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Advancing Science Through Collaborative Data Sharing and Synthesis.

, Tatiana Perrino1, George Howe2

  • 1University of Miami's Miller School of Medicine tperrino@med.miami.edu.

Perspectives on Psychological Science : a Journal of the Association for Psychological Science
|November 19, 2013
PubMed
Summary

Researchers face challenges in sharing data, hindering scientific replication and synthesis. This article promotes data sharing collaborations and presents a model for advancing scientific knowledge, particularly in adolescent depression research.

Keywords:
collaborative researchdata sharingdata synthesisintegrative data analysisscientific equity

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Area of Science:

  • Mental Health Research
  • Data Science
  • Collaborative Research

Background:

  • Growing demand for data sharing to ensure scientific rigor and replication.
  • Data synthesis across trials is crucial for answering complex research questions.
  • Barriers to data sharing and synthesis persist, limiting scientific advancement.

Purpose of the Study:

  • To advocate for increased researcher collaboration in data sharing and synthesis.
  • To present a model for establishing data synthesis collaborations.
  • To improve understanding of intervention efficacy, especially for minority populations, advancing scientific equity.

Main Methods:

  • Utilizing the National Institute of Mental Health's Collaborative Data Synthesis on Adolescent Depression Trials as a case study.
  • Integrating datasets from multiple adolescent depression prevention and treatment trials.
  • Bringing together researchers and stakeholders to analyze individual-level data.

Main Results:

  • Demonstrates a collaborative model for synthesizing individual-level data across studies.
  • Addresses critical questions regarding intervention effectiveness ('for whom') and mechanisms ('by what pathways').
  • Highlights the potential for enhanced intervention improvement and scientific equity.

Conclusions:

  • Collaborative data synthesis is essential for advancing scientific knowledge beyond individual trials.
  • The presented model offers a framework for fields requiring synthesized individual-level data.
  • Promoting data sharing and synthesis is key to improving intervention outcomes and achieving scientific equity.