Purpose Advances in computer vision heavily depend on large data sets, making ethical research data management (RDM) increasingly crucial. This paper, an interdisciplinary study, aims to examine RDM practices and challenges among computer vision scholars and explores a GenAI-assisted qualitative analysis workflow as a methodological contribution. Design/methodology/approach Through qualitative case studies of six scholars from five research intensive universities, the authors investigate their data management strategies and challenges. The primary analytical approach involves manual thematic coding with NVivo, complemented by ChatGPT 4o (Qualibot) as an AI-assisted verification tool and a comparative triangulation of both methods to ensure consistency and trustworthiness. Findings Key RDM themes identified include data types and sizes, storage and management, sharing and accessibility, services and training and data management struggles. Drawing on the experiences of six computer vision scholars, the findings reveal a strong reliance on hybrid storage systems combining cloud and local storage for large-scale visual data sets. These experience-based insights suggest that major challenges include financial and logistical burdens of data storage, backup, sharing and the need for data management training. Practical implications The findings exemplify the need for better institutional support, scalable tools and standardized protocols, offering crucial insights for library and information science professionals developing research data services tailored to big data environments. Originality/value This interdisciplinary study provides one of the few empirical, qualitative account of RDM practices specifically within the computer vision community. Methodologically, the authors introduce a novel GenAI-assisted qualitative analysis workflow (Qualibot) rigorously evaluated against manual coding as a complementary validation tool, providing a replicable model for AI-assisted qualitative research in data-intensive domains.
Purpose This study aims to examine how users and communities are integrated into the design and evaluation of semantic or neurosymbolic (NeSy) information systems in digital libraries. Through a structured scoping review and content analysis of academic databases, it identifies and describes multidisciplinary participative practices in the development of these systems; maps methodological trends, gaps and practical recommendations for collaboration in epistemically just designs to support inclusion in digital libraries. Design/methodology/approach The study combines a search, appraisal, synthesis, analysis-based scoping review with bibliometric mapping and qualitative content analysis. It is useful for identifying multidisciplinary participative practices and for observing their methodologies in analytical variables: 1-Timing and process-driven patterns, 2-Methods, 3-Engagement and levels of user participation and 4-Ownership, inclusion, representational gaps and epistemic-justice considerations. Findings Analysis reveals that participative practices are integrated and documented in all 22 analyzable cases, with uneven reporting across phases: early user integration occurs in seven practices (32%), while users are positioned in the late evaluation phase or in narrowly scoped continuous contribution in other cases. Participatory methods cluster around co-design workshops, usability testing and crowd-science. The participation levels concentrate in consultative and collaborative stages, with some empowerment actions. Epistemic-justice considerations are under-documented. Bias-aware modeling and provenance tracking are found in specific cases. Evaluation practices tend to rely on usability and information-retrieval metrics, leaving participation impacts, trust-building and inclusion outcomes under-documented. Research limitations/implications The analysis is constrained by uneven documentation across practices, a temporal focus on the past decade and a corpus dominated by English-language and Global North sources, which limits generalizability. Geographic and linguistic coverage remain uneven: cases cluster in Europe and the Global North and several source papers do not report site geography explicitly. Hybrid initiatives often resist neat categorization, which underscores the need for more transparent reporting of participatory processes and evaluation methods. Future research should combine longitudinal and comparative designs, broaden geographic and linguistic coverage and refine tools for assessing participation, trust and epistemic justice in human-centered AI systems for digital libraries. Originality/value By mapping who is involved, the timing, the methodology, the engagement and the ownership, the study offers a structured lens on the inclusiveness, trustworthiness and accountability of human-centered semantic and AI practices in digital libraries. To authors’ knowledge, no prior synthesis integrates participatory design, citizen science, semantic/NeSy and epistemic-justice literatures in a single cross case map of existing documentation of participative practices on digital library and cultural heritage information systems. The five-lens framework and four-dimension codebook serve as analytical tools; corpus findings and recommendations serve as a pragmatic guide for practitioners seeking bias-aware participation in digital library infrastructures.
Purpose Drawing from recent advancements, this study aims to highlight how the increasing integration of large language models (LLMs) necessitates a novel governance scheme regarding data integration, ethical oversight and bias mitigation. Design/methodology/approach Central to the scheme are the concepts of LLMs companionship and stewardship, which ensure responsible governance to promote repeatability and robust assessment. Findings This study proposes an operational scheme for librarian-led stewardship of LLM-supported research, detailing governance roles, data curation workflows, and accountability mechanisms for attribution and dating tasks. Research limitations/implications This paper offers a conceptual viewpoint rather than large-scale empirical validation. Its framework relies on an illustrative case study and conceptual reasoning rather than extensive implementation data. The author invites the digital library and cyber humanities (CH) communities to test, adapt and empirically evaluate the proposed governance practices. Practical implications Library-based AI governance has practical implications in managing the relationship between human CH researchers and LLMs. Practical policy adjustments are required to position librarians as active stewards of ethical, repeatable and provenance-rich LLM-assisted research. Social implications By positioning libraries as ethical stewards of LLM-supported CH research, this framework advances epistemic justice, mitigates cultural and algorithmic biases against marginalized voices, promotes inclusive representations of global heritage and strengthens public trust in transparent, equitable and democratized knowledge production in the AI era. Originality/value The scheme advocates library-based AI governance to address challenges in the relationship between human researchers and LLMs in humanities scholarship, positioning librarians as active stewards of ethical, repeatable and provenance-rich computational research.
Purpose This study aims to map the specific terrain of prevalent security threats to digital library services, dissect the critical privacy risks associated with patron information and culminate in the proposal of a resilient framework designed to address these multifaceted challenges. Design/methodology/approach To ground this analysis in empirical reality, a quantitative survey of 127 Ukrainian public library professionals was conducted. The study synthesizes established cybersecurity best practices, data protection principles and current information science literature to examine vulnerabilities ranging from technical infrastructure to human-factor risks. Findings The results reveal a critical governance gap: 70% of surveyed institutions operate without a formal information security policy, and 85% lack full-time information technology (IT) staff (with 60% having no dedicated IT resource at all). These findings replace previous assumptions with concrete data, highlighting the urgency of the issue. The investigation identifies a spectrum of common threats, including malware, phishing and denial-of-service attacks and highlights the profound privacy implications of the Diia.Digital Education initiative. Originality/value This research replaces previous assumptions with concrete data regarding the security maturity of libraries in a war zone, specifically analyzing the risks of the Diia ecosystem integration. It proposes a conceptual framework with actionable strategies tailored to empower Ukrainian public libraries to fortify their digital offerings and champion user data privacy.
