Development of an Open, Automated Workflow for Gaining Bibliometric Insights into University Research Data Publishing

Bryan Gee

Interest in research data has been rapidly growing across the research ecosystem, from growing funder mandates to evolving best practices around data management. Concurrently, there is also increased interest in using bibliometrics to gain insights into the outputs of research institutions and to inform decision-making around service provision. However, tracking data publications is significantly more complicated than tracking journal articles or books, for which various “one-stop” solutions exist, because there are substantially more nuances around pathways and best practices for publishing research data. While some subscription-based commercial solutions claim to be able to perform this work for research datasets, their closed, proprietary nature renders them difficult to fully assess, among other shortcomings. Here I discuss efforts at the University of Texas at Austin to develop a scripted approach to acquire insights into data publishing by university researchers. This presentation summarizes the current workflow, through the querying of a large number of public APIs of non-profit repositories and other scholarly platforms, and various challenges encountered along the way. This approach enables scalable, real-time discovery and analysis of these objects across systems of varying architecture, metadata quantity and quality, and connectivity. It provides a more resilient solution than commercial options by ensuring the reproducibility of the work by others, permitting modification as needed to adapt to the rapidly evolving landscape, providing a solution that will remain viable in the long-term, and facilitating easy adoption by other stakeholders and institutions. This workflow in turn lays the foundation for identifying connections between related scholarly outputs (e.g., an article and its associated data and code) and for developing a data-driven approach to library strategies around research data services.