Data standards promote the efficient data sharing, data interoperability, data interpretability, and reuse of data and software. Exact standards and associated metadata will vary by scientific area, study design, characteristics of the dataset and data type. Employing data type-specific standards provides a uniform framework that maximizes compatibility with existing tools and resources and the reuse potential of the data.
Data standards were developed with input from data generators (RMIP investigators and the In-depth Cell Characterization Hub), the NHLBI Data Management Core, and NIH colleagues using the BDC Request for Comment framework.
The linked document describes the proposed standards for clinical and pre-clinical data including phenotype data, clinical observations, and laboratory measurements.
Jeran Stratford
Data standards promote the efficient data sharing, data interoperability, data interpretability, and reuse of data and software. Exact standards and associated metadata will vary by scientific area, study design, characteristics of the dataset and data type. Employing data type-specific standards provides a uniform framework that maximizes compatibility with existing tools and resources and the reuse potential of the data.
Data standards were developed with input from data generators (RMIP investigators and the In-depth Cell Characterization Hub), the NHLBI Data Management Core, and NIH colleagues using the BDC Request for Comment framework.
The linked document describes the proposed standards for clinical and pre-clinical data including phenotype data, clinical observations, and laboratory measurements.
BDC-Draft-RFC-24_NHLBI BioData Catalyst Data Standards for Clinical and Preclinical Data