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Data Soup is a collaboration between the Journal of eScience Librarianship (JeSLIB) and the Data Curation Network to host a series of community focused webinars/discussions to exchange practices for curating research data of different formats or subject areas among data curators. The lineup of the inaugural webinar includes the following speakers and topics from the recent JeSLIB Special Issue: Data Curation in Practice:
Objective: This paper examines a unique data set disclosure process at a medium sized, land grant, research university and the campus collaboration that led to its creation.
Methods: The authors utilized a single case study methodology, reviewing relevant documents and workflows. As first-hand participants in the collaboration and disclosure process development, their own accounts and experiences also were utilized.
Results: A collaborative approach to enhancing research data sharing is essential, considering the wide array of stakeholders involved across the life cycle of research data. A transparent, inclusive data set disclosure process is a viable route to ensuring research data can be appropriately shared.
Conclusions: Successful sharing of research data impacts a range of university units and individuals. The establishment of productive working relationships and trust between these stakeholders is critical to expanding the sharing of research data and to establishing shared workflows.
Objective: While diversity, equity, inclusion, and accessibility (DEIA) principles and practices have been incorporated into much of academic librarianship, there has been less focus on the job postings.
Methods: In order to quantify ways in which DEIA is being integrated into job postings, we analyzed 48 job positions for engineering librarians posted in 2018 and 2019 via deductive thematic analysis, looking for trends in salary and qualifications related to education and academic or professional experience.
Results: Of postings that listed a quantitative salary value, salary ranged from $45,000 to $81,606; the median was $60,750. However, only 33% (n = 16) of positions listed a quantitative salary value. For educational qualifications, we found that 98% of job postings (n = 47) listed a Master’s in Library and Information Science (MLIS) as a required qualification; however, 34% of these postings (n = 16) would accept an equivalent degree in lieu of the MLIS. Additionally, 73% (n = 35) of positions sought candidates with an MLIS and another degree; 91% of these positions (n = 32) wanted the additional degree to be in a science, technology, engineering, and mathematics discipline. For academic or professional experience, 56% of positions (n = 27) sought candidates with previous academic library experience.
Conclusions: Using this data, we provide actionable recommendations on how to incorporate DEIA principles into any academic librarian job posting. Our study provides quantitative data and evidence-based recommendations that can be used to make DEIA an integral part of the job postings in academic librarianship.
This study of data services librarians is part of a series of studies examining the current roles and perspectives on Research Data Management (RDM) services in higher education. Reviewing current best practices provides insights into the role-based responsibilities for RDM services that data services librarians perform, as well as ways to improve and create new services to meet the needs of their respective university communities.
Objectives: The objectives of this article are to provide the context of research data services through a review of past studies, explain how they informed this qualitative study, and provide the methods and results of the current study. This study provides an in-depth overview of the overall job responsibilities of data services librarians and as well as their perspectives on RDM through job analyses.
Methods: Job analysis interviews provide insight and context to the tasks employees do as described in their own words. Interviews with 10 data services librarians recruited from the top 10 public and top 10 private universities according to the 2020 Best National University Rankings in the US News and World Reports were asked 30 questions concerning their overall job tasks and perspectives on RDM. Five public and five private data services librarians were interviewed. The interviews were recorded and transcribed. The transcriptions were analyzed in NVivo using a grounded theory application of open, axial, and selective coding to generate categories and broad themes based on the responses using synonymous meanings.
Results: The results presented here provide the typical job tasks of data services librarians that include locating secondary data, reviewing data management plans (DMPs), conducting outreach, collaborating, and offering RDM training. Fewer data services librarians assisted with data curation or manage an institutional repository.
Discussion: The results indicate that there may be different types of data services librarians depending on the mix of responsibilities. Academic librarianship will benefit from further delineation of job titles using tasks while planning, advertising, hiring, and evaluating workers in this emerging area. There remain many other explorations needed to understand the challenges and opportunities for data services librarians related to RDM.
Conclusions: This article concludes with a proposed matrix of job tasks that indicates different types of data services librarians to inform further study. Future job descriptions, training, and education will all benefit from differentiating between the many associated research data services roles and with increased focus on research data greater specializations will emerge.
Objective: An underexplored area in Library and Information Science (LIS) is the development of educational offerings and partnerships in Health-Related Informatics (HRI) (e.g., bioinformatics, clinical informatics, health informatics). The purpose of this study is to identify which disciplines are collaborating in HRI education and how partnerships developed.
Methods: This study was conducted in two parts: a website review and survey. Seventy-seven North American ALA-accredited and iSchool member websites were searched between November 2019-March 2020 for HRI-related educational offerings and which academic units were involved. Two hundred sixteen individuals involved in LIS and/or HRI education were contacted for a 40-question survey that included: their roles and responsibilities regarding HRI education; the alignment of this education with strategic plans or competencies; and how HRI partnerships developed. The survey also asked those who were not currently partnering in HRI education which factors influenced their circumstances.
Results: 352 HRI educational offerings existed within ALA-accredited or iSchool programs. A total of 38 (17.5%) responded to the survey. For almost two-thirds of these, there was no indication of partnership in that education (213/352, 60.5%). LIS or iSchool involvement in HRI is just under one-third of all offerings (111/352, 31%). “Health or healthcare” informatics (35) or “biomedical or bioinformatics” were the most common types of HRI offered from the website review and survey.
Conclusions: Opportunities exist for LIS programs to form HRI educational partnerships that will provide richer educational offerings for LIS students and health sciences librarians.
Objective: This study explores the root causes that undermine successful collaborations between scientists and their library liaisons to improve outreach to this population.
Methods: This paper uses the Five Whys Technique to explore the reasons why many scientists are unaware of the breadth of services offered by liaison librarians. Existing outreach strategies that address these obstacles are interpreted through the lens of implementation science theories and process models, including Normalization Process Theory.
Results: A total of four recommendations—two for liaison librarians and two for libraries as institutions—are provided to enhance the perceived value of liaison services. The recommendations for individuals include aiming to understand scientists’ needs more comprehensively and actively increasing the visibility of services that respond to those needs. Those for libraries focus on cross-functional teams and new forms of assessment.
Conclusions: These recommendations emphasize the benefits of collaboration to liaisons, to library programs at large, and to the faculty that liaisons serve. Implementation science can help librarians to understand why certain outreach strategies bring success, and how new services can be implemented more effectively.
Due to the COVID-19 pandemic, Delft University of Technology in the Netherlands (TU Delft) stopped its activities on campus until autumn 2021 and moved all teaching activities to an online setting. This article describes the challenges and lessons learned from successfully moving basic programming workshops, Software Carpentry workshops, online. The article details the local TU Delft context, the online workshop tools that were employed, and the roles that the organising team played to organise and run these online workshops. To successfully adapt to the online context, it was important to adjust the original planning and programme for the Carpentry workshops. General challenges of online workshops and solutions that worked for the TU Delft team are also shared. Through iteratively developing the online workshops over the past year, the team has enhanced both learners’ and organisers’ experience. The lessons learned will continue to be valuable when the workshops are transitioned back to a physical setting when COVID-19 protective measures are lifted.
Objective: The purpose of this study is to examine the usability of the Texas Data Repository (TDR) for the data depositors who are unfamiliar with its interface and use the results to improve user experience.
Methods: This mixed-method research study collected qualitative and quantitative data through a pre-survey, a task-oriented usability test with a think-aloud protocol, and an exit questionnaire. Analysis of the quantitative (i.e., descriptive statistics) and qualitative data (e.g., content analysis of the thinking-aloud protocols) were employed to examine the TDR’s usability for first-time data depositors at Texas A&M University.
Results: While the study revealed that the users were generally satisfied with their experience, the data suggest that a majority of the participants had difficulty understanding the difference between a dataverse collection and dataset, and often found adding or editing metadata overwhelming. The platform’s tiered model for metadata description is core to its function, but many participants did not have an accurate mental model of the platform, which left them scrolling up and down the page or jumping back and forth between different tabs and pages to perform a single task. Based on the results, the authors made some recommendations.
Conclusions: While this paper relies heavily on the context of the Harvard Dataverse repository platform, the authors posit that any self-deposit model, regardless of platform, could benefit from these recommendations. We noticed that completing various metadata fields in the TDR required participants to pivot their mindset from a data creator to that of a data curator. Moreover, the methods used to investigate the usability of the repository can be used to develop additional studies in a variety of repository and service model contexts.
It takes a strong data science community and many stakeholders to make disparate types of data work so that you and I can continue to explore and learn. Being proactive by including diversity, equity, inclusion, and accessibility (DEIA) policies and practices, along with evaluating liaison/outreach roles and established programs and tools, will go a long way in strengthening the library, its staff and services, and the institution. Fighting for the “food” you need to grow the profession and data services is key to the future of the RDM library community.
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