Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

and ENVIRONMENT) were obtained from the BiodivTagger [65]. The results are presented in Table 10. ORGANISM annotations are contained in 85% of the analyzed Figshare files, in 36% of Dryad files, in 32.5% of the selected PANGAEA files, in 29% of Zenodo files and in around 12% of the selected GBIF files. The number of ORGANISM annotations in GBIF files is low since datasets mostly describe the overall study and do not contain concrete species names but rather broader taxonomic terms such as ‘Family’ or ‘Order’. Probably, the number of ORGANISM annotations in Figshare files is that high due to the large amount of collection data. The numbers for all other biological entity types in Figshare are low because, apart from the taxonomic classification, no further relevant keywords are provided in the descriptive fields. While the inspected PANGAEA files achieved the highest results for the categories MATERIAL (86%) and QUALITY (78%), Dryad provides the largest amount of files contain- ing annotations of the categories ENVIRONMENT (52%) and PROCESS (67%). That shows that the content of descriptive metadata fields actually contains relevant information for search, which can be made accessible through additional semantic enrichment. Some of the text mining pipelines were originally developed and evaluated with text cor- pora and not sparse datasets. Hence, annotations might be missing or the results could contain wrong (false positive) annotations. However, the outcome indicates that NLP tools can support

the identification of biological entities. That could be an approach for generalist repositories to additionally enrich metadata. All scripts and the final results are available in our repository. Summary. In all repositories, we determined an increasing amount of datasets over the years. In some of them, the growth rate is exponential. This confirms the importance of data repositories and the increasing demand for data publication services. Our results reveal that generalist repositories tend to use only general standards such as Dublin Core and DataCite. Even if it is common understanding that generalist repositories only support general metadata standards, it would be a great advantage for data discoverability if generalist repositories offer several different metadata standards. This would additionally emphasize the message of gener- alist repositories to be able to handle any type of scientific data. Discipline-specific repositories, e.g., GBIF and PANGAEA are more likely to provide domain-related standards such as EML or Pan-MD. In GBIF’s case, we are aware that we could not provide a full picture as we did not analyze the occurrence records. Here, only a deeper analysis of the provided fields in the search index would deliver more answers. However, that would require technical staff support as the access to GBIF’s search indices is limited. Overall, the only metadata fields that matched researchers’ search interests closely were TIME and PERSON, which were present in almost all inspected repositories. However, impor-

tant categories such as information on environments, materials or species are only covered if Table 10. NLP analysis: Number of datasets with named entities (out of 10,000 processed files in a reduced OAI-DC structure) per repository. Each file contains a subset of the original metadata, namely, dc:title, dc:description, dc:subject and dc:date. PANGAEA GBIF Dryad Zenodo Figshare Location 9088 5718 3530 4644 9978 Person 3789 6687 3030 3201 1773 Organization 3307 1762 1486 1674 9542 Environment 2918 1533 5166 2050 15 Quality 7794 1830 6548 3183 29 Process 1756 1055 6743 1847 15 Material 8593 3358 3716 2859 644 Organism 3251 1217 3603 2891 8542 https://doi.org/10.1371/journal.pone.0246099.t010 PLOS ONE Dataset search in biodiversity research PLOS ONE | https://doi.org/10.1371/journal.pone.0246099 March 24, 2021 28 / 36