ArticleArXiv2025
Harmonizing the Generation and Pre-publication Stewardship of FAIR bioimage data.
Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
46 authors.
Funding
Abstract
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,
Identifiers
38351940PMC10862930What 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.