Advancing Science Through Collaborative Data Sharing and Synthesis. (July 2013)

• Record Type: Journal Article
• Authors: Perrino, Tatiana; Howe, George; Sperling, Anne; Beardslee, William; Sandler, Irwin; Shern, David; Pantin, Hilda; Kaupert, Sheila; Cano, Nicole; Cruden, Gracelyn; Bandiera, Frank; Brown, C. Hendricks
• Is Part Of: Perspectives on psychological science. Volume 8:Number 4(2013)
• Page Start: 433
• Page End: 444
• Publication Date: 2013-07
• Subjects: collaborative research -- data sharing -- data synthesis -- scientific equity -- integrative data analysis; Psychology -- Periodicals; 150
• DOI: 10.1177/1745691613491579
• Languages: English
• ISSNs: 1745-6916

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.