[Collective Board Meeting] High Value HEAL Datasets

The May Collective Board (CB) Meeting discussion converged on the idea that “high-value datasets” should not be defined by a single standard but rather as a set of context-dependent, reuse-driven signals shaped by audience and research purpose. CB members consistently emphasized interoperability and reuse potential—especially the ability to link, combine, and “re-mix” datasets across systems—as the strongest indicator of value, alongside high-quality documentation (e.g., clear variable definitions, missingness rationale, collection methods, and study constraints) and technical usability, including AI- and ML-readiness. The group highlighted that value varies by use case (e.g., training, clinical trials, disparities research, implementation science), and that datasets gain importance when they support question generation, secondary analysis, or scaling insights. There was also recognition that rare or difficult-to-reproduce datasets (e.g., specialized biological or longitudinal administrative data) may be inherently high value even if imperfect, and that support is often needed to enhance curation, standardization, and alignment beyond what primary study teams can feasibly deliver.