@article{1655a51be2584614b4ecb04945982a96,
title = "Advancing Science Through Collaborative Data Sharing and Synthesis",
abstract = "The demand for researchers to share their data has increased dramatically in recent years. There is a need to replicate and confirm scientific findings to bolster confidence in many research areas. Data sharing also serves the critical function of allowing synthesis of findings across trials. As innovative statistical methods have helped resolve barriers to synthesis analyses, data sharing and synthesis can help answer research questions that cannot be answered by individual trials alone. However, the sharing of data among researchers remains challenging and infrequent. This article aims to (a) increase support for data sharing and synthesis collaborations among researchers to advance scientific knowledge and (b) provide a model for establishing these collaborations using the example of the ongoing National Institute of Mental Health's Collaborative Data Synthesis on Adolescent Depression Trials. This study brings together datasets from existing prevention and treatment trials in adolescent depression, as well as researchers and stakeholders, to answer questions about {"}for whom interventions work{"} and {"}by what pathways interventions have their effects.{"} This is critical to improving interventions, including increasing knowledge about intervention efficacy among minority populations, or what we call {"}scientific equity.{"} The collaborative model described is relevant to fields with research questions that can only be addressed by synthesizing individual-level data.",
keywords = "collaborative research, data sharing, data synthesis, integrative data analysis, scientific equity",
author = "Tatiana Perrino and George Howe and Anne Sperling and William Beardslee and Irwin Sandler and David Shern and Hilda Pantin and Sheila Kaupert and Nicole Cano and Gracelyn Cruden and Frank Bandiera and Brown, {C. Hendricks}",
note = "Funding Information: In a time of expanding data processing capabilities, increased calls for public access to research findings, and increased scrutiny into the outcomes of research investments, scientists, treatment professionals, and advocates have begun to rally around the cause of rapid, widespread data sharing. The critical importance of data sharing has been addressed across disciplines (e.g., Hernan & Wilcox, 2009 ; Kell, 2008 ; Piwowar, Becich, Bilofsky, Crowley, & caBIG Data Sharing and Intellectual Capital Workspace, 2008 ) and the rise of data processing solutions and methodological advances have transformed the potential for research collaborations and synthesis work across different substantive fields. The most direct way to ensure collaboration in data sharing and synthesis is to integrate it into a study as a central research objective during the inception of the study with early and explicit commitment from investigators to collaborate and share data. This approach permits proper planning that can avoid later challenges, addressing issues of participant confidentiality, informed consent, selection of common assessment instruments, and comprehensive data documentation. However, rich opportunities also exist in sharing datasets that have already been collected and combining the expertise of researchers and experts in a particular substantive field for the purposes of synthesis studies. Collaborative data sharing and synthesis that utilizes a community-based participatory research approach provides a model for establishing and maintaining effective working relationships among stakeholders that can overcome barriers to data sharing and synthesis and help answer key research questions that cannot be answered by individual investigators working on their own. We thank Julie Totten, President and Founder of Families for Depression Awareness, for her comments on sections of this article. Declaration of Conflicting Interests The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article. Funding This work was supported by National Institute of Mental Health Grant No. MH040859- “Collaborative Data Synthesis on Adolescent Depression Trials” (C. Hendricks Brown, PI) and National Institutes of Health Grant No. UL1TR000460- “Miami Clinical and Translational Science Institute” (J. Szapocznik, PI). 1. A unique feature of behavioral interventions, in comparison with pharmacological interventions as identified in the CDSADT study, is that investigators of behavioral interventions have been willing to share the parts of their datasets that have been sufficiently analyzed, delaying full data sharing until all of their primary analyses are completed. ",
year = "2013",
month = jul,
doi = "10.1177/1745691613491579",
language = "English (US)",
volume = "8",
pages = "433--444",
journal = "Perspectives on Psychological Science",
issn = "1745-6916",
publisher = "SAGE Publications Inc.",
number = "4",
}