GerBI Reserach
Check out our past and current reserach!
2026
Journal of Microscopy, 08. September 2026
Image analysis in the life sciences is constrained by fragmented software ecosystems, heterogeneous data formats, and limited reproducibility. These barriers hinder the reuse of image analysis methods and the sustainability of tools. In this article, we describe how the Galaxy platform enables FAIR (Findable, Accessible, Interoperable, and Reusable) image analysis by providing an integrated environment for data access, workflow execution, provenance tracing, and training.
We present Galaxy as a computational workbench that supports diverse image formats and integrates with public, institutional, and private repositories. We describe a reference structure for FAIR image analysis workflows and illustrate how this pattern supports reproducibility, interoperability, and reuse. We also describe community-driven training and sustainability practices that embed FAIR principles directly into executable tutorials and shared workflows.
Together, these foundations position Galaxy as a reproducible, scalable, and community-maintained platform for FAIR image analysis across the life sciences and beyond.
DOI: https://doi.org/10.1111/jmi.70165
PMID: 42709042
, , , , , , , , , , , , , , , , , , , … (2026). Bioimage management and analysis in Galaxy: Tools, workflows, training, and community practices. Journal of Microscopy, 1–15. https://doi.org/10.1111/jmi.70165
Zenodo, 10. June 2026
The German National Research Data Infrastructure (NFDI) brings together research communities across all scientific domains with the shared goal of enabling FAIR and interoperable research data management. While disciplinary diversity remains a defining strength, it also poses challenges for cross-consortial metadata alignment. Addressing these challenges is the mission of the NFDI Taskforce Metadata within the Section (Meta)data, Terminologies and Provenance.
This presentation reports on the outcomes of the 2nd Interdisciplinary NFDI Metadata Workshop, held in June 2025 at the TIB Leibniz Information Centre for Science and Technology, and discusses their relevance for implementation within Text+ and other NFDI consortia. Building on the first workshop in January 2025, the second workshop shifted the focus from identifying suitable generic metadata schemas to their practical application across disciplines. Sixty-one participants from 25 NFDI consortia collaboratively assessed existing standards and developed community-driven recommendations, documented in the workshop report (DOI: 10.5281/zenodo.19709639).
A central result is the definition of an NFDI Core Metadata Profile: a schema-agnostic set of mandatory and recommended elements that enables cross-domain interoperability while preserving disciplinary specificity. The workshop reaffirmed the suitability of three established generic metadata standards – DataCite, DCAT-AP and schema.org – as interoperable discovery layers. Rather than prescribing a single schema, the recommendations encourage consortia to adopt at least one of these standards and to align them with their domain-specific metadata models through documented mappings.
For Text+, these results provide a concrete framework for harmonising metadata practices across heterogeneous text- and language-related data types. The recommendations offer best practices for improving interoperability within the consortium and across NFDI as a whole, without imposing mandatory technical requirements. The presentation outlines how Text+ partners can operationalise the Core Metadata Profile, integrate it with existing standards such as ISO 24622, and contribute to a shared, sustainable metadata landscape for the NFDI.
Nature Methods, 15. June 2026
Finding the right image data can be surprisingly difficult, especially across large, siloed and inconsistently labeled datasets. As biological imaging grows in scale and collaboration, most laboratories routinely record some metadata alongside acquisition, but these annotations are often sparse and fragmented across filenames, spreadsheets or unstructured notes, making datasets difficult to search or interpret in practice. As a result, the field increasingly requires tools that make it easier to find, explore, reuse and share data. We developed BioFile Finder (BFF) to address this gap: a free, easy-to-use, open-source application that transforms a simple spreadsheet of image metadata describing what is interesting or important about each image file into an interactive, searchable, and browsable dataset (https://bff.allencell.org).
DOI: https://doi.org/10.1038/s41592-026-03130-w
PMID: 42298069
Meharry, S.L., Borensztejn, A., Gaudreault, N. et al. Search, organize, aggregate and share image data with BioFile Finder (BFF). Nat Methods 23, 1256–1258 (2026). https://doi.org/10.1038/s41592-026-03130-w
Figure from this Publication:
Zenodo, 22. June 2026
This document specifies a method, known as RO-Crate (Research Object Crate), of aggregating and describing data for distribution, re-use, publishing, preservation and archiving. RO-Crates aggregate data into a Dataset, and may describe any resource including files, URI-addressable resources, or use other addressing schemes to locate digital or physical data. Describing resources includes technical metadata such as file sizes and types as well as contextual information including how datasets and files were created, and where, how they were collated and collected, who was involved in the process, what equipment and software was used, who funded the work, how to cite it, and crucially, how it may be reused, and by whom.
The core of RO-Crate is a machine-readable linked-data document in JSON-LD format known as an RO-Crate Metadata Document. RO-Crate metadata documents can to a large extent be created and processed just like any other JSON: knowledge of JSON-LD is not needed, unless extending RO-Crate with additional concepts or combining RO-Crate with other Linked Data technologies.
This document, the RO-Crate specification, introduces the general RO-Crate concepts through a running example, adding normative pages that define in more detail these and other concepts using separate examples and recommendations.
Single-cell omics technologies have transformed our understanding of cellular diversity by enabling high-resolution profiling of individual cells. However, the unprecedented scale and heterogeneity of these datasets demand robust frameworks for data integration and annotation. The Cell Ontology (CL) has emerged as a pivotal resource for achieving FAIR (Findable, Accessible, Interoperable, and Reusable) data principles by providing standardized, species-agnostic terms for canonical cell types, forming a core component of a wide range of platforms and tools. In this paper, we describe the wide variety of uses of CL in these platforms and tools and detail ongoing work to improve and extend CL content including the addition of transcriptomic types, working closely with major atlasing efforts including the Human Cell Atlas and the Brain Initiative Cell Atlas Network to support their needs. We cover the challenges and future plans for harmonising classical and transcriptomic cell type definitions, integrating markers and using Large Language Models (LLMs) to improve content and efficiency of CL workflows.
DOI: https://doi.org/10.1038/s41597-026-07173-8
PMID: 40735089
Tan, S.Z.K., Puig-Barbe, A., Goutte-Gattat, D. et al. The Cell Ontology in the age of single-cell omics. Sci Data 13, 946 (2026). https://doi.org/10.1038/s41597-026-07173-8
Figure from this publication:
23. March 2026
Bioimaging research increasingly relies on complex, high-volume microscopy data that is still rarely shared in a FAIR and interoperable way. While formats such as OME-Zarr address modern, cloud-native data storage and access, the bioimaging community continues to lack Linked Data-ready metadata standards that capture experimental context and relationships between research artefacts. Research Object Crates (RO-Crates) provide a promising, JSON-LD–based mechanism to package data together with rich, standardized metadata. In thiswork, we explore how RO-Crates can complement existing Open Microscopy Environment standards, with a focus on OME-Zarr. We present recent practical experiences, including the use of RO-Crates in the OME 2024 NGFF Challenge, and discuss ongoing efforts toward formal RO-Crate profiles and metadata pipelines for major bioimaging repositories. Together, these efforts highlight RO-Crate as a valuable addition to the modern bioimaging metadata toolkit.
Link: https://app.oxfordabstracts.com/content/events/76267/submitters/1459811/submissions/fe-164832b5-3dbe-4b14-9738-46a18ab8828a/questions/132492/file/118e90b7-0673-4320-9708-f4b6ccf13cfa.pdf
Journal of Microscopy, 24. March 2026
Effective management of imaging data is critical to ensuring reproducibility, accessibility, and long-term value of image-based biological research. However, good data management remains a major challenge across imaging facilities, with lack of standardised practices and limited training opportunities. Additionally, there is a need to upskill Research Technical Professionals (RTPs) in areas such as image data stewardship, as well as opening new career pathways. Here, we report on an approach to identify and design a training curriculum for imaging data stewardship in the UK bioimaging community. An initial survey captured current practices, challenges, and training requirements. These findings were presented at a community event, where structured feedback activities further identified pain points across the imaging data life cycle, and prioritised training needs. These consultations resulted in a three-module training framework covering data management and metadata, data storage and sharing, and public repository submission. The iterative approach provided technical guidance for curriculum design and community input on delivery models, target audiences, and incentives for engagement. This work highlights the need for training resources that address critical skill gaps, increase RTP visibility and recognition, and improve the quality of image data management.
DOI: https://doi.org/10.1111/jmi.70078
PMID: 41873830
, , , , , , , , , , , , , , , , , , , … (2026). Building capacity in imaging data management. Journal of Microscopy, 302, 334–345. https://doi.org/10.1111/jmi.70078
Journal of Cell Biology, 25. February 2026
Incomplete reporting of microscopy methods undermines transparency, reproducibility, and data reuse. Despite recent initiatives, comprehensive, broadly endorsed, and accessible reporting guidelines are still lacking. Here, we present a bare minimal microscopy reporting requirements checklist that integrates human- and machine-readable input to provide clear, actionable guidance for researchers, reviewers, and publishers and to advance community standards in microscopy.
DOI: https://doi.org/10.1083/jcb.202601032
PMID: 41739099
2025
CEUR Workshop, Barcelona, 24.-27.02.2025
Paper 51
OMExcavator: a tool for exporting and connecting domain-specific metadata in a wider knowledge graph
Paper 53
FAIR Linked Data Framework for Glioblastoma Research.
Paper 58
Leveraging Zarr to handle RDF data
Paper 66
Imaging and curation of human TB lung tissue with linked data
Link: https://ceur-ws.org/Vol-4196/
BioArXiv, 04. September 2025
Biological imaging, combined with molecular insights into genes and proteins, holds immense promise for deepening our understanding of complex cellular systems and accelerating the development of predictive, personalized therapies for human health. To fully realize this potential at scale-and harness the power of AI/ML to extract novel biological insights and therapeutic interventions-it is necessary to transition from siloed datasets to globally shared, rigorously annotated, and computationally ready image data. This demands systematic harmonization of multidimensional bioimaging data, where interoperable formats, and standardized context-rich annotation, quality controls, and analytical pipelines transform scattered observations into a coherent knowledge base ripe for computational mining. Only this machine-actionable aggregation can provide the substrate for AI/ML to extract mechanistic insights into fundamental biological mechanisms, novel diagnostic biomarkers and intervention targets. Enabling seamless image data sharing in the life sciences requires addressing two key areas. The first is outlined in an accompanying publication, Enabling Global Image Data Sharing in the Life Sciences, which focuses on the publicly available repositories needed to share digital array data 1. This White Paper details a comprehensive set of requirements for integrated image data and metadata management – from acquisition through dissemination – ensuring the contextual information necessary for assessing quality, interpreting scientific validity, and enabling meaningful reuse remains intrinsically linked to the data throughout its lifecycle. Critically, it recognizes that generating harmonized, well-annotated publicly available corpora of FAIR bioimage data requires these datasets to be “FAIR-from-the-start” – an objective that can only be achieved by enabling experimental scientists to manage, organize, and analyze their data according to community standards from the very first experiment. Building on recent progress made by the bioimaging field towards establishing shared practices for bioimaging Quality Control (QC) and metadata capture, we present actionable recommendations to advance these efforts through embedding researcher-friendly integrated software infrastructure directly into pre-publication workflows, thus transforming disorganized data capture into structured, shareable resources ready for aggregation and reuse. Our ultimate goal is to expand the use of streamlined tools and practices thus transforming how researchers capture, annotate, analyze and eventually publish bioimaging data thus laying the foundation for a new era of data-driven discovery.
DOI: https://doi.org/10.48550/arXiv.2401.13022
PMID: 38351940
Nature Methods, 15. September 2025
Artificial intelligence (AI) methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new algorithms, but access to such data is often hindered by the lack of standards for sharing datasets. We discuss the barriers to sharing annotated image datasets and suggest specific guidelines to improve the reuse of bioimages and annotations for AI applications. These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets. We are sure that the Metadata, Incentives, Formats and Accessibility (MIFA) recommendations will accelerate the development of AI tools for bioimage analysis by facilitating access to high-quality training and benchmarking data.
DOI: https://doi.org/10.1038/s41592-025-02835-8
PMID: 40954297
Zulueta-Coarasa, T., Jug, F., Mathur, A. et al. MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis. Nat Methods 22, 2245–2252 (2025). https://doi.org/10.1038/s41592-025-02835-8
EMBO Reports, 03. December 2025
Since Wilhelm Röntgen discovered X-rays in 1895, imaging technologies have transformed the life sciences. These technologies—from early radiography and light microscopy to bioluminescent and fluorescent imaging to high-resolution microscopy—have enabled scientists to visualize cells, tissues and molecular processes in unprecedented detail. By making the microworld visible, imaging has accelerated breakthroughs across nearly every domain of science, from anatomy to physiology to genetics to disease research. Today, high-precision imaging modalities like MRI, CT, and advanced microscopy continue to drive innovation across biomedical research, pharmaceutical development, and biotechnology, generating vast and valuable datasets. …
DOI: https://doi.org/10.1038/s44319-025-00652-w
Winter, S.L., Moore, J., Tavares, A.A.S. et al. A global effort toward standards for data sharing in biomedical imaging. EMBO Rep 27, 10–14 (2026). https://doi.org/10.1038/s44319-025-00652-w
Arxiv, 17. December 2025
Modern bioimage analysis approaches are data hungry, making it necessary for researchers to scavenge data beyond those collected within their (bio)imaging facilities. In addition to scale, bioimaging datasets must be accompanied with suitable, high-quality annotations and metadata. Although established data repositories such as the Image Data Resource (IDR) and BioImage Archive offer rich metadata, their contents typically cannot be directly consumed by image analysis tools without substantial data wrangling. Such a tedious assembly and conversion of (meta)data can account for a dedicated amount of time investment for researchers, hindering the development of more powerful analysis tools. Here, we introduce BioimageAIpub, a workflow that streamlines bioimaging data conversion, enabling a seamless upload to HuggingFace, a widely used platform for sharing machine learning datasets and models.
2024
CEUR Workshop, 20. -26. February 2024
Paper 40
There and back again: RDF as a bridge to domain-platforms like OMERO
Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.
DOI: https://doi.org/10.1038/s41592-024-02212-x
PMID: 38509327
Marconato, L., Palla, G., Yamauchi, K.A. et al. SpatialData: an open and universal data framework for spatial omics. Nat Methods 22, 58–62 (2025). https://doi.org/10.1038/s41592-024-02212-x
Nature Methods, 19. August 2024
Multimodal tissue atlasing datasets pose two key challenges for online dissemination and equitable access. First, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics data objects are often saved in non-unified sequencing and imaging file formats that perform poorly with web technologies. Second, existing software platforms do not readily support simultaneous browsing of multiple integrated data modalities.
DOI: https://doi.org/10.1038/s41592-024-02371-x
PMID: 39160302
Li, T., Horsfall, D., Basurto-Lozada, D. et al. WebAtlas pipeline for integrated single-cell and spatial transcriptomic data. Nat Methods 22, 3–5 (2025). https://doi.org/10.1038/s41592-024-02371-x
Nature, 16. October 2024
Human prenatal skin is populated by innate immune cells, including macrophages, but whether they act solely in immunity or have additional functions in morphogenesis is unclear. Here we assembled a comprehensive multi-omics reference atlas of prenatal human skin (7–17 post-conception weeks), combining single-cell and spatial transcriptomics data, to characterize the microanatomical tissue niches of the skin. This atlas revealed that crosstalk between non-immune and immune cells underpins the formation of hair follicles, is implicated in scarless wound healing and is crucial for skin angiogenesis. We systematically compared a hair-bearing skin organoid (SkO) model derived from human embryonic stem cells and induced pluripotent stem cells to prenatal and adult skin1. The SkO model closely recapitulated in vivo skin epidermal and dermal cell types during hair follicle development and expression of genes implicated in the pathogenesis of genetic hair and skin disorders. However, the SkO model lacked immune cells and had markedly reduced endothelial cell heterogeneity and quantity. Our in vivo prenatal skin cell atlas indicated that macrophages and macrophage-derived growth factors have a role in driving endothelial development. Indeed, vascular network remodelling was enhanced following transfer of autologous macrophages derived from induced pluripotent stem cells into SkO cultures. Innate immune cells are therefore key players in skin morphogenesis beyond their conventional role in immunity, a function they achieve through crosstalk with non-immune cells.
DOI: https://doi.org/10.1038/s41586-024-08002-x
PMID: 39415002
Gopee, N.H., Winheim, E., Olabi, B. et al. A prenatal skin atlas reveals immune regulation of human skin morphogenesis. Nature 635, 679–689 (2024). https://doi.org/10.1038/s41586-024-08002-x
Cell Science, 23. October 2024
The increasing technical complexity of all aspects involving bioimages, ranging from their acquisition to their analysis, has led to a diversification in the expertise of scientists engaged at the different stages of the discovery process. Although this diversity of profiles comes with the major challenge of establishing fruitful interdisciplinary collaboration, such collaboration also offers a superb opportunity for scientific discovery. In this Perspective, we review the different actors within the bioimaging research universe and identify the primary obstacles that hinder their interactions. We advocate that data sharing, which lies at the heart of innovation, is finally within reach after decades of being viewed as next to impossible in bioimaging. Building on recent community efforts, we propose actions to consolidate the development of a truly interdisciplinary bioimaging culture based on open data exchange and highlight the promising outlook of bioimaging as an example of multidisciplinary scientific endeavour.
DOI: https://doi.org/10.1242/jcs.262139
PMID: 39440474
BioHackrXiv, 19.12.2024
2023
Nature Methods, 10. February 2023
Modern microscopy produces massive image datasets that enable detailed multi-scale analysis and can combine several modalities. Visualizing, exploring and sharing such data are challenges both during the execution of a research project and after publication to enable open access. To this end we have developed MoBIE, a Fiji1 plugin for multi-modal big image data sharing and exploration. It supports visualization of multi-scale data of heterogeneous dimensionality (that is, combined 2D, 3D or 4D data) and several-terabyte image data, as well as the exploration of image segmentations, corresponding measurements and annotations. MoBIE uses next-generation image file formats, such as OME-Zarr2, that enable access to multi-scale data on local or cloud storage, permitting the transparent sharing and publication of data without the need to run a web service. In addition, MoBIE allows users to easily configure and share fully reproducible ‘views’ of their data. MoBIE has enabled integration of multiple modalities and open access for data from different domains of the life sciences. This includes data from studies in developmental biology3 (Fig. 1a), correlative microscopy, high-throughput screening microscopy4, plant biology and spatial transcriptomics5 (all Fig. 1c). Further applications can be found in Supplementary Note 8 and Supplementary Figs. 1–4. Video tutorials for MoBIE are available at https://www.youtube.com/@MoBIE-Viewer and documentation at https://mobie.github.io/.
DOI: https://doi.org/10.1038/s41592-023-01776-4
PMID: 36765247
Pape, C., Meechan, K., Moreva, E. et al. MoBIE: a Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data. Nat Methods 20, 475–476 (2023). https://doi.org/10.1038/s41592-023-01776-4
CEUR Workshop, 13.-16. February 2023
Paper 27
NFDI4BIOIMAGE: Perspective for a national bioimaging standard
Learning Health Systems, 18. April 2023
Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.
DOI: https://doi.org/10.1002/lrh2.10365
PMID: 38249839
, , , et al. Toward a common standard for data and specimen provenance in life sciences. Learn Health Sys. 2024; 8(1):e10365. doi:10.1002/lrh2.10365
ARPHA Preprints, 05.12.2023
File formats incompatibility has become a major obstacle in biological imaging, complicating downstream processes such as image processing and analysis. One way to address this challenge is to convert the acquired image data into standard image file formats. Here we introduce BatchConvert, a command line tool for parallelised conversion of image collections into OME-TIFF or OME-Zarr using the workflow management system Nextflow. BatchConvert offers functionalities such as remote input-output support, optional execution on Slurm clusters and pattern-based filtering of input files. Conversion can be coupled to image concatenation, allowing selected images to be merged along specified dimensions. Support for remote locations includes an option to submit the output data to S3-compatible object stores or public archives such as BioImage Archive. Overall, BatchConvert is a flexible tool for researchers who are routinely managing and analysing large multidimensional image data that is either locally or remotely stored.
Springer Nature, 10. July 2023
A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself—OME-Zarr—along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain—the file format that underlies so many personal, institutional, and global data management and analysis tasks.
DOI: https://doi.org/10.1007/s00418-023-02209-1
PMID: 36865282
Moore, J., Basurto-Lozada, D., Besson, S. et al. OME-Zarr: a cloud-optimized bioimaging file format with international community support. Histochem Cell Biol 160, 223–251 (2023). https://doi.org/10.1007/s00418-023-02209-1
Open Publishing, 07. September 2023
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.
DOI: https://doi.org/10.52825/cordi.v1i.285
Moore, J., & Kunis, S. (2023). Zarr: A Cloud-Optimized Storage for Interactive Access of Large Arrays. Proceedings of the Conference on Research Data Infrastructure , 1. https://doi.org/10.52825/cordi.v1i.285
bioRxiv, 12. October 2023
Human prenatal skin is populated by innate immune cells including macrophages, and whether they act solely in immunity or have additional functions in morphogenesis is unclear. We assembled the first comprehensive multi-omic reference atlas of prenatal human skin (7-16 post-conception weeks), combining single cell and spatial transcriptomic data, to characterise the skin’s microenvironmental cellular organisation. This revealed that crosstalk between non-immune and immune cells underpins formation of hair follicles, has implications for scarless wound healing, and is critical for skin angiogenesis. We benchmarked a skin organoid model, derived from human embryonic stem (ES) and induced pluripotent stem (iPS) cells, against prenatal and adult skin, demonstrating close recapitulation of the epidermal and dermal skin components during hair follicle development. Notably, the skin organoid lacked immune cells and had markedly diminished endothelial cell heterogeneity and quantity. From our in vivo skin cell atlas data, we found that macrophages and macrophage-derived growth factors play a key role in driving endothelial development prenatally. Indeed, vascular network formation was enhanced following transfer of autologous iPS-derived macrophages into both endothelial cell angiogenesis assays and skin organoid cultures. In summary, innate immune cells moonlight as key players in skin morphogenesis beyond their conventional immune roles, a function they achieve via extensive crosstalk with non-immune cells. Finally, we leveraged our human prenatal skin cell atlas to further our understanding of the pathogenesis of genetic hair and skin disorders.
DOI:
2022
F1000 Research, 20. September 2022
Background: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany’s National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs.
Methods: An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels.
Results: The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication.
Conclusion: The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.
DOI: https://doi.org/10.12688/f1000research.121714.2
Schmidt C, Hanne J, Moore J et al. Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey [version 2; peer review: 2 approved]. F1000Research 2022, 11:638 (https://doi.org/10.12688/f1000research.121714.2)
2021
Nature Methods, 18. December 2021
Bioimaging data have significant potential for reuse, but unlocking this potential requires systematic archiving of data and metadata in public databases. We propose draft metadata guidelines to begin addressing the needs of diverse communities within light and electron microscopy. We hope this publication and the proposed Recommended Metadata for Biological Images (REMBI) will stimulate discussions about their implementation and future extension.
DOI: https://doi.org/10.1038/s41592-021-01166-8
PMID: 34021280
Sarkans U, Chiu W, Collinson L, Darrow MC, Ellenberg J, Grunwald D, Hériché JK, Iudin A, Martins GG, Meehan T, Narayan K, Patwardhan A, Russell MRG, Saibil HR, Strambio-De-Castillia C, Swedlow JR, Tischer C, Uhlmann V, Verkade P, Barlow M, Bayraktar O, Birney E, Catavitello C, Cawthorne C, Wagner-Conrad S, Duke E, Paul-Gilloteaux P, Gustin E, Harkiolaki M, Kankaanpää P, Lemberger T, McEntyre J, Moore J, Nicholls AW, Onami S, Parkinson H, Parsons M, Romanchikova M, Sofroniew N, Swoger J, Utz N, Voortman LM, Wong F, Zhang P, Kleywegt GJ, Brazma A. REMBI: Recommended Metadata for Biological Images-enabling reuse of microscopy data in biology. Nat Methods. 2021 Dec;18(12):1418-1422. doi: 10.1038/s41592-021-01166-8. PMID: 34021280; PMCID: PMC8606015.
2020
Wiley Cytometry, 24. June 2020
Operating shared resource laboratories (SRLs) in times of pandemic is a challenge for research institutions. In a multiuser, high-turnover working space, the transmission of infectious agents is difficult to control. To address this challenge, imaging core facility managers being members of German BioImaging discussed how shared microscopes could be operated with minimal risk of spreading SARS-CoV-2 between users and staff. Here, we describe the resulting guidelines and explain their rationale, with a focus on separating users in space and time, protective face masks, and keeping surfaces virus-free. These recommendations may prove useful for other types of SRLs. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.
DOI: https://doi.org/10.1002/cyto.a.24178
Dietzel, S., Ferrando-May, E., Fried, H., Kukat, C., Naumann, A., Nitschke, R., Pasierbek, P., Peychl, J., Rasse, T.M., Schroth-Diez, B., Stöckl, M.T., Terjung, S., Thuenauer, R., Tulok, S., Weidtkamp-Peters, S. and German BioImaging-Society for Microscopy and Image Analysis (2020), A Joint Action in Times of Pandemic: The German BioImaging Recommendations for Operating Imaging Core Facilities During the SARS-Cov-2 Emergency. Cytometry, 97: 882-886. https://doi.org/10.1002/cyto.a.24178
2016
Wiley Analytical Science, 04. April 2016
Core Facilities (CF) for advanced light microscopy (ALM) have become indispensable support units for research in the life sciences. Their organizational structure and technical characteristics are quite diverse, although the tasks they pursue and the services they offer are similar. Therefore, throughout Europe, scientists from ALM-CFs are forming networks to promote interactions and discuss best practice models. Here, we present recommendations for ALM-CF operations elaborated by the workgroups of the German network of ALM-CFs, German Bio-Imaging (GerBI). We address technical aspects of CF planning and instrument maintainance, give advice on the organization and management of an ALM-CF, propose a scheme for the training of CF users, and provide an overview of current resources for image processing and analysis. Further, we elaborate on the new challenges and opportunities for professional development and careers created by CFs. While some information specifically refers to the German academic system, most of the content of this article is of general interest for CFs in the life sciences.
DOI: https://doi.org/10.1002/jemt.22648
Ferrando-May, E., Hartmann, H., Reymann, J., Ansari, N., Utz, N., Fried, H.-U., Kukat, C., Peychl, J., Liebig, C., Terjung, S., Laketa, V., Sporbert, A., Weidtkamp-Peters, S., Schauss, A., Zuschratter, W., Avilov, S. and and the German BioImaging network (2016), Advanced light microscopy core facilities: Balancing service, science and career. Microsc. Res. Tech., 79: 463-479. https://doi.org/10.1002/jemt.22648
