Dr. Tom Pollard, Ph.D., is a Research Scientist at MIT’s Institute of Medical Engineering and Science, and the Technical Director of PhysioNet. His efforts center on sharing data for use in research, education, and industry, with a focus on critical care.
Drs. Roger Mark and Tom Pollard will present "PhysioNet: A Quarter Century of Open Health Data" at the NIH-ODSS Data Sharing and Reuse Seminar on December 9, 2022 at 12 p.m. EDT.
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PhysioNet is a data sharing platform that began as an outreach component for an NIH research project in 1999. Rebuilt in 2019 following FAIR principles (Findable, Accessible, Interoperable, Reusable), the platform has grown rapidly. It now serves over 55,000 registered users around the world with >30TB of data and is heavily used across research, education, and industry.
PhysioNet is a recommended repository for journals including the Springer Nature collection, eLife, and PLOS. It also supports regular “datathons” around the world, which bring together clinicians and data scientists to focus on important, unanswered questions in health research. PhysioNet has been a close collaborator of MIT Libraries and it is piloting their data citation service, helping to help establish datasets as primary research objects and to reward those who share.
While the vast majority of data on PhysioNet is fully open access, the platform is unique in supporting training requirements and access control where necessary. This allows researchers to share sensitive resources that would not be possible through typical data sharing platforms. Over half of all PhysioNet users (approx 35,000) have been “credentialed”, providing evidence of their identity and training in human research. PhysioNet was recently featured in an ORCID showcase due to its novel use of the ORCID Trust Markers as part of this process.
The software that underpins PhysioNet has been made completely open source and we are working to create a network of new, partner platforms. Repositories are being piloted by University of Mbarara in Uganda and University of Toronto, as part of the Temerty Centre for AI Research and Education in Medicine (T-CAIREM). More institutes are expected to follow suit, leading us towards a network of interconnected repositories.