Open data sharing: what could possibly go wrong?

Abstract

Open data sharing in clinical trials holds tremendous promise for accelerating scientific discovery yet lacks standardized approaches for communicating critical contextual information necessary for valid secondary analyses. This perspective examines emerging evidence that inadequate documentation of clinical trial datasets leads to misinterpretation and potentially flawed conclusions. Using examples from neuroimaging, pain research, and multisite trials, we demonstrate how complex trial designs contain inherent constraints that, when not properly documented, can result in inappropriate analyses violating core design principles. We highlight concerning statistics: only 30% of shared trial datasets include sufficient metadata for replication, 40% have been analyzed in ways violating primary design constraints, and 15% of secondary analyses misinterpret data due to missing context. Current initiatives like FAIR principles provide high-level guidance but require practical implementation frameworks. We propose a unified approach to clinical trial data documentation that addresses both technical interoperability and contextual knowledge critical for valid secondary analyses. This framework would build upon existing standards while adding crucial design constraints, analytical boundaries, and key assumptions. The scientific community must invest in robust documentation standards, sustained curation funding, and scientific oversight to realize open science’s promise while protecting research integrity. These improvements are essential as shared datasets increasingly guide clinical practice and research directions in pain medicine and beyond.