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Key research themes
1. How can data citation infrastructures and persistent identifiers (PIDs) enhance precise data findability and reuse in scholarly research?
This theme examines the development and implementation of data citation infrastructures, especially the role of persistent identifiers at granular levels, to better support data discoverability, reuse, and scholarly credit—core components of FAIR data principles. It focuses on challenges and solutions related to uniquely identifying datasets and their subcomponents, such as variables, to promote precise attribution and reproducibility across disciplines.
Key finding: This paper identifies a significant gap in current Social Sciences data citation practices where Persistent Identifiers (PIDs) are assigned only at the study level but not at the finer attribute or variable level, hindering... Read more
Key finding: This study details VAMDC’s implementation of Research Data Alliance (RDA) recommendations on dynamic data citation, including the use of versioned, time-stamped queries stored in a Query Store to enable reproducible... Read more
Key finding: Springer Nature’s development of a standardized, tiered framework of research data policies across journals — ranging from encouragement of data sharing up to mandatory open data and peer review of data — illustrates a... Read more
2. What are the prevalent practices and challenges in formal vs. informal data citation in scholarly publications, particularly in biomedical and social sciences?
This theme investigates the dynamics between formal data citation—references included in bibliographies or reference lists using standardized metadata—and informal citation practices embedded in main texts or acknowledgments. It explores disciplinary differences, barriers to standardized data citation, and the implications of informal citation on researcher recognition and data discoverability.
Key finding: Through an automated and manual analysis of biomedical literature, this study finds that informal mentions of datasets within article texts far exceed formal data citations indexed in reference lists. This discrepancy leads... Read more
Key finding: This paper showcases the workflows of ICPSR librarians who monitor formal and informal mentions of their datasets in social science literature, underscoring the complexity of detecting uncited or informally referenced uses.... Read more
Key finding: Highlighting the lag in proper data citation despite advances in data management infrastructure and FAIR principles, this editorial argues data sharing without formal citation undermines researcher credit and open science... Read more
3. What conceptual distinctions between software and data influence their citation and attribution practices in scientific research?
Research outputs encompass both software and data, yet these have distinct natures that affect how each should be cited, credited, and reused. This theme interrogates the epistemological and legal differences between software and data, implications for citation norms, and how these distinctions inform best practices for scholarly attribution.
Key finding: The paper delineates critical distinctions between software and data: software is executable, creative, and generally copyright-protected, whereas data are empirical observations meant to provide evidence. This difference... Read more