[Paper] The HEAL Data Platform
Source: arXiv - 2512.17506v1
Overview
The paper describes the HEAL Data Platform, a cloud‑native, federated system that gives researchers a single searchable gateway to more than a thousand NIH‑funded studies from the Helping to End Addiction Long‑term (HEAL) Initiative. By stitching together dozens of NIH and third‑party data repositories, the platform makes diverse addiction‑related datasets FAIR (Findable, Accessible, Interoperable, Reusable) and ready for secondary analysis.
Key Contributions
- Unified discovery layer for >1,000 HEAL studies across 19 heterogeneous data repositories.
- Open‑source Gen3‑based architecture that leverages a minimal set of reusable framework services (authz/authn, persistent identifiers, metadata management).
- API‑first design enabling programmatic access and easy integration with external tools and commons.
- Secure, on‑demand cloud compute environments (via NIH STRIDES) that sit next to the data, supporting reproducible secondary analyses.
- FAIR compliance baked into the platform’s data model, indexing, and access controls, dramatically increasing data reuse potential.
Methodology
The authors built the platform on Gen3, an open‑source data commons framework that provides a “mesh” of services rather than a monolithic stack. The core components are:
| Service | Role |
|---|---|
| Authentication & Authorization | Uses industry‑standard OAuth2/OpenID Connect to federate user identities across NIH and partner institutions. |
| Persistent Identifier (PID) Service | Assigns globally unique IDs (e.g., DOI‑like) to each data object, ensuring stable references. |
| Metadata Service | Stores rich, schema‑driven descriptors (study, modality, consent, etc.) that power the search UI and API queries. |
| Data Indexing & Search | Aggregates metadata from all connected repositories into a single searchable catalog. |
| Compute Integration | Links to STRIDES cloud environments (AWS, GCP) where analysts can spin up Jupyter notebooks, RStudio, or custom containers without moving data. |
Developers interact with the platform through RESTful APIs and a GraphQL endpoint, making it straightforward to embed discovery or analysis workflows into existing pipelines.
Results & Findings
- Discovery: The platform indexes metadata from 19 external repositories, exposing >1,000 HEAL studies to a searchable UI and API.
- Adoption: Hundreds of unique users per month (researchers, data scientists, policy analysts) have accessed the catalog and launched compute jobs.
- Interoperability: Seamless hand‑off between the catalog and STRIDES compute environments enables “bring‑the‑analysis‑to‑the‑data” without data duplication.
- FAIR Impact: By providing persistent IDs and standardized metadata, the platform improves dataset citation, reproducibility, and cross‑study meta‑analyses.
Practical Implications
- Accelerated Research: Developers can programmatically query the catalog, pull down only the metadata they need, and launch analysis notebooks in the same cloud environment—cutting weeks of data wrangling.
- Tool Integration: The API‑first approach means existing bio‑informatics pipelines (e.g., Nextflow, Snakemake) can be extended to fetch HEAL datasets on demand.
- Enterprise Use Cases: Companies building AI‑driven health solutions can leverage the FAIR‑compliant data to train models on real‑world addiction data while staying compliant with NIH security requirements.
- Scalable Architecture: The mesh design demonstrates a reusable blueprint for other large‑scale, multi‑repository initiatives (e.g., genomics, environmental data) that need a single discovery front‑end without forcing data migration.
- Compliance & Security: Integration with NIH STRIDES ensures that compute workloads meet federal data‑security standards, a critical factor for any organization handling protected health information (PHI).
Limitations & Future Work
- Metadata Heterogeneity: Despite a common schema, source repositories still vary in metadata depth, which can limit search precision for niche queries.
- Scalability of Compute Integration: Current STRIDES integration supports a limited set of cloud providers; expanding to additional clouds or on‑premise HPC clusters is planned.
- User Experience: Early feedback points to a learning curve for non‑technical users; the team aims to add guided workflows and richer visualizations.
- Extending FAIR Features: Future releases will incorporate automated provenance tracking and richer licensing metadata to further enhance data reuse.
The HEAL Data Platform showcases how a lightweight, API‑driven mesh of services can turn a fragmented landscape of research data into a cohesive, developer‑friendly ecosystem—paving the way for faster, more reproducible science in addiction research and beyond.
Authors
- Brienna M. Larrick
- L. Philip Schumm
- Mingfei Shao
- Craig Barnes
- Anthony Juehne
- Hara Prasad Juvvla
- Michael B. Kranz
- Michael Lukowski
- Clint Malson
- Jessica N. Mazerik
- Christopher G. Meyer
- Jawad Qureshi
- Erin Spaniol
- Andrea Tentner
- Alexander VanTol
- Peter Vassilatos
- Sara Volk de Garcia
- Robert L. Grossman
Paper Information
- arXiv ID: 2512.17506v1
- Categories: cs.DC
- Published: December 19, 2025
- PDF: Download PDF