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

C—Metadata usage in selected data repositories In the following analysis, we examine how well existing metadata cover scholarly information needs in biodiversity research. Therefore, we analyzed metadata from selected data repositories along the following criteria: • Statistics: At first, we inspected what metadata standards are offered in the repositories’ metadata API. For the further processing, we only selected standards that are relevant for biodiversity research. • Timelines: Timelines indicate how many datasets per year are published in a data repository and when particular standards have been introduced. • Metadata Field Usage: In order to get an overview of which metadata elements are used we determined how many fields of a standard are filled. • Category-Field-Match: Focusing on the standard that best covers the identified categories, we explored what fields reflect information needs for biodiversity research and how often they are filled. • Content Analysis: Counting alone does not provide further insights on the actual content in the metadata. Therefore, we analyzed free text metadata fields such as title, description and further general fields, and we inspected whether they contain useful information for search. Keywords in general fields were extracted and sorted by frequency for all parsed files and from all data repositories. A deeper content analysis of the descriptive metadata fields was limited to 10,000 files per data repository due to performance limitations of the used text mining tools.

We first introduce the methodology comprising data collection, data processing and the used strategy and tools for the content analysis. Afterwards, we present the results along the introduced criteria. Methodology Scholarly publishers increasingly demand scientific data to be submitted along with publica- tions. Since publishers usually do not host the data on their own, they ask scholars to publish data with a repository for their research domain. According to Nature’s list of recommended data repositories [16], we selected five archives for our further analysis: three generalist ones (Dryad, Figshare and Zenodo) and two domain-specific ones (PANGAEA—environmental data, GBIF—taxonomic data). In the biodiversity projects we are involved in, scholars also often mention these repositories as the ones they mainly use. Data collection. We parsed all available metadata from Figshare, Dryad, GBIF, PANGAEA and Zenodo in May 2019 via their respective OAI-PMH (https://www.openarchives.org/pmh/) interfaces. The Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) is a protocol primarily developed for providing and consuming metadata. GBIF only offers the metadata of their datasets in the OAI-PMH interface. The individual occurrence records, which belong to a dataset and which are provided in metadata structures based on the Darwin Core metadata standard [61], are not present in the OAI-PMH interface. Hence, we only ana- lyzed the metadata of the datasets. Our script parses the metadata structure of all public records per metadata standard for

each of the selected data repositories (Table 4). Apart from the metadata standards introduced in the previous section, a few more standards appear in this list. OAI-DC is an abbreviation for Dublin Core, a mandatory standard in OAI-PMH interfaces. QCD means qualified Dublin PLOS ONE Dataset search in biodiversity research PLOS ONE | https://doi.org/10.1371/journal.pone.0246099 March 24, 2021 20 / 36