Enabling FAIR data stewardship in complex international multi-site studies: Data Operations for the Accelerating Medicines Partnership® Schizophrenia Program

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 .

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Publication Details

Journal
Schizophrenia
Published
2025-04-03
DOI
https://doi.org/10.1038/s41537-025-00560-x
Citations
7
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
29.18

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article

Enabling FAIR data stewardship in complex international multi-site studies: Data Operations for the Accelerating Medicines Partnership® Schizophrenia Program

Michael J. Coleman, Phillip Wolff, Sylvain Bouix, Alan Anticevic et al.
7 citations
Schizophrenia
Scientific Computing and Data Management
29.18
article

Enabling FAIR data stewardship in complex international multi-site studies: Data Operations for the Accelerating Medicines Partnership® Schizophrenia Program

Michael J. Coleman, Phillip Wolff, Sylvain Bouix, Alan Anticevic, John M. Kane, Diana O. Perkins, Nora Penzel, Jai L. Shah, Carrie E. Bearden, Alessia McGowan, Daniel H. Mathalon, Anastasia Haidar, John Blake Torous, Justine Chen, Dominic Dwyer, Sinéad Kelly, René S. Kahn, Tashrif Billah, Habiballah Rahimi-Eichi, Priya Matneja, Michael P. Harms, Scott Richard Clark, Ofer Pasternak, Yoonho Chung, Kristen Laulette, Kosha Ruparel, Jessica Spark, Barnaby Nelson, Jean Addington, Cheryl M. Corcoran, Einat Liebenthal, Susan Ray, William S. Stone, Patrick D. McGorry, Ruben C. Gur, Kathryn E. Lewandowski, Paolo Fusar‐Poli, Michaela Ennis, Marek Kubicki, Beau‐Luke Colton, Guillermo Cecchi, Zailyn Tamayo, Monica E. Calkins, Kate Buccilli, Kelly Allott, Sophie Tod, Hok Pan Yuen, Scott W. Woods, Justin T. Baker, Patricia Marcy, Omar John, Alison Ruth Yung, Eirini Zoupou, Martha E. Shenton, Holly Carrington, Daniel H. Wolf, Grace R. Jacobs, Spero Nicholas, Vijay Anand Mittal, Inge Winter-van Rossum, Tina Kapur, Owen Borders, Simone Veale, Kang Ik K. Cho, Ángela Núñez, Larry D. Hendricks, Dheshan Mohandass
article en
7 citations

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 .

SchizophreniaVol. 11(1)
Northwestern University (US), University of North Carolina at Chapel Hill (US), Brigham and Women's Hospital (US), Northwell Health (US), Beth Israel Deaconess Medical Center (US), Feinstein Institute for Medical Research (US), Harvard University (US), Deakin University (AU), Emory University (US), University of California, Los Angeles (US), The University of Melbourne (AU), University of Calgary (CA), Douglas College (CA), University of California, San Francisco (US), King's College London (GB), Washington University in St. Louis (US), University of Pavia (IT), Manchester Academic Health Science Centre (GB), McLean Hospital (US), University of Manchester (GB), Yale University (US), Massachusetts General Hospital (US), Basil Hetzel Institute (AU), Donald & Barbara Zucker School of Medicine at Hofstra/Northwell (US), Orygen (AU), IBM Research - Thomas J. Watson Research Center (US), King's College School (GB), San Francisco VA Health Care System (US), Connecticut Mental Health Center (US), Hotchkiss Brain Institute (CA), McGill University (CA), The University of Adelaide (AU), University of Pennsylvania (US), École de Technologie Supérieure (CA), Icahn School of Medicine at Mount Sinai (US)
Canada Research Chairs, National Institutes of Health, Natural Sciences and Engineering Research Council of Canada, National Institute of Mental Health
Partnerships for the goals
Openalex Percentile: Top 1%
Scientific Computing and Data Management
29.18
DOIOpen Access PDFOpenAlex
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