Abstract
We present an analytical study of the quality of metadata about samples used in biomedical experiments. The metadata under analysis are stored in two well-known databases: BioSample—a repository managed by the National Center for Biotechnology Information (NCBI), and BioSamples—a repository managed by the European Bioinformatics Institute (EBI). We tested whether 11.4 M sample metadata records in the two repositories are populated with values that fulfill the stated requirements for such values. Our study revealed multiple anomalies in the metadata. Most metadata field names and their values are not standardized or controlled. Even simple binary or numeric fields are often populated with inadequate values of different data types. By clustering metadata field names, we discovered there are often many distinct ways to represent the same aspect of a sample. Overall, the metadata we analyzed reveal that there is a lack of principled mechanisms to enforce and validate metadata requirements. The significant aberrancies that we found in the metadata are likely to impede search and secondary use of the associated datasets.
Highlights
The metadata about scientific experiments are essential for finding, retrieving, and reusing the scientific data stored in online repositories
We present an analysis of the quality of metadata in two online databases: the NCBI BioSample[9], which is maintained by the U.S National Center for Biotechnology Information (NCBI), and the EBI BioSamples[10,11], which is maintained by the European Bioinformatics Institute (EBI)
We carried out an empirical assessment of the quality of metadata in two well-known online repositories of metadata about samples used in biomedical experiments: the NCBI BioSample and the EBI BioSamples
Summary
The metadata about scientific experiments are essential for finding, retrieving, and reusing the scientific data stored in online repositories. Finding relevant scientific data requires that the data be accompanied by metadata, and that the metadata be of sufficient quality for the corresponding datasets to be discovered and reused. Bruce et al.[1] define various metadata quality metrics, such as completeness (e.g., all necessary fields should be filled in), accuracy (e.g., the values filled in should be specified as appropriate for the field), and provenance (e.g., information about the metadata author). Park et al.[2,3] specified several high-level principles for the creation of good-quality metadata. The FAIR principles specify desirable criteria that metadata and their corresponding datasets should meet to be Findable, Accessible, Interoperable, and Reusable
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