Updated Abstract Text: Perhaps the biggest challenge in designing and implementing a data management & sharing strategy for HEAL is its broad range of programs, projects and types of data being collected. While individual programs or projects are supported by Data Coordinating Centers (DCCs) and Data Management Centers (DMCs), broad sharing of HEAL data will require a platform that can link together the DMCs and extend their reach while working smoothly with them. The range of HEAL data types is extensive, spanning multiple measurement modalities (e.g., clinical data, bioassays, wearables and self-report data) as well as varying in size and complexity. In addition, the range of scientific disciplines represented not only among the HEAL investigators but among other researchers likely to use HEAL data, together with the range of scientific questions that might be pursued, also have implications for how data should be organized, documented and made accessible to maximize their scientific value. Specifically, a single monolithic system is likely to fail, or at least be sub-optimal. At the same time, asking all programs or projects to build their own systems conforming to a set of common requirements would be both expensive and unsustainable. We propose to build and maintain a HEAL Platform that will interoperate with the DMCs to enable discoverability and access of HEAL data stored in the DMCs or other repositories. The platform will allow search and query of the HEAL metadata, and selected data, within and across studies, and provide access to a secure, scalable computing environment to conduct independent analysis on HEAL data along with the user?s own data.

OT2OD030208

2020-09-01

2025-08-31