Evidence Summary
Data Librarians’ Skills and Competencies Are Heterogeneous and Cluster
into Those for Generalists and Specialists
A Review of:
Federer, L. (2018). Defining data librarianship: A survey of
competencies, skills, and training. Journal
of the Medical Library Association 106(3),
294–303. https://doi.org/10.5195/jmla.2018.306
Reviewed by:
Scott Goldstein
Web Librarian
Appalachian State University Libraries
Boone, North Carolina, United States of America
Email: goldsteinsl@appstate.edu
Received: 29 Oct. 2018 Accepted:
27
Dec. 2018
2019 Goldstein.
This is an Open Access article distributed under the terms of the Creative
Commons‐Attribution‐Noncommercial‐Share Alike License 4.0
International (http://creativecommons.org/licenses/by-nc-sa/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly attributed, not used for commercial
purposes, and, if transformed, the resulting work is redistributed under the
same or similar license to this one.
DOI:
10.18438/eblip29516
Abstract
Objective – To better define the skills, knowledge, and
competencies necessary to data librarianship.
Design – Electronic survey.
Setting – Unknown number of research institutions in English-speaking
countries with a focus on North America.
Subjects – Unknown number of information professionals who
follow data-related interest group electronic mail lists or discussions on
Twitter.
Methods – Author distributed an electronic survey via electronic
mail lists and Twitter to information professionals, particularly those in
biomedicine and the sciences, who self-determined that they spend a significant
portion of their work providing data services. The survey asked respondents to
rate the importance of various skills and expertise that had been selected from
a review of the literature. In addition to other quantitative analysis, author
performed cluster analysis on the final dataset to detect subgroups of similar
respondents.
Main Results – 82 valid responses were received. Most respondents
supported more than one academic discipline and spent at least half of their
time on data-related work. Competencies in the “Personal Attributes” category
(such as interpersonal, written, and presentation skills) were rated as most
important, while those in the “Library Skills” category were rated as least
important. A cluster analysis detected two groups that could best be described
as subject specialists and data generalists. Subject specialists focus on a
smaller number of disciplines and view a smaller number of tasks as important
to their work compared to data generalists. In addition, data generalists are
more likely to report spending most of their time on data-related work.
Conclusion – Data librarianship is a heterogeneous profession
with many skillsets at play depending on the work environment, but the
existence of two overarching subgroups – subject specialists and data
generalists – deserves further study and may have implications for a number of
stakeholders. Hiring institutions may consider the breadth of their user
population’s needs before recruitment. Educational institutions as well as
other on-the-job training opportunities may do well to focus more on “soft
skills” as this is deemed more important by data librarians.
Commentary – In the past decade, there has been growth in the
number of libraries offering data services, defined as services to researchers
in relation to managing data. Examples of data services include data management
guidance, data curation, and data visualization. Whereas Tenopir et al. (2012)
found that only a small minority of U.S. and Canadian libraries offer any sort
of services, a recent content analysis of library websites by Yoon and Shultz
(2017) has revealed over 180 schools with services in place, though to varying
degrees. Other scholarship has focused on the competencies required of
librarians in data services roles, such as Xia and Wang’s (2014) analysis of
social science data librarian job postings. The author’s survey contributes to
the literature by asking self-designated data librarians in biomedical and
scientific fields how these competencies and skills are actually utilized in
practice.
This summary relies on Boynton and Greenhalgh’s (2004)
critical appraisal tool. Two aspects of the paper are worth highlighting here.
First, the author performs cluster analysis on the categorical survey data to
group respondents into the categories of generalist and specialist. This is an
innovative and welcome analytical technique in LIS practitioner research. In
addition, the data used in the analysis are openly available, well-documented,
and reproducible. The questionnaire generally fares well against the critical
appraisal tool. The items were developed in conjunction with a review of the
literature and pilot tested. Skills were rated on importance using a five-point
Likert scale ranging from “Not at all important” to “Absolutely essential” with
an additional option for “Don’t know or N/A.” The biggest issue was the sampling
method, which relied on a convenience sample from electronic mail lists and
social media. This limits the generalizability of the results, although the
author mentions this as a limitation and generally refrains from making
wide-sweeping claims in the paper. (Differences between subject specialists and
data generalists were tested with unpaired-samples t-tests and p-values were
reported, but these should have been adjusted for multiple tests or else
explicitly presented as exploratory.)
The study has unconventional implications for library
science students and librarians looking to get into data librarianship. For
students, it suggests they may be served more by developing soft skills and
seeking discipline-specific skills rather than focusing on library-specific
data management or curation courses. For librarians, on-the-job training and
professional development opportunities, especially in a specific discipline if
they wish to specialize, might prove more beneficial. Strong comfort with
self-education may be a highly valuable skill to develop and certainly one to
promote during the hiring process. The study also highlights potential barriers
to data librarians that deserve further study. For instance, an open-ended
survey comment indicated that “our researchers have shown a strong bias towards
working with ‘one of their own.’” It is unclear where this reluctance comes
from, but it may be from a lack of awareness of the skills and competencies
data librarians possess, suggesting that proactively demonstrating what they
have done and can do to assist researchers would do much to reverse
misconceptions.
References
Boynton, P. M., & Greenhalgh, T. (2004). Selecting, designing, and
developing your questionnaire. BMJ, 328(7451), 1312–1315. https://doi.org/10.1136/bmj.328.7451.1312
Tenopir, C., Birch, B., & Allard, S. (2012). Academic libraries and
research data services: Current practices and plans for the future [White
paper]. Retrieved October 26, 2018, from Association of College & Research
Libraries: http://www.ala.org/acrl/sites/ala.org.acrl/files/content/publications/whitepapers/Tenopir_Birch_Allard.pdf
Xia, J., & Wang, M. (2014). Competencies and responsibilities of
social science data librarians: An analysis of job descriptions. College & Research Libraries, 75(3),
362–388. https://doi.org/10.5860/crl13-435
Yoon, A., & Schultz, T. (2017). Research data management services in
academic libraries in the US: A content analysis of libraries’ websites. College & Research Libraries,
78(7), 920–933. https://doi.org/10.5860/crl.78.7.920