Over the past decade, more than thirty books have been published which seek to provide data management foundational and continuing education materials for an academic librarian audience. This annotated bibliography centrally collects and reviews these titles, providing not only a quantitative overview of publisher, price, and availability but also a topical overview for each book and a brief critical summary. Concluding with recommendations for the most useful titles for targeted audiences, the authors address trends, gaps, and limitations of the present corpus.
This paper introduces the Open Data Format (ODF), a new, non-proprietary, metadata-enriched, and zip-compressed data format for tabular data alongside statistical software packages. It embeds structured, multilingual metadata directly within the data file, using the well-established DDI-Codebook standard (DDI Alliance, 2014), and enables access to both data and documentation within common statistical environments. Dedicated software packages for R, Stata, and Python allow users to seamlessly import, export, and explore both data and metadata, moving documentation significantly closer to the user. The data format is specified as a CSV file for the data and an XML file containing the metadata, both compressed into a zip file with the '.odf.zip' extension.
The IPUMS Bibliography tool plays a crucial role harnessing over three decades of IPUMS organizational knowledge to support and strengthen institutional goals, demonstrating the impact of IPUMS datasets and the Institute for Social Research and Data Innovation (ISRDI, the home of IPUMS), and strengthening broader social science infrastructure. The IPUMS Bibliography captures, organizes, and makes discoverable: research analyses on tens of thousands of variables of microdata and aggregate variables over time; processes for developing and maintaining well-defined harmonized classifications; and technological innovation and advancement supporting IPUMS systems. The challenge for the IPUMS Bibliography team is threefold: refine bibliographic data capture methods; develop an efficient, scalable, and sustainable workflow for managing the expanding bibliographic database; and improve the user interface for internal and external users to discover and link to publications using IPUMS data. This article will trace the IPUMS Bibliography development from 1994 to 2025, demonstrate the benefit of harnessing organizational knowledge within an organization, and suggest the potential for leveraging organizational knowledge to support robust social science research.
In numerous research projects, researchers generate a rich variety of qualitative research data. It is generated through the application of different research methods and manifests itself in different data types and formats. In their heterogeneity, these data allow insights into people's everyday lives from different perspectives. Qualitative research data is rich and often offers analytical potential for further research. However, in comparison to quantitative social research, where the secondary use of data is established practice, qualitative data is still used rather hesitantly. This is where the article comes in. Infrastructurally established approaches to data sharing, that take into account data protection and research ethics aspects and enable FAIR data sharing of qualitative data, are presented. It is based on the solutions of various Research Data Centers (RDCs) of a network of qualitative research data infrastructures (QualidataNet), which was established as part of the National Research Data Infrastructure in Germany. The QualidataNet network is open to all RDCs, repositories and other archiving partners that hold qualitative data. It enables as a ‘central point of entry' 1) to provide researchers with easier access to qualitative data, to support the management of qualitative data and to help finding suitable archiving partners and 2) to promote the exchange among data providers and the development of best practices for the management, sharing and reuse of qualitative data. The article addresses how ‘cooperative data management’ makes it possible to integrate and accompany important steps of data preparation for data sharing into the research process, so that synergies also arise for the primary research itself.
Qualitative data and their collections are valuable treasures for re-use, re-analysis, and training. However, there is a tension inherent in qualitative data sharing between the desire for transparency and the complex ethical, methodological, and labor-intensive realities of sharing non-numeric materials. As the push for Open Science accelerates, researchers are increasingly required to navigate the high standards set by the FAIR, CARE, and TRUST principles. In response to data sharing expectations, qualitative researchers have expressed concerns about the ethical challenges, methodological concerns, and labor involved in making their data and materials publicly available. The collection of papers in this special issue examines the challenges of data sharing in the context of qualitative research. The first paper, “Evaluating data sharing practices: A case study for federally funded research using FAIR Standards,” examines qualitative and quantitative data from a federally funded project on undergraduate academic success. Author Jung Mi Scoulas evaluates how these shared data sets align with FAIR principles, offering critical insights for applying these standards across diverse and complex data types. In “Giving structure to the "everything else" box: Creating curation standards for qualitative data,” Amanda Draft, Matthew Johnston, Aubrey Garman, Zachary Bennett, and Annie Beaubien examine the role played by data curators in applying FAIR standards to the unique, often variable structures of qualitative and mixed-methods research. The authors highlight how the Inter-university Consortium for Political and Social Research (ICPSR) has refined its workflows for qualitative data and present a framework that categorizes curation tasks into specific "curation levels" based on their intensity. The curation of qualitative data requires special considerations in archival infrastructure, workflow, and relationships with other archival collections. The article, “IPUMS qualitative resources: Supporting robust social science research”, looks at the development of archival infrastructure to manage, preserve, annotate, and distribute qualitative data at an established archive with a long history of curating census data. Diana Magnuson presents a model for how data archives can extend their data curation workflows to qualitative data, even if it is not the primary data product. In “Managing, sharing and reusing qualitative data - approaches that integrate data protection, research ethics and research interests, or: How researchers and data providers work together to make qualitative research data available for scientific re-use,” Kati Mozygemba describes archival infrastructure designed to address the data protection and ethical challenges in qualitative data sharing. This article explores the QualidataNet, a network of qualitative research data infrastructures established as part of the National Research Data Infrastructure in Germany that enables cooperative data management, data access, and data exchange among data producers. The article, “Stewarding qualitative data: A hermeneutic and relational reframing of qualitative data governance” explores the challenges of applying deidentification and disclosure control practices designed for structured data to qualitative data. Taking a critical approach, Elizabeth Green proposes a nuanced model where qualitative data governance aligns with ethics, FAIR, and CARE principles for a responsive and practical stewardship of qualitative data. The article, “From labourer to information service expert: FSD's journey in qualitative data archiving” by Arja Kuula-Luumi, Jarkko Päivärinta, and Tuomas J. Alaterä, highlights how a data repository extended their archiving services to qualitative data. The Finnish Social Science Data Archive (FSD) has slowly evolved qualitative data archiving support since 2003 including practical guidance for researchers, procedures, and software. Given the recent global and disciplinary trends in open science and data, we believe it is timely to revisit the qualitative data landscape. This special issue contributes to the literature on concepts, theory, and practices that can support transparent, trustworthy, and accessible qualitative research now and into the future.
Qualitative data governance is increasingly formalised within infrastructures originally designed for quantitative research. These systems rely on tools such as suppression, generalisation and output checking, underpinned by epistemological assumptions that treat data as detachable, stable and decontextualisable. Such logics misalign with qualitative inquiry, where narrative meaning is relational, historically situated and co-constructed through interpretation. As a result, conventional governance practices risk enacting epistemic harms- flattening lived experience, distorting participant voice, and prioritising procedural defensibility over interpretive integrity. Drawing on hermeneutics, feminist epistemology and theories of epistemic injustice, this paper reframes qualitative data as meaning-bearing and relational rather than fragmentary or object-like. It critically examines how CARE, FAIR, the Five Safes and the Belmont principles offer valuable ethical resources but require reinterpretation to support qualitative epistemologies. In response, the paper develops the Interpretive and Relational Data Stewardship (IRDS) model, a framework grounded in interpretive awareness, relational accountability, epistemic justice and ethical stewardship. Through worked examples, it demonstrates how governance decisions actively reshape meaning and how over-abstraction can reproduce the very harms governance seeks to prevent. The paper argues that qualitative data governance must shift from logics of containment to practices that preserve the conditions under which meaning, dignity and justice can emerge.
Dear IASSISTers, Welcome to IASSIST Quarterly, Vol. 50 No. 1! It is our great pleasure to present the first issue of the 50th volume of IQ. The journal has come a long way since its humble beginnings in 1977, when it served as a newsletter reporting on the activities of IASSIST action groups. Over the years IQ has been through several format changes, transferred from a print format to a fully online journal in 2008, and became a full-fledged open access journal, switching to the Open Journal System in 2017. Beyond the changes in formatting, over the years, IASSIST Quarterly developed and transitioned into a peer-reviewed publication, offering our community members a venue to share their research, experience, insight, and best practices for using information technology and data to support research and teaching. We are delighted to open the new volume with a special double issue, guest edited by members of the Qualitative Social Science and Humanities Data Interest Group (QSSHDIG). This is the second special issue dedicated to qualitative research. While the first one (Vol. 43, No 2) focused on extending data services to qualitative research, this second issue examines the unique challenges of data sharing in the context of qualitative research. Articles in this issue examine qualitative data through the lens of FAIR, CARE, open science, and access. Guest editors Hilary Bussell, Cheryl A. Thompson, Maureen Haaker, and Michael Beckstrand have skillfully guided the editorial process to deliver this collection of research. We are grateful for their expert stewardship. Finally, we were saddened to learn recently of the passing of Walter Giesbrecht, a long-time IASSIST member. Walter is remembered for his many contributions to the IASSIST community and his mentoring and kindness to those new to the field. We are hoping to include an In-Memoriam honoring Walter in an upcoming issue. We hope you enjoy engaging with the scholarly work presented in this issue, and we wish you continued inspiration and a sense of renewal in the months ahead. Ofira Schwartz and Michele Hayslett, March 2026
To produce high-quality data for future use, Curators must be both proactive in setting FAIR (Findability, Accessibility, Interoperability, and Reuse) standards and flexible in allowing curation standards to evolve alongside methodological and technological advances. This is especially pertinent when working with qualitative and mixed-methods data, which can vary immensely in structure and form depending on the study, even when researchers are willing to share these data. Standards at the Inter-university Consortium for Political and Social Research (ICPSR) for curating qualitative data have evolved over time to account for the varying types of data ingested while building consistency in workflows for Curators and in the data sharing experience for end users more familiar with quantitative data curation. We first describe the factors we consider when suggesting curation tasks grouped by intensity ('curation levels') specifically for qualitative data. Building on these factors, we then propose our levels of qualitative data curation. Finally, we briefly discuss how technological advances may impact our curation standards in the future.
IPUMS at the University of Minnesota has created the world’s largest accessible database of census and survey microdata. The IPUMS suite contains nine harmonized census and survey microdata and aggregate geographic data products. In addition to archiving these data products, the IPUMS archival staff is also responsible for curating, preserving, and making discoverable three key pieces of qualitative IPUMS data: ancillary census and survey materials acquired primarily by IPUMS International for their data harmonization work; DDI-Codebook metadata and documentation produced during IPUMS data product preservation; and working papers authored by IPUMS staff documenting technical innovation around IPUMS data products. This paper describes the development of IPUMS infrastructure to manage, preserve, annotate, and disseminate its qualitative collection, tightly connecting these materials to the IPUMS quantitative data produced for the purpose of supporting robust social science research. The IPUMS archival staff has developed processes to receive, organize, tag, and distribute a large and diverse body of qualitative data. Attention to the range of processing and research uses of this qualitative collection has been instrumental in identifying useful tags to provide targeted access to support these uses. Operationalizing this work has informed archival organizational knowledge control and efforts to raise awareness of archival work and research possibilities for those who provide qualitative content in all its forms. These efforts support strong social science research and offer a path forward for archivists preserving qualitative data in an organizational setting in which archival curation, preservation, discoverability, and dissemination activities are essential, although often considered secondary to the main product.
This article explores the evolution of the Finnish Social Science Data Archive (FSD) in archiving qualitative research data. It highlights the initial challenges faced due to ethical concerns and methodological assumptions, and the gradual shift towards openness in qualitative research. The article discusses the influence of national and international policies, the role of ethical principles, and the impact of the open science movement in Finland. It also details the practical steps taken by FSD to facilitate data archiving, including the development of data management guidelines and the use of specific technical tools. The article underscores the importance of detailed guidance for researchers and the growing acceptance of qualitative data archiving within the research community.
While open science and access trends emphasize data sharing, the focus often prioritizes quantitative data. This emphasis leads to a lack of attention for sharing qualitative data and evaluating such practices. This paper examines both quantitative and qualitative data generated from a federally funded research project, led by the author, with an evaluation of its data management and sharing practices guided by FAIR principles. The research project aimed to develop assessment tools to understand undergraduate students’ academic experiences, including library use, and how these experiences shaped their definitions of academic success. Conducted at public research universities, the two-year project (2022–2024) collected both quantitative and qualitative data including students' personal definitions of success. Following the project’s completion, the author revisited the data management plan and data sharing policies to assess the data generated from the project, guided by FAIR standards. This self-evaluation highlights key findings and critical takeaways in the management and sharing of various types of data, addressing a significant gap in current discussions that often prioritize datasets itself. The findings offer actionable insights for researchers and information professionals, aiming to enhance data sharing, improve reusability, and refine their institutional practices.
The role of Data Stewards (DSs) in academic institutions has become increasingly complex as research data management (RDM) policies evolve under the pressures of open science, data protection regulations, and funding mandates. This paper examines the challenges DSs face through an autoethnographic approach, analysing four cases that highlight tensions between global compliance requirements and researchers' practical needs. The findings illustrate that DSs operate in a “buffer zone,” mediating between top-down global imperatives, such as the principles of findability, accessibility, interoperability, and reusability (FAIR), national data-sharing policies, and legal constraints, as well as bottom-up pressures from researchers prioritizing knowledge production, academic freedom, and project-specific requirements. Rather than offering generalisations or fixed solutions, this paper provides a practice-based perspective that seeks to open the debate on the current positioning of DSs within academic institutions. By highlighting recurring frictions and underexplored issues, it identifies key areas for reflection and improvement, such as integrating DSs into institutional decision-making and promoting more flexible, context-sensitive RDM frameworks. This study contributes to a growing conversation on how data stewardship can evolve to better support both regulatory compliance and research innovation.
Over the past few years, the United States has implemented a second round of data management policies, exemplified by the 2023 NIH Data Management and Sharing Policy and 2022 “Nelson Memo.” Effectively supporting public access to data and a data sharing culture at an academic research institution requires collaboration across various research support staff and central offices as well as knowledge of the current practices of researchers. Two research support groups at Duke University, the University Libraries (DUL) and the Office of Scientific Integrity (DOSI), have forged a strong working relationship for supporting data management and sharing practices, including an active Teams channel for communication, developing tools collaboratively, delivering trainings, and providing co-consults for data management. To more effectively understand “the state of play” at our institution, DUL and DOSI analyzed data management and sharing plans (DMSPs) submitted to the National Science Foundation (NSF) in 2021. The project team used a modified version of the DART rubric (https://osf.io/qh6ad/) to score DMSPs against required elements in key areas, including types of data; standards for data and metadata; access, sharing, and preservation; limitations on access, distribution, and reuse; and roles and responsibilities. In this paper we will present the key findings from the DMSP assessment project and discuss how, as data management specialists, we can use this information to plan for ongoing education, training, and resource development using a cross-campus collaboration model.
Data literacy is an increasingly important skill in our data-driven world, and librarians and other information professionals can play a key role in creating a data literate population due to data literacy’s close association with information literacy. However, the definition of data literacy and the attention paid to certain competencies varies greatly between fields: what librarians and statisticians mean by “data literacy” is not the same thing. A scoping review of data literacy articles within the field of statistics education reveals the landscape of data literacy education in statistics, giving librarians and other information professionals a map for coordinating their data literacy work with disciplinary faculty. The areas of data discovery, evaluating and ensuring the quality of data and its sources, and reproducibility are closely examined. These areas are defined and valued inconsistently amongst information professionals and statisticians, but their close associations to traditional library services creates an ideal opportunity for libraries and data archives to contribute to data literacy education.
The Canadian census is a primary source of information about Canada and the people who live there, and that information is used by researchers, the private sector, public servants and residents. However, access to Canadian census data is fragmented and inconsistent, with no single source of Census data for all census years, or in all census data formats. This is a barrier to research, making systematic analysis, discovery, and reuse difficult. This article provides an overview of the current landscape of Canadian census portals by data format. It includes an analysis of the coverage and usability of census portals and demonstrates the outstanding need for a single comprehensive access point for Canadian census data.
In 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.
In 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.
