🔴FAIR & Open Science Resource

FAIR & Open Science Resource

FAIR & Open Science Resource (Life Sciences Focus)

Category	Purpose	Key Open Resources	Strengths	Limitations / Caveats
Data & Metadata Standards	Define interoperable formats and metadata schemas for research data	OME-TIFF, OME-Zarr, Bio-Formats, MIABIS (biobanking), MIAME (microarray), REMBI (Recommended Metadata for Biological Images), RO-Crate	Ensure data is FAIR (Findable, Accessible, Interoperable, Reusable); community-agreed best practices	Adoption varies; may require technical expertise; incomplete metadata often limits reusability
Repositories – Bioimaging	Public deposition of raw image datasets	BioImage Archive (EMBL-EBI), IDR (Image Data Resource), EMPIAR (EM), Cell Image Library	Long-term archiving, DOI assignment, policy compliance; accessible by global community	Requires metadata curation; submission can be complex
Repositories – Other Life Sciences	Public deposition of datasets beyond imaging	ArrayExpress (transcriptomics), GEO (Gene Expression Omnibus), PRIDE (proteomics), ENA (nucleotides), dbGaP (controlled genomic data)	Trusted by funders; enables cross-disciplinary data reuse	Different submission rules; genomic data often restricted
Data Management & Infrastructure	Institutional / collaborative systems for storing, managing, and serving research data	OMERO (images), Dataverse, CKAN, eLabFTW (ELN), ERDA (UCPH), Zenodo (general-purpose OA repository)	Control access, integrate metadata, align with FAIR principles	Requires IT support; learning curve for researchers
Open Access Publishing	Dissemination of research outputs under OA models	Plan S, DOAJ (Directory of Open Access Journals), BioRxiv, Europe PMC, Open Research Europe (EC platform)	Increases visibility; aligns with funder mandates; rapid dissemination (preprints)	APC (Article Processing Charges) can be high; quality varies among journals
Identifiers & Registries	Persistent IDs for FAIR data citation & linking	DOI (DataCite, CrossRef), ORCID (researcher IDs), ROR (Research Organization Registry), RRID (antibodies, cell lines, resources)	Ensure unambiguous references; enable machine-actionable links	Requires adoption at data entry; not always enforced
FAIR & Policy Frameworks	Principles, roadmaps, and governance for FAIR data	FAIR Principles, EOSC (European Open Science Cloud), RDA (Research Data Alliance), CODATA, GO FAIR	Community-wide frameworks; guide funders, infrastructures, researchers	Implementation uneven; FAIR ≠ Open (controlled access allowed)
Training & Community Networks	Build researcher capacity, provide best practices, sustain culture change	NEUBIAS (bioimage analysts), ELIXIR (bioinformatics), FocalPlane, Global BioImaging, Carpentries (data skills), FAIRsharing.org	Widely used training hubs; community-driven; accessible learning resources	Limited regional coverage; workshops not always continuous
Open Science Monitoring & Impact	Track uptake, compliance, and engagement with FAIR/Open Science	OpenAIRE (European OA infrastructure), Altmetric, OpenAlex, Dimensions (open citation graphs)	Evidence of impact, adoption, and policy compliance	Metrics can be biased (field-dependent); qualitative impact harder to capture

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Key Insights

• Bioimaging-specific FAIR resources sit at the intersection of OME standards (data formats), OMERO (infrastructure), and BioImage Archive / IDR / EMPIAR (repositories).

• Cross-domain resources like Zenodo, Dataverse, and CKAN ensure interoperability with broader FAIR/Open Science practices.

• Identifiers (DOI, ORCID, ROR) are crucial to link datasets ↔ publications ↔ researchers ↔ institutions, enabling credit and reuse.

• Training & community networks (NEUBIAS, Global BioImaging, Carpentries) are just as necessary as the technical tools for sustaining FAIR culture.

• FAIR ≠ Open: controlled-access resources (e.g., dbGaP for genomics) remain FAIR if metadata is discoverable.