Measuring the impact of biodiversity datasets: data reuse, citations and altmetrics

Abstract

Despite growing evidence of open biodiversity data reuse by scientists, information about how data is reused and cited is rarely openly accessible from research data repositories. This study explores data citation and reuse practices in biodiversity by using openly available metadata for 43,802 datasets indexed in the Global Biodiversity Information Facility (GBIF) and content analyses of articles citing GBIF data. Results from quantitative and content analyses suggest that even though the number of studies making use of openly available biodiversity data has been increasing steadily, best practice for data citation is not yet common. It is encouraging, however, that an increasing number of recent articles (16 out of 23 in 2019) in biodiversity cite datasets in a standard way. A content analysis of a random sample of unique citing articles (n=100) found various types of background (n=18) and foreground (n=81) reuse cases for GBIF data, ranging from combining with other data sources to create species distribution modelling to software testing. This demonstrates some unique research opportunities created by open data. Among the citing articles, 27% mentioned the dataset in references and 13% in data access statements in addition to the methods section. Citation practice was inconsistent especially when a large number of subsets (12~50) were used. Even though many GBIF dataset records had altmetric scores, most posts only mentioned the articles linked to those datasets. Among the altmetric mentions of datasets, blogs can be the most informative, even though rare, and most tweets and Facebook posts were for promotional purposes.

Are data repositories fettered? A survey of current practices, challenges and future technologies

“…Khan et al (2020) suggest further enhancements of the Scholix schema and enrichment of Scholexplorer metadata using controlled vocabularies and the adoption of standardized data citations by journals to establish links between datasets and literature. Google Dataset Search also displays citation counts for datasets, but Khan et al (2021) found discrepancies between these numbers and the citation counts displayed by Google and by GBIF. These services can be potentially used to identify data reuse cases when they mature in the future.…”

“…Sharing meaningful data can be time consuming, so researchers often want to know how their shared data is reused (Kratz & Strasser, 2015;Wallis et al, 2013). Data reuse is defined as the retrieval and use of a dataset by someone other than the originator, including the first use of data collected for a community (e.g., astronomy datasets from sky surveys: (Faniel et al, 2016) for social science data, the National Heart, Lung, and Blood Institute (NHLBI) data repository for clinical trials data (Coady et al, 2017), and the Global Biodiversity Information Facility (GBIF) for biodiversity and zoology data (Khan et al, 2021). While data reuse has been of increasing interest to the research community, most research has focused on scientists' data reuse practices rather than technological feasibility of data repositories to track secondary data reuse.…”

“…Several studies indicate that data reuse is growing with the availability of open data through established data repositories. Examples of data reuse cases and repositories include the UK Data Service (Bishop and Kuula-Luumi, 2017) and the Inter-University Consortium for Political and Social Research (ICPSR) (Faniel et al , 2016) for social science data, the National Heart, Lung, and Blood Institute (NHLBI) data repository for clinical trials data (Coady et al , 2017) and the Global Biodiversity Information Facility (GBIF) for biodiversity and zoology data (Khan et al , 2021).…”

“…Data repositories play an important role for this by providing the policy and infrastructure to support effective curation and long-term preservation. Nevertheless, the development and implementation of repositories has been sporadic and varies between disciplines, with genomics, chemical crystallography and biodiversity extensively sharing open data (Faniel and Yakel, 2017; Robinson-García et al , 2016; Khan et al , 2021). Hence, the disciplinary data repositories in these areas seem to be relatively mature and robust in terms of technology and policy.…”

Introducing guidelines for publishing DNA-derived occurrence data through biodiversity data platforms

“…A hurdle towards the goal of integrating DNA-based occurrences into routine biological practice is the somewhat poor track record of biology when it comes to making actual research data available to begin with (e.g., Hinchliff et al 2015;Khan et al 2021). The final, published research paper is all too often seen as the sole end product of the research project in question, and little, if any, effort is made to facilitate re-interpretation and re-use of the underlying results and data for the same or other purposes (Durkin et al 2020;Abarenkov et al 2022).…”