Journal of the Association for Information Science and Technology最新文献

Despite their professed enthusiasm for open science, faculty researchers have been documented as not freely sharing their data; instead, if sharing data at all, they take a minimal approach. A robust research agenda in LIS has documented the data under-sharing practices in which they engage, and the motivations they profess. Using theoretical frameworks from sociology to complement research in LIS, this article examines the broader context in which researchers are situated, theorizing the social relational dynamics in academia that influence faculty decisions and practices relating to data sharing. We advance a theory that suggests that the academy has entered a period of transition, and faculty resistance to data sharing through foot-dragging is one response to shifting power dynamics. If the theory is borne out empirically, proponents of open access will need to find a way to encourage open academic research practices without undermining the social value of academic researchers.

COVID-19 has emerged as a major research hotspot and trending topic in recent years, leading to increased publications and citations of related papers. While concerns exist about the potential citation boost in journals publishing these papers, the specifics are not fully understood. This study uses a generalized difference-in-differences approach to examine the impact of publishing COVID-19 papers on journal citation metrics in the Health Sciences fields. Findings indicate that journals publishing COVID-19 papers in 2020 received significantly higher citation premiums due to COVID-19 in 2020 and continued to benefit from the premium in 2021 in certain fields. In contrast, journals that began publishing COVID-19 papers in 2021 experienced weaker citation premiums. The citation premiums exhibit some negative spillover effect: Although the publication volume of non-COVID-19 papers also surged, these papers experienced insignificant or negative citation gains, even when published in the same journals as COVID-19 papers. COVID-19 papers published in high-impact journals brought higher citation premiums than those in low-impact journals in most fields, indicating a potential Matthew effect. These citation premiums can affect various citation-based journal metrics, such as our simulated impact factor and SCImago Journal Rank, to different degrees. Compared to the simulated impact factor, other normalized journal metrics are less influenced by citation premiums. The results highlight a “gold rush” pattern in which early entrants establish their citation advantage in research hotspots and caution against using citation-based metrics for research assessment.

Data literacy, a multifaceted competency in working with data, has emerged as an essential skill that holds significance in both personal and professional lives. Nonetheless, there is a lack of a precise definition of data literacy, and individuals' perceptions of their data literacy have not been thoroughly investigated. This study aims to develop and validate a scale designed for measuring self-efficacy in data literacy within the context of higher education. Both exploratory and confirmatory factor analyses were conducted to determine construct validity and reliability. The resulting data literacy self-efficacy scale comprises 31 items organized into three factors: data identification, data processing, and data management and sharing. These factors represent distinct yet interconnected dimensions, highlighting the multifaceted nature of data literacy.

Most studies of trusted digital repositories have focused on the internal factors delineated in the Open Archival Information System (OAIS) Reference Model—organizational structure, technical infrastructure, and policies, procedures, and processes. Typically, these factors are used during an audit and certification process to demonstrate a repository can be trusted. The factors influencing a repository's designated community of users to trust it remains largely unexplored. This article proposes and tests a model of trust in a data repository and the influence trust has on users' intention to continue using it. Based on analysis of 245 surveys from quantitative social scientists who published research based on the holdings of one data repository, findings show three factors are positively related to data reuser trust—integrity, identification, and structural assurance. In turn, trust and performance expectancy are positively related to data reusers' intentions to return to the repository for more data. As one of the first studies of its kind, it shows the conceptualization of trusted digital repositories needs to go beyond high-level definitions and simple application of the OAIS standard. Trust needs to encompass the complex trust relationship between designated communities of users that the repositories are being built to serve.