Table 1.

Summary of challenges in data sharing discussed at the FAIR Share Workshop

•Data collection and organization schemes vary across laboratories - a ‘one-size-fits-all’ approach to data collection and storage may not work for every  laboratory

•Training laboratory members in best practices for data collection and storage requires additional time and expertise for lab managers and PIs

•Infrastructure for data storage and upload to repositories is expensive and not available in all labs

•Data repository security is essential to protecting researchers and research subjects

•Repositories must have metrics of data quality to ensure that data shared are accurate and sufficiently described - variety in data quality checks will exist across labs and study types

•Shared data must be attributable and citable