ArticleAI magazine2026
Knowledge Engineering for Open Science: Building and Deploying Knowledge Bases for Metadata Standards.
Article in AI magazine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- The HuBMAP Framework for Advancing Data FAIRness.bioRxiv : the preprint server for biology · 2026Article
- All Models are Wrong, Some are Annotated: Automating Metadata in Biomedical Repositories.bioRxiv : the preprint server for biology · 2026Article
- The FAIRSCAPE AI-readiness Framework for Biomedical Research.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
For more than a decade, scientists have been striving to make their datasets available in open repositories, with the goal that they be findable, accessible, interoperable, and reusable (FAIR). Although it is hard for most investigators to remember all the "guiding principles" associated with FAIR data, there is one overarching requirement: The data need to be annotated with "rich," discipline-specific, standardized metadata that can enable third parties to understand who performed the experiment, who or what the subjects were, what the experimental conditions were, and what the results appear to show. Most areas of science lack standards for such metadata and, when such standards exist, it can be difficult for investigators or data curators to apply them. The Center for Expanded Data Annotation and Retrieval (CEDAR) builds technology that enables scientists to encode descriptive metadata standards 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.