Evidence map›Paper›PMID 42317826›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2026

Leadership, Informatics Expertise, and Resources: Determinants of Institutional Data Sharing in the National Clinical Cohort Collaborative (N3C).

Carly M Rose, William S Bush, Mark F Beno, Scott M Williams, Jonathan L Haines, Dana C Crawford

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Carly M RoseProgram in Biomedical & Health Informatics; Cleveland Institute for Computational Biology; Department of Population and Quantitative Health Sciences; Department of Genetics and Genome Sciences Case Western Reserve University, Cleveland, OH.
William S BushProgram in Biomedical & Health Informatics; Cleveland Institute for Computational Biology; Department of Population and Quantitative Health Sciences; Department of Genetics and Genome Sciences Case Western Reserve University, Cleveland, OH.
Mark F BenoProgram in Biomedical & Health Informatics; Cleveland Institute for Computational Biology; Department of Population and Quantitative Health Sciences; Department of Genetics and Genome Sciences Case Western Reserve University, Cleveland, OH.
Scott M WilliamsProgram in Biomedical & Health Informatics; Cleveland Institute for Computational Biology; Department of Population and Quantitative Health Sciences; Department of Genetics and Genome Sciences Case Western Reserve University, Cleveland, OH.
Jonathan L HainesProgram in Biomedical & Health Informatics; Cleveland Institute for Computational Biology; Department of Population and Quantitative Health Sciences; Department of Genetics and Genome Sciences Case Western Reserve University, Cleveland, OH.
Dana C CrawfordProgram in Biomedical & Health Informatics; Cleveland Institute for Computational Biology; Department of Population and Quantitative Health Sciences; Department of Genetics and Genome Sciences Case Western Reserve University, Cleveland, OH.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The National Clinical Cohort Collaborative (N3C), initially developed in response to the SARS-CoV-2/COVID-19 pandemic, is one of the largest centralized, individual-level clinical data repositories available for research in the US. Some institutions do not share data with N3C. We accessed public data to derive institutional variables representative of leadership, informatics expertise, and resources, potentially addressable factors we hypothesized were associated with N3C clinical data sharing. N3C data sharing institutions were more likely to have active NCATS funding, have affiliated departments, centers, or institutes of biomedical informatics, and active Fellows of the American College of Medical Informatics faculty compared with institutions that did not share data with N3C. While we identified institutional characteristics associated with N3C data sharing, there are likely other characteristics associated with institutional data sharing decisions. Understanding factors that motivate or deter institutional data sharing for research is key to addressing barriers and making more data open.

Identifiers

PMID42317826
PMCPMC13274281

What OpenQuestion holds

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Registered trials

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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.