The influence of community recommendations on metadata completeness

Many communities use standard, structured documentation that is machine-readable, i.e. metadata, to make discovery, access, use, and understanding of scientific datasets possible. Organizations and communities have also developed recommendations for metadata content that is required or suggested for their data developers and users. These recommendations are typically specific to metadata representations (dialects) used by the community. By considering the conceptual content of the recommendations, quantitative analysis and comparison of the completeness of multiple metadata dialects becomes possible. This is a study of completeness of EML and CSDGM metadata records from DataONE in terms of the LTER recommendation for Completeness. The goal of the study is to quantitatively measure completeness of metadata records and to determine if metadata developed by LTER is more complete with respect to the recommendation than other collections in EML and in CSDGM. We conclude that the LTER records are broadly more complete than the other EML collections, but similar in completeness to the CSDGM collections.