Zenodo.org (76)#
“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
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 Glimpse of the Open-Source FLIM Analysis Software Tools FLIMfit, FLUTE and napari-flim-phasor-plotter#
Anca Margineanu, Chiara Stringari, Marcelo Zoccoler, Cornelia Wetzker
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.
Tags: Bioimage Analysis, Flim
Content type: Slides
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
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, Tu Dresden, Bioimage Data, Nfdi4Bioimage
Content type: Slide
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.
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: Large Language Models, Python
Content type: Slide
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, Deep Learning, Microscopy Image Analysis, Python
Content type: Slides
Building FAIR image analysis pipelines for high-content-screening (HCS) data using Galaxy#
Riccardo Massei, Matthias Berndt, Beatriz Serrano-Solano, Wibke Busch, Stefan Scholz, Hannes Bohring, Jo Nyffeler, Luise Reger, Jan Bumberger, Lucille Lopez-Delisle
Published 2024-11-06
Licensed CC-BY-4.0
Imaging is crucial across various scientific disciplines, particularly in life sciences, where it plays a key role in studies ranging from single molecules to whole organisms. However, the complexity and sheer volume of image data, especially from high-content screening (HCS) experiments involving cell lines or other organisms, present significant challenges. Managing and analysing this data efficiently requires well-defined image processing tools and analysis pipelines that align with the FAIR principles—ensuring they are findable, accessible, interoperable, and reusable across different domains. In the frame of NFDI4BioImaging (the National Research Data Infrastructure focusing on bioimaging in Germany), we want to find viable solutions for storing, processing, analysing, and sharing HCS data. In particular, we want to develop solutions to make findable and machine-readable metadata using (semi)automatic analysis pipelines. In scientific research, such pipelines are crucial for maintaining data integrity, supporting reproducibility, and enabling interdisciplinary collaboration. These tools can be used by different users to retrieve images based on specific attributes as well as support quality control by identifying appropriate metadata. Galaxy, an open-source, web-based platform for data-intensive research, offers a solution by enabling the construction of reproducible pipelines for image analysis. By integrating popular analysis software like CellProfiler and connecting with cloud services such as OMERO and IDR, Galaxy facilitates the seamless access and management of image data. This capability is particularly valuable in bioimaging, where automated pipelines can streamline the handling of complex metadata, ensuring data integrity and fostering interdisciplinary collaboration. This approach not only increases the efficiency of HCS bioimaging but also contributes to the broader scientific community’s efforts to embrace FAIR principles, ultimately advancing scientific discovery and innovation. In the present study, we proposed an automated analysis pipeline for storing, processing, analysing, and sharing HCS bioimaging data. The (semi)automatic workflow was developed by taking as a case study a dataset of zebrafish larvae and cell lines images previously obtained from an automated imaging system generating data in an HCS fashion. In our workflows, images are automatically enriched with metadata (i.e. key-value pairs, tags, raw data, regions of interest) and uploaded to the UFZ-OME Remote Objects (OMERO) server using a novel OMERO tool suite developed with GALAXY. Workflows give the possibility to the user to intuitively fetch images from the local server and perform image analysis (i.e. annotation) or even more complex toxicological analyses (dose response modelling). Furthermore, we want to improve the FAIRness of the protocol by adding a direct upload link to the Image Data Resource (IDR) repository to automatically prepare the data for publication and sharing.
https://zenodo.org/records/14044640
https://doi.org/10.5281/zenodo.14044640
https://galaxyproject.org/news/2024-11-08-galaxy-imaging-fair-pipelines/
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
CZI: Open Science Program Collection#
Content type: Collection
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
Combining the BIDS and ARC Directory Structures for Multimodal Research Data Organization#
Torsten Stöter, Tobias Gottschall, Andrea Schrader, Peter Zentis, Monica Valencia-Schneider, Niraj Kandpal, Werner Zuschratter, Astrid Schauss, Timo Dickscheid, Timo Mühlhaus, Dirk von Suchodoletz
Licensed CC-BY-4.0
Interdisciplinary collaboration and integrating large, diverse datasets are crucial for answering complex research questions, requiring multimodal data analysis and adherence to FAIR principles. To address challenges in capturing the full research cycle and contextualizing data, DataPLANT developed the Annotated Research Context (ARC), while the neuroimaging community extended the Brain Imaging Data Structure (BIDS) for microscopic image data, both providing standardized, file system-based storage structures for organizing and sharing data with metadata.
Tags: Research Data Management, FAIR-Principles
Content type: Poster
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 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: Slide
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
DALIA Interchange Format#
Jonathan Geiger, Petra Steiner, Abdelmoneim Amer Desouki, Frank Lange
Published 2024-06-07
Licensed CC-BY-SA-4.0
The DALIA (Data Literacy Alliance) project aims to develop a knowledge graph for FAIR teaching and learning materials on data literacy, data competencies and research data management (RDM) skills within the National Research Data Infrastructure (NFDI) and the RDM landscape. Such a platform thrives on the participation of users who want to search, create, manage or use teaching and learning materials. A schematization of the metadata is necessary for the interoperability of teaching and learning materials. This is done by the DALIA Interchange Format (DIF), which provides a framework for making the metadata of teaching and learning materials transparent, comparable and smooth to integrate into the DALIA platform. It includes the description and explanation of the data fields for the online publication of educational resources. The DIF was developed in close consultation with the scientific community. This development process included several feedback rounds in which valuable feedback was provided and subsequently incorporated into the DIF. This not only contributed to the clear, transparent and user-oriented definitions of the data fields, and to a clear structure, but also to the integration of many existing data standards and to the (special) requirements of the scientific community. The selection of elements is based on the Dublin Core Application Profile. The DIF is provided as a PDF document and in table form (ODS) to convey the attributes of the teaching and learning materials and their definitions in an easily understandable form and to facilitate communication. It also includes a legend and an example in tabular form. In addition, a template (CSV) with the attributes as column headers is provided, which can be used for recording the metadata of the teaching and learning materials. The tables can also be transferred to technical application profiles. We would like to thank all the commentators of the previous versions, especially Susanne Arndt, Sophie Boße, Sonja Felder, Marc Fuhrmans, Jan-Michael Haugwitz, Marina Lemaire, Karoline Lemke, Birte Lindstädt, Juliane Röder, and Jakob Voß. Without their feedback and advice, the DIF would be less transparent.
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
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
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
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)
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.
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.
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”
Hackaton Results - Conversion of KNIME image analysis workflows to Galaxy#
Riccardo Massei
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)
Tags: Research Data Management
Content type: Slides
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
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: Slide, Video
https://zenodo.org/records/8323588
https://www.youtube.com/playlist?list=PL2k-L-zWPoR7SHjG1HhDIwLZj0MB_stlU
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
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
Large Language Models: An Introduction for Life Scientists#
Robert Haase
Published 2024-08-27
Licensed CC-BY-4.0
Large Language Models (LLMs) are changing the way how humans interact with computers. This has impact on all scientific fields by enabling new ways to achieve for example data analysis goals. In this talk we will go through an introduction to LLMs with respect to applications in the life sciences, focusing on bio-image analysis. We will see how to generate text and images using LLMs and how LLMs can extract information from reproducibly images through code-generation. We will go through selected prompt engineering techniques enabling scientists to tune the output of LLMs towards their scientific goal and how to do quality assurance in this context.
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.
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: Image Segmentation, Bioimage Analysis, Deep Learning
Content type: Slides
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
Licensed CC-BY-4.0
Presentation was given at the 2nd MPG-NFDI Workshop on April 18th about e NFDI4BIOIMAGE Consortium, FAIRification of Image (meta)data, Zarr, RFC, Training (TA5), contributing.
Tags: Research Data Management, Bioimage Analysis, FAIR-Principles, Zarr, 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#
Marcelo Zoccoler
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 - 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
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: Slide
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
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: Slide
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
Research Data Management Seminar - Slides#
Della Chiesa, Stefano
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: Slide
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
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
Structuring of Data and Metadata in Bioimaging: Concepts and technical Solutions in the Context of Linked Data#
Susanne Kunis
Published 2022-08-24
Licensed CC-BY-4.0
guided walkthrough of poster at https://doi.org/10.5281/zenodo.6821815
which provides an overview of contexts, frameworks, and models from the world of bioimage data as well as metadata and the techniques for structuring this data as Linked Data.
You can also watch the video in the browser on the I3D:bio website.
Tags: Nfdi4Bioimage, Research Data Management
Content type: Video
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
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.
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 2024-08-29
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.
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.
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
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
Who you gonna call? - Data Stewards to the rescue#
Vanessa Fuchs, Aphaia Fiona, Jens Wendt, Maximilian Müller, Mohsen Ahmadi, Riccardo Massei, Cornelia Wetzker
Licensed CC-BY-4.0
The Data Steward Team of the NFDI4BIOIMAGE consortium presents themselves and the services (including the Helpdesk) that we offer.
Tags: Research Data Management, Bioimage Analysis, Data Stewardship, Nfdi4Bioimage
Content type: Poster
[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/
[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
[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
[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.
Tags: Research Data Management
Content type: Slides
[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
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.
Tags: Research Data Management, Bioimage Analysis, Nfdi4Bioimage
Content type: Slides
[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 2024-10-30
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
[Workshop] FAIR data handling for microscopy: Structured metadata annotation in OMERO#
Vanessa Fuchs, Fiona Aphaia, 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
ilastik: interactive machine learning for (bio)image analysis#
Anna Kreshuk, Dominik Kutra
Licensed CC-BY-4.0
Tags: Artificial Intelligence, Bioimage Analysis
Content type: Slide
quantixed/TheDigitalCell: First complete code set#
Stephen Royle
Published 2019-04-17
Licensed GPL-3.0
First complete code set for The Digital Cell book.
Tags: Bioimage Analysis
Content type: Code
https://zenodo.org/records/2643411
https://doi.org/10.5281/zenodo.2643411