Recently added (9)#
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).
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.
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.
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 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
Pasteur-BioImage-Analysis-Course-2025#
Jean-Yves Tinevez
Published 2025-04-10T13:26:22+00:00
Teaching materials and outline for the Pasteur Bioimage Analysis vour
Tags: Bioimage Analysis
Content type: Github Repository
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.
bioimageio-chatbot#
Wei Ouyang, Wanlu Lei, Caterina Fuster-Barceló, Gabe Reder, arratemunoz, Weize, Curtis Rueden, Matt McCormick
Published 2023-10-10T16:05:49+00:00
Licensed MIT
Your Personal Assistant in Computational Bioimaging.
Tags: Artificial Intelligence, Bioimage Analysis
Content type: Github Repository
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.
https://zenodo.org/records/15001649
https://doi.org/10.5281/zenodo.15001649