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

From our perspective, this definition of a dataset is a bit too restrictive. All kinds of scien- tific data such as experimental data, observations, environmental and genome data, simula- tions and computations can be considered as datasets. We therefore extend the definition of Chapman et al [22] as follows: Definition 1. A dataset is a collection of scientific data including primary data and metadata organized and formatted for a particular purpose. We agree with Chapman et al.’s definition of dataset search. We use dataset search and dataset retrieval synonymously and define it as follows: Definition 2. Dataset retrieval comprises the search process, the ranking and the return of scientific datasets. Unger et al. [23] introduced three dimensions to take into account in question answering namely the User and Data perspective as well as the Complexity of a task. We argue that these dimensions can also be applied in dataset retrieval. In this work, we do not further explore the complexity of questions. We focus on the analysis of user interests and metadata, only. How- ever, for completeness and possible future research in computer science towards question answering in dataset retrieval, the analysis of the complexity of search questions and queries is essential. Therefore, we briefly introduce all three dimensions. User perspective In conventional retrieval systems users’ search interests are represented as a few keywords that are sent to the system as a search query. Keywords are usually embedded in a search context that can be expressed in a full sentence or a question.

In order to understand what users are looking for, a semantic analysis is needed. Infor- mation Extraction is a technique from text mining that identifies main topics (also called entity types) occurring in unstructured text [24]. Noun entities are extracted and catego- rized based on rules. Common, domain-independent entity types are for instance Person, Location and Time. When it comes to specific domains, additional entity types correspond- ing to core user interests need to be taken into consideration. In bio-medicine, according to [25], the main topics are Data Type, Disease Type, Biological Process and Organism. In new research fields such as biodiversity research these main entity types still need to be identified in order to get insights into users’ information needs and to be able to later adapt systems to user requirements. Data perspective From the data perspective, a dataset search can be classified into two types based on the source of data: primary data and metadata. Definition 3. Primary data are scientific raw data. They are the result of scientific experi- ments, observations, or simulations and vary in type, format, and size. Definition 4. Metadata are structured, descriptive information of primary data and answer the W-questions: What has been measured by Whom, When, Where and Why?. Metadata are created for different purposes such as search, classification, or knowledge derivation. Dataset retrieval approaches focusing on primary data as source data have to deal with dif-

ferent data formats such as tabular data, images, sound files, or genome data. This requires spe- cific query languages such as QUIS [26] to overcome the ensuing heterogeneity, which is out of scope of this paper. Here, we solely focus on dataset retrieval approaches that use metadata as input for search. A variety of metadata standards in the Life Sciences are introduced in Sec- tion “B—Metadata Standards in the Life Sciences”. PLOS ONE Dataset search in biodiversity research PLOS ONE | https://doi.org/10.1371/journal.pone.0246099 March 24, 2021 4 / 36