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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.
... 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., 2017Cox et al., , 2019McBurney 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., 2017Cox et al., , 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). ...
... 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., 2017Cox et al., , 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). ...
Data librarianship is becoming more common as a means of developing and integrating data-driven library services. Consequently, the academic and research libraries’ traditional role in providing information support and training has been expanded to include support in all aspects of the research lifecycle. Hence, this study systematically reviews the data librarianship literature focusing on current data librarianship services, challenges, skills, and motivational factors. A systematic review was conducted following the PRISMA guidelines. A comprehensive search strategy was formulated to extract maximum relevant results. The bibliographic data were retrieved from the Scopus, Web of Science, Library, Information Science and Technology Abstracts (LISTA), and Library and Information Science Abstracts (LISA). Finally, 27 studies that fulfill the criteria were included in this study. The findings revealed that two main factors that contribute to the success or failure in this emerging data librarianship roles are skills, knowledge, and expertise; and limited support and advocacy from library leadership and higher authorities. One is on the part of library professionals who can develop the required skills, knowledge, and expertise and the other is on the part of library leadership. The library professionals are hesitant to embrace this new role due to non-additional benefits, no relevant job description, and lacking leadership support. Overall, the findings revealed that the data librarianship scope is dynamic and has been expanded, albeit the progress is slow. The theoretical, practical, policy, and social implications described that the data librarianship services tend to be improved, and the relevant skills, knowledge, and expertise should be developed. The policy initiatives need to be taken, improved, and expanded to advance technical services related to data librarianship.
... Academic and research libraries are visibly restructuring and reassigning employees to meet the growing demand for RDM services, as well as ensure that existing staff assigned to new RDM tasks participate in various professional development programs. McBurney and Kubas (2021) identified various data support challenges in the USA, Canada and South Africa. These challenges mainly relate to technology and resource limitations (such as poor searching and navigation, data export limitations, limited documentation, inadequate data curation and data access issues), institutional limitations (such as limited job duties, lack of administration support, lacking data cleaning and analysis skills) and personal limitations (no self-investment in skills, limited time and training, librarians' lack of data management skills and lack of involvement in research). ...
... Usually, the job title is "librarian," who performs traditional as well as diverse job duties. Nevertheless, as compared to developed countries (such as the USA, UK, Canada and European countries), the findings are quite different as these countries are moving forward from these basic challenges and now face advanced and technical challenges such as data analysis and visualization, documentation and metadata, copyright and technical issues (Mani et al., 2021;McBurney and Kubas, 2021;Cox et al., 2019). ...
Purpose
Data librarianship, or data-driven librarianship, is the combination of information science, data science and e-science fields and is gaining gradual importance in the library and information science (LIS) profession. Hence, this study investigates the data librarianship core concepts (motivational factors, challenges, skills and appropriate training platforms) to learn and successfully launch data librarianship services.
Design/methodology/approach
A survey method was used and the data were collected through online questionnaire. Purposive sampling method was applied and 132 responses were received with 76 respondents from the public and 56 from the private sector universities of Pakistan. The statistical package for social sciences (SPSS version 25) was used, and descriptive and inferential statistics were applied to analyzed the data.
Findings
LIS professionals understand the importance of data-driven library services and perceive that such services are helpful in evolving the image of the library, helping with the establishment of institutional data repositories/data banks, developing data resources and services for library patrons and especially researchers, and receiving appreciation and acknowledgment from the higher authorities. The major challenges that emerged from the data were: missing data policies, limited training opportunities for data librarianship roles, no additional financial benefits, lack of infrastructure and systems, lack of organizational support for the initiation of data-driven services, and lack of skills, knowledge and expertise. Data librarianship is in its early stages in Pakistan, and consequently, the LIS professionals are lacking basic, advanced and technical data-driven skills.
Research limitations/implications
The policy, theoretical and practical implications describe an immediate need for framing data policies. Such policies will help the libraries or any other relevant entities to store the data and assign metadata and documentation in such a way that it is easy to retrieve and reusable for others.
Originality/value
This is the first study in Pakistan to investigate the perceptions of LIS professionals about data librarianship core concepts: motivational factors, challenges, skills and appropriate training platforms to grasp data-driven skills and successfully launch library services.
... Given the emergent research data management, Abankwa and Yuan (2019) and Chigwada, Chiparausha, and Kasiroori, (2017) report the absence of institutional support and leadership in African Universities and the paucity of synergy between librarians and researchers in Ghana and Zimbabwe respectively. McBurney and Kubas (2022) identified hindrances to library data support in South Africa and the United States of America. The challenges relate to limited documentation, data privacy and protection, inadequate data curation, and technology resources limitation like security and access. ...
The purpose of this study was to investigate data aspects in Library and Information Science curricula at all levels in selected African Universities. Design/methodology/approach review of existing literature on data aspects and LIS Curricula. Secondly, the study juxtaposed web-based courses for fourteen LIS schools to establish data aspects incorporated into their curricula. The study's findings found that although many LIS schools have some form of data aspects in curricula, coverage is still very limited and basic.
... As much as funding for collections is important, administrative support for data services is equally critical. The library should devote sufficient staff time and funds to supporting data services to succeed (McBurney and Kubas, 2022). Libraries have been accused of not supporting DDM practices as one of the main barriers, due to a lack of institutional leadership support and lack of established data management plans, policies and procedures are the major problems LIS Professionals face. ...
Purpose
The study aims to identify the prospects and challenges associated with current practices regarding digital data sets management in university libraries in Pakistan.
Design/methodology/approach
A cross-sectional survey approach was used to collect the data from library and information science (LIS) professionals working in public sector university libraries in Pakistan. A four-part questionnaire was used to collect the data from the respondents. The collected data from 371 participants were analyzed using a statistical package for social sciences (SPSS-24 version) and analysis of moment structure (AMOS-24).
Findings
LIS professionals are better placed to support digital data management practices, such as finding, collecting, assessing and analyzing digital data sets and making digital data publicly discoverable and accessible via open access. In spite of this, a lack of leadership support, interest and cooperation among university departments and the absence of a data management plan, policies and procedures were reported as significant challenges.
Practical implications
To meet the needs of data users, LIS professionals must become knowledgeable about managing and reusing digital data sets. Due to the demands of the information society, university librarians need to learn about data-centric practices that can enhance research outputs and provide new insights.
Originality/value
This research paper is extracted from a PhD dissertation to present a contemporary picture of library data management services and the challenges LIS professionals face to provide possible solutions.
As data skills are incorporated into academic curriculum and data becomes more widely available and used in everyday life, many librarians find themselves serving as 'accidental' data librarians in their subject areas. Due to this evolving landscape and growing data need, it is increasingly important for librarians to be familiar with data resources and able to answer secondary data reference questions. To learn more about this area of librarianship, this study uses survey responses from librarians who answer data questions to explore the challenges and frustrations that arise from data reference questions and interactions. Our key findings reveal that frustrations are ever present in data reference regardless of how much experience a librarian has, and many frustrations arise due to factors such as patron expectations, subject-specific and data-related jargon, and data formats and accessibility.
Understanding Academic Patrons’ Data Needs through Virtual Reference Transcripts: Preliminary Findings from New York University Libraries
An insider’s guide to data librarianship packed full of practical examples and advice for any library and information professional learning to deal with data.
Interest in data has been growing in recent years. Support for this peculiar class of digital information – its use, preservation and curation, and how to support researchers’ production and consumption of it in ever greater volumes to create new knowledge, is needed more than ever. Many librarians and information professionals are finding their working life is pulling them toward data support or research data management but lack the skills required.
The Data Librarian’s Handbook, written by two data librarians with over 30 years’ combined experience, unpicks the everyday role of the data librarian and offers practical guidance on how to collect, curate and crunch data for economic, social and scientific purposes.
With contemporary case studies from a range of institutions and disciplines, tips for best practice, study aids and links to key resources, this book is a must-read for all new entrants to the field, library and information students and working professionals.
Key topics covered include:
•the evolution of data libraries and data archives
•handling data compared to other forms of information
•managing and curating data to ensure effective use and longevity
•how to incorporate data literacy into mainstream library instruction and information literacy training
•how to develop an effective institutional research data management (RDM) policy and infrastructure
•how to support and review a data management plan (DMP) for a project, a key requirement for most research funders
•approaches for developing, managing and promoting data repositories
•handling and sharing confidential or sensitive data
•supporting open scholarship and open science, ensuring data are discoverable, accessible, intelligible and assessable.
Readership: This title is for the practising data librarian, possibly new in their post with little experience of providing data support. It is also for managers and policy-makers, public service librarians, research data management coordinators and data support staff. It will also appeal to students and lecturers in iSchools and other library and information degree programmes where academic research support is taught.
The proliferation of online access to social science statistical and numeric data sources, such as the U.S. Census Bureauâs American Fact Finder, has lead to an increased interest in supporting these sources in academic libraries. Many large libraries have been able to devote staff to data services for years, and recently smaller academic libraries have recognized the need to provide numeric data services and support. This guidebook serves as a primer to developing and supporting social science statistical and numerical data sources in the academic library. It provides strategies for the establishment of data services and offers short descriptions of the essential sources of free and commercial social science statistical and numeric data. Finally, it discusses the future of numeric data services, including the integration of statistics and data into library instruction and the use of Web 2.0 tools to visualize data.
Data reference and instruction in journalism and the social sciences
- S Hoffman
Data reference: Strategies for subject librarians
- B Bordelon
The Reference Guide to Data Sources
- J Bauder
Recoding the academic librarian: Our developing role as data detectives
- J Mcburney
- A Kubas
Data basics: An introductory text
- D Geraci
- C Humphrey
- J Jacobs