Cc-by-4.0 (349)#
“ZENODO und Co.” Was bringt und wer braucht ein Repositorium?#
Elfi Hesse, Jan-Christoph Deinert, Christian Löschen
Published 2021-01-25
Licensed CC-BY-4.0
Die Online-Veranstaltung fand am 21.01.2021 im Rahmen der SaxFDM-Veranstaltungsreihe “Digital Kitchen - Küchengespräche mit SaxFDM” statt. SaxFDM-Sprecherin Elfi Hesse (HTW Dresden) erläuterte zunächst Grundsätzliches zum Thema Repositorien. Anschließend teilten Nutzer (Jan Deinert – HZDR) und Anbieter (Christian Löschen – TU Dresden/ZIH) lokaler Repositorien ihre Erfahrungen mit uns.
Tags: Research Data Management
Content type: Slides
.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.
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.
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
Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/ai/analysed-dataset/S-BIAD1518/
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.
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.
Tags: Ai-Ready
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
Content type: Data
6 Steps Towards Reproducible Research#
Heidi Seibold
Licensed CC-BY-4.0
A short book with 6 steps that get you closer to making your work reproducible.
Tags: Reproducibility, Research Data Management
Content type: Book
A Cloud-Optimized Storage for Interactive Access of Large Arrays#
Josh Moore, Susanne Kunis
Licensed CC-BY-4.0
Tags: Nfdi4Bioimage, Research Data Management
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.
A Hitchhiker’s guide through the bio-image analysis software universe#
Robert Haase, Elnaz Fazeli, David Legland, Michael Doube, Siân Culley, Ilya Belevich, Eija Jokitalo, Martin Schorb, Anna Klemm, Christian Tischer
Licensed CC-BY-4.0
This article gives an overview about commonly used bioimage analysis software and which aspects to consider when choosing a software for a specific project.
Tags: Bioimage Analysis
Content type: Publication
https://febs.onlinelibrary.wiley.com/doi/full/10.1002/1873-3468.14451
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.
Tags: Ai-Ready
Content type: Data
A journey to FAIR microscopy data#
Stefanie Weidtkamp-Peters, Janina Hanne, Christian Schmidt
Published 2023-05-03
Licensed CC-BY-4.0
Oral presentation, 32nd MoMAN “From Molecules to Man” Seminar, Ulm, online. Monday February 6th, 2023
Abstract:
Research data management is essential in nowadays research, and one of the big opportunities to accelerate collaborative and innovative scientific projects. To achieve this goal, all our data needs to be FAIR (findable, accessible, interoperable, reproducible). For data acquired on microscopes, however, a common ground for FAIR data sharing is still to be established. Plenty of work on file formats, data bases, and training needs to be performed to highlight the value of data sharing and exploit its potential for bioimaging data.
In this presentation, Stefanie Weidtkamp-Peters will introduce the challenges for bioimaging data management, and the necessary steps to achieve data FAIRification. German BioImaging - GMB e.V., together with other institutions, contributes to this endeavor. Janina Hanne will present how the network of imaging core facilities, research groups and industry partners is key to the German bioimaging community’s aligned collaboration toward FAIR bioimaging data. These activities have paved the way for two data management initiatives in Germany: I3D:bio (Information Infrastructure for BioImage Data) and NFDI4BIOIMAGE, a consortium of the National Research Data Infrastructure. Christian Schmidt will introduce the goals and measures of these initiatives to the benefit of imaging scientist’s work and everyday practice.
Tags: Nfdi4Bioimage, Research Data Management
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
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
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.
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.
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
Content type: Document
Advancing FAIR Image Analysis in Galaxy: Tools, Workflows, and Training#
Chiang Jurado, Diana, Riccardo Massei, Pavankumar Videm, Anup Kumar, Anne Fouilloux, Leonid Kostrykin, Beatriz Serrano-Solano, Björn Grüning
Published 2025-03-06
Licensed CC-BY-4.0
Alles meins – oder!? Urheberrechte klären für Forschungsdaten#
Stephan Wünsche
Published 2024-06-04
Licensed CC-BY-4.0
Wem gehören Forschungsdaten? Diese Frage stellt sich bei Daten, an deren Entstehung mehrere Personen beteiligt waren, und besonders bei Textdaten, Bildern und Videos. Hier lernen Sie, für Ihr eigenes Forschungsvorhaben zu erkennen, wessen Urheber- und Leistungsschutzrechte zu berücksichtigen sind. Sie erfahren, wie Sie mit Hilfe von Vereinbarungen frühzeitig Rechtssicherheit herstellen, etwa um Daten weitergeben oder publizieren zu können.
Tags: Research Data Management, Licensing
Content type: Slides
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
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
Content type: Data
https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD599-ai.html
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
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.
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.
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
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: Bioinformatics, OMERO
Content type: Github Repository
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
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
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).
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
Content type: Publication
Bio-Image Data Strudel for Workshop on Research Data Management in TU Dresden Core Facilities#
Cornelia Wetzker
Published 2023-11-08
Licensed CC-BY-4.0
This presentation gives a short outline of the complexity of data and metadata in the bioimaging universe. It introduces NFDI4BIOIMAGE as a newly formed consortium as part of the German ‘Nationale Forschungsdateninfrastruktur’ (NFDI) and its goals and tools for data management including its current members on TU Dresden campus.
Tags: Research Data Management, Nfdi4Bioimage
Content type: Slides
Bio-image Analysis Code Generation#
Robert Haase
Published 2024-10-28
Licensed CC-BY-4.0
Large Language Models are changing the way we interact with computers, especially how we write code. In this tutorial, we will generate bio-image analysis code using two LLM-based code generators, bia-bob and git-bob. haesleinhuepf/bia-bob haesleinhuepf/git-bob
Bio-image Analysis Code Generation using bia-bob#
Robert Haase
Published 2024-10-09
Licensed CC-BY-4.0
In this presentation I introduce bia-bob, an AI-based code generator that integrates into Jupyter Lab and allows for easy generation of Bio-Image Analysis Python code. It highlights how to get started with using large language models and prompt engineering to get high-quality bio-image analysis code.
Tags: Artificial Intelligence, Bioimage Analysis
Bio-image Analysis with the Help of Large Language Models#
Robert Haase
Published 2024-03-13
Licensed CC-BY-4.0
Large Language Models (LLMs) change the way how we use computers. This also has impact on the bio-image analysis community. We can generate code that analyzes biomedical image data if we ask the right prompts. This talk outlines introduces basic principles, explains prompt engineering and how to apply it to bio-image analysis. We also compare how different LLM vendors perform on code generation tasks and which challenges are ahead for the bio-image analysis community.
Tags: Artificial Intelligence, Python
Content type: Slides
Bio-image Data Science#
Robert Haase
Licensed CC-BY-4.0
This repository contains training resources for Students at Uni Leipzig who want to dive into bio-image data science with Python.
Tags: Research Data Management, Artificial Intelligence, Bioimage Analysis, Python
Content type: Notebook
Bio-image Data Science Lectures 2025 @ Uni Leipzig / ScaDS.AI#
Robert Haase
Published 2025-05-29
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.
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
Content type: Slides
Bio-image analysis, biostatistics, programming and machine learning for computational biology#
Anna Poetsch, Biotec Dresden, Marcelo Leomil Zoccoler, Johannes Richard Müller, Robert Haase
Licensed CC-BY-4.0
Tags: Python, Bioimage Analysis, Napari
Content type: Notebook
Bio.tools database#
Licensed CC-BY-4.0
Tags: Bioinformatics
Content type: Collection
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.
Content type: Documentation
https://bio-formats.readthedocs.io/en/v8.0.0/users/comlinetools/index.html
BioImage Analysis Notebooks#
Robert Haase et al.
Licensed [‘CC-BY-4.0’, ‘BSD-3-CLAUSE’]
Tags: Python, Bioimage Analysis
Content type: Book, Notebook
https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html
BioImage.IO Chatbot, GloBIAS Seminar#
Caterina Fuster-Barcelo
Published 2024-10-02
Licensed CC-BY-4.0
The dynamic field of bioimage analysis continually seeks innovative tools to democratize access to analysis tools and its documentation. The BioImage.IO Chatbot, leveraging state-of-the-art AI technologies including Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), provides an interactive platform that significantly integrates the exploration and application of bioimage analysis tools and models. This seminar will introduce the BioImage.IO Chatbot’s capabilities, focusing on how it facilitates access to advanced analysis tools and documentation, allows for the execution of complex models, and enables users to create customized extensions adjusted to specific research needs. In a live demo, attendees will see how to interact with the chatbot and all its assistants and capabilities. Join us to explore how the BioImage.IO Chatbot ca transform your research by making sophisticated analysis more intuitive and accessible.
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.
Tags: Ai-Ready
Content type: Data
Browsing the Open Microscopy Image Data Resource with Python#
Robert Haase
Licensed CC-BY-4.0
Tags: OMERO, Python
Content type: Blog Post
https://biapol.github.io/blog/robert_haase/browsing_idr/readme.html
Building FAIR image analysis pipelines for high-content-screening (HCS) data using Galaxy#
Riccardo Massei, Matthias Berndt, Lucille Lopez-Delisle, Beatriz Serrano-Solano, Wibke Busch, Stefan Scholz, Hannes Bohring, Jo Nyffeler, Luise Reger, Jan Bumberger
Published 2025-02-25
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.
Building a FAIR image data ecosystem for microscopy communities#
Isabel Kemmer, Antje Keppler, Beatriz Serrano-Solano, Arina Rybina, Bugra Özdemir, Johanna Bischof, Ayoub El Ghadraoui, John E. Eriksson, Aastha Mathur
Published 2023-03-31
Licensed CC-BY-4.0
Bioimaging has now entered the era of big data with faster than ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis and management practices, which are currently hampering the full potential of image data being realized. Here we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimage data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field.
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.
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
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
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.
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
Tags: Ai-Ready
Content type: Data
Challenges and opportunities for bio-image analysis core-facilities#
Robert Haase
Licensed CC-BY-4.0
Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage
Content type: Slides
Challenges and opportunities for bioimage analysis core-facilities#
Johannes Richard Soltwedel, Robert Haase
Licensed CC-BY-4.0
This article outlines common reasons for founding bioimage analysis core-facilities, services they can provide to fulfill certain need and conflicts of interest that arise from these services.
Tags: Bioimage Analysis, Research Data Management
Content type: Publication
ChatGPT for Image Analysis#
Robert Haase
Published 2024-08-25
Licensed CC-BY-4.0
Large Language Models (LLMs) such as ChatGPT are changing the way we interact with computers, including how we analye microscopy imaging data. In this talk I introduce basic concepts of asking LLMs to write code and how to modify the questions to get the best out of it. For trying out these prompt engineering basics there are additional online resources available: https://scads.github.io/prompt-engineering-basics-2024/intro.html
Collaborative Working and Version Control with git[hub]#
Robert Haase
Published 2024-01-10
Licensed CC-BY-4.0
This slide deck introduces the version control tool git, related terminology and the Github Desktop app for managing files in Git[hub] repositories. We furthermore dive into:* Working with repositories* Collaborative with others* Github-Zenodo integration* Github pages* Artificial Intelligence answering Github Issues
Tags: Nfdi4Bioimage, Globias, Research Data Management, Research Software Management
Collaborative bio-image analysis script editing with git#
Robert Haase
Licensed CC-BY-4.0
Introduction to version control using git for collaborative, reproducible script editing.
Tags: Sharing, Research Data Management
Content type: Blog Post
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
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)
Tags: Ai-Ready
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)
Tags: Ai-Ready
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)
Tags: Ai-Ready
Content type: Data
Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization#
Torsten Stöter, Tobias Gottschall, Andrea Schrager, Peter Zentis, Monica Valencia-Schneider, Niraj Kandpal, Werner Zuschratter, Astrid Schauss, Timo Dickscheid, Timo Mühlhaus, Dirk von Suchodoletz
Published 2023-09-12
Licensed CC-BY-4.0
Interdisciplinary collaboration and integration of large and diverse datasets are becoming increasingly important. Answering complex research questions requires combining and analysing multimodal datasets. Research data management follows the FAIR principles making data findable, accessible, interoperable, and reusable. However, there are challenges in capturing the entire research cycle and contextualizing data according, not only for the DataPLANT and NFDI4BIOIMAGE communities. To address these challenges, DataPLANT developed a data structure called Annotated Research Context (ARC). The Brain Imaging Data Structure (BIDS) originated from the neuroimaging community extended for microscopic image data. Both concepts provide standardised and file system based data storage structures for organising and sharing research data accompanied with metadata. We exemplarily compare the ARC and BIDS designs and propose structural and metadata mapping.
Tags: Research Data Management
Content type: Poster
Conda, Container and Bots - How to Build and Maintain Tool Dependencies in Workflows and Training Materials#
Paul Zierep, Sanjay Kumar Srikakulam, Sebastian Schaaf, Bjoern Gruening
Published 2023-09-07
Licensed CC-BY-4.0
The lifecycle of scientific tools comprises the creation of code releases, packages and containers which can be deployed into cloud platforms, such as the Galaxy Project, where they are run and integrated into workflows. The tools and workflows are further used to create training material that benefits a broad community. The need to organize and streamline this tool development lifecycle has led to a sophisticated development and deployment architecture.
Tags: Research Data Management
Content type: Publication
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.
Crashkurs Forschungsdatenmanagement#
Barbara Weiner, Stephan Wünsche, Stefan Kühne, Pia Voigt, Sebastian Frericks, Clemens Hoffmann, Romy Elze, Ronny Gey
Published 2020-04-30
Licensed CC-BY-4.0
Diese Präsentation bietet einen Einstieg in alle relevanten Bereiche des Forschungsdatenmanagements an der Universität Leipzig. Behandelt werden Grundlagen des Forschungsdatenmanagements, technische, ethische und rechtliche Aspekte sowie die Archivierung und Publikation von Forschungsdaten. Die Präsentation enthält zahlreiche weiterführende Links (rot) und Literaturhinweise.
Ergänzend hierzu wird eine Präsentation mit Übungsaufgaben angeboten, die helfen soll, das Gelernte zu festigen und in der eigenen Forschungspraxis umzusetzen. Den Aufgaben folgen jeweils eine Antwortfolie sowie deren Auflösung.
Tags: Research Data Management
Content type: Slides
Creating Workflows and Advanced Workflow Options#
Licensed CC-BY-4.0
Tags: Workflow
Content type: Tutorial, Online Tutorial
Creating a Research Data Management Plan using chatGPT#
Robert Haase
Published 2023-11-06
Licensed CC-BY-4.0
In this blog post the author demonstrates how chatGPT can be used to combine a fictive project description with a DMP specification to produce a project-specific DMP.
Tags: Research Data Management, Artificial Intelligence
Content type: Blog Post
https://focalplane.biologists.com/2023/11/06/creating-a-research-data-management-plan-using-chatgpt/
Creating open computational curricula#
Kari Jordan, Zhian Kamvar, Toby Hodges
Published 2020-12-11
Licensed CC-BY-4.0
In this interactive session, Carpentries team members will guide attendees through three stages of the backward design process to create a lesson development plan for the open source tool of their choosing. Attendees will leave having identified what practical skills they aim to teach (learning objectives), an approach for designing challenge questions (formative assessment), and mechanisms to give and receive feedback.
Content type: Slides
Cultivating Open Training#
Robert Haase
Published 2024-03-14
Licensed CC-BY-4.0
In this SaxFDM Digital Kitchen, I introduced current challenges and potential solutions for openly sharing training materials, softly focusing on bio-image analysis. In this field a lot of training materials circulate in private channels, but openly shared, reusable materials, according to the FAIR-principles, are still rare. Using the CC-BY license and uploading materials to publicly acessible repositories are proposed to fill this gap.
Tags: Open Science, Research Data Management, FAIR-Principles, Bioimage Analysis, Licensing
Content type: Slides
Cultivating Open Training to advance Bio-image Analysis#
Robert Haase
Published 2024-04-25
Licensed CC-BY-4.0
These slides introduce current challenges and potential solutions for openly sharing training materials, focusing on bio-image analysis. In this field a lot of training materials circulate in private channels, but openly shared, reusable materials, according to the FAIR-principles, are still rare. Using the CC-BY license and publicly acessible repositories are proposed to fill this gap.
Tags: Research Data Management, Licensing, FAIR-Principles
Content type: Slides
DL4MicEverywhere#
Iván Hidalgo, et al.
Licensed CC-BY-4.0
Tags: Bioimage Analysis
Content type: Notebook, Collection
Data Carpentry for Biologists#
Licensed [‘CC-BY-4.0’, ‘MIT’]
Content type: Tutorial, Code
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.
Tags: Data Life Cycle, Research Data Management
Content type: Collection, Website, Online Tutorial
Data stewardship and research data management tools for multimodal linking of imaging data in plasma medicine#
Mohsen Ahmadi, Robert Wagner, Philipp Mattern, Nick Plathe, Sander Bekeschus, Markus M. Becker, Torsten Stöter, Stefanie Weidtkamp-Peters
Published 2023-11-03
Licensed CC-BY-4.0
A more detailed understanding of the effect of plasmas on biological systems can be fostered by combining data from different imaging modalities, such as optical imaging, fluorescence imaging, and mass spectrometry imaging. This, however, requires the implementation and use of sophisticated research data management (RDM) solutions to incorporate the influence of plasma parameters and treatment procedures as well as the effects of plasma on the treated targets. In order to address this, RDM activities on different levels and from different perspectives are started and brought together within the framework of the NFDI consortium NFDI4BIOIMAGE.
DataPLANT knowledge base#
Published 2022-12-14
Licensed CC-BY-4.0
Explore fundamental topics on research data management (RDM), how DataPLANT implements these aspects to support plant researchers with RDM tools and services, read guides and manuals or search for some teaching materials.
Tags: Research Data Management, Dataplant
Content type: Collection
https://nfdi4plants.org/nfdi4plants.knowledgebase/index.html
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.
Datenmanagement#
Robert Haase
Published 2024-04-14
Licensed CC-BY-4.0
In dieser Data Management Session wird der Lebenszyklus von Daten näher beleuchtet. Wie entstehen Daten, was passiert mit ihnen, wenn sie verarbeitet werden? Wem gehören die Daten und wer ist dafür verantwortlich, sie zu veröffentlichen, zu archivieren und gegebenenfalls wiederzuverwenden? Wir werden einen Datenmanagementplan in Gruppenarbeit entwerfen, ggf. mit Hilfe von ChatGPT.
Tags: Research Data Management
Content type: Slides
Datenmanagement im Fokus: Organisation, Speicherstrategien und Datenschutz#
Pia Voigt, Carolin Hundt
Published 2024-04-19
Licensed CC-BY-4.0
Workshop zum Thema „Datenmanagement im Fokus: Organisation, Speicherstrategien und Datenschutz“ auf der Data Week Leipzig Der Umgang mit Daten ist im Alltag nicht immer leicht: Wie und wo speichert man Daten idealerweise? Welche Strategien helfen, den Überblick zu behalten und wie geht man mit personenbezogenen Daten um? Diese Fragen möchten wir gemeinsam mit Ihnen anhand individueller Datenprobleme besprechen und Ihnen Lösungen aufzeigen, wie Sie ihr Datenmanagement effizient gestalten können.
Tags: Research Data Management
Content type: Slides
Datenmanagementpläne erstellen - Teil 1#
Pia Voigt, Barbara Weiner
Published 2021-03-23
Licensed CC-BY-4.0
Was ist ein Datenmanagementplan? Welche Vorgaben sollte ich beachten? Wie erstelle ich einen solchen für mein Forschungsprojekt und welche nützlichen Tools kann ich hierfür verwenden?
Die Anforderungen der Forschungsförderer zum Datenmanagement steigen stetig. Damit verbunden ist häufig auch das Erstellen eines Datenmanagementplans. Dabei erwarten DFG, BMBF oder die EU jeweils unterschiedliche Angaben zur Erhebung, Speicherung und Veröffentlichung von projektbezogenen Forschungsdaten. Zudem bietet das Erstellen eines Datenmanagementplans viele Vorteile und hilft Ihnen nicht zuletzt, die Anforderungen der guten wissenschaftlichen Praxis strukturiert umzusetzen.
Was im ersten Moment unübersichtlich und überfordernd wirkt, soll in diesem Kurs anhand einer grundlegenden theoretischen Einführung im ersten und praxisorientierter Beispiele im zweiten Teil der Veranstaltung handhabbar gemacht werden. Sie lernen, was hinter den Anforderungen der Forschungsförderer steckt, welche Elemente ein Datenmanagementplan enthalten sollte und wie sie einen solchen mithilfe interaktiver Tools selbst erstellen können.
Tags: Research Data Management
Content type: Slides
Datenmanagementpläne erstellen - Teil 2#
Pia Voigt, Barbara Weiner
Published 2021-03-30
Licensed CC-BY-4.0
Was ist ein Datenmanagementplan? Welche Vorgaben sollte ich beachten? Wie erstelle ich einen solchen für mein Forschungsprojekt und welche nützlichen Tools kann ich hierfür verwenden?
Die Anforderungen der Forschungsförderer zum Datenmanagement steigen stetig. Damit verbunden ist häufig auch das Erstellen eines Datenmanagementplans. Dabei erwarten DFG, BMBF oder die EU jeweils unterschiedliche Angaben zur Erhebung, Speicherung und Veröffentlichung von projektbezogenen Forschungsdaten. Zudem bietet das Erstellen eines Datenmanagementplans viele Vorteile und hilft Ihnen nicht zuletzt, die Anforderungen der guten wissenschaftlichen Praxis strukturiert umzusetzen.
Was im ersten Moment unübersichtlich und überfordernd wirkt, soll in diesem Kurs anhand einer grundlegenden theoretischen Einführung im ersten und praxisorientierter Beispiele im zweiten Teil der Veranstaltung handhabbar gemacht werden. Sie lernen, was hinter den Anforderungen der Forschungsförderer steckt, welche Elemente ein Datenmanagementplan enthalten sollte und wie sie einen solchen mithilfe interaktiver Tools selbst erstellen können.
Version 2 enthält aktuelle Links und weiterführende Hinweise zu einzelnen Aspekten eines Datenmanagementplans.
Version 3 ist die überarbeitete und aktualisierte Version der ersten beiden und enthält u.a. Hinweise zur Lizenzierung und zu Nutzungsrechten an Forschungsdaten.
Tags: Research Data Management
Content type: Slides
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
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).
Tags: Ai-Ready
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
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
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
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
Tags: Ai-Ready
Content type: Data
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
Content type: Poster
Developing a Training Strategy#
Robert Haase
Published 2024-11-08
Licensed CC-BY-4.0
When training people in topics such as programming, bio-image analysis or data science, it makes sense to define a training strategy with a wider perspective than just trainees needs. This slide deck gives insights into aspects to consider when defining a training strategy. It considers funder’s interests, financial aspects, metrics / goals, steps towards sustainability and opportunities for outreach and for founding future collaborations.
Developing open-source software for bioimage analysis: opportunities and challenges#
Florian Levet, Anne E. Carpenter, Kevin W. Eliceiri, Anna Kreshuk, Peter Bankhead, Robert Haase
Licensed CC-BY-4.0
This article outlines common challenges and practices when developing open-source software for bio-image analysis.
Tags: Neubias
Content type: Publication
Development of a platform for advanced optics education, training and prototyping#
Nadine Utz, Sabine Reither, Ruth Hans, Christian Feldhaus
Published 2023-10-05
Licensed CC-BY-4.0
In bio-medical research we often need to combine a broad range of expertise to run complex experiments and analyse and interpret their results. Also, it is desirable that all stakeholders of a project understand all parts of the experiment and analysis to draw and support the right conclusions. For imaging experiments this usually requires a basic understanding of the underlying physics. This has not necessarily been part of the professional training of all stakeholders, e.g. biologists or data scientists. Therefore an affordable platform for easily demonstrating and explaining imaging principles would be desirable. Building up on a commercially available STEM Optics kit we developed extensions with widely available and affordable components to demonstrate advanced imaging techniques like e.g. confocal, lightsheet, OPT, spectral imaging. All models are quick and easy to build, yet demonstrate the important physical principles each imaging technique is based on. Further use cases for this kit are training courses, demonstrations for imaging newbies when designing an experiment and outreach activities but also basic level prototyping.
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 )
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
Content type: Poster
EDAM-bioimaging: The ontology of bioimage informatics operations, topics, data, and formats (update 2020)#
Matúš Kalaš, Laure Plantard, Joakim Lindblad, Martin Jones, Nataša Sladoje, Moritz A Kirschmann, Anatole Chessel, Leandro Scholz, Fabianne Rössler, Laura Nicolás Sáenz, Estibaliz Gómez de Mariscal, John Bogovic, Alexandre Dufour, Xavier Heiligenstein, Dominic Waithe, Marie-Charlotte Domart, Matthia Karreman, Raf Van de Plas, Robert Haase, David Hörl, Lassi Paavolainen, Ivana Vrhovac Madunić, Dean Karaica, Arrate Muñoz-Barrutia, Paula Sampaio, Daniel Sage, Sebastian Munck, Ofra Golani, Josh Moore, Florian Levet, Jon Ison, Alban Gaignard, Hervé Ménager, Chong Zhang, Kota Miura, Julien Colombelli, Perrine Paul-Gilloteaux
Licensed CC-BY-4.0
Tags: Metadata
Content type: Publication, Poster
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).
Tags: Ai-Ready
Content type: Data
Efficiently starting institutional research data management#
Katarzyna Biernacka, Katrin Cortez, Kerstin Helbig
Published 2019-10-15
Licensed CC-BY-4.0
Researchers are increasingly often confronted with research data management (RDM) topics during their work. Higher education institutions therefore begin to offer services for RDM at some point to give support and advice. However, many groundbreaking decisions have to be made at the very beginning of RDM services. Priorities must be set and policies formulated. Likewise, the staff must first be qualified in order to provide advice and adequately deal with the manifold problems awaiting. The FDMentor project has therefore bundled the expertise of five German universities with different experiences and levels of RDM knowledge to jointly develop strategies, roadmaps, guidelines, and open access training material. Humboldt-Universität zu Berlin, Freie Universität Berlin, Technische Universität Berlin, University of Potsdam, and European University Viadrina Frankfurt (Oder) have worked together on common solutions that are easy to adapt. With funding of the German Federal Ministry of Education and Research, the collaborative project addressed four problem areas: strategy development, legal issues, policy development, and competence enhancement. The aim of the project outcomes is to provide other higher education institutions with the best possible support for the efficient introduction of research data management. Therefore, all project results are freely accessible under the CC-BY 4.0 international license. The early involvement of the community in the form of workshops and the collection of feedback has proven its worth: the FDMentor strategies, roadmaps, guidelines, and training materials are applied and adapted beyond the partner universities.
Tags: Research Data Management
Content type: Document
Einblicke ins Forschungsdatenmanagement - Darf ich das veröffentlichen? Rechtsfragen im Umgang mit Forschungsdaten#
Stephan Wünsche, Pia Voigt
Published 2021-05-11
Licensed CC-BY-4.0
Diese Präsentation wurde im Zuge der digitalen Veranstaltungsreihe “Einblicke ins Forschungsdatenmanagement” erstellt. Diese findet seit dem SS 2020 an der Universität Leipzig für alle Interessierten zu verschiedenen Themen des Forschungsdatenmanagements statt.
Dieser Teil der Reihe dreht sich um Rechtsfragen im Umgang mit Forschungsdaten und deren Bedeutung für die wissenschaftliche Praxis. Sie finden in der vorliegenden Präsentation einen Überblick über relevante Rechtsbereiche sowie Erläuterungen zum Datenschutz, Urheberrecht und den Grundsätzen der guten wissenschaftlichen Praxis mit Fokus auf deren Bedeutung im Forschungsdatenmanagement.
Tags: Research Data Management, Data Protection
Content type: Slides
End-to-End Tissue Microarray Image Analysis with Galaxy-ME#
Cameron Watson, Allison Creason
Published 2023-02-14
Licensed CC-BY-4.0
This tutorial will demonstrate how to use the Galaxy multiplex imaging tools to process and analyze publicly available TMA test data provided by MCMICRO (Figure 1); however, the majority of the steps in this tutorial are the same for both TMAs and WSIs and notes are made throughout the tutorial where processing of these two imaging types diverge.
Tags: Galaxy, Multiplex Imaging
Content type: Tutorial
Erstellung und Realisierung einer institutionellen Forschungsdaten-Policy#
Uli Hahn, Kerstin Helbig, Gerald Jagusch, Jessica Rex
Published 2018-10-22
Licensed CC-BY-4.0
Die vorliegende Empfehlung sowie die zugehörigen Erfahrungsberichte geben einen Überblick über die verschiedenen Möglichkeiten der Gestaltung einer Forschungsdatenmanagement Policy sowie Wege zu deren Erstellung.
Tags: Research Data Management
Content type: Publication
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”.
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.
Euro-BioImaging’s Guide to FAIR BioImage Data - Practical Tasks#
Isabel Kemmer, Euro-BioImaging ERIC
Published 2024-06-04
Licensed CC-BY-4.0
Hands-on exercises on FAIR Bioimage Data from the interactive online workshop “Euro-BioImaging’s Guide to FAIR BioImage Data 2024” (https://www.eurobioimaging.eu/news/a-guide-to-fair-bioimage-data-2024/). Types of tasks included: FAIR characteristics of a real world dataset Data Management Plan (DMP) Journal Policies on FAIR data sharing Ontology search Metadata according to REMBI scheme (Image from: Sarkans, U., Chiu, W., Collinson, L. et al. REMBI: Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology. Nat Methods 18, 1418–1422 (2021). https://doi.org/10.1038/s41592-021-01166-8) Matching datasets to bioimage repositories Browsing bioimage repositories
Tags: Bioimage Analysis, FAIR-Principles, Research Data Management
Content type: Slides, Tutorial
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
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
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)
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
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
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.
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:
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.
Explainable AI for Computer Vision#
Robert Haase
Published 2025-03-09
Licensed CC-BY-4.0
In this slide deck we learn about the basics of Explainable Artificial Intelligence with a soft focus on Computer Vision. We take a deeper dive in one method: Gradient Class Activation Maps. Releated exercise materials are available online: https://haesleinhuepf.github.io/xai/
FAIR BioImage Data#
Licensed CC-BY-4.0
Tags: Research Data Management, Fair, Bioimage Analysis
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
Content type: Publication
FAIR Priciples#
Licensed CC-BY-4.0
In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets.
Tags: FAIR-Principles, Data Stewardship, Research Data Management
Content type: Collection
FAIRy deep-learning for bioImage analysis#
Estibaliz Gómez de Mariscal
Licensed CC-BY-4.0
Introduction to FAIR deep learning. Furthermore, tools to deploy trained DL models (deepImageJ), easily train and evaluate them (ZeroCostDL4Mic and DeepBacs) ensure reproducibility (DL4MicEverywhere), and share this technology in an open-source and reproducible manner (BioImage Model Zoo) are introduced.
Tags: Artificial Intelligence, FAIR-Principles, Bioimage Analysis
Content type: Slides
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.
Tags: Ai-Ready
Content type: Data
File Naming Convention Worksheet#
Kristin Briney
Published 2020-06-02
Licensed CC-BY-4.0
This worksheet walks researchers through the process of creating a file naming convention for a group of files. This process includes: choosing metadata, encoding and ordering the metadata, adding version information, and properly formatting the file names. Two versions of the worksheet are available: a Caltech Library branded version and a generic editable version.
Tags: Research Data Management
Content type: Worksheet
Finding and using publicly available data#
Anna Swan
Published 2024-01-01
Licensed CC-BY-4.0
Sharing knowledge and data in the life sciences allows us to learn from each other and built on what others have discovered. This collection of online courses brings together a variety of training, covering topics such as biocuration, open data, restricted access data and finding publicly available data, to help you discover and make the most of publicly available data in the life sciences.
Tags: Open Science, Teaching, Sharing
Content type: Collection, Tutorial, Video
https://www.ebi.ac.uk/training/online/courses/finding-using-public-data/
Forschungsdaten.org#
Licensed CC-BY-4.0
Research Data Management Wiki in German
Tags: Research Data Management
Content type: Collection
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”
From Cells to Pixels: Bridging Biologists and Image Analysts Through a Common Language#
Elnaz Fazeli, Haase Robert, Doube Michael, Miura Kota, Legland David
Published 2024-08-16
Licensed CC-BY-4.0
Bioimaging has transformed our understanding of biological processes, yet extracting meaningful information from complex datasets remains a challenge, particularly for early career scientists. This paper proposes a simplified, systematic approach to bioimage analysis, focusing on categorizing commonly observed structures and shapes, and providing relevant analysis methods. Our approach includes illustrative examples and a visual flowchart, enabling researchers to define analysis objectives clearly. By understanding the diversity of bioimage structures and aligning them with appropriate analysis approaches, the framework empowers researchers to navigate bioimage datasets more efficiently. It also aims to foster a common language between researchers and analysts, thereby enhancing mutual understanding and facilitating effective communication.
Galaxy Training#
Published None
Licensed CC-BY-4.0
Collection of tutorials developed and maintained by the worldwide Galaxy community.
Tags: Bioimage Analysis, Data Analysis
Content type: Collection, Tutorial
Galaxy meets OMERO! Overview on the Galaxy OMERO-suite and Vizarr Viewer#
Riccardo Massei, Matthias Bernt, Beatriz Serrano-Solano, Lucille Lopez-Delisle, Jan Bumberger, Björn Grüning, Leonid Kostrykin
Published 2025-03-05
Licensed CC-BY-4.0
Generative artificial intelligence for bio-image analysis#
Robert Haase
Licensed CC-BY-4.0
Tags: Python, Bioimage Analysis, Artificial Intelligence
Content type: Slides
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.
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.
Getting started with Mambaforge and Python#
Mara Lampert
Licensed CC-BY-4.0
Tags: Python, Conda, Mamba
Content type: Blog Post
https://biapol.github.io/blog/mara_lampert/getting_started_with_mambaforge_and_python/readme.html
Getting started with Python: intro and set-up a conda environment#
Riccardo Massei
Published 2024-10-09
Licensed CC-BY-4.0
YMIA python event 2024 Presentation : “Getting started with Python: intro and set-up a conda environment with Dr. Riccardo Massei”
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.
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.
Tags: Ai-Ready
Content type: Data
Guidance for Developing a Research Data Management (RDM) Policy#
Published 2017
Licensed CC-BY-4.0
This document provides the essential elements of a Research Data Management (RDM) Policy and is part of the LEARN Toolkit containing the Model Policy for Research Data Management (RDM) at Research Institutions/Institutes.
Tags: Research Data Management
Content type: Book
https://discovery.ucl.ac.uk/id/eprint/1546596/1/26_Learn_Guidance_137-140.pdf
Gut Analysis Toolbox#
Luke Sorensen, Ayame Saito, Sabrina Poon, Myat Noe Han, Ryan Hamnett, Peter Neckel, Adam Humenick, Keith Mutunduwe, Christie Glennan, Narges Mahdavian, Simon JH Brookes, Rachel M McQuade, Jaime PP Foong, Estibaliz Gómez-de-Mariscal, Arrate Muñoz Barrutia, Julia A. Kaltschmidt, Sebastian K. King, Robert Haase, Simona Carbone, Nicholas A. Veldhuis, Daniel P. Poole, Pradeep Rajasekhar
Published 2024-09-10
Licensed CC-BY-4.0
What’s Changed
Updating User Dialogs by @mattyrowey in pr4deepr/GutAnalysisToolbox#18 Added Dialog Boxes and Grammar Corrections by @mattyrowey in pr4deepr/GutAnalysisToolbox#19 Updated Dialog Prompts for Clarity by @mattyrowey in pr4deepr/GutAnalysisToolbox#20 Batch analysis option added. fixed a bunch of bugs related to ganglia segmentation and user workflow
New Contributors
@mattyrowey made their first contribution in pr4deepr/GutAnalysisToolbox#18
Full Changelog: https://github.com/pr4deepr/GutAnalysisToolbox/compare/v0.6…v0.7
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.
HPA Nucleus Segmentation (DPNUnet)#
Hao Xu, Wei Ouyang
Published 2023-03-02
Licensed CC-BY-4.0
Download RDF Package
Tags: Ai-Ready
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)
Tags: Ai-Ready
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)
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
High throughput & automated data analysis and data management workflow with Cellprofiler and OMERO#
Sarah Weischer, Jens Wendt, Thomas Zobel
Licensed CC-BY-4.0
In this workshop a fully integrated data analysis solutions employing OMERO and commonly applied image analysis tools (e.g., CellProfiler, Fiji) using existing python interfaces (OMERO Python language bindings, ezOmero, Cellprofiler Python API) is presented.
Tags: OMERO, Data Analysis, Bioimage Analysis
Content type: Collection
Highlights from the 2016-2020 NEUBIAS training schools for Bioimage Analysts: a success story and key asset for analysts and life scientists#
Gabriel G. Martins, Fabrice P. Cordelières, Julien Colombelli, Rocco D’Antuono, Ofra Golani, Romain Guiet, Robert Haase, Anna H. Klemm, Marion Louveaux, Perrine Paul-Gilloteaux, Jean-Yves Tinevez, Kota Miura
Published 2021
Licensed CC-BY-4.0
Tags: Bioimage Analysis, Neubias
Content type: Publication
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
Content type: Slides, Presentation
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.
I3D:bio’s OMERO training material: Re-usable, adjustable, multi-purpose slides for local user training#
Christian Schmidt, Michele Bortolomeazzi, Tom Boissonnet, Carsten Fortmann-Grote, Julia Dohle, Peter Zentis, Niraj Kandpal, Susanne Kunis, Thomas Zobel, Stefanie Weidtkamp-Peters, Elisa Ferrando-May
Published 2023-11-13
Licensed CC-BY-4.0
The open-source software OME Remote Objects (OMERO) is a data management software that allows storing, organizing, and annotating bioimaging/microscopy data. OMERO has become one of the best-known systems for bioimage data management in the bioimaging community. The Information Infrastructure for BioImage Data (I3D:bio) project facilitates the uptake of OMERO into research data management (RDM) practices at universities and research institutions in Germany. Since the adoption of OMERO into researchers’ daily routines requires intensive training, a broad portfolio of training resources for OMERO is an asset. On top of using the OMERO guides curated by the Open Microscopy Environment Consortium (OME) team, imaging core facility staff at institutions where OMERO is used often prepare additional material tailored to be applicable for their own OMERO instances. Based on experience gathered in the Research Data Management for Microscopy group (RDM4mic) in Germany, and in the use cases in the I3D:bio project, we created a set of reusable, adjustable, openly available slide decks to serve as the basis for tailored training lectures, video tutorials, and self-guided instruction manuals directed at beginners in using OMERO. The material is published as an open educational resource complementing the existing resources for OMERO contributed by the community.
Tags: OMERO, Research Data Management, Nfdi4Bioimage, I3Dbio
Content type: Slides, Video
https://zenodo.org/records/8323588
https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU
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
If you license it, it’ll be harder to steal it. Why we should license our work#
Robert Haase
Licensed CC-BY-4.0
Blog post about why we should license our work and what is important when choosing a license.
Tags: Licensing, Research Data Management
Content type: Blog Post
Image Analysis Training Resources#
Licensed CC-BY-4.0
Tags: Neubias, Bioimage Analysis
Content type: Book
Image Analysis using Galaxy#
Beatriz Serrano-Solano, Leonid Kostrykin, Anne Fouilloux, Riccardo Massei
Published 2025-02-28
Licensed CC-BY-4.0
GloBIAS seminar series Part 3 in the topic: Infrastructure for deploying image analysis workflows Image analysis using Galaxy
Beatrix Serrano-Solano, Euro-BioImaging ERIC Bio-Hub, EMBL Heidelberg, Germany & Anne Fouilloux , Simula Research Laboratory, Oslo, Norway & Leonid Kostrykin, Biomedical Computer Vision Group, Heidelberg University, BioQuant, IPMB, Heidelberg, Germany & Ricardo Massei, Helmholtz Center for Environmental Research, UFZ, Leipzig, Germany Abstract: This webinar will introduce the Galaxy Image Analysis Community and highlight our mission to advance the development of FAIR and reproducible image analysis workflows. As part of our commitment to making image data analysis more accessible and collaborative, we will showcase how Galaxy can serve the imaging community. The session will explore Galaxy’s capabilities for integrating popular image analysis tools, interactive environments, and notebooks, making it a versatile platform for researchers across various scientific domains. We will also present how Galaxy facilitates the creation and sharing of reusable workflows, promoting open science and fostering collaboration. To give participants hands-on insight, we’ll provide a live demonstration on designing and running image analysis workflows within Galaxy.
Image Processing with Python#
Mark Meysenburg, Toby Hodges, Dominik Kutra, Erin Becker, David Palmquist, et al.
Licensed CC-BY-4.0
This lesson shows how to use Python and scikit-image to do basic image processing.
Tags: Bioimage Analysis, Python
Content type: Tutorial, Workflow
Image Repository Decision Tree - Where do I deposit my imaging data#
Isabel Kemmer, Feriel Romdhane, Euro-BioImaging ERIC
Published 2025-05-15
Licensed CC-BY-4.0
Depositing data in quality data repositories is one crucial step towards FAIR (Findable, Accessible, Interoperable, and Reusable) data. Accordingly, Euro-BioImaging strongly encourages sharing scientific imaging data in established, thematic repositories. To guide you in the selection of appropriate repositories, we have created an overview of available repositories for different types of image data, including their scope and requirements. This decision tree guides you through questions about your data and directs you to the correct repository, and/or provides instructions for further processing to meet the critera of the repositories. Three seperate trees are provided for different classes of imaging data: open bioimage data, preclinical data, and human imaging data. These versions with three trees can be used for web-view. Update: also the editable versions in powerpoint format (.pptx) are now provided. Please be aware that opening the versions with another program might lead to shifted formatting. Update: we now also provide ready-to-print versions designed to be printed on A3 format. One page shows the open bioimaging data tree and one page combines the preclinical and human imaging data trees. Also the editable versions of these are provided.
ImageJ tool for percentage estimation of pneumonia in lungs#
Martin Schätz, Olga Rubešová, Jan Mareš, Alan Spark
Published 2023-05-02
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.
Underlying data: DOI:10.5281/zenodo.5805939 The 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 analysis Olga RUBEŠOVÁ: Code review, tutorial preparation, tool testing, data set analysis Jan MAREŠ: Tool testing, data set analysis Alan 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.
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
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
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
Insights from Acquiring Open Medical Imaging Datasets for Foundation Model Development#
Stefan Dvoretskii
Published 2024-04-10
Licensed CC-BY-4.0
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.
Interactive Image Data Flow Graphs#
Martin Schätz
Published 2022-10-17
Licensed CC-BY-4.0
The slides were presented during the Macro programming with ImageJ workshop (https://www.16mcm.cz/programme/#workshops) which was part of the 16th Multinational Congress on Microscopy. It is a collection and “reshuffle” of slides originally made by Robert Haase on topics from Image Analysis in general up to User-friendly GPU-accelerated bio-image analysis and CLIJ2.
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.
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.
Introduction to Bioimage Analysis#
Pete Bankhead
Licensed CC-BY-4.0
Tags: Python, Imagej, Bioimage Analysis
Content type: Book, Notebook
Introduction to High Performance Computing for Life Scientists#
Julien Sindt
Published 2021-03-22
Licensed CC-BY-4.0
This course introduces life science researchers to high-performance computing (HPC), covering essential concepts and providing hands-on experience using the UK’s ARCHER2 supercomputing service. It aims to help participants understand how HPC can benefit their research and prepare them to use it effectively for tasks like biomolecular simulation.
Tags: High Performance Computing
Content type: Github Repository
https://epcced.github.io/20210322-intro-hpc-life-scientists/
Introduction to OMERO - Frankfurt - online#
Michele Bortolomeazzi, Tom Boissonnet
Published 2025-04-05
Licensed CC-BY-4.0
These slides were presented during an online introductory session to OMERO for the UB Frankfurt. The two-hour session consisted of a first part highlighting the benefits that image data management brings to the lab. The second part showcased image analysis workflows with a Fiji macro and a Python notebook.
Introduction to Research Data Management and Open Research#
Shanmugasundaram
Published 2024-05-17
Licensed CC-BY-4.0
Introduction to RDM primarily for researchers. Can be seen as primer to all other materials in this catalogue.
Tags: Research Data Management, Open Science
Content type: Slides
JIPipe Spring Course (JSC) 2025: Workshop Recordings, Slides, Homework, and Materials#
Ruman Gerst, Zoltán Cseresnyés, Marc Thilo Figge
Published 2025-05-12:T13:37:00+00:00
Licensed CC-BY-4.0
The course gives a basic introduction into microscopy, optics, and image analysis. This is followed by interactive tutorials that explain the basics of creating fully automated image analysis workflows in JIPipe using a simple blobs analysis and intermediate-level quantification of LSFM kidney images. JIPipe-specific features including annotation-guided batch processing, organization with graph compartments, expressions and path processing, and project-wide metadata and parameters are also established. Finally, an advanced real-world pipeline is showcased with detailed guidance through the individual components that include integrations of Cellpose and TrackMate.
Tags: Jipipe, Bioimage Analysis
Content type: Workshop, Video, Tutorial, Slides
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
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
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)
Kollaboratives Arbeiten und Versionskontrolle mit Git#
Robert Haase
Published 2024-04-15
Licensed CC-BY-4.0
Gemeinsames Arbeiten im Internet stellt uns vor neue Herausforderungen: Wer hat eine Datei wann hochgeladen? Wer hat zum Inhalt beigetragen? Wie kann man Inhalte zusammenfuehren, wenn mehrere Mitarbeiter gleichzeitig Aenderungen gemacht haben? Das Versionskontrollwerkzeug git stellt eine umfassende Loesung fuer solche Fragen bereit. Die Onlineplatform github.com stellt nicht nur Softwareentwicklern weltweit eine git-getriebene Platform zur Verfuegung und erlaubt ihnen effektiv zusammen zu arbeiten. In diesem Workshop lernen wir:
Infuerung in FAIR-Prinzipien im Softwarecontext Arbeiten mit git: Pull-requests Aufloesen von Merge-Konflikten Automatisiertes Archivieren von Inhalten nach Zenodo.org Eigene Webseiten auf github.io publizieren
Tags: Research Data Management, FAIR-Principles, Git, Zenodo
Content type: Slides
Kriterienkatalog für Materialien aus dem Themenbereich Forschungsdatenmanagement#
Linda Zollitsch, Swantje Piotrowski
Published 2025-01-24
Licensed CC-BY-4.0
Im Rahmen von FDM-SH Kontor – einem Projekt, das im Kontext der AG Kompetenzentwicklung von der Landesinitiative FDM-SH durchgeführt wurde - haben wir zum Ziel, eine kuratierte Materialbasis für Fortbildungen und Schulungen zu schaffen. Dies stellte uns vor die Herausforderung, festzulegen, wie die Materialien ausgewählt werden sollen. Dieser Kriterienkatalog ist ein Versuch, erste Qualitätskriterien (insbesondere hinsichtlich der Nachnutzbarkeit und den FAIR-Prinzipien) für Materialien auf Basis von Metadaten zu erstellen. Dabei wurde das Vorgehen des Open Science Learning Gates (https://zenodo.org/records/12772135), als Vorbild genommen. Neben dem Metadatenschema der RDA (https://zenodo.org/records/6769695#.YrrP9-xBybQ) haben wir auf das Metadatenschema der DINI/nestor UAG Schulungen/Fortbildungen (https://zenodo.org/records/3760398) sowie das DALIA Interchange Format (https://zenodo.org/records/11521029) zurückgegriffen.
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)
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.
Tags: Ai-Ready
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.
Tags: Ai-Ready
Content type: Data
LSM example J. Dubrulle#
Salama Lab Fred Hutchinson Cancer Center
Published 2024-12-17
Licensed CC-BY-4.0
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.
Large Language Models: An Introduction for Life Scientists#
Robert Haase
Published 2024-12-12
Licensed CC-BY-4.0
This slide deck introduces Large Language Models to an audience of life-scientists. We first dive into terminology: Different kinds of Language Models and what they can be used for. The remaining slides are optional slides to allow us to dive deeper into topics such as tools for using LLMs in Science, Quality Assurance, Techniques such as Retrieval Augmented Generation and Prompt Engineering.
Tags: Globias, Artificial Intelligence
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 )
Learning and Training Bio-image Analysis in the Age of AI#
Robert Haase
Published 2025-04-07
Licensed CC-BY-4.0
The advent of large language models (LLMs) such as ChatGPT changes the way we analyse images. We ask LLMs to generate code, apply it to images and spend less time on learning implementation details. This also has impact on how we learn image analysis. While coding skills are still required, we can use LLMs to explain code, make proposals how to analyse the images and yet still decide how the analysis is done.
Leitfaden zur digitalen Datensparsamkeit (mit Praxisbeispielen)#
Maximilian Heber, Moritz Jakob, Matthias Landwehr, Jan Leendertse, Maximilian Müller, Gabriel Schneider, Dirk von Suchodoletz, Robert Ulrich
Published 2024-06-03
Licensed CC-BY-4.0
Im Zuge der stetig wachsenden Brisanz des Forschungsdatenmanagements fallen immer größere Mengen an Forschungsdaten an. Diese an sich begrüßenswerte Entwicklung führt zu technischen und organisatorischen Herausforderungen nicht nur im Bereich der Speicherung von Forschungsdaten, sondern in allen Phasen des Forschungsdatenlebenszyklus. Der vorliegende Beitrag erläutert vor diesem Hintergrund mögliche Motivationen hinter digitaler Datensparsamkeit mit Blick auf organisatorische, technische und ethische Kriterien, Datenschutz und Nachhaltigkeit. Anschließend werden vor dem Hintergrund zentraler Herausforderungen Umsetzungsvorschläge für das Vorfeld sowie den Verlauf eines Forschungsvorhabens gemacht. Zudem werden grundlegende Empfehlungen zur digitalen Datensparsamkeit ausgesprochen. Eine kürzere Ausgabe des Leitfadens ist im Mai 2024 in der Zeitschrift o | bib erschienen: https://doi.org/10.5282/o-bib/6036 Diese Ausgabe enthält ein zusätzliches Kapitel (4.2) mit konkreten Praxisbeispielen. Dieser Artikel wurde ins Englische übersetzt: Heber, M., Jakob, M., Landwehr, M., Leendertse, J., Müller, M., Schneider, G., von Suchodoletz, D., & Ulrich, R. (2024). A Users’ Guide to Economical Digital Data Usage. Zenodo. https://doi.org/10.5281/zenodo.13752220
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
Content type: Publication
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.
Linked (Open) Data for Microbial Population Biology#
Carsten Fortmann-Grote
Published 2024-03-12
Licensed CC-BY-4.0
Liver Micrometastases area quantification using QuPath and pixel classifier#
Laia Simó-Riudalbas, Romain Guiet, Olivier Burri, Julien Duc, Didier Trono
Published 2022-05-06
Licensed CC-BY-4.0
Sample: Mouse (NSG) liver slices with human colorectal cancer cells metastases, stained with Hematoxylin & Eosin.
Image Acquisition: Images were acquired on an Olympus VS120 Whole Slide Scanner, using a 20x objective (UPLSAPO, N.A. 0.75) and a color camera (Pike F505 Color) with an image pixel size of 0.345 microns.
Image Processing and Analysis: Obtained images were analyzed using the software QuPath [1] (version 0.3.2) using groovy scripts, making use of a pixel classifier to segment and measure cancer cell clusters.
Files :
Detailed_worflow.pdf : contains a detailed description of how pixel classifier was created
images_for_classifier_training.zip : contains all the vsi file obtained from the microscope and used for the training
project_for_classifier_training.zip : contains the QuPath project, with Training Image, annotations, classifiers and scripts for analysis
PythonCode.txt : code ran to transform output results from QuPath to final results
[1] Bankhead, P. et al. QuPath: Open source software for digital pathology image analysis. Scientific Reports (2017). https://doi.org/10.1038/s41598-017-17204-5
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).
Tags: Ai-Ready
Content type: Data
Making the most of bioimaging data through interdisciplinary interactions#
Virginie Uhlmann, Matthew Hartley, Josh Moore, Erin Weisbart, Assaf Zaritsky
Published 2024-10-23
Licensed CC-BY-4.0
Tags: Bioimage Analysis, Open Science, Microscopy
Content type: Publication
Making your package available on conda-forge#
Kevin Yamauchi
Licensed CC-BY-4.0
Tags: Deployment, Python
Content type: Documentation
https://kevinyamauchi.github.io/open-image-data/how_tos/conda_forge_packaging.html
Managing Scientific Python environments using Conda, Mamba and friends#
Robert Haase
Licensed CC-BY-4.0
Tags: Python, Conda, Mamba
Content type: Blog Post
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.
Meeting in the Middle: Towards Successful Multidisciplinary Bioimage Analysis Collaboration#
Anjalie Schlaeppi, Wilson Adams, Robert Haase, Jan Huisken, Ryan B. MacDonald, Kevin W. Eliceiri, Elisabeth C. Kugler
Licensed CC-BY-4.0
Tags: Bioimage Analysis
Content type: Publication
https://www.frontiersin.org/articles/10.3389/fbinf.2022.889755/full
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.
Tags: Ai-Ready
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.
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
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
Methods in bioimage analysis#
Christian Tischer
Licensed CC-BY-4.0
Tags: Bioimage Analysis
Content type: Online Tutorial, Video, Slides
https://www.ebi.ac.uk/training/events/methods-bioimage-analysis/
https://doi.org/10.6019/TOL.BioImageAnalysis22-w.2022.00001.1
https://drive.google.com/file/d/1MhuqfKhZcYu3bchWMqogIybKjamU5Msg/view
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
Content type: Slides
Microscopy data analysis: machine learning and the BioImage Archive#
Andrii Iudin, Anna Foix-Romero, Anna Kreshuk, Awais Athar, Beth Cimini, Dominik Kutra, Estibalis Gomez de Mariscal, Frances Wong, Guillaume Jacquemet, Kedar Narayan, Martin Weigert, Nodar Gogoberidze, Osman Salih, Petr Walczysko, Ryan Conrad, Simone Weyend, Sriram Sundar Somasundharam, Suganya Sivagurunathan, Ugis Sarkans
Licensed CC-BY-4.0
The Microscopy data analysis: machine learning and the BioImage Archive course, which focused on introducing programmatic approaches used in the analysis of bioimage data via the BioImage Archive, ran in May 2023.
Tags: Bioimage Analysis, Python, Artificial Intelligence
Content type: Video, 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
Content type: Publication
https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.871228/full
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
Content type: Publication
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
Morphological analysis of neural cells with WEKA and SNT Fiji plugins#
Daniel Waiger
Published 2022-07-14
Licensed CC-BY-4.0
A simple workflow to detect Soma and neurite paths, from light microscopy datasets.
Using open-source tools for beginners.
Multi-Template-Matching for object-detection (slides)#
Laurent Thomas
Published 2022-05-16
Licensed CC-BY-4.0
This presentations describes Multi-Template-Matching, a novel method extending on template-matching for object-detection in images.
The project was part of the PhD project of Laurent Thomas between 2017 and 2020, under supervision of Jochen Gehrig. The project was hosted at ACQUIFER Imaging with collaboration of the medical university of Heidelberg, and part of the ImageInLife Horizon2020 ITN (PhD program).
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.
Multiplexed tissue imaging - tools and approaches#
Agustín Andrés Corbat, OmFrederic, Jonas Windhager, Kristína Lidayová
Licensed CC-BY-4.0
Material for the I2K 2024 “Multiplexed tissue imaging - tools and approaches” workshop
Tags: Bioimage Analysis
Content type: Github Repository, Slides, Workshop
BIIFSweden/I2K2024-MTIWorkshop
https://docs.google.com/presentation/d/1R9-4lXAmTYuyFZpTMDR85SjnLsPZhVZ8/edit#slide=id.p1
My Journey Through Bioimage Analysis Teaching Methods From Classroom to Cloud#
Elnaz Fazeli
Published 2024-02-19
Licensed CC-BY-4.0
In these slides I introducemy journey through teaching bioimage analysis courses in different formats, from in person courses to online material. I have an overview of different training formats and comparing these for different audiences.
Tags: Teaching
Content type: Slides
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.
NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and BioImage Analysis - Online Kick-Off 2023#
Stefanie Weidtkamp-Peters
Licensed CC-BY-4.0
NFDI4BIOIMAGE core mission, bioimage data challenge, task areas, FAIR bioimage workflows.
Tags: Research Data Management, FAIR-Principles, Bioimage Analysis, Nfdi4Bioimage
Content type: 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
Content type: Slides
NFDI4BIOIMAGE - National Research Data Infrastructure for Microscopy and Bioimage Analysis#
NFDI4BIOIMAGE Consortium
Published 2024-08-07
Licensed CC-BY-4.0
Bioimaging refers to a collection of methods to visualize the internal structures and mechanisms of living organisms. The fundamental tool, the microscope, has enabled seminal discoveries like that of the cell as the smallest unit of life, and continues to expand our understanding of biological processes. Today, we can follow the interaction of single molecules within nanoseconds in a living cell, and the development of complete small organisms like fish and flies over several days starting from the fertilized egg. Each image pixel encodes multiple spatiotemporal and spectral dimensions, compounding the massive volume and complexity of bioimage data. Proper handling of this data is indispensable for analysis and its lack has become a growing hindrance for the many disciplines of the life and biomedical sciences relying on bioimaging. No single domain has the expertise to tackle this bottleneck alone. As a method-specific consortium, NFDI4BIOMAGE seeks to address these issues, enabling bioimaging data to be shared and re-used like they are acquired, i.e., independently of disciplinary boundaries. We will provide solutions for exploiting the full information content of bioimage data and enable new discoveries through sharing and re-analysis. Our RDM strategy is based on a robust needs analysis that derives not only from a community survey but also from over a decade of experience in German BioImaging, the German Society for Microscopy and Image Analysis. It considers the entire lifecycle of bioimaging data, from acquisition to archiving, including analysis and enabling re-use. A foundational element of this strategy is the definition of a common, cloud-compatible, and interoperable digital object that bundles binary images with their descriptive and provenance metadata. With members from plant biology to neuroscience, NFDI4BIOIMAGE will champion the standardization of bioimage data to create a framework that answers discipline-specific needs while ensuring communication and interoperability with data types and RDM systems across domains. Integration of bioimage data with, e.g., omics data as the basis for spatial omics, holds great promise for fields such as cancer medicine. Unlocking the full potential of bioimage data will rely on the development and broad availability of exceptional analysis tools and training sets. NFDI4BIOIMAGE will make these accessible and usable including cutting-edge AI-based methods in scalable cloud environments. NFDI4BIOIMAGE intersects with multiple NFDI consortia, most prominently with GHGA for linking image and genomics data and with DataPLANT on the definition of FAIR data objects. Last but not least, NFDI4BIOIMAGE is internationally well connected and represents the opportunity for German scientists to keep path with and have a voice in several international initiatives focusing on the FAIRification of bioimage data as one of the main challenges for the advancement of knowledge in the life and biomedical sciences.
NFDI4BIOIMAGE data management illustrations by Henning Falk#
NFDI4BIOIMAGE Consortium
Published 2024-11-29
Licensed CC-BY-4.0
These illustrations were contracted by the Heinrich Heine University Düsseldorf in the frame of the consortium NFDI4BIOIMAGE from Henning Falk for the purpose of education and public outreach. The illustrations are free to use under a CC-BY 4.0 license.AttributionPlease include an attribution similar to: “Data annoation matters”, NFDI4BIOIMAGE Consortium (2024): NFDI4BIOIMAGE data management illustrations by Henning Falk, Zenodo, https://doi.org/10.5281/zenodo.14186100, is used under a CC-BY 4.0 license. Modifications to this illustration include cropping.
Tags: Nfdi4Bioimage, Research Data Management
NFDI4BIOIMAGE: Perspective for a national bioimaging standard#
Josh Moore, Susanne Kunis
Licensed CC-BY-4.0
Tags: Nfdi4Bioimage
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
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
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
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
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
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.
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.
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
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
Content type: Data
OME Documentation#
Licensed CC-BY-4.0
Tags: OMERO
Content type: Documentation
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
Content type: Publication
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
OMExcavator: a tool for exporting and connecting Bioimaging-specific metadata in wider knowledge graphs#
Stefan Dvoretskii, Klaus Maier-Hein, Marco Nolden, Christian Schmidt, Michele Bortolomeazzi, Josh Moore
Published 2025-05-15
Licensed CC-BY-4.0
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
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
Content type: Github Repository
Open Image Data Handbook#
Kevin Yamauchi
Licensed CC-BY-4.0
Tags: Neubias, Research Data Management, Napari, Python, Bioimage Analysis
Content type: Book, Notebook
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
Content type: Video, Collection
Open Science, Sharing & Licensing#
Robert Haase
Published 2024-04-18
Licensed CC-BY-4.0
Wir tauchen ein in die Welt der Open Science und definieren Begriffe wie Open Source, Open Access und die FAIR-Prinzipien (Findable, Accessible, Interoperable and Reuasable). Wir diskutieren, wie diese Methoden der [wissenschaftlichen] Kommunikation und des Datenmanagements die Welt verändern und wie wir sie praktisch in unsere Arbeit integrieren können. Dabei spielen Aspekte wie Copyright und Lizenzierung eine wichtige Rolle.
Tags: Research Data Management, Open Access, FAIR-Principles, Licensing
Content type: Slides
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.
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
Overview of the Galaxy OMERO-suite - Upload images and metadata in OMERO using Galaxy#
Riccardo Massei, Björn Grüning
Published 2024-12-02
Licensed CC-BY-4.0
Tags: OMERO, Galaxy, Metadata, Nfdi4Bioimage
Content type: Tutorial, Framework, Workflow
Parallelization and heterogeneous computing: from pure CPU to GPU-accelerated image processing#
Robert Haase
Licensed CC-BY-4.0
Content type: Slides
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.
Tags: Ai-Ready
Content type: Data
Photonic data analysis in 2050#
Oleg Ryabchykov, Shuxia Guo, Thomas Bocklitz
Licensed CC-BY-4.0
Photonic data analysis, combining imaging, spectroscopy, machine learning, and computer science, requires flexible methods and interdisciplinary collaborations to advance. Essential developments include standardizing data infrastructure for comparability, optimizing data-driven models for complex investigations, and creating techniques to handle limited or unbalanced data and device variations.
Tags: FAIR-Principles, Machine Learning, Research Data Management
Content type: Publication
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.
Tags: Ai-Ready
Content type: Data
PoL Bio-Image Analysis Training School on GPU-Accelerated Image Analysis#
Stephane Rigaud, Brian Northan, Till Korten, Neringa Jurenaite, Apurv Deepak Kulkarni, Peter Steinbach, Sebastian Starke, Johannes Soltwedel, Marvin Albert, Robert Haase
Licensed CC-BY-4.0
This repository hosts notebooks, information and data for the GPU-Accelerated Image Analysis Track of the PoL Bio-Image Analysis Symposium.
Tags: Gpu, Clesperanto, Dask, Python
Content type: Notebook
BiAPoL/PoL-BioImage-Analysis-TS-GPU-Accelerated-Image-Analysis
Practical Guide to the International Alignment of Research Data Management - Extended Edition#
Licensed CC-BY-4.0
Content type: Book
Practical considerations for data exploration in quantitative cell biology#
Joanna W. Pylvänäinen, Hanna Grobe, Guillaume Jacquemet
Published 2025-04-07
Licensed CC-BY-4.0
This article emphasizes the importance of structured, hands-on data exploration in quantitative cell biology, offering practical advice for analyzing bioimage datasets. It also highlights how generative AI and large language models can enhance and streamline data workflows for more reliable and transparent research.
Tags: Bioimage Analysis, Data Exploration
Content type: Publication
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
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.
Tags: Ai-Ready
Content type: Data
Prompt Engineering, Agentic Workflows and Multi-modal Large Language Models#
Robert Haase
Published 2025-01-19
Licensed CC-BY-4.0
In these two slide-decks we explore applications of large language models. In the first slide deck we dive into prompt engineering, function calling and how to build agentic workflows. In the second slide-deck we explore multi-modal large language models focusing on vision language models and image generation models.
QI 2024 Analysis Lab Manual#
Beth Cimini, Florian Jug, QI 2024
Licensed CC-BY-4.0
This book contains the quantitative analysis labs for the QI CSHL course, 2024
Tags: Python
Content type: Notebook
https://bethac07.github.io/qi_2024_analysis_lab_manual/intro.html
QM Course Lectures on Bio-Image Analysis with napari 2024#
Marcelo Leomil Zoccoler
Licensed CC-BY-4.0
In these lectures, we will explore ways to analyze microscopy images with Python and visualize them with napari, an nD viewer open-source software. The analysis will be done in Python mostly using the scikit-image, pyclesperanto and apoc libraries, via Jupyter notebooks. We will also explore some napari plugins as an interactive and convenient alternative way of performing these analysis, especially the napari-assistant, napari-apoc and napari-flim-phasor-plotter plugins.
Tags: Napari, Python
Content type: Notebook
https://zoccoler.github.io/QM_Course_Bio_Image_Analysis_with_napari_2024
QUAREP-LiMi: A community-driven initiative to establish guidelines for quality assessment and reproducibility for instruments and images in light microscopy#
Glyn Nelson, Ulrike Boehme, et al.
Licensed CC-BY-4.0
Tags: Quareo-Limi
Content type: Publication
QuPath: Open source software for analysing (awkward) images#
Peter Bankhead
Published 2020-12-16
Licensed CC-BY-4.0
Slides from the CZI/EOSS online meeting in December 2020.
Tags: Bioimage Analysis
Content type: Slides
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
Content type: Slides
RDM Starter Kit#
GO FAIR
Licensed CC-BY-4.0
This page is supposed to serve as a Starter Kit for research data management (RDM). It lists resources designed to help researchers get started to organize their data.
Tags: Research Data Management
Content type: Website
RDM4Mic Presentations#
Licensed CC-BY-4.0
Tags: Research Data Management
Content type: Collection
RDMKit Training Resources#
Licensed CC-BY-4.0
Tags: Research Data Management
Content type: Collection
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).
Rechtsfragen bei Open Science - Ein Leitfaden#
Till Kreutzer, Henning Lahmann
Published 2021-05-25
Licensed CC-BY-4.0
Die Digitalisierung ermöglicht eine offene Wissenschaft (Open Science). Diese hat viele Aspekte, insbesondere den freien Zugang zu wissenschaftlichen Veröffentlichungen und Materialien (Open Access), transparente Begutachtungsverfahren (Open Peer Review) oder quelloffene Technologien (Open Source). Das Programm Hamburg Open Science (Laufzeit 2018–2020) unterstützt unter anderem den Kulturwandel in der Wissenschaft. In diesem Kontext entstand der nun vorliegende Leitfaden, der das rechtliche Umfeld greifbar machen soll. Der Leitfaden erarbeitet die betroffenen Rechtsgebiete zunächst systematisch. Im zweiten Teil werden rechtliche Fragen zu Open Science beantwortet, die direkt aus den Universitäten und Bibliotheken kommen.
Tags: Open Science, Open Access, Copyright
Content type: Book
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.
Report on a pilot study: Implementation of OMERO for microscopy data management#
Silke Tulok, Gunar Fabig, Andy Vogelsang, Thomas Kugel, Thomas Müller-Reichert
Published 2023-11-10
Licensed CC-BY-4.0
The Core Facility Cellular Imaging (CFCI) at the Faculty of Medicine Carl Gustav Carus (TU Dresden) is currently running a pilot project for testing the use and handling of the OMERO software. This is done together with interested users of the imaging facility and a research group. Currently, we are pushing forward this pilot study on a small scale without any data steward. Our experiences argue so far for giving data management issues into the hands of dedicated personnel not fully involved in research projects. As funding agencies will ask for higher and higher standards for implementing FAIRdata principles in the future, this will be a releva
Research Data Management Seminar - Slides#
Stefano Della Chiesa
Published 2022-05-18
Licensed CC-BY-4.0
This Research Data Management (RDM) Slides introduce to the multidisciplinary knowledge and competencies required to address policy compliance and research data management best practices throughout a project lifecycle, and beyond it.
Module 1 - Introduces the RDM giving its context in the Research Data Governance
Module 2 - Illustrates the most important RDM policies and principles
Module 3 - Provides the most relevant RDM knowledge bricks
Module 4 - Discuss the Data Management Plans (DMPs), examples, templates and guidance
Tags: Research Data Management
Content type: Slides
Research Data Managemet and how not to get overwhelmed with data#
Martin Schätz
Published 2023-09-23
Licensed CC-BY-4.0
Research data management and how not to get overwhelmed with data presentation is an overview of bioimage analysis with a focus on the basics for data management planning, FAIR principles, and how to practically organize folders and prepares naming convention. The presentation includes an overview of metadata, Creative Common licenses, and a sum up of electronic laboratory notebooks. The last two slides go through how all of that works in practice in open access core microscopy facility.
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
Content type: Publication
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.
Tags: Research Data Management
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
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).
Tags: Ai-Ready
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.
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.
Running Deep-Learning Scripts in the BiA-PoL Omero Server#
Marcelo Zoccoler
Licensed CC-BY-4.0
Tags: Python, Artificial Intelligence, Bioimage Analysis
Content type: Blog Post
https://biapol.github.io/blog/marcelo_zoccoler/omero_scripts/readme.html
SWC/GCNU Software Skills#
Licensed CC-BY-4.0
Computational skills training at the UCL Sainsbury Wellcome Centre and Gatsby Computational Neuroscience Unit, delivered by members of the Neuroinformatics Unit.
Content type: Collection, Online Course, Video, Tutorial
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.
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.
Tags: Ai-Ready
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
Tags: Ai-Ready
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: OMERO, Bioimage Analysis
Content type: Publication
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.
Tags: Ai-Ready
Content type: Data
Slides about FLUTE: a Python GUI for interactive phasor analysis of FLIM data#
Chiara Stringari
Published 2024-03-19
Licensed CC-BY-4.0
This presentation introduces the open source software to analyze FLIM data: FLUTE – (F)luorescence (L)ifetime (U)ltima(T)e (E)xplorer: a Python GUI for interactive phasor analysis of FLIM data The software is available on GitHub: LaboratoryOpticsBiosciences/FLUTE and it is published on Biological imaging Journal: Gottlieb, D., Asadipour, B., Kostina, P., Ung, T., & Stringari, C. (2023). FLUTE: A Python GUI for interactive phasor analysis of FLIM data. Biological Imaging, 1-22. doi:10.1017/S2633903X23000211 The lecture was part of the short talks on community developed FLIM-software at the German BioImaging workshop on FLIM in Munich.
So geschlossen wie nötig, so offen wie möglich - Datenschutz beim Umgang mit Forschungsdaten#
Pia Voigt
Published 2024-05-30
Licensed CC-BY-4.0
Der Umgang mit personenbezogenen Daten stellt Forschende oft vor rechtliche Herausforderungen: Unter welchen Bedingungen dürfen personenbezogene Daten verarbeitet werden? Welche Voraussetzungen müssen erfüllt sein und welche Strategien können angewendet werden, um Daten sicher speichern, verarbeiten, teilen und aufbewahren zu können? Mit Hilfe dieses Foliensatzes erhalten Sie Einblicke in datenschutzrechtliche Aspekte beim Umgang mit Ihren Forschungsdaten.
Tags: Research Data Management, Data Protection, FAIR-Principles
Content type: Slides
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
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
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
Tags: Ai-Ready
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.
Tags: Ai-Ready
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
Tags: Ai-Ready
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
Tags: Ai-Ready
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
Tags: Ai-Ready
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
Tags: Ai-Ready
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
Tags: Ai-Ready
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
Tags: Ai-Ready
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
Tags: Ai-Ready
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
Tags: Ai-Ready
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)
Tags: Ai-Ready
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
Tags: Ai-Ready
Content type: Data
Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data#
Sarah Weischer, Jens Wendt, Thomas Zobel
Published 2022-07-12
Licensed CC-BY-4.0
Provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata. Visualizes the techniques for structuring this data as Linked Data. (Walkthrough Video: https://doi.org/10.5281/zenodo.7018928 )
Content:
Types of metadata
Data formats
Data Models Microscopy Data
Tools to edit/gather metadata
ISA Framework
FDO Framework
Ontology
RDF
JSON-LD
SPARQL
Knowledge Graph
Linked Data
Smart Data
...
Tags: Nfdi4Bioimage, Research Data Management
Sustainable Data Stewardship#
Stefano Della Chiesa
Published 2024-03-25
Licensed CC-BY-4.0
These slides were presented at the 2. SaxFDM-Beratungsstammtisch and delve into the strategic integration of Research Data Management (RDM) within research organizations. The Leibniz IOER presented an insightful overview of RDM activities and approaches, emphasizing the criticality of embedding RDM strategically within research institutions. The presentation showcases some best practices in RDM implementation through practical examples, offering valuable insights for optimizing data stewardship processes.
Tags: Research Data Management, Data Stewardship
Content type: Slides
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
Tags: Ai-Ready
Content type: Data
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.
Tags: Ai-Ready
Content type: Data
Ten simple rules for making training materials FAIR#
Leyla Garcia, Bérénice Batut, Melissa L. Burke, Mateusz Kuzak, Fotis Psomopoulos, et al.
Published 2020-05-21
Licensed CC-BY-4.0
The authors offer trainers some simple rules, to help make their training materials FAIR, enabling others to find, (re)use, and adapt them.
Tags: Metadata, Bioinformatics, FAIR-Principles
Content type: Publication
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854
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.
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.
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
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
Content type: Publication
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
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
Content type: Publication
https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r47
The Turing Way: Guide for reproducible research#
Licensed [‘CC-BY-4.0’, ‘MIT’]
A guide which covers topics related to skills, tools and best practices for research reproducibility.
Content type: Book
https://the-turing-way.netlify.app/reproducible-research/reproducible-research
The crucial role of bioimage analysts in scientific research and publication#
Beth A. Cimini, Peter Bankhead, Rocco D’ Antuono, Elnaz Fazeli, Julia Fernandez-Rondriguez, Caterina Fuster-Barcelo, Robert Haase, Helena Klara Jambor, Martin L. Jones, Florian Jug, Anna H. Klemm, Anna Kreshuk, Stefania Marcotti, Gabriel G. Martins, Sara Mc Ardle, Kota Miura, Arrate Muñoz-Barrutia, Laura C. Murphy, Michael S. Nelson, Simon F. Nørrelykke, Perrine Paul-Gilloteaux, Thomas Pengo, Joanna W. Pylvänäinen, Lior Pytowski, Arianna Ravera, Annika Reinke, Yousr Rekik, Caterina Strambio-De-Castillia, Daniel Thédié, Virginie Uhlmann, Oliver Umney, Laura Wiggins, Kevin W. Eliceiri
Published 2024-10-30
Licensed CC-BY-4.0
Bioimage analysis (BIA), a crucial discipline in biological research, overcomes the limitations of subjective analysis in microscopy through the creation and application of quantitative and reproducible methods. The establishment of dedicated BIA support within academic institutions is vital to improving research quality and efficiency and can significantly advance scientific discovery. However, a lack of training resources, limited career paths and insufficient recognition of the contributions made by bioimage analysts prevent the full realization of this potential. This Perspective – the result of the recent The Company of Biologists Workshop ‘Effectively Communicating Bioimage Analysis’, which aimed to summarize the global BIA landscape, categorize obstacles and offer possible solutions – proposes strategies to bring about a cultural shift towards recognizing the value of BIA by standardizing tools, improving training and encouraging formal credit for contributions
Tags: Bioimage Analysis
Content type: Publication
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
Thinking data management on different scales#
Susanne Kunis
Licensed CC-BY-4.0
Presentation given at PoL BioImage Analysis Symposium Dresden 2023
Tags: Research Data Management, Nfdi4Bioimage
Content type: Slides
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
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.
Towards open and standardised imaging data: an introduction to Bio-Formats, OME-TIFF, and OME-Zarr#
Josh Moore
Published 2025-05-28
Licensed CC-BY-4.0
https://www.ebi.ac.uk/training/events/towards-open-and-standardised-imaging-data-introduction-bio-formats-ome-tiff-and-ome-zarr/ Microscopy and bioimaging technologies are fundamental tools for exploring biological systems, generating large, multidimensional datasets rich in experimental detail. However, the bioimaging community has historically faced major challenges around data handling: vendor-specific proprietary formats, fragmented metadata storage, and increasingly large dataset sizes that outstrip traditional storage and computing solutions. In this webinar, key open technologies developed by the Open Microscopy Environment (OME) to address these challenges were presented. Specifically, the Bio-Formats library for accessing diverse proprietary file formats, the OME-TIFF standard for archival data storage, and the OME-Zarr format for cloud-native, scalable bioimaging workflows were presented.
Tracking of mitochondria and capturing mitoflashes#
Leonid Kostrykin, Diana Chiang Jurado
Published 2024-11-20
Licensed CC-BY-4.0
Tags: Bioinformatics, Bioimage Analysis
Content type: Workflow, Tutorial
Train-the-Trainer Concept on Research Data Management#
Katarzyna Biernacka, Maik Bierwirth, Petra Buchholz, Dominika Dolzycka, Kerstin Helbig, Janna Neumann, Carolin Odebrecht, Cord Wiljes, Ulrike Wuttke
Published 2020-11-04
Licensed CC-BY-4.0
Within the project FDMentor, a German Train-the-Trainer Programme on Research Data Management (RDM) was developed and piloted in a series of workshops. The topics cover many aspects of research data management, such as data management plans and the publication of research data, as well as didactic units on learning concepts, workshop design and a range of didactic methods.
After the end of the project, the concept was supplemented and updated by members of the Sub-Working Group Training/Further Education (UAG Schulungen/Fortbildungen) of the DINI/nestor Working Group Research Data (DINI/nestor-AG Forschungsdaten). The newly published English version of the Train-the-Trainer Concept contains the translated concept, the materials and all methods of the Train-the-Trainer Programme. Furthermore, additional English references and materials complement this version.
Tags: Research Data Management
Content type: Book
Training Computational Skills in the Age of AI#
Robert Haase
Published 2024-11-06
Licensed CC-BY-4.0
Artificial intelligence (AI) and large language models (LLMs) are changing the way we use computers in science. This slide deck introduces ways for using AI and LLMs for making training materials and for exchanging knowledge about how to use AI in joint discussions between humans and LLM-based AI-systems.
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
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
Content type: Github Repository
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
Content type: Publication, Preprint
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
Content type: Data
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
Content type: Slides
What is Open Data?#
Daniel Dietrich, Jonathan Gray, Tim McNamara, Antti Poikola, Rufus Pollock, et al.
Licensed CC-BY-4.0
This handbook is about open data but what exactly is it? In particular what makes open data open, and what sorts of data are we talking about?
Tags: Open Science
Content type: Collection
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.
Workflow for user introduction into microscopy, OMERO and data management at Center for Advanced imaging#
Ksenia Krooß, Fuchs, Vanessa Aphaia Fiona, Tom Boissonnet, Stefanie Weidtkamp-Peters
Published 2025-03-07
Licensed CC-BY-4.0
At the Center for Advanced Imaging (CAi) at the Heinrich Heine University Düsseldorf, Germany, we have established a workflow to guide users through all aspects of bioimaging. The process begins with an initial consultation with our imaging specialists regarding microscopy techniques for their specific project. Users then receive training in microscope operation, ensuring they can handle the equipment effectively. If needed, our specialists also provide support in image analysis. Next, we introduce users to OMERO, highlighting its features and the advantages of using a bioimage data management system. They are then trained to structure and annotate their data within OMERO according to the Recommended Metadata for Biological Images (REMBI), taking their specific research topics into account. As users prepare for data publication, we assist with data organization and repository uploads. Our goal is to educate researchers in managing bioimage data throughout its entire lifecycle, with a strong emphasis on the FAIR (findable, accessible, interoperable, reusable) principles.
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.
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: Bioimage Analysis
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.
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.
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.
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.
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
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
Content type: Data
[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/
[CIDAS] Scalable strategies for a next-generation of FAIR bioimaging#
Josh Moore
Published 2025-01-23
Licensed CC-BY-4.0
Talk given at Georg-August-Universität Göttingen Campus Institute Data Science23rd January 2025 https://www.uni-goettingen.de/en/653203.html
[CMCB] Scalable strategies for a next-generation of FAIR bioimaging#
Josh Moore
Published 2025-01-16
Licensed CC-BY-4.0
CMCB LIFE SCIENCES SEMINARSTechnische Universität Dresden16th January 2025 https://tu-dresden.de/cmcb/crtd/news-termine/termine/cmcb-life-sciences-seminar-josh-moore-german-bioimaging-e-v-society-for-microscopy-and-image-analysis-constance
Tags: Nfdi4Bioimage
[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
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
[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?
[ELMI 2024] AI’s Dirty Little Secret: Without#
FAIR Data, It’s Just Fancy Math
Josh Moore, Susanne Kunis
Published 2024-05-21
Licensed CC-BY-4.0
Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/)
Tags: Nfdi4Bioimage, Research Data Management
[ELMI 2024] AI’s Dirty Little Secret: Without FAIR Data, It’s Just Fancy Math#
Josh Moore, Susanne Kunis
Licensed CC-BY-4.0
Poster presented at the European Light Microscopy Initiative meeting in Liverpool (https://www.elmi2024.org/)
Tags: Research Data Management, FAIR-Principles, Bioimage Analysis, Nfdi4Bioimage
Content type: Poster
[ELMI2025] Bridging communities with OME-Zarr#
Christian Schmidt, Aastha Mathur, Josh Moore
Published 2025-06-04
Licensed CC-BY-4.0
Presented at ELMI2025 https://www.embl.org/about/info/course-and-conference-office/events/elmi2025/
[ELMI2025] The Road to OME-Zarr 1.0#
Josh Moore
Published 2025-06-05
Licensed CC-BY-4.0
Presented at https://www.embl.org/about/info/course-and-conference-office/events/elmi2025/ Abstract For over 20 years, the Open Microscopy Environment (OME) has developed tools and specifications to support bioimaging data sharing. Technologies such as Bio-Formats, OMERO, and OME-TIFF have helped researchers manage the growing size, complexity, and acquisition rates of imaging datasets. However, with increasing mandates for research data management, such as the Nelson memo in the United States, and the shift toward cloud-native workflows, the bioimaging community faces new challenges in ensuring scalable and FAIR data infrastructure. In 2024, following expanding community engagement, the focus of the Next-Generation File Format (NGFF) community was on building consensus around a Request for Comments (RFC) process. This collaborative effort has laid the foundation for future refinements and wider adoption. In parallel, we hosted the “OME2024 NGFF Challenge,” bringing together over the course of just four months hundreds of terabytes of data in a first prototype of federated image hosting, showcasing the power of OME-Zarr for handling large-scale, distributed datasets. In 2025, we are set to take a major step toward a stable FAIR solution with OME-Zarr 1.0. This milestone marks a crucial phase towards an international standard, providing an open, cloud-optimized, and scalable solution for handling terabyte- and petabyte-scale imaging data. The 1.0 release will introduce long-awaited transforms, enabling robust support for multimodal datasets, followed by collections and an extensibility mechanism to accommodate evolving scientific needs. These additions emphasize a solid foundation on which future capabilities can be built while providing the stability needed for broader adoption of the format. This presentation will outline the path to 1.0, including community-driven refinements, vendor engagement to ensure complete metadata representation, and alignment with global bioimaging initiatives. As imaging data continues to grow in scale and complexity, consensus-driven evolution of infrastructure will be key to ensuring a truly FAIR future for bioimaging.
[GBI EOE VII] Five (or ten) must-have items for making IT infrastructure for managing bioimage data#
Josh Moore
Published 2024-05-26
Licensed CC-BY-4.0
Presentation made to the GBI Image Data Management Working Group during the 7th Exchange of Experience in Uruguay.
[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.
[I2K] Scalable strategies for a next-generation of FAIR bioimaging#
Josh Moore
Published 2024-10-25
Licensed CC-BY-4.0
or, “OME-Zarr: ‘even a talk on formats [can be] interesting’” Presented at https://events.humantechnopole.it/event/1/
[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.
[NFDI Tech Talk] Cloud Based Image Science#
Josh Moore, Yi Sun
Published 2025-06-02
Licensed CC-BY-4.0
Slides for the NFDI Tech Talk live streamed to https://www.youtube.com/live/bzfmE29S270 See http://nfdi.de/talks for more information.
[SWAT4HCLS 2023] NFDI4BIOIMAGE: Perspective for a national bioimage standard#
Josh Moore, Susanne Kunis
Licensed CC-BY-4.0
Poster presented at Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2023), Feb 13–16, 2023, Basel, Switzerland. NFDI4BIOIMAGE is a newly established German consortium dedicated to the FAIR representation of biological imaging data. A key deliverable is the definition of a semantically-compatible FAIR image object integrating RDF metadata with web-compatible storage of large n-dimensional binary data in OME-Zarr. We invite feedback from and collaboration with other endeavors during the soon-to-begin 5 year funding period.
Tags: Research Data Management, FAIR-Principles, Nfdi4Bioimage
Content type: Poster
[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.
[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
[Workshop] Bioimage data management and analysis with OMERO#
Riccardo Massei, Michele Bortolomeazzi, Christian Schmidt
Published 2024-05-13
Licensed CC-BY-4.0
Here we share the material used in a workshop held on May 13th, 2024, at the German Cancer Research Center in Heidelberg (on-premise) Description:Microscopy experiments generate information-rich, multi-dimensional data, allowing us to investigate biological processes at high spatial and temporal resolution. Image processing and analysis is a standard procedure to retrieve quantitative information from biological imaging. Due to the complex nature of bioimaging files that often come in proprietary formats, it can be challenging to organize, structure, and annotate bioimaging data throughout a project. Data often needs to be moved between collaboration partners, transformed into open formats, processed with a variety of software tools, and exported to smaller-sized images for presentation. The path from image acquisition to final publication figures with quantitative results must be documented and reproducible. In this workshop, participants learn how to use OMERO to organize their data and enrich the bioimage data with structured metadata annotations.We also focus on image analysis workflows in combination with OMERO based on the Fiji/ImageJ software and using Jupyter Notebooks. In the last part, we explore how OMERO can be used to create publication figures and prepare bioimage data for publication in a suitable repository such as the Bioimage Archive. Module 1 (9 am - 10.15 am): Basics of OMERO, data structuring and annotation Module 2 (10.45 am - 12.45 pm): OMERO and Fiji Module 3 (1.45 pm - 3.45 pm): OMERO and Jupyter Notebooks Module 4 (4.15 pm - 6. pm): Publication-ready figures and data with OMERO The target group for this workshopThis workshop is directed at researchers at all career levels who plan to or have started to use OMERO for their microscopy research data management. We encourage the workshop participants to bring example data from their research to discuss suitable metadata annotation for their everyday practice. Prerequisites:Users should bring their laptops and have access to the internet through one of the following options:- eduroam- institutional WiFi- VPN connection to their institutional networks to access OMERO Who are the trainers? Dr. Riccardo Massei (Helmholtz-Center for Environmental Research, UFZ, Leipzig) - Data Steward for Bioimaging Data in NFDI4BIOIMAGE Dr. Michele Bortolomeazzi (DKFZ, Single cell Open Lab, bioimage data specialist, bioinformatician, staff scientist in the NFDI4BIOIMAGE project) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg, Project Coordinator of the NFDI4BIOIMAGE project)
Tags: Nfdi4Bioimage, Research Data Management
[Workshop] FAIR data handling for microscopy: Structured metadata annotation in OMERO#
Vanessa Fiona Aphaia Fuchs, Christian Schmidt, Tom Boissonnet
Published 2024-05-06
Licensed CC-BY-4.0
Description Microscopy experiments generate information-rich, multi-dimensional data, allowing us to investigate biological processes at high spatial and temporal resolution. Image processing and analysis is a standard procedure to retrieve quantitative information from biological imaging. Due to the complex nature of bioimaging files that often come in proprietary formats, it can be challenging to organize, structure, and annotate bioimaging data throughout a project. Data often needs to be moved between collaboration partners, transformed into open formats, processed with a variety of software tools, and exported to smaller-sized images for presentation. The path from image acquisition to final publication figures with quantitative results must be documented and reproducible. In this workshop, participants learn how to use structured metadata annotations in the image data management platform OMERO (OME Remote Objects) to optimize their data handling. This strategy helps both with organizing data for easier processing and analysis and for the preparation of data publication in journal manuscripts and in public repositories such as the BioImage Archive. Participants learn the principles of leveraging object-oriented data organization in OMERO to enhance findability and usability of their data, also in collaborative settings. The integration of OMERO with image analysis tools, in particular ImageJ/Fiji, will be trained. Moreover, users learn about community-accepted metadata checklists (REMBI) to enrich the value of their data toward reproducibility and reusability. In this workshop, we will provide hands-on training and recommendations on:
Structured metadata annotation features in OMERO and how to use them Types of metadata in bioimaging: Technical metadata, sample metadata, analysis metadata The use of ontologies and terminologies for metadata annotation REMBI, the recommended metadata for biological images Metadata-assisted image analysis streamlining Tools for metadata annotation in OMERO
The target group for this workshop This workshop is directed at researchers at all career levels who have started using OMERO for their microscopy research data management. We encourage the workshop participants to bring example data from their research to discuss suitable metadata annotation for their everyday practice. Who are the trainers (see trainer description below for more details)
Dr. Vanessa Fuchs (NFDI4BIOIMAGE Data Steward, Center for Advanced Imaging, Heinrich-Heine University of Düsseldorf) Dr. Tom Boissonnet (OMERO admin and image metadata specialist, Center for Advanced Imaging, Heinrich-Heine University of Düsseldorf) Dr. Christian Schmidt (Science Manager for Research Data Management in Bioimaging, German Cancer Research Center, Heidelberg)
Material Description Published here are the presentation slides that were used for input from the trainers during the different sessions of the programme. Additionally, a Fiji Macro is published that depends on the OMERO Extensions Plugin by Pouchin et al, 2022, F100Research, https://doi.org/10.12688/f1000research.110385.2 Programme Overview Day 1 - April 29th, 2024 09.00 a.m. to 10.00 a.m.: Session 1 - Welcome and Introduction 10.00 a.m. to 10.30 a.m.: Session 2 - Introduction to the FAIR principles & data annotation 10:30 a.m. to 10:45 a.m.: Coffee break 10.45 a.m. to 12.00 a.m.: Session 3 - Data structure (datasets in OMERO) and organization with Tags 12.00 a.m. to 1.00 p.m.: Lunch Break 1.00 p.m. to 2.00 p.m.: Session 4 - REMBI, Key-Value pair annotations in bioimaging 2:00 p.m. to 2.30 p.m.: Session 5 - Ontologies for Key-Value Pairs in OMERO 2:30 p.m. to 2:45 p.m. Coffee break 2.45 p.m. to 3.45 p.m.: Wrap-up, discussion, outlook on day 2 Day 2 - April 30th, 2024 09.00 a.m. to 09.30 a.m.: Arrival and Start into day 2 09.30 a.m. to 11.30 a.m.: Session 6 - Hands-on : REMBI-based Key-Value Pair annotation in OMERO 11.30 a.m. to 12.30 a.m.: Lunch Break 12.30 a.m. to 1.15 p.m.: Session 7 - OMERO and OMERO.plugins 1.15 p.m. to 2.00 p.m.: Session 8 - Loading OMERO-hosted data into Fiji 2.00 p.m. to 2.15 p.m.: Coffee break 2.15 p.m. to 3.00 p.m.: Discussion, Outlook
[Workshop] Managing FAIR microscopy data at scale for universities and research institutions: an introduction for non-imaging stakeholders#
Christian Schmidt, Michele Bortolomeazzi, Ksenia Krooß, Jan-Philipp Mallm, Elisa Ferrando-May, Stefanie Weidtkamp-Peters
Published 2025-03-14
Licensed CC-BY-4.0
These slides were used in a workshop at the 2025 E-Science Tage in Heidelberg. Workshop Abstract: Effective Research Data Management (RDM) requires collaboration between infrastructure providers, support units, and domain-specific experts across scientific disciplines. Microscopy, or bioimaging, is a widely used technology at universities and research institutions, generating large, multi-dimensional datasets. Scientists now routinely produce microscopy data using advanced imaging modalities, often through centrally-provided instruments maintained by core facilities. Bioimaging data management presents unique challenges: files are often large (e.g., 15+ GB for whole slide images), come in various proprietary formats, and are accessed frequently for viewing as well as for complex image processing and analysis workflows. Collaboration between experimenters, clinicians, group leaders, core facility staff, and image analysts adds to the complexity, increasing the risk of data fragmentation and metadata loss. The DFG-funded project I3D:bio and the consortium NFDI4BIOIMAGE, part of Germany’s National Research Data Infrastructure (NFDI), are addressing these challenges by developing solutions and best practices for managing large, complex microscopy datasets. This workshop introduces the challenges of bioimaging RDM to institutional support personnel, including, for example, library staff, IT departments, and data stewards. Participants will explore the bioimaging RDM system OMERO, and apply structured metadata annotation and object-oriented data organization to a simple training dataset. OMERO offers centralized, secure access to data, allowing collaboration and reducing the data fragementation risk. Moreover, participants will experience the benefits of OME-Zarr, a chunked open file format designed for FAIR data sharing and remote access. OME-Zarr enables streaming of large, N-dimensional array-typed data over the Internet without the need to download whole files. An expanding toolbox for leveraging OME-Zarr for bioimaging data renders this file type a promising candidate for a standard file format suitable for use in FAIR Digital Object (FDO) implementations for microscopy data. OME-Zarr has become a pillar for imaging data sharing in two bioimaging-specific data repositories, i.e., the Image Data Resource (IDR) and the BioImage Archive (BIA). The team of Data Stewards from both abovenmentioned projects help researchers and research support staff to manage und publish bioimaging data. By the end of the workshop, participants will have gained hands-on experience with bioimaging data and will be aware of support resources like the NFDI4BIOIMAGE Help Desk for addressing specific local use cases. Our goal is to promote collaboration across disciplines to effectively manage complex bioimaging data in compliance with the FAIR principles.
[Workshop] Research Data Management for Microscopy and BioImage Analysis#
Christian Schmidt, Tom Boissonnet, Michele Bortolomeazzi, Ksenia Krooß
Published 2024-09-30
Licensed CC-BY-4.0
Research Data Management for Microscopy and BioImage Analysis
Introduction to BioImaging Research Data Management, NFDI4BIOIMAGE and I3D:bioChristian Schmidt /DKFZ Heidelberg OMERO as a tool for bioimaging data managementTom Boissonnet /Heinrich-Heine Universität Düsseldorf Reproducible image analysis workflows with OMERO software APIsMichele Bortolomeazzi /DKFZ Heidelberg Publishing datasets in public archives for bioimage dataKsenia Krooß /Heinrich-Heine Universität Düsseldorf
Date & Venue:Thursday, Sept. 26, 5.30 p.m.Haus 22 / Paul Ehrlich Lecture Hall (H22-1)University Hospital Frankfurt
Tags: Nfdi4Bioimage, Research Data Management
ilastik: interactive machine learning for (bio)image analysis#
Anna Kreshuk, Dominik Kutra
Licensed CC-BY-4.0
Tags: Artificial Intelligence, Bioimage Analysis
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.
introduction-to-generative-ai#
Bruna Piereck, Alexander Botzki
Published 2024-09-27T14:38:51+00:00
Licensed CC-BY-4.0
Course repository for Strategic Use of Generative AI
Tags: Artificial Intelligence
Content type: Github Repository, Tutorial
nextflow-workshop#
Tuur Muyldermans, Kris Davie, Alexander, Nicolas Vannieuwkerke, Kobe Lavaerts, Marcel Ribeiro-Dantas, Bruna Piereck, Steff Taelman
Published 2023-03-29T10:40:04+00:00
Licensed CC-BY-4.0
Nextflow workshop materials March 2023
Tags: Workflow, Nextflow
Content type: Github Repository, Tutorial
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
Content type: Website
scikit-learn MOOC#
Loïc Estève et al.
Licensed CC-BY-4.0
Machine learning in Python with scikit-learn MOOC
Tags: Bioimage Analysis, Machine Learning
Content type: Github Repository
training-resources#
Christian Tischer, Antonio Politi, Toby Hodges, maulakhan, grinic, bugraoezdemir, Tim-Oliver Buchholz, Elnaz Fazeli, Aliaksandr Halavatyi, Dominik Kutra, Stefania Marcotti, AnniekStok, Felix, jhennies, Severina Klaus, Martin Schorb, Nima Vakili, Sebastian Gonzalez Tirado, Stefan Helfrich, Yi Sun, Ziqiang Huang, Jan Eglinger, Constantin Pape, Joel Lüthi, Matt McCormick, Oane Gros
Published 2020-04-23T07:51:38+00:00
Licensed CC-BY-4.0
Resources for teaching/preparing to teach bioimage analysis
Tags: Bioimageanalysis, Neurobias
Content type: Github Repository