Improving the discoverability, accessibility and citability of 'omics datasets

Although discovery-scale biomedical datasets represent valuable assets for hypothesis generation, model testing and data validation, the infrastructure supporting their re-use lacks 

organization and consistency. Here, using relative abundance transcriptomic datasets in the field of nuclear receptor signaling as a proof-of-principle data type, we established a model for improving the discoverability, accessibility and 

citability of published ‘omics datasets.