ArticleSchizophrenia (Heidelberg, Germany)2025
Enabling FAIR data stewardship in complex international multi-site studies: Data Operations for the Accelerating Medicines Partnership® Schizophrenia Program.
Article in Schizophrenia (Heidelberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- The electroencephalography protocol for the Accelerating Medicines Partnership® Schizophrenia Program: Reliability and stability of measures.Schizophrenia (Heidelberg, Germany) · 2025Article
- Pathways to prevention: the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ) Program.Schizophrenia (Heidelberg, Germany) · 2025Article
- The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia Program.Schizophrenia (Heidelberg, Germany) · 2025Article
- Baseline Clinical Characterization of Participants in the Accelerating Medicines Partnership Schizophrenia Program.Schizophrenia bulletin open · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
68 authors.
Funding
Abstract
Modern research management, particularly for publicly funded studies, assumes a data governance model in which grantees are considered stewards rather than owners of important data sets. Thus, there is an expectation that collected data are shared as widely as possible with the general research community. This presents problems in complex studies that involve sensitive health information. The latter requires balancing participant privacy with the needs of the research community. Here, we report on the data operation ecosystem crafted for the Accelerating Medicines Partnership® Schizophrenia project, an international observational study of young individuals at clinical high risk for developing a psychotic disorder. We review data capture systems, data dictionaries, organization principles, data flow, security, quality control protocols, data visualization, monitoring, and dissemination through the NIMH Data Archive platform. We focus on the interconnectedness of these steps, where our goal is to design a seamless data flow and an alignment with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles while balancing local regulatory and ethical considerations. This process-oriented approach leverages automated pipelines for data flow to enhance data quality, speed, and collaboration, underscoring the project's contribution to advancing research practices involving multisite studies of sensitive mental health conditions. An important feature is the data's close-to-real-time quality assessment (QA) and quality control (QC). The focus on close-to-real-time QA/QC makes it possible for a subject to redo a testing session, as well as facilitate course corrections to prevent repeating errors in future data acquisition. Watch Dr. Sylvain Bouix discuss his work and this article: https://vimeo.com/1025555648 .
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.