Identifying and Tracing Persistent Identifiers of Research Resources : Automation, Metrics and Analytics

Abstract:

Formal and informal citations and acknowledgements for research infrastructures, such as data collections, software packages, and facilities, are an increasingly important function of attribution in scholarly literature. While such citations provide the appropriate links, even if informally, to their origins, they are often done so inconsistently, making such citations hard to analyze. While significant progress has been made in the past few years in the development of recommendations, policies, and procedures for creating and promoting citable identifiers, progress has been mixed in tracking how data sets and other digital infrastructures have actually been identified and cited in the literature.

Understanding the full extent and value of research infrastructures through the lens of scholarly literature requires significant resources, and thus, we argue must rely on automated approaches that mine and track persistent identifiers to scientific resources. Such automated approaches, however, face a number of unique challenges, from the inconsistent and informal referencing practices of authors, to unavailable, embargoed or hard-to-obtain full-text resources for text analytics, to inconsistent and capricious impact metrics.

This presentation will discuss work to develop and evaluate tools for automating the tracing of research resource identification and referencing in the research literature via persistent citable identifiers. Despite the impediments, automated processes are of considerable importance in enabling these traceability efforts to scale, as the numbers of identifiers being created for unique scientific resources continues to grow rapidly. Such efforts, if successful, should improve the ability to answer meaningful questions about research resources as they continue to grow as a target of advanced analyses in research metrics.

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