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.
Efficiently curating metadata with controlled terminology is a critical yet time-consuming task in social science data management and archiving. Data depositors often provide insufficient metadata, compelling data repository staff to extensively enhance the metadata. This process traditionally involves navigating a wide array of controlled terms, a task demanding substantial time and expertise, sometimes necessitating the creation of new terms. Addressing these challenges, we introduce an innovative model employing Generative AI technology (ChatGPT) coupled with a web application. This tool is engineered to complement the knowledge of the human experts who are responsible for generating metadata by presenting them with keyword suggestions from controlled vocabularies. This reduces the burden on individual data curators who prepare data collections for archiving by giving them a starting point when selecting keywords. It achieves this by rapidly analyzing text and extracting pertinent keywords from established thesauri, including the ICPSR Subject Thesaurus, the European Language Social Science Thesaurus (ELSST), Library of Congress Subject Headings (LCSH), and Medical Subject Headings (MeSH), along with ChatGPT's intelligent recommendations. This approach expedites the curation process and augments the terms selected.
It is worthwhile to make institutional repositories interoperable with Web-based computational reproducibility tools to foster the reuse of scholarship. While repositories excel at long-term preservation, they were not made with the interactive capabilities required to execute complex code or analyze dynamic data directly. There are several open-source platforms, including JupyterHub, BinderHub, Whole Tale, and Emulation-as-a-Service Infrastructure, which provide ‘sandbox’ environments for replaying research that can be integrated with repositories to add this functionality. By integrating these scholarly tools, institutions can move beyond static file storage to offer seamless, in-browser execution of scholarly materials. This unified approach aims to reduce technical barriers for secondary researchers, ensuring that computational work remains both FAIR and re-executable. This approach advocates for a research lifecycle where active preservation and active discovery and reuse are deeply interconnected through sustainable infrastructure.
Numerous investments have been made in different data infrastructures in the UK to support often specialist, discipline-led discovery, access and use of different data resources adding to a busy and fragmented data infrastructure landscape. Data increasingly straddles traditional academic topics and the pandemic highlighted the importance of ready access to a range of linked economic, environmental, health and social data. Despite the increasing availability of data, our Future Data Services (FDS) review describes how finding and accessing data is now more complex for the researcher and this experience needs smoothing out. The current UK government’s manifesto referenced the creation of a ‘National Data Library’ (NDL). We ask how this can provide a solution to the fragmented landscape and how it might build upon existing infrastructures to address the challenges that our review documented including the need for closer connections between existing infrastructures that support the federation of data services. Considering this, we describe a progressive approach to data infrastructure development, modeled as a ‘hierarchy of needs’. Assessing the core components to make data discoverable and accessible allows us to evaluate additional services, and the need for greater innovative capacity and maturity. From this assessment, a vision for an NDL becomes apparent and we can frame our ambition for a federated data service landscape and monitor our progress to achieving this goal.
Colleagues share fond memories of Walter Giesbrecht, his dedication to helping researchers understand data, his long association with IASSIST and DLI, and his deep impact on many.
