IASSIST Quarterly (Journal)
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Data literacy in undergraduate research: A case study from student poster competitions
Full text linkAt universities, research involving data is often regarded as the domain of graduate students and faculty. However, undergraduate students also work with data within the research process, and it can be a core experience to prepare them for future education and careers. Research products from undergraduate students can demonstrate the extent of their data literacy skills and understanding, which are becoming central to success in graduate studies and the world of work. Since a leading way for undergraduate students to share research is through posters, this paper examines undergraduate posters at Brigham Young University (BYU) in the context of data literacy skills. The paper defines data literacy and the importance of undergraduate students becoming data literate. This case study shares the BYU context for the undergraduate poster competitions and the resulting strengths and gaps in data literacy education followed by suggestions for supporting and encouraging undergraduate research and data literacy development beyond the traditional area of data analysis
The role of FAIR principles in high-quality research data documentation: Looking at national election studies
Full text linkThe FAIR principles as a framework for evaluating and improving open science and research data management have gained much attention over the last years. By defining a set of properties that indicates good practice for making data findable, accessible, interoperable, and reusable (FAIR), a quality measurement is created, which can be applied to diverse research outputs, including research data. There are some software tools available to help with the assessment, with the F-UJI tool being the most prominent of them. It uses a set of metrics which defines tests for each of the FAIR components, and it creates an overall assessment score.
The article examines differences between manually and automatically assessing FAIR principles, shows that there are significantly different results by using national election studies as examples. An evaluation of progress is done by comparing the automatically assessed FAIRness scores of the datasets from 2018 with those of 2024, showing that there is only a very slight yet not significant difference. Specific measures which have improved the FAIRness scores are described by the example of the Politbarometer 2022 dataset at the GESIS Data Archive. The article highlights the role of archives in securing a high level of data and metadata quality and technically sound implementation of the FAIR principles to help researchers benefit from getting the most of their valuable research data
How are we FAIR-ing? Creating a FAIR self-assessment checklist for data repositories
Full text linkIn 2023, a team from a local grant-funded medical data repository requested guidance from Penn Libraries on evaluating the extent to which their repository was FAIR-enabling. After a consultation with the repository team, our research data experts discovered that many of the current self-assessments of the FAIR guidelines were for data creators rather than data repository managers. In addition, we wanted a self-assessment tool similar to the process and guidance created by CoreTrustSeal but focusing explicitly on the FAIR Principles. In answer to their request, the Penn Libraries Research Data Engineer conducted a literature review and coalesced current guidance and assessment tools on the principles. After this review of the existing documentation, a small team developed a FAIR Principles self-assessment tool for repository teams. In addition to several iterations of the tool, we also met with the repository team for feedback on making the tool more understandable. Our conversation provided insights into the challenges of explaining the FAIR Principles to those without information or data science backgrounds. The discussion and creation of this self-assessment tool helped develop a more transparent and trustworthy repository. This paper will discuss our process for developing the assessment, the goals for utilizing the tool, and the lessons learned. Reporting our findings as they currently stand will prompt the research data management field to ruminate on the adoption of FAIR Principles for data repositories. We also intend to encourage conversation on the usability of the FAIR Principles for professionals without an information or data science background
Using common data elements to foster interoperability of research on health disparities
Full text linkCommon data elements (CDEs) are standardized questions, variables, or measures with specific sets of responses that are used across multiple studies. They are organized around a particular research topic or question, validated, and defined via a consensus building process. Their use fosters comparability of results and findings across studies. CDEs are more common in NIH-funded clinical and biomedical research than in social, behavioral, and economic (SBE) research. Yet the community-driven, consensus-building approach to defining CDEs makes them well suited to measuring complex social phenomena. The Social, Behavioral, and Economic COVID Coordinating Center at ICPSR (SBE CCC) is leading the effort to establish CDEs for SBE research into the effects of the COVID-19 pandemic. We are collaborating with fifteen NIH-funded research teams who are examining pandemic-related health disparities related to race, ethnicity, sex, geography, income, and other factors. In this article, we discuss ways in which CDEs support research into health disparities and describe our process for identifying, validating, and building consensus on CDEs related to COVID public health policies
Literature review on the competencies of data literacy for middle-grade learners
Full text linkIn today’s data-driven world, it is crucial for students to be data literate; able to view, understand, and reason with data in multimodal forms representing real-world phenomena. Despite its importance, data literacy is rarely integrated into K-12 curricula, and its definition remains unclear for this age group. This paper reviews existing literature to define the competencies relevant to adolescent learners and highlights those crucial for middle-grade students. A literature review of theoretical and empirical discussions on data literacy concepts, instructional practices, and assessments revealed eight key competencies. Among these, two were identified as most critical for middle-grade students: interpreting data representations and evaluating claims based on data representations. This paper aims to serve as a conceptual and practical guide to enhance data literacy in educational settings, providing a foundation for educators and researchers to collaboratively support middle-grade learners
Reflecting on past practice and research to innovate
Full text linkWelcome to the first issue of IASSIST Quarterly for 2025, IQ 49(1).
We are excited to welcome two new members to our Editorial Team, Mary Carter, the Finance and Operations Research Librarian at Princeton University, and Jessica (Jess) Hagman, the Social Sciences Research Librarian and an Assistant Professor at the University of Illinois Urbana-Champaign. Mary and Jess have graciously volunteered to serve as our new Managing Editors and will share the responsibility. They have already taken an active role in the production of this issue.
The Editorial Team together with the Editorial Board continue to develop policies for authors, reviewers, and the editorial team. We hope to share these policies with the IASSIST community in the near future.
The current issue, IQ 49(1), presents three excellent papers. All three review services offered by data librarians or issues important to them, and identify opportunities to incorporate innovative approaches to enhance those services for users and researchers.
Author Madison Golden shares the adaptive approach she uses as a research data librarian. The article ”Adaptive Data Governance for Research Data Management” builds on the author’s experience working in data governance at a corporation as well as her more recent experience as a research data librarian in an academic institution. The author introduces four styles of data governance that provide a framework for librarians and data governance specialists alike to prioritize competing needs and guide researchers through the data lifecycle. This approach offers increased flexibility in data management practices, continuous improvement of services and resources, efficiency, and empowerment of researchers and related stakeholders.
In the article ”Literature review on the competencies of data literacy for middle-grade learners” author Semi Yeom reviews the literature related to data literacy guidelines and practices for students in K-12 school settings, focusing on middle-grade learners. The author identifies eight main competencies that are important for data-literate adolescents, and highlights the two competencies that were pointed out by researchers as essential skills for academic success and critical engagement in our increasingly data-driven world.
The article ”Support for Computer-Assisted Qualitative Data Analysis Software in ARL libraries” authored by Paul Rival surveys support for Computer-Aided Qualitative Data Analysis Software (CAQDAS) among members of the Association of Research Libraries (ARL). By visiting institutional websites and LibGuides, the author tries to understand the level of qualitative data analysis expertise and support provided to researchers at these academic institutions. Peer institutions that do not offer such services are encouraged to explore this possibility to better support their researchers.
We hope you enjoy the reading! We are looking forward to seeing many of you in June, at IASSIST 50th anniversary conference, the ”best IASSIST ever” in Bristol, UK.
Ofira Schwartz and Michele Hayslett, March 202
From FAIR principles to data competencies: Evolving library support for data-driven scholarship
Full text linkAdaptive data governance for research data management
Full text linkThe field of research data management librarianship has grown significantly in past years but continues to face the challenges of knowledge gaps, frequent changes to policy and guidance, and the complexity and context that comes from data that varies both in type and format. As a research data librarian, I face these issues on a daily basis and have adopted an adaptive approach that combines multiple styles to balance the individual needs of researchers while complying with policies and best practices. This approach was adopted from my past experience in data governance at a corporation in which we faced the same core challenges. Incorporating the four styles of data governance as laid out by Gartner provides a framework for librarians and data governance specialists alike to prioritize competing needs and guide researchers through the data lifecycle. The benefits of this approach include increased flexibility in data management practices, continuous improvement of services and resources, efficiency, and empowerment of researchers and related stakeholders
Adventures in data literacy: When the gap you were trying to identify turns out to be a chasm.
Full text linkIn an era where post-secondary students are seen as digital natives and novel knowledge mobilization is becoming an expected part of scholarly discourse, this paper synthesizes insights from multiple surveys about this topic. This research was conducted in 2020 and 2022 with participants from programs across the University of Manitoba (a Canadian public research university of around 30,000 students). This paper aims to illuminate the campus landscape and assess library support and resources for research visualization; additionally, the authors also explore challenges and potential pathways for improvement