ReviewNature methods2025
Enabling global image data sharing in the life sciences.
Review in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Search, organize, aggregate and share image data with BioFile Finder (BFF).Nature methods · 2026Article
- The HuBMAP Framework for Advancing Data FAIRness.bioRxiv : the preprint server for biology · 2026Article
- Lung cancer screening with AI can discover cures for many early diseases. A public utility can make sure it happens.Frontiers in oncology · 2026Article
- Computational whole-body-exposome models for global precision brain health.Nature communications · 2025Article
- Article
- Reporting light microscopy data in our pages.Nature structural & molecular biology · 2025Article
- SSBD: an ecosystem for enhanced sharing and reuse of bioimaging data.Nucleic acids research · 2025Article
- Making the most of bioimaging data through interdisciplinary interactions.Journal of cell science · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
Abstract
Despite the importance of imaging in biological and medical research, a large body of informative and precious image data never sees the light of day. To ensure scientific rigor as well as the reuse of data for scientific discovery, image data need to be made FAIR (findable, accessible, interoperable and reusable). Image data experts are working together globally to agree on common data formats, metadata, ontologies and supporting tools toward image data FAIRification. With this Perspective, we call on public funders to join these efforts to support their national scientists. What researchers most urgently need are openly accessible resources for image data storage that are operated under long-term commitments by their funders. Although existing resources in Australia, Japan and Europe are already collaborating to enable global image data sharing, these efforts will fall short unless more countries invest in operating and federating their own open data resources. This will allow us to harvest the enormous potential of existing image data, preventing substantial loss of unrealized value from past investments in imaging acquisition infrastructure.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.