Measuring the Impact of Data Sharing: From Author-Level Metrics to Quantification of Economic and Non-tangible Benefits

In early 2023, the National Institutes of Health (NIH) implemented its Data Management and Sharing (DMS) Policy, requiring researchers to share scientific data produced with NIH funding. The policy's objective is to amplify the benefits of public investment in research by promoting the dissemination and reusability of primary data. Given this backdrop, identifying a robust methodology to assess the impact of data sharing across diverse research domains is essential. In this review, we adopted two methodological paradigms, the bottom-up and top-down strategies, and employed content analysis to pinpoint established methodologies and innovative practices within this intricate field. Although numerous author-level metrics are available to gauge the impact of data sharing, their application is still limited. Non-traditional metrics, encompassing economic (e.g., cost savings) and intangible benefits, presently appear to hold more potential for evaluating the impact of primary data sharing. Finally, we address the primary obstacles encountered by open data policies and introduce an innovative "Shared model for shared data" framework to bolster data sharing practices and refine evaluation metrics.