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.

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.

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.

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.

This retrospective scoping review explores the data-related competencies required by liaison and subject librarians to effectively support academic researchers. Despite the growing demand for research data assistance, many librarians lack formal training (Tenopir et al., 2014) or confidence (Cox et al., 2012) in this area, often relying on self-taught skills. The objective of this review was to map data-related competencies over a ten-year period (2012-2022) with particular attention given to the skill sets that liaisons or non-data librarians may need to develop or hone. Overall, the findings indicate a surprisingly stable list of skills over this period. This review finds that to support research data services on campus, librarians must rely on traditional skills including reference/consulting, teaching/training and collaboration/engagement as well as data-specific competencies, including metadata creation, data preparation for repositories, data preservation, data management plan (DMP) creation, and programming/data analysis. These competencies are essential for librarians to assist researchers with data queries. The study highlights the need for structured training and suggests which competencies to prioritize. The findings aim to guide the development of self-training resources and cross-training initiatives to better equip librarians in supporting data-rich research.

Comprising longitudinal data on around 1.1 million individuals, the Office for National Statistics Longitudinal Study (ONS-LS) is the largest nationally representative longitudinal dataset in the United Kingdom. It follows a 1% sample of the England & Wales population drawn from the decennial census data (1971 – 2011), linked to some administrative data. Currently comprising up to 46 years of data (1971 – 2017) on sample members, the forthcoming linkage of the 2021 England and Wales Census data to the ONS-LS will extend this follow-up to 50 years. The Centre for Longitudinal Study Information and User Support (CeLSIUS) provides assistance for researchers wishing to use the ONS-LS in their research. Based at University College London (UCL) and the Office for National Statistics (ONS), it has been supporting academic and voluntary sector users of the ONS-LS since 2001. Its work includes helping researchers with their applications to use the ONS-LS, supporting research projects and advising on research outputs.