Wei Zakharov

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.

The purpose of this study was to develop and validate an effective instrument to measure undergraduate students’ self-efficacy in engaging with information for online coursework. Recognizing the increasing importance of information literacy in online learning environments, the Online Information Literacy Self-Efficacy (OILS) instrument was designed to assess key competencies associated with students’ abilities to engage with information effectively. The validity and reliability of the OILS instrument were examined using exploratory factor analysis (EFA) and reliability analysis. The instrument initially comprised 30 items, grounded in two authoritative frameworks: the Association of College and Research Libraries (ACRL) Framework for Information Literacy and the Association of American Colleges and Universities (AAC&U) Information Literacy VALUE Rubric. Survey responses from 259 undergraduate students enrolled in online courses at a large Midwestern US R1 institution were analyzed using EFA and reliability analysis. EFA results revealed a four-factor structure explaining substantial variance in the item response patterns: scoping research topics, obtaining information, evaluating information quality and producing research documents, and crediting sources. These four factors demonstrated high reliability, with omega coefficients ranging from 0.891 to 0.960. One item was recommended for removal due to cross-loadings and skewness. Exploratory measurement invariance analyses suggested that the factor structure was comparable for students with and without prior online learning experience. The findings support the OILS instrument’s utility in measuring students’ confidence in applying information literacy skills in online learning contexts, offering valuable insights for educators and librarians to enhance instructional strategies and support student success.

A systematic review is a specialized type of literature review used to collect and synthesize all the available evidence related to a research question. The methods for systematic reviews should be transparent and reproducible so that other researchers can use, replicate, and build upon the findings. Systematic reviews have been published for decades in medical literature where it is necessary to bring together all the studies related to a particular disease or condition for healthcare professionals to make evidence-based recommendations. Conducting systematic reviews has expanded to other disciplines, including education, business, and engineering. The authors of this study conducted a scoping review to investigate how prevalent systematic reviews are in engineering and to what extent engineering authors are following published reporting guidelines. To conduct this study, the authors searched the databases Compendex, Inspec, and ERIC and retrieved 11,588 records. After removing duplicates and applying inclusion and exclusion criteria, 3,066 articles remained in the final data set. The authors then used a checklist of bibliographic and quality-related items to extract data from each of these studies. The findings show that systematic reviews are a popular and rapidly growing methodology in engineering, internationally, but the quality of the studies, in terms of how authors construct and report their methods, is often low, resulting in many studies that are irreproducible and may fall short of the goal of gathering all the available evidence on a topic. The authors include recommendations for engineering authors, higher education administrators, and publishers.