Objective: The objective was to develop a cloud-based, federated system to serve as a single point of search, discovery and analysis for data generated under the NIH Helping to End Addiction Long-term (HEAL) Initiative. Materials and methods: The HEAL Data Platform is built on the open source Gen3 platform, utilizing a small set of framework services and exposed APIs to interoperate with both NIH and non-NIH data repositories. Framework services include those for authentication and authorization, creating persistent identifiers for data objects, and adding and updating metadata. Results: The HEAL Data Platform serves as a single point of discovery of over one thousand studies funded under the HEAL Initiative. With hundreds of users per month, the HEAL Data Platform provides rich metadata and interoperates with data repositories and commons to provide access to shared datasets. Secure, cloud-based compute environments that are integrated with STRIDES facilitate secondary analysis of HEAL data. The HEAL Data Platform currently interoperates with nineteen data repositories. Discussion: Studies funded under the HEAL Initiative generate a wide variety of data types, which are deposited across multiple NIH and third-party data repositories. The mesh architecture of the HEAL Data Platform provides a single point of discovery of these data resources, accelerating and facilitating secondary use. Conclusion: The HEAL Data Platform enables search, discovery, and analysis of data that are deposited in connected data repositories and commons. By ensuring that these data are fully Findable, Accessible, Interoperable and Reusable (FAIR), the HEAL Data Platform maximizes the value of data generated under the HEAL Initiative.
@article{arxiv.2512.17506,
title = {The HEAL Data Platform},
author = {Brienna M. Larrick and L. Philip Schumm and Mingfei Shao and Craig Barnes and Anthony Juehne and Hara Prasad Juvvla and Michael B. Kranz and Michael Lukowski and Clint Malson and Jessica N. Mazerik and Christopher G. Meyer and Jawad Qureshi and Erin Spaniol and Andrea Tentner and Alexander VanTol and Peter Vassilatos and Sara Volk de Garcia and Robert L. Grossman},
journal= {arXiv preprint arXiv:2512.17506},
year = {2025}
}