Limitations to success in academic data reference support

Abstract

As secondary data become increasingly integrated into research and coursework across a widening variety of fields and disciplines, data reference is gaining traction as a major area of library research support. To examine the current landscape of data reference, we distributed a survey via regional and international library listservs asking librarians about their experiences and opinions related to their data reference work. For this paper, the full collected dataset was limited to only academic librarians who answer at least one data reference question per month in order to identify the unique needs of respondents doing reference work in academic institutions, with the ultimate goal of improving our own work as academic librarians at our institution. We used a grounded theory approach to analyze the qualitative survey response data, and supplemented this analysis with descriptive statistics and chi-square tests for the quantitative responses. Through this analysis, we identify a theoretical framework consisting of three themes relating to limitations to success where librarians must advocate for change in order to maintain and improve high-quality data reference work in the academic sphere: (1) technology and resource limitations, such as substandard database interfaces; (2) institutional limitations, such as insufficient staff time or resources dedicated to data reference; and (3) personal limitations, such as a lack of data skills. While librarians have varying levels of influence over each of these three areas, identifying and targeting these categories can help librarians and other data professionals focus resources and build cases for additional support from their library and campus administrators.

A systematized review on data librarianship literature: Current services, challenges, skills, and motivational factors

“…The participants ranged with minimum n = 7 to maximum n = 223. Further, there are some studies that recruited participants from multiple countries (Corrall et al, 2013; Cox et al, 2017, 2019; McBurney and Kubas, 2021). Overall, the methodological details show a holistic approach of study design, data collection, participants, and across countries.…”

“…These instructions involved in overall research data lifecycle (Cox et al, 2019; Jefferson, 2020), DMPs (Knight, 2015; Si et al, 2015), data sharing and data use or reuse (Chiware, 2020; Yoon and Donaldson, 2019). Similarly, the consultancy and advisory services involved in DMPs (Chiware, 2020; Cox et al, 2019; Faniel and Connaway, 2018; Hamad et al, 2021; Knight, 2015; Si et al, 2015; Tenopir et al, 2017; Yoon and Donaldson, 2019), copy right and licensing (Chiware and Becker, 2018; Cox et al, 2017, 2019;), data archives (Cox et al, 2019; Tenopir et al, 2017), research lifecycle consultation (McBurney and Kubas, 2021; Ohaji et al, 2019). The technical services involved organizing data sets (Johnson, 2012), allocating metadata and standards (Chiware, 2020; Johnson, 2012; McBurney and Kubas, 2021), data curation (Cox et al, 2019; Jefferson, 2020; Yoon and Donaldson, 2019), data preservation, storage and repositories (Chiware and Becker, 2018; Cox et al, 2019; Faniel and Connaway, 2018; Knight, 2015; McBurney and Kubas, 2021; Yoon and Donaldson, 2019) and few involved in data quality control (Cox et al, 2019; Yoon and Donaldson, 2019) and data analysis and visualization (Chiware and Becker, 2018; Cox et al, 2019).…”

“…The knowledge, skills, and education gap involved limited RDS skill sets (Mani et al, 2021; Tenopir et al, 2017), limited RDS technical experience (Brown et al, 2015), lack of involvement in research processes (Bresnahan and Johnson, 2013; Chiware, 2020; Johnson, 2012), and discipline disparities (Corrall et al, 2013; Cox et al, 2019), which ultimately resulted in professionals’ vague understanding of RDM (Hamad et al, 2021; Mohammed and Ibrahim, 2019) and reluctant to accept the emerging data librarianship roles (Ashiq et al, 2021b; Corrall et al, 2013; Cox et al, 2019; Huang et al, 2021). The technical issues involved data scale, variety, and complexity (Johnson, 2012; Knight, 2015), no prober documentation and metadata (Chawinga and Zinn, 2020; Chiware, 2020; Knight, 2015; McBurney and Kubas, 2021; Si et al, 2015), data curation issues (Chiware, 2020; McBurney and Kubas, 2021; Yoon and Donaldson, 2019), data archiving, storage, and preservation (Ashiq et al, 2021b; Knight, 2015; Yoon and Donaldson, 2019), data security and privacy (Cox et al, 2019; Perrier and Barnes, 2018), and absence of data analysis and visualization support (Chiware, 2020; Cox et al, 2019; Hamad et al, 2021). The technological issues mainly related with the cost of RDS tools, software, and insufficient infrastructure (Chiware and Becker, 2018; Johnson, 2012; Mohammed and Ibrahim, 2019; Perrier and Barnes, 2018).…”

“…Some studies reported that leadership and institutional limitations are also affecting data librarianship. They stated that advocacy and support from the leadership as well as lack of shared vision was key hurdles (Chawinga and Zinn, 2020; Cox et al, 2017; Faniel and Connaway, 2018; McBurney and Kubas, 2021; Ohaji et al, 2019; Yoon and Donaldson, 2019), especially lacking policy making (Ashiq et al, 2021b; Huang et al, 2021; Mohammed and Ibrahim, 2019), inadequate cooperation and collaboration among services entities within or outside the campus (Cox et al, 2017), and limited staff, time, job description, and non-additional financial benefits who are involved in RDS (Chiware and Becker, 2018; Huang et al, 2021; Si et al, 2015). Few studies also reported the academic and library gap also affecting the RDS (Chawinga and Zinn, 2020; Jefferson, 2020) and researchers reluctant for depositing and data sharing (Ashiq et al, 2021b; Si et al, 2015).…”

“…The data and research literacy skills reported in various studies and considered equally important for researchers and library professionals. Some studies reported that librarians need to get knowledge about research methods and procedures (Corrall et al, 2013; Cox et al, 2019; Federer, 2018), especially data analysis (Bresnahan and Johnson, 2013; Johnson, 2012; McBurney and Kubas, 2021). Some highlighted the importance of knowledge of various subject/discipline domains (Corrall et al, 2013; Johnson, 2012), computational literacy skills (Oliver et al, 2019), data literacy skills (Jefferson, 2020; Ohaji et al, 2019), data lifecycle knowledge (Bresnahan and Johnson, 2013), and advising on DMPs (Knight, 2015).…”