Data Quality
Data quality is likely to be the most difficult element to standardize in any given set of rules of participation, considering that the usual standard of “fit for purpose” varies so much from use case to use case. There are two mechanisms to ensure appropriate data quality. The first derives from FAIR principles, as interoperable and reusable data implies that the data set has a given minimum amount of metadata.
Data quality is likely to be the most difficult element to standardize in any given set of rules of participation, considering that the usual standard of “fit for purpose” varies so much from use case to use case. There are two mechanisms to ensure appropriate data quality. The first derives from FAIR principles, as interoperable and reusable data implies that the data set has a given minimum amount of metadata.