 2024-08-30T12:00:53+00:00
Licensed BSD-3-CLAUSE
Project planning and material repository for the 2024 challenge to generate 1 PB of OME-Zarr data
Tags: Sharing, Nfdi4Bioimage, Research Data Management, Exclude From Dalia
Content type: Github Repository
omero-arc#
Christoph Moehl, Peter Zentis, Niraj Kandpal
Published 2023-12-18T16:11:04+00:00
Licensed GNU GENERAL PUBLIC LICENSE V3.0
Library to export OMERO projects to ARC repositories
Tags: OMERO, Research Data Management, Exclude From Dalia
Content type: Github Repository, Software
omero-ontop-mappings#
Carsten Fortmann-Grote, andrawaag, Jerven Bolleman
Published 2024-09-13T08:01:09+00:00
ONTOP module for querying OMERO with SPARQL
Tags: OMERO, Exclude From Dalia
Content type: Github Repository
omero-quay#
Alexis Lebon, Anatole Chessel, Raphael Braud-Mussi, Marine Breuilly, Denis Ressnikoff, Dorian Kauffmann, Elvire Guiot, Emmanuel Faure, Perrine Gilloteaux, Guillaume Gay, Guillaume Jean-François, Jerome Mutterer, Paulette Lieby, Julio Mateos-Langerak, Guillaume Maucort, Marc Mongy, Mylene Pezet, Sotirios Papadiamantis, Théo Barnouin, Mathieu Vigneau
Published 2023-06-16
Licensed MPL-2.0
omero-quay is a microscopy data transport layer between data management tools. Currently, it supports the iRODS — OMERO architecture built at France BioImaging fbi-omero.
Tags: Data Management, OMERO, Exclude From Dalia
Content type: Gitlab Repository
omero-vitessce#
Michele Bortolomeazzi
Published 2024-11-25T10:51:01+00:00
Licensed AGPL-3.0
OMERO.web plugin for the Vitessce multimodal data viewer.
Tags: OMERO, Exclude From Dalia
Content type: Github Repository
patho_prompt_injection#
JanClusmann, Tim Lenz
Published 2024-11-08T08:32:03+00:00
Licensed GPL-3.0
Tags: Histopathology, Bioimage Analysis, Exclude From Dalia
Content type: Github Repository, Notebook
quantixed/TheDigitalCell: First complete code set#
Stephen Royle
Published 2019-04-17
Licensed GPL-3.0
First complete code set for The Digital Cell book.
Tags: Bioimage Analysis, Exclude From Dalia
Content type: Code
raw2ometiff Converter#
Melissa Linkert, Chris Allan, Sébastien Besson, Josh Moore
Licensed GPL-2.0
Java application to convert a directory of tiles to an OME-TIFF pyramid. This is the second half of iSyntax/.mrxs => OME-TIFF conversion.
Tags: Open Source Software, Exclude From Dalia
Content type: Application, Github Repository
re3data.org - registry of Research Data Repositories#
Licensed CC-BY-4.0
Re3data is a global registry of research data repositories that covers research data repositories from different academic disciplines. It includes repositories that enable permanent storage of and access to data sets to researchers, funding bodies, publishers, and scholarly institutions.
Tags: Research Data Management, Exclude From Dalia
Content type: Website
training#
Erick Martins Ratamero, dependabot[bot]
Published 2020-03-09T13:25:54+00:00
Licensed MIT
repo for training materials
Tags: Exclude From Dalia
Content type: Github Repository