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 nucleotide-based approaches including Sanger sequencing, whole genome sequencing, ATAC-seq, GUIDE-seq, optical genome mapping, insertion site validation, RNA sequencing (bulk and single cell), and RT-qPCR.
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 nucleotide-based approaches including Sanger sequencing, whole genome sequencing, ATAC-seq, GUIDE-seq, optical genome mapping, insertion site validation, RNA sequencing (bulk and single cell), and RT-qPCR.
BDC-Draft-RFC-23_NHLBI BioData Catalyst Data Standards for Nucleotide-Based Profiles - Google Docs