2026-07-11T16:16:18Z https://publishing.escholarship.umassmed.edu/jeslib/api/oai/

oai:jeslib:id:1196 2026-04-01T09:50:00Z

Review of <em>The Data Management Workbook</em> by Kristin Briney Review of <em>The Data Management Workbook</em> by Kristin Briney Tatarian, Allie This review is a critique of The Data Management Workbook by Kristin Briney. Briney’s Workbook offers a selection of hands-on data management activities for research projects at all stages of the research data lifecycle. It provides a valuable resource for both researchers at all career stages and the data librarians who support them. 2026-04-01T09:50:00Z info:eu-repo/semantics/article 15 2 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1196 https://doi.org/10.7191/jeslib.1196 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1196/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1196

oai:jeslib:id:1159 2026-01-30T11:00:00Z

Building a lightweight dataset catalog in Digital Commons  Building a lightweight dataset catalog in Digital Commons  Warner, Claire Reese, Amy Hertz, Marla Introduction: Many academic libraries compile dataset catalogs to make the research data produced by their institutions more findable, accessible, interoperable, and reusable (FAIR). There are a variety of approaches to gather metadata for dataset records and catalogs may be hosted on one of several platforms.  Methods: We developed Python code and leveraged APIs to locate relevant datasets and harvest their metadata automatically. The metadata is then manually curated and enhanced. We employ the hosted institutional repository Digital Commons to display the dataset records. Results: The University of Alabama at Birmingham’s Research Data Catalog (RDC) currently contains over 260 dataset records from multiple generalist repositories. The code we developed, and the customized Digital Commons collection, are available for reuse. Conclusion: Combining API harvesting with the ready-made features of Digital Commons yielded efficient ingestion of many dataset records, allowing us to prioritize manual curation and enhancement of the dataset metadata. This approach is ideal for launching a dataset catalog at institutions with limited personnel time and minimal technical resources.  2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1159 https://doi.org/10.7191/jeslib.1159 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1159/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1159

oai:jeslib:id:1172 2026-01-30T11:00:00Z

Building Open Qualitative Science with Open Curriculum Building Open Qualitative Science with Open Curriculum Porter, Nathaniel D. Karcher, Sebastian Open science has been gradually embraced over recent decades both in libraries and across a wide range of disciplinary communities. It promises paths toward transparency, reproducibility, and evidence synthesis — all in service of the ultimate goals of trustworthy and high-impact scientific research. Qualitative researchers, however, have been slow to accept open science goals as unqualified goods and adopt open science practices, such as data and process sharing. Their hesitancy comes for a variety of reasons, some as prosaic as software and data limitations and others embedded in epistemological assumptions. This paper describes how recent developments in infrastructure, technology, and methodology have begun to reduce this adoption gap between quantitative and qualitative researchers. Capitalizing on these opportunities, however, requires those training new qualitative researchers (including an increasing number of instructional librarians and faculty) have the technical and methodological skills to integrate open science throughout qualitative workshops and courses. We then detail how we developed and pilot tested a pair of open qualitative research curricula for novice learners and instructors who may have limited qualitative research training themselves. The curricula use entirely open resources, including the Qualitative Data Repository, two open-source qualitative data analysis software packages (Taguette and QualCoder), the open REFI-QDA standard for the exchange of coded qualitative data, and the Carpentries Workbench curriculum infrastructure. This allows wide adoption of the curriculum with the ability to re-mix and finetune lessons and data to the target audience and without the need to maintain licenses for any services. 2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1172 https://doi.org/10.7191/jeslib.1172 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1172/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1172

oai:jeslib:id:1193 2026-01-30T11:00:00Z

Editorial for Special Issue: 2025 Research Data Access and Preservation (RDAP) Summit Editorial for Special Issue: 2025 Research Data Access and Preservation (RDAP) Summit Chaput, Jennifer Tang, Yijing Angel Zakharov, Wei Owen, Heather Charlotte The Research Data Access and Preservation Association (RDAP) held its 2025 Summit from March 11th to 13th, centered around the theme Evolutions in Data Services: Forging Resiliency. This Special Issue presents 12 full-length articles from Summit presenters and showcases how data librarians continue to adapt to changing circumstances and expectations. Topics covered in these articles range from library outreach to staff researchers to the development of a data catalog for an institutional repository. 2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1193 https://doi.org/10.7191/jeslib.1193 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1193/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1193

oai:jeslib:id:1174 2026-01-30T11:00:00Z

Evolving the 3-2-1 backup rule for more resilient data Evolving the 3-2-1 backup rule for more resilient data McCaffrey, Deb Foster, Erin D. Gorenstein, Lev Magle, Tobin Bayrd, Venice Background: Many resources and professionals reference the 3-2-1 backup rule as an effective strategy to prevent active research data loss. However, the changes in storage technology and the pace of research data growth have outgrown the 3-2-1 rule. Objectives: The authors want to contribute background information and invite community input to evolve the 3-2-1 rule to fit modern research data and storage better. This evolution would provide better information to research data management professionals and researchers for more resilient research data. Methods: The authors facilitated a workshop at the Research Data Access and Preservation (RDAP) Summit in 2025 to present the necessary information for understanding the current storage and backup landscape. Backups were reframed as failure modes for data loss and corresponding preventative data protection measures. Results: The workshop resulted in an overview and summary of data protection methods and the ways in which they mitigate different “failures,” which allows for a more nuanced discussion of data protection that is not enabled by use of the 3-2-1 rule nor the term “backup” alone. Workshop participants brainstormed ways that the information presented in the workshop could be synthesized and incorporated into various learning materials, including materials for data professionals and researchers.  2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1174 https://doi.org/10.7191/jeslib.1174 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1174/ 2161-3974 https://creativecommons.org/licenses/by-nc-sa/4.0/ e1174

oai:jeslib:id:1168 2026-01-30T11:00:00Z

Forging Resilience and Building Capacity: Professional Development in Early-Career Research Data Services  Forging Resilience and Building Capacity: Professional Development in Early-Career Research Data Services  Harmon, Samantha As research and information landscapes evolve, shaped through the intentional incorporation of data services and emerging technologies like artificial intelligence (AI), academic libraries are being asked to expand service capacities, develop new literacies, and respond to complexity with integrity and equity. This commentary draws on two contributions to the 2025 Research Data Access and Preservation (RDAP) Summit: a poster exploring how professional development supports early-career growth in research data services, and a presentation on navigating AI’s ethical tensions through a library-centered literacy framework (Research Data Access &amp; Preservation Association [RDAP], 2025). Grounded in my experience as a new data services librarian without a formal background in library and information science (LIS), I reflect on how professional development has not only accelerated my learning but also shaped my approach to service design, provided access to critical resources, and helped build a much-needed professional network. These opportunities have strengthened my resilience in a field where roles, technologies, and expectations continue to shift. This commentary also considers the realities of institutional resource constraints and the value of inclusive, collaborative spaces that make professional growth possible. For librarians navigating emerging areas like data services or AI literacy, particularly those early in their careers or entering from adjacent fields, it offers insight into how we can meet uncertainty not with expertise alone, but with connection, collaboration, and care. 2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1168 https://doi.org/10.7191/jeslib.1168 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1168/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1168

oai:jeslib:id:1165 2026-01-30T11:00:00Z

Identifying restricted data repositories supporting mediated access via data usage agreements Identifying restricted data repositories supporting mediated access via data usage agreements Oberlies, Mary K. Potterbusch, Megan In the modern era, the near impossibility of true anonymization means we must provide tangible recommendations for researchers who need to share de-identified, person-level data that could potentially be re-identified due to the presence of quasi-identifiers. This calls for data stewards to support researchers in depositing sensitive data in public repositories while still following institutional, ethical, and legal requirements. While various repository aggregators like re3data and DataCite Repository Finder provide lists of data repositories, navigating these can be cumbersome when trying to locate options for depositing restricted data. These listings rarely include certain necessary details, making the process of recommending third-party repositories to researchers time-consuming — or even limited, and we often end up relying on a short list of well-known repositories. An additional challenge is the difficulty of identifying repositories that mediate access via data usage agreements, where the repository handles access requests to ensure potential users meet established security and privacy requirements and have taken the necessary steps to protect confidentiality and commit to appropriate data use. The need to provide tangible recommendations to help researchers deposit data in public repositories while still protecting individual privacy served as the inception to this project to identify and create a spreadsheet of restricted data repositories with mediated access processes for researchers. This practical solution empowers data sharing while upholding essential ethical and institutional privacy requirements and, while currently limited to US based social sciences repositories, in sharing this resource, we hope others will continue to contribute and expand this work.  2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1165 https://doi.org/10.7191/jeslib.1165 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1165/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1165

oai:jeslib:id:1122 2026-01-30T11:00:00Z

Investigating and Addressing the Needs of Research Support Staff  Investigating and Addressing the Needs of Research Support Staff  Link Cilfone, Alissa Ferguson, Jen Our academic library, like many others, has primarily focused its outreach and engagement efforts on faculty and students. We had less insight into the needs of a harder-to-reach population: the staff researchers — postdocs, research scientists, lab managers, technicians, and study coordinators — who are on the front lines of campus research activity. We identified a target population of nearly 500 staff members in these roles in several STEM colleges at our institution and invited them to respond to a survey about the library resources, support, and services they would find most valuable for their work. We found higher-than-expected building usage among this population, identified their most requested service and support needs, and learned that repeated promotion of key library resources would be beneficial to increase awareness. In this paper, we share what we have learned from them and the steps we’re taking to address their needs. Our hope is that others may be able to apply what we have learned to investigate and meet the needs of research support staff at their own institutions.  2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1122 https://doi.org/10.7191/jeslib.1122 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1122/ 2161-3974 https://creativecommons.org/licenses/by-nc/4.0 e1122

oai:jeslib:id:1171 2026-01-30T11:00:00Z

Investigating Graduate Mentor Perspectives on Data Information Literacy and Stewardship in Undergraduate Engineering Research Projects Investigating Graduate Mentor Perspectives on Data Information Literacy and Stewardship in Undergraduate Engineering Research Projects Arigye, Joreen Zakharov, Wei Zoltowski, Carla B. Sewell, Sarah Purzer, Senay Objectives: The purpose of this study is to explore graduate mentors’ perspectives on the data information literacy of the undergraduate engineering research teams they support, and to identify the challenges these teams face in managing data across the research data lifecycle. Methods: Two graduate mentors supporting undergraduate engineering research teams were interviewed in fall 2024 using the Data Information Literacy (DIL) Toolkit. The interviews examined graduate mentors’ perceptions of the importance of key data competencies for undergraduate researchers and explored how students engage with the stages of the research data lifecycle. Transcripts were analyzed using qualitative descriptive methods and organized around the DIL modules to provide a structured account of data information literacy practices. Results: Graduate mentors identified different priorities for undergraduate researchers. One mentor emphasized Data Management and Organization as most important, while the other mentor highlighted a wide range of priorities from Data Conversion and Interoperability, to Data Curation and Reuse. Descriptive findings, structured around the DIL Toolkit modules, showed student engagement in technical tasks such as coding, and version control under mentor guidance. However, mentors reported persistent challenges in areas such as documentation quality, metadata creation, ethical practices, and long-term data preservation. Conclusion: Graduate mentors offer essential insight into the data information literacy of undergraduate engineering research teams. Their insights examined through this study revealed that while students are supported in technical aspects of the data lifecycle, skills like documentation, ethics, and data preservation remain underdeveloped. Investing in training around data stewardship can equip graduate mentors with the skills and language needed to strengthen their undergraduate teams’ data literacy and better prepare students to serve as competent data stewards in an increasingly data-driven research environment. 2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1171 https://doi.org/10.7191/jeslib.1171 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1171/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1171

oai:jeslib:id:1160 2026-01-30T11:00:00Z

Resilient by Design: Assessment Strategies for Data Services from RDAP 2025 Resilient by Design: Assessment Strategies for Data Services from RDAP 2025 Fredrick, Rose The RDAP Summit 2025 theme of Evolutions in Data Services: Forging Resiliency prompted a range of presentations focused on using assessment to help libraries develop more responsive and sustainable research data services. This commentary highlights strategies and case examples shared at the conference that show how assessment through surveys, interviews, and local feedback can support service development, improve coordination with campus partners, and help libraries better meet the needs of their research communities. 2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1160 https://doi.org/10.7191/jeslib.1160 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1160/ 2161-3974 https://creativecommons.org/licenses/by-nc-sa/4.0/ e1160

oai:jeslib:id:1175 2026-01-30T11:00:00Z

Send It Away, or Put It On Display? How librarians and research computing staff can collaborate across language barriers Send It Away, or Put It On Display? How librarians and research computing staff can collaborate across language barriers Bayrd, Venice Foster, Erin D. Gorenstein, Lev Magle, Tobin McCaffrey, Deb The data services landscape continues to change rapidly. From shifting cloud file storage platform quotas, to rising storage costs, to increasing focus on multi-disciplinary projects, rapid changes warrant an evolving collaborative approach toward data services. Data librarians and research computing professionals in this context are increasingly becoming “boundary-spanners,” a term we invoke to articulate the role data librarians and data professionals play in exploring collaborations across disciplines while managing uncertainty, change, and communication disconnects in data services. In line with a long-standing dilemma, concepts and ideas defined in one domain do not always find parity when also defined within the context and value system of a different discipline. Although semantic ambiguities in the data services arena present challenges, their presence suggests an opportunity to reflect. How do we begin collaborations across silos? How do we avoid the pitfalls of cross-disciplinary collaborations? This article will explore terminology and services differentiation in libraries and research computing organizations. To illustrate different perspectives, value systems, and language, we will highlight divergent uses of common terms such as metadata, storage and backup, sharing, and archiving, then outline some implications for collaborative conversations. Throughout, we will share ideas for building research computing and library partnerships, from conversation starters to shared initiatives to shared funding proposals. The goal of this piece is to bring together a set of practical collaboration ideas and strategies to use in meetings with data services stakeholders, with an aim to help transform operational differences into understanding. 2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1175 https://doi.org/10.7191/jeslib.1175 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1175/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1175

oai:jeslib:id:1173 2026-01-30T11:00:00Z

Strengthening Data Management Systems: Insights from the Machine Actionable Plans (MAP) Project's Institutional Pilots Strengthening Data Management Systems: Insights from the Machine Actionable Plans (MAP) Project's Institutional Pilots Murray, Matthew Wham, Briana Harp, Matthew Carson, Matthew B. Gonzales, Sara Data Management and Sharing Plans (DMSPs) are typically viewed only as a requirement researchers must meet as part of grant proposals to funding agencies; machine actionable DMSPs (maDMSPs) offer the potential to enhance the value of research data produced at institutions by making it more discoverable and connected to other parts of the research ecosystem. In recent years, maDMSPs have emerged as key mechanisms for the United States federal funding agencies’ policies for public access to research data. This paper provides overviews and insights from four institutions that were part of the Machine Actionable Plans (MAP) Pilot Project with the goal to develop projects related to maDMSPs. The case studies cover using generative AI for DMSP feedback, organizing a cross-campus workshop on maDMSPs, attempting to track and connect campus systems related to grant-related research, and creating a proof-of-concept for networked RDM and DMSP workflows. Finally, this paper concludes by acknowledging the rapid changes happening in federal funding agencies in the United States, highlighting potential failure points, and emphasizing the importance of staying up to date with changes to US federal policies. 2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1173 https://doi.org/10.7191/jeslib.1173 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1173/ 2161-3974 https://creativecommons.org/licenses/by-nc/4.0 e1173

oai:jeslib:id:1170 2026-01-30T11:00:00Z

The hunt for research data: Development of an open-source workflow for tracking institutionally-affiliated research data publications The hunt for research data: Development of an open-source workflow for tracking institutionally-affiliated research data publications Gee, Bryan M. The ability to find data is central to the FAIR principles and open scholarship. As with the ability to reuse data, efforts to ensure findability have historically focused on discoverability by other researchers, but there is growing recognition of the importance of stewarding data in a fashion that makes them FAIR for a broader range of potential reusers and stakeholders. One such stakeholder, research institutions, have various motivations for discovering affiliated data, from facilitating compliance with funder provisions to gathering data to inform institutional research data services. However, many datasets and data repositories are not optimized for institutional discovery, which creates downstream obstacles for workflows designed for comprehensive institutional discovery.  This study describes an open-source workflow for institutional tracking of research datasets at The University of Texas at Austin. This workflow comprises a multi-faceted approach that utilizes multiple open application programming interfaces (APIs) and a combination of broad and targeted searches to address common challenges to institutional discovery, such as variation in whether affiliation metadata are recorded, and if so, how metadata are standardized, structured, and crosswalked. It currently identifies more than 4,300 affiliated datasets across over 70 distinct platforms, including certain “invisible” deposits that lack DOIs or that lack affiliation metadata. However, there remain major gaps that stem from suboptimal practices of data repositories, many of which were identified in previous studies. The persistence of these barriers underscores the importance of advocating for institutional discovery as an important data (re)use case and for critical self-examination of practices by repositories.  2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1170 https://doi.org/10.7191/jeslib.1170 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1170/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1170

oai:jeslib:id:1176 2026-01-30T11:00:00Z

The Repository of Last Resort? Exploring the Role of Institutional Repositories in the Data Repository Ecosystem through Researcher Perspectives The Repository of Last Resort? Exploring the Role of Institutional Repositories in the Data Repository Ecosystem through Researcher Perspectives Key, Cara Park, Diana E. Nichols, Jane Llebot, Clara Borland, L. K. Objective: The data repository ecosystem is robust, and researchers have many choices when it comes to sharing their research data. This study aims to better understand what researchers value when choosing a data repository and how they perceive the role of the institutional repository in the larger ecosystem. Methods: We sent out a survey to researchers who had deposited datasets in identified repositories or the institutional repository (IR). We received 40 responses, a 7.5% response rate. Ten survey participants were also invited to take part in interviews to expand on their experiences. Results: Overwhelmingly, data repository users consider cost and convenience over repository features. Institutional repository users value the services offered by library staff and cite trust as a primary factor for using the IR. Differences in the two groups of users confirm our hypothesis that there is value in maintaining the IR for data deposits. Discussion: Based on our results, we identified three user personas to guide our outreach strategy in the future. A targeted outreach strategy can help improve IR awareness and also attract additional users who may discover that the IR meets their data repository needs. 2026-01-30T11:00:00Z info:eu-repo/semantics/article 15 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1176 https://doi.org/10.7191/jeslib.1176 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1176/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1176

oai:jeslib:id:1177 2025-08-20T09:15:00Z

David Bowie Had it Right: Challenges and Opportunities in Research Data Management David Bowie Had it Right: Challenges and Opportunities in Research Data Management Raboin, Regina Changes in the academic research enterprise environment come with challenges and opportunities for librarians in data services. From integrating AI literacy into information literacy classes, to creating services addressing the current volatile research environment, or embracing the opportunities of practicing, accessing, and interpreting data, change is happening, and librarians must meet it head on…including the Journal of eScience Librarianship team. 2025-08-20T09:15:00Z info:eu-repo/semantics/article 14 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1177 https://doi.org/10.7191/jeslib.1177 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1177/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1177

oai:jeslib:id:1076 2025-08-14T09:15:00Z

AI Literacy and Adoption Readiness Among Librarians in Nigerian Private University Libraries: A Technology Acceptance Model Perspective AI Literacy and Adoption Readiness Among Librarians in Nigerian Private University Libraries: A Technology Acceptance Model Perspective Alao, Adekunle Victor Olajide, Afolabi Akanbiemu, Adetola A. Ailakhu, Ugonna V. Ajao, Samson O. This study investigates artificial intelligence (AI) literacy and adoption readiness among 102 librarians in private university libraries in Osun State, Nigeria, using the Technology Acceptance Model (TAM). A quantitative survey across eight institutions reveals high AI awareness (87.3%, mean = 3.18 on a 4-point Likert scale) and positive perceptions (57.8% strongly agree AI is transformative, mean = 3.42), surpassing Nigeria’s public university benchmarks (65%). Chi-square tests (p &gt; 0.05) and regression (R² = 0.058, p = 0.119) show no significant variation by qualifications, position, or experience, while ANOVA (F = 3.497, p = 0.001) identifies institutional differences (e.g., Adeleke mean = 3.40 vs. Bowen mean = 2.95). Sensitivity analysis (standardized difference = 0.23) highlights Likert scales’ superiority over binary measures in detecting variance. Extending TAM, the study positions awareness as a stable antecedent to perceived usefulness, moderated by institutional factors rather than demographics—a novel refinement in library and information science (LIS). Despite high awareness, practical AI use remains limited (8.8%), reflecting infrastructural and training gaps. Findings contrast with public-sector studies and align with global trends (80% awareness in developed contexts), offering a developing-region lens on AI readiness. Recommendations include institution-specific training (₦500,000/library) and pilot investments (₦2 million/library) to bridge adoption gaps. This research advances TAM’s application in LIS, contributes to AI literacy discourse, and informs strategic planning for technology integration in resource-constrained academic libraries, with implications for global-south contexts. 2025-08-14T09:15:00Z info:eu-repo/semantics/article 14 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1076 https://doi.org/10.7191/jeslib.1076 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1076/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1076

oai:jeslib:id:1059 2025-07-28T12:00:00Z

Exploring Workforce Factors in the Data Fields Exploring Workforce Factors in the Data Fields Soyka, Heather Murillo, Angela As the data fields continue to grow and evolve, it is critical to examine factors that impact the workforce. This study explores various workforce development factors through a survey that asked data workers to describe their time spent on data-related activities, training needed for data-related activities, facilitators and barriers to workforce entry, facilitators and barriers to workforce retention, and the impact of diversity, equity, and inclusion efforts. Drawing together these varied factors allows for examination and triangulation of overlapping factors that shape and contribute to workplace experiences for data professionals.   Author Contributions: Authors made equal contributions 2025-07-28T12:00:00Z info:eu-repo/semantics/article 14 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1059 https://doi.org/10.7191/jeslib.1059 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1059/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1059

oai:jeslib:id:961 2025-07-18T12:30:00Z

Data Please!: Expanding the Role of Libraries in Data Science through Digital Scholarship Data Please!: Expanding the Role of Libraries in Data Science through Digital Scholarship Kerns, Halie Objective: As data science becomes more integrated into research and teaching, libraries are well-positioned to support this work. This study examines how a digital scholarship team at Binghamton University enhanced engagement with data science by assessing faculty, staff, and graduate student needs. Through focus group interviews, the study identifies key support gaps and outlines strategic initiatives to strengthen interdisciplinary data science programming within the library. Methods: A qualitative approach was used, involving 26 focus group interviews with faculty, staff, and graduate students across STEM and related fields. Participants discussed their data science work, tools, training, and perceived resource gaps. Qualitative coding analysis identified key areas for library support. Results: The study revealed three primary areas for library expansion in data science: (1) fostering interdisciplinary collaboration through outreach, (2) developing structured data science programming aligned with campus needs, and (3) establishing physical and digital infrastructure for data-intensive research. In response, the Digital Scholarship team implemented a three-semester data science programming plan, enhanced research community engagement, and contributed to a dedicated data science space in the upcoming Digital Scholarship Center. Conclusions: Findings support the library’s role as a vital hub for data science. By aligning digital scholarship services with campus needs, the library can bridge gaps in data literacy, tool accessibility, and collaborative opportunities. While initial implementations show promising engagement, ongoing assessment will be necessary to refine services, particularly for undergraduates and emerging technologies. This study provides a model for other libraries to expand data science programming effectively. 2025-07-18T12:30:00Z info:eu-repo/semantics/article 14 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.961 https://doi.org/10.7191/jeslib.961 https://publishing.escholarship.umassmed.edu/jeslib/article/id/961/ 2161-3974 https://creativecommons.org/licenses/by-nc/4.0 e961

oai:jeslib:id:806 2025-07-16T11:50:00Z

Research Data Management at a Medical Facility in Uganda: Practices, Awareness, Challenges, and Recommendation Research Data Management at a Medical Facility in Uganda: Practices, Awareness, Challenges, and Recommendation Mukiibi, Edward Bukirwa, Joyce The paper explored research data management practices at a medical research facility in Uganda. It focused on the researchers' perception about research data practices, awareness, and challenges. Mixed methods were applied in which thirty (30) respondents out of a population of sixty (60) research community members. The research community was comprised of both research teams and research support members. Whereas the research team respondents were selected randomly and subjected to the questionnaire, the four key informants were purposively selected from the research support members and subjected to the interview. The findings showed variations in perception, management, and understanding of research data practices. Identified challenges were inadequate legal framework, lack of institutionalised storage facilities, and limited competencies in writing Data Management Plans. The recommendations were: the formation of a unit for the development of research data management policy, support services, and the introduction of formal research data management skills training to equip the research community at the facility. 2025-07-16T11:50:00Z info:eu-repo/semantics/article 14 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.806 https://doi.org/10.7191/jeslib.806 https://publishing.escholarship.umassmed.edu/jeslib/article/id/806/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e806

oai:jeslib:id:851 2025-07-14T11:20:00Z

The Data Drill: An Opportunity for Researchers to Practice Accessing and Interpreting Data The Data Drill: An Opportunity for Researchers to Practice Accessing and Interpreting Data Caldrone, Sandi Feng, Yali It is difficult to engage researchers in workshops on data management best practices when there are so many other demands on their time and attention. Even when interest is high, attendance is often low. In response to this challenge, the Research Data Service and the School of Social Work at the University of Illinois Urbana-Champaign partnered to develop a new data management learning activity, the data drill. Like a fire drill, the data drill is a safe way to practice a stressful scenario, in this case, accessing and interpreting a dataset. In this paper, we describe how we designed the data drill, discuss the results of three pilot drills we conducted, and outline our plans to improve and expand upon this activity based on our experiences. Each data drill participant selected a dataset they deemed important to their research but that they were not currently using, and attempted to locate, access, and interpret the data during a virtual meeting with one to two librarian facilitators who helped troubleshoot issues as they arose. This allowed participants to stress-test how well their data is organized and documented and provided facilitators with a window into the researcher-data relationship and a unique opportunity to provide highly individualized support with immediate and long-term benefits. 2025-07-14T11:20:00Z info:eu-repo/semantics/article 14 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.851 https://doi.org/10.7191/jeslib.851 https://publishing.escholarship.umassmed.edu/jeslib/article/id/851/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e851

oai:jeslib:id:937 2025-07-09T13:45:00Z

The Research Data Management Workbook: Building a Collection of Data Management Exercises to Bridge Data Information Literacy and Data Management Implementation The Research Data Management Workbook: Building a Collection of Data Management Exercises to Bridge Data Information Literacy and Data Management Implementation Briney, Kristin Objective: There are limited opportunities and resources for data information literacy at small universities, requiring instructors to make the most of the time they have in the classroom. This article describes the creation of a collection of data management exercises, collectively called The Research Data Management Workbook, which supplement one-shot instruction and help students implement specific data management tasks. Methods: Exercises were developed using backward design and authentic assessment, with the goal of scaffolding data management implementation yet allowing for customization to research workflows. Exercises cover activities across data lifecycle and take the form of worksheets, checklists, and procedures. The exercises were collectively formatted as a book using the tool bookdown. Results: For a one-hour library session, students can work through one or two exercises during class and the instructor can refer to specific exercises for follow up on various data management topics. The exercises have also proved useful for consultation, as a researcher can develop an understanding of a way to address the data problem ahead of a more in-depth consultation. Conclusions: The workbook has been a useful supplement to limited data management instruction time at a small university. Further work needs to be done to quantify the efficacy of this form of data information literacy. 2025-07-09T13:45:00Z info:eu-repo/semantics/article 14 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.937 https://doi.org/10.7191/jeslib.937 https://publishing.escholarship.umassmed.edu/jeslib/article/id/937/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e937

oai:jeslib:id:1061 2025-01-27T09:30:00Z

Editorial for Special Issue: 2024 Research Data Access and Preservation (RDAP) Summit Editorial for Special Issue: 2024 Research Data Access and Preservation (RDAP) Summit Cappello, Alicia Tang, Angel Jackson, Carolyn Jones, Jamaica Goben, Abigail The 2024 Research Data Access and Preservation (RDAP) Summit, with the theme Bridging Boundaries: Interoperability in the Data Community, was held virtually between March 12th and 14th, 2024. This year’s summit focused on the “I” in FAIR: interoperability. While very important, interoperability is often the overlooked FAIR principle. Its goal, to be able to integrate multiple formats and systems of data together, can be achieved through a variety of activities, including data formatting, metadata standards, and collaborative protocols. In other words, interoperability is meant to “bridge the boundaries” between different data types, sources, communities, and institutions. The 2024 RDAP Summit looked at the interoperability between the various social components and perspectives that also need to be considered when integrating data. 2025-01-27T09:30:00Z info:eu-repo/semantics/article 13 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1061 https://doi.org/10.7191/jeslib.1061 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1061/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1061

oai:jeslib:id:969 2024-12-16T09:30:00Z

Staking out the Stakeholders: Using NIST’s Research Data Framework Within a Public University System Staking out the Stakeholders: Using NIST’s Research Data Framework Within a Public University System Kilcer, Emily Almas, Bridget Koos, Jessica A. Medina-Smith, Andrea Stollar Peters, Catherine Purpose: This article first introduces and contextualizes the National Institute of Standards and Technology (NIST) Research Data Framework (RDaF) and then explores its application in a local context. Setting/Participants: The State University of New York (SUNY) System, both at a system-wide level and at two individual SUNY campuses, developed an approach to applying RDaF to improve research data management (RDM) practices. Brief Description: As institutions work to establish sound, coordinated services and infrastructure that meet local needs, they look to strategic guidance and established best practices for doing so responsibly and successfully. Modeled after their Cybersecurity and Privacy Frameworks, NIST began developing RDaF in 2019 to address pressing research data community needs. The RDaF provides a comprehensive, structured approach to be used by diverse stakeholders to better understand the benefits, risks, and costs of research data management (RDM).  Results/Outcome: NIST continues to work with other organizations on RDaF’s utility in different contexts, and SUNY’s application offers both a use case and lessons learned that may offer other institutions a practical, grounded approach for leveraging the power of RDaF to improve their RDM strategy. Conclusions: RDaF’s comprehensive guidance offers a robust, flexible framework for building thorough RDM strategy, whatever an organization's institutional readiness. 2024-12-16T09:30:00Z info:eu-repo/semantics/article 13 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.969 https://doi.org/10.7191/jeslib.969 https://publishing.escholarship.umassmed.edu/jeslib/article/id/969/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e969

oai:jeslib:id:965 2024-12-13T09:00:00Z

Understanding how to identify and manage personal identifying information (PII) to further data interoperability Understanding how to identify and manage personal identifying information (PII) to further data interoperability Nie, Zixin Respect for research participant rights is a key aspect for consideration when creating and utilizing interoperable data. From that perspective, requirements for sharing research data often call for the data to be de-identified, i.e., the removal of all personal identifying information (PII) prior to data sharing, to ensure that the participant’s data privacy rights are not infringed upon. However, what constitutes PII is often a point of confusion amongst researchers who are not familiar with privacy laws and regulations. This paper hopes to provide some clarity around what makes research data identifiable by presenting it under a different perspective from what most researchers are familiar with. It also provides a framework to help researchers determine where PII could exist within their data that they can use to help with privacy impact evaluations. The goal is to empower researchers to share their data with greater confidence that the privacy rights of their research subjects have been sufficiently protected, enabling access to greater amounts of data for research use. 2024-12-13T09:00:00Z info:eu-repo/semantics/article 13 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.965 https://doi.org/10.7191/jeslib.965 https://publishing.escholarship.umassmed.edu/jeslib/article/id/965/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e965

oai:jeslib:id:970 2024-12-04T11:30:00Z

Bridging Data Communities: Interoperability through inclusive, cross-institutional collaboration Bridging Data Communities: Interoperability through inclusive, cross-institutional collaboration Sackmann, Anna Ngo, Lisa Smith, Elliott Coleman, Misha Objectives: To demonstrate how librarians can use engagement strategies to foster the exchange of knowledge and skills for data analysis and to build bridges between data communities. A second objective is to help student instructors to develop effective live-coding pedagogical practices and to gain practical experience in leading participatory workshop sessions.  Methods: Librarians developed a low-barrier introductory peer-to-peer data science workshop series to support students seeking to develop coding, data analysis, and visualization skills, with a focus on Python and SQL. We guided undergraduate peer instructors in participatory live-coding pedagogy, organized practice sessions for instructors, and managed the scheduling, logistics, outreach, and hosting of the workshops. Results: In Fall 2023 sessions in the workshop series were delivered synchronously to over 100 participants, including students from our home institution and more than a dozen community colleges; one workshop was delivered twice—once in English, once in Spanish. Workshop recordings posted online have been viewed over 1000 times. Conclusions: We successfully identified strategies for building upon existing relationships and strengthening connections among diverse data communities; designing programs and outreach efforts to lower barriers to participation in data science; and fostering a culture of diversity, equity, and inclusion in data science knowledge sharing. 2024-12-04T11:30:00Z info:eu-repo/semantics/article 13 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.970 https://doi.org/10.7191/jeslib.970 https://publishing.escholarship.umassmed.edu/jeslib/article/id/970/ 2161-3974 https://creativecommons.org/licenses/by-sa/4.0 e970

oai:jeslib:id:966 2024-12-04T11:30:00Z

Collaborative Development of a Statistics Microlearning Course for Health Professionals Collaborative Development of a Statistics Microlearning Course for Health Professionals Bohman, Lena Vitiello, Regina Teaching is often core to a librarian’s duties. However, at large institutions, there is often not enough librarian manpower to deliver in-person instruction on specialized topics to all who could benefit. In this case, librarians must look beyond the traditional in-person session to deliver educational content at scale. At our library, serving a large healthcare system with 85,000 employees, we constantly tackle issues of delivering library services at scale with limited manpower. In this article, we discuss how we tackled developing an asynchronous microlearning-based course for health care professionals on statistical analysis. We start out with background on microlearning, a strategy for e-learning based on short “bites” of information (Gagne et al. 2019). Then we move on to the process of developing the course, which was built on an existing library program to offer GraphPad Prism licenses to health system employees. We detail how we collaborated with units across the health system, especially an e-learning specialist based in the office of data strategy and the director of biostatistics. We describe in detail the planning and development of the course, including how we decided what to cover, creating synthetic electronic health record data for video examples, and recording the microlearning videos. Thus far, our microlearning course has received more than 1,400 views, which we consider to be very successful. However, our strategy to assess the course could be more robust, and we also talk about future strategies to gauge the success of similar projects. 2024-12-04T11:30:00Z info:eu-repo/semantics/article 13 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.966 https://doi.org/10.7191/jeslib.966 https://publishing.escholarship.umassmed.edu/jeslib/article/id/966/ 2161-3974 https://creativecommons.org/licenses/by-nc-sa/4.0/ e966

oai:jeslib:id:967 2024-12-04T11:30:00Z

Data Sharing Practices in Agricultural Research: Findings from a Systematized Review Data Sharing Practices in Agricultural Research: Findings from a Systematized Review Baxter, Isabella Antognoli, Erin Aria, Paria Albro, Maggie Boice, Jocelyn McCullough, Michal Jackson, Carolyn Objective: Agricultural researchers who follow data sharing best practices advance the state of research in a variety of critical areas including plant breeding, cropping systems, and climate change adaptation. Data sharing makes research more reliable and reproducible, therefore, data sharing practices of researchers are integral to advancing science. To assess how agricultural researchers adhere to these practices, we conducted a systematized review of their published output and examined different ways data were shared. Methods: Our study focused on corn and soybean production research published from 2017 to 2022 by authors at our institutions. We searched five databases, retrieved 8,271 articles, and created a randomized sample of 1,250 papers that contained an equal number of examples from each year. Following a rigorous set of criteria, we screened each article for inclusion and recorded the characteristics of the data, funder information, and whether the researchers shared data. Results: Of the articles that met the inclusion criteria, less than 15% shared the full dataset associated with the research. The rate of articles sharing data did not change appreciably over time and was low regardless of funding source. Methods for sharing data varied widely, both in data availability statements and in storage options. Conclusions: These results indicate a need for improved agricultural data sharing and suggest an important role for librarians and data professionals in promoting best data practices to meet increasingly strict funder requirements. 2024-12-04T11:30:00Z info:eu-repo/semantics/article 13 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.967 https://doi.org/10.7191/jeslib.967 https://publishing.escholarship.umassmed.edu/jeslib/article/id/967/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e967

oai:jeslib:id:964 2024-12-03T09:50:00Z

Persistent Identifiers for Instruments and Facilities: Current State, Challenges, and Opportunities Persistent Identifiers for Instruments and Facilities: Current State, Challenges, and Opportunities Mayernik, Matthew Johnson, Andrew Julian, Renaine Murray, Matthew Mundoma, Claudius Ranganath, Aditya Stossmeister, Greg Objective: Persistent Identifiers (PIDs) are central to the vision of open science described in the FAIR Principles. However, the use of PIDs for scientific instruments and facilities is decentralized and fragmented. This project aims to develop community-based standards, guidelines, and best practices for how and why PIDs can be assigned to facilities and instruments. Methods: We hosted several online and in-person focus groups and discussions, cumulating in a two-day in-person workshop featuring stakeholders from a variety of organizations and disciplines, such as instrument and facilities operators, PID infrastructure providers, researchers who use instruments and facilities, journal publishers, university administrators, federal funding agencies, and information and data professionals. Results: Our first-year efforts resulted in four main areas of interest: developing a better understanding of the current PID ecosystem; clarifying how and when PIDs could be assigned to scientific instruments and facilities; challenges and barriers involved with assigning PIDs; incentives for researchers, facility managers, and other stakeholders to encourage the use of PIDs. Conclusions: The potential for PIDs to facilitate the discovery, connection, and attribution of research instruments and facilities indicates an obvious value in their use. The lack of standards of how and when they are created, assigned, updated, and used is a major barrier to their widespread use. Data and information professionals can work to create relationships with stakeholders, provide relevant education and outreach activities, and integrate PIDs for instruments and facilities into their data curation and publication workflows.  2024-12-03T09:50:00Z info:eu-repo/semantics/article 13 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.964 https://doi.org/10.7191/jeslib.964 https://publishing.escholarship.umassmed.edu/jeslib/article/id/964/ 2161-3974 https://creativecommons.org/publicdomain/zero/1.0/ e964

oai:jeslib:id:1026 2024-09-25T09:30:00Z

Good Omens: New Services, Accurate Research Data Good Omens: New Services, Accurate Research Data Raboin, Regina Fisher Research data management and data curation need strong relationships with colleagues, collaborators, and researchers. Because change comes fast, assessment of current services and practices are important so that new paths and initiatives can be developed. Creating strong metadata will support the FAIR Principles and provide a more equitable and accessible path to data.  2024-09-25T09:30:00Z info:eu-repo/semantics/article 13 2 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.1026 https://doi.org/10.7191/jeslib.1026 https://publishing.escholarship.umassmed.edu/jeslib/article/id/1026/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e1026

oai:jeslib:id:959 2024-09-10T11:15:00Z

Academic Library Pricing Dataset for SciFinder Scholar, Web of Science, and Scopus: 2018-2024 Academic Library Pricing Dataset for SciFinder Scholar, Web of Science, and Scopus: 2018-2024 Brundy, Curtis Thornton, Joel This dataset contains database pricing and agreements received through public records requests made to members of the Association of Research Libraries (ARL) and a few non-ARL research libraries. Pricing from the years 2018 to 2024, depending on the institution, is included for three premium academic databases: SciFinder from the American Chemical Society’s Chemical Abstract Service, Web of Science from Clarivate, and Scopus from Elsevier. The pricing in this dataset is difficult to acquire and of significant interest to libraries that license these products and those wishing to investigate pricing strategies and approaches in the library database market. 2024-09-10T11:15:00Z info:eu-repo/semantics/article 13 2 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.959 https://doi.org/10.7191/jeslib.959 https://publishing.escholarship.umassmed.edu/jeslib/article/id/959/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e959

oai:jeslib:id:907 2024-08-16T09:15:00Z

Identifying metadata commonalities across restricted health data sources: A mixed methods study exploring how to improve the discovery of and access to restricted datasets Identifying metadata commonalities across restricted health data sources: A mixed methods study exploring how to improve the discovery of and access to restricted datasets Read, Kevin B. Gibson, Grant Leahey, Ambery Peterson, Lynn Rutley, Sarah Shi, Julie Smith, Victoria Stathis, Kelly Background: While open datasets are adopting FAIR principles to improve their discovery and use, restricted data—those only accessible via request or application—have fallen behind. Metadata is not an inherent characteristic of restricted data, which limits its ability to be found and used. To better understand discoverability and accessibility of restricted data, this study reviewed restricted health data sources to determine how they describe their datasets and access procedures, what descriptive commonalities exist across data sources, and to what extent the commonalities we found can be accommodated within existing metadata schemas. Methods: This study extracted dataset and access information provided by a sample of 48 restricted data sources, identified commonalities across these data sources to develop possible metadata elements for restricted data, and mapped these metadata elements to existing metadata schemas (e.g., DataCite) to evaluate how well they accommodate information supplied by restricted data sources. Results: Restricted data sources describe their datasets (35 commonalities) and access procedures (27 commonalities) in similar ways. Dataset descriptions aligned with existing metadata schemas, with the DDI-Lifecycle and -Codebook schemas receiving 91.4% and 85.7% exact matches respectively with the dataset elements we identified. Access procedures did not align with metadata available in existing schemas. Discussion: While descriptive dataset metadata for restricted data sources will make their data more findable, the accessibility of these datasets could be significantly improved by structured metadata capturing data access information. Presently, metadata schemas do not accommodate the level of detail restricted data sources provide about access procedures and requirements. 2024-08-16T09:15:00Z info:eu-repo/semantics/article 13 2 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.907 https://doi.org/10.7191/jeslib.907 https://publishing.escholarship.umassmed.edu/jeslib/article/id/907/ 2161-3974 https://creativecommons.org/licenses/by-nc/4.0 e907

oai:jeslib:id:931 2024-08-09T09:30:00Z

Back to Basics: Considering Categories of Data Services Consults Back to Basics: Considering Categories of Data Services Consults Wink, Isaac Consultations are fundamental to data librarianship, serving as a vital means of one-on-one support for researchers. However, the topics and forms of support unique to data services consults are not always carefully considered. This commentary addresses five common services offered by data librarians—dataset reference, data management support, data analysis and software support, data curation, and data management (and sharing) plan writing—and considers strategies for successful patron support within the boundaries of a consultation. 2024-08-09T09:30:00Z info:eu-repo/semantics/article 13 2 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.931 https://doi.org/10.7191/jeslib.931 https://publishing.escholarship.umassmed.edu/jeslib/article/id/931/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e931

oai:jeslib:id:935 2024-08-09T09:30:00Z

Navigating the Currents: Reflections from the Community Data Toolkits Workshop Navigating the Currents: Reflections from the Community Data Toolkits Workshop Narlock, Mikala The Community Data Toolkits Workshop (CDTW) was held March 21-22, 2024, in Hamilton, Ontario. The CDTW sought to cross boundaries, and to forge relationships and connections between data professionals and community-oriented organizations. Drawing participants from across Canada and the local Hamilton community, the CDTW provided a distinct opportunity to reflect on the role of community, data, and community data, especially in an era of seemingly-never-ending change. Below, I share a summary of the workshop, intertwined with meditations from Undrowned: Black Feminist Lessons from Marine Mammals. I reflect on the key themes that emerged, and end with a gratitude to all of the workshop organizers and participants. 2024-08-09T09:30:00Z info:eu-repo/semantics/article 13 2 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.935 https://doi.org/10.7191/jeslib.935 https://publishing.escholarship.umassmed.edu/jeslib/article/id/935/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e935

oai:jeslib:id:685 2024-06-20T10:15:00Z

Toward Enhanced Reusability: A Comparative Analysis of Metadata for Machine Learning Objects and Their Characteristics in Generalist and Specialist Repositories Toward Enhanced Reusability: A Comparative Analysis of Metadata for Machine Learning Objects and Their Characteristics in Generalist and Specialist Repositories Labou, Stephanie G. Pennington, Abigail Yoo, Ho Jung S. Baluja, Michael Objective: The rapidly increasing prevalence and application of machine learning (ML) across disciplines creates a pressing need to establish guidance for data curation professionals. However, we must first understand the characteristics of ML-related objects shared in generalist and specialist repositories and the extent to which repository metadata fields enable findability and reuse of ML objects. Methods: We used a combination of API queries and web scraping to retrieve metadata for ML objects in eight commonly used generalist and ML-specific data repositories. We assessed both metadata schema and characteristics of deposited ML objects, within the context of the widely adopted FAIR Principles. We also calculated summary statistics for properties of objects, including number of objects per year, dataset size, domains represented, and availability of related resources. Results: Generalist repositories excelled at providing provenance metadata, specifically unique identifiers, unambiguous citations, clear licenses, and related resources, while specialist repositories emphasized ML-specific descriptive metadata, such as number of attributes and instances and task type. In terms of object content, we noted a wide range of file formats, as well as licenses, all of which impact reusability. Conclusions: Generalist repositories will benefit from some of the practices adopted by specialists, and specialist repositories will benefit from adopting proven data curation practices of generalist repositories. A step forward for repositories will be to invest more into use of labels and persistent identifiers to improve workflow documentation, provenance, and related resource linking of ML objects, which will increase their findability, interoperability, and reusability. 2024-06-20T10:15:00Z info:eu-repo/semantics/article 13 2 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.685 https://doi.org/10.7191/jeslib.685 https://publishing.escholarship.umassmed.edu/jeslib/article/id/685/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e685

oai:jeslib:id:681 2024-05-21T14:50:00Z

The use of data management planning among researchers in higher learning institutions: The case of the Nelson Mandela African Institution of Science and Technology in Tanzania The use of data management planning among researchers in higher learning institutions: The case of the Nelson Mandela African Institution of Science and Technology in Tanzania Mosha, Neema Florence Ngulube, Patrick This study assessed the use of data management plans among researchers at a selected higher learning institution (HLI) in Tanzania. A pretested structured questionnaire was administered to registered postgraduate students. Many of the respondents reported that a data management plan (DMP) was required before writing a research project and when a research project was submitted. The results also demonstrated that many respondents did not use any online DMP template tools to formulate their DMP although most of them were aware of available DMP template tools such as OpenDMP. Many respondents stated that the requirement of using a DMP were selection of a DMP format, updating the DMP regularly, having a short and to-the-point DMP and a well-structured DMP specifying the kinds and formats of the data to be acquired, generated, produced, and preserved. Meeting funders&rsquo; institutions, and publishers&rsquo; requirements, and ensuring that data are accurate, complete, and reliable were among the DMP benefits in HLIs identified by the respondents. Several challenges were revealed including a lack of awareness, competence, and guidelines to assist researchers using a DMP for their research projects. The conclusion is that researchers need to develop and use DMP template tools to plan, organize, and work on their research projects in addition to ensuring that they meet funders' requirements. It is recommended that HLIs should provide extensive training programs for raising awareness about DMPs among the researchers and to make DMPs a mandatory requirement for finalizing research projects among researchers, and not only for funding purposes. 2024-05-21T14:50:00Z info:eu-repo/semantics/article 13 2 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.681 https://doi.org/10.7191/jeslib.681 https://publishing.escholarship.umassmed.edu/jeslib/article/id/681/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e681

oai:jeslib:id:780 2024-04-23T09:00:00Z

Data services at the academic library: a natural history of horses and unicorns Data services at the academic library: a natural history of horses and unicorns Oliver, Jeffrey Rios, Fernando Carini, Kiriann Ly, Chun Objective: Increases in data-intensive research at colleges and universities is driving demand for data services provided by academic libraries. The current work investigates the distribution of library data services, how such services are offered, and the effect of resourcing on the amount of services offered by a library.  Methods: We used a web-based inventory of 25 academic libraries at U.S. Research 1 (R1) Carnegie institutions to assess the state of data services at university libraries. We categorized and quantified services, and tested for an effect of library resourcing on the size of library data service portfolios. Results: Support for data management and geospatial services was relatively widespread, with increasing support in areas of data analyses and data visualization. There was significant variation among services in the modality in which they were offered (web, consult, instruction) and library resourcing had a significant effect on the number of data services a library offered. Conclusions: While a core subset of these data services are offered at most academic libraries, more specialized topics are restricted to well-resourced libraries. In light of the influence of resource scarcity on the number of services a library can offer, intra- and inter-campus partnerships will be critical to ensure campus support for data service needs. 2024-04-23T09:00:00Z info:eu-repo/semantics/article 13 2 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.780 https://doi.org/10.7191/jeslib.780 https://publishing.escholarship.umassmed.edu/jeslib/article/id/780/ 2161-3974 https://creativecommons.org/licenses/by-sa/4.0 e780

oai:jeslib:id:845 2024-03-06T10:15:00Z

Automatic Expansion of Metadata Standards for Historic Costume Collections Automatic Expansion of Metadata Standards for Historic Costume Collections McIrvin, Caleb Miller, Chreston Smith-Glaviana, Dina Ng, Wen Nie Objective: This project focuses on Artificial Intelligence (AI) supported enhancement of descriptive metadata for fashion collections (otherwise known as costume or dress and textile collections) through expanding costume-specific controlled terms. The authors use Natural Language Processing (NLP) techniques along with a human-in-the-loop process to support selection of descriptive terms for inclusion in the controlled terms of a metadata schema. This project seeks to expand upon existing domain-specific schema, Costume Core, by enhancing the schema with a comprehensive set of descriptors. This enhancement will allow for more accurate and detailed descriptions of costume artifacts. This article describes this process and the outcomes of AI approaches for providing this metadata expansion, who this process is for, ethical considerations, and lessons learned.Methods: We approached our problem with an investigation into using word embeddings to aid in supporting the suggesting of new metadata terms. Several word embedding models were applied with the more descriptive one chosen for final use in a human-in-the-loop selection process. This selection process provided domain experts to identify which terms chosen by the model are of relevant value. We then compare what was chosen by the domain experts and what the model produced to get an idea as to the value the model provides in the process of metadata expansion.Results: The metadata expansion process was a success. An AI supported process aided domain experts in choosing relevant terms to include in their metadata schema. Therefore, the results were a methodology for using identified AI models for the problem, an interactive system to aid the domain experts (software system), and how to evaluate the results.Conclusion: The application of AI technologies (word embeddings) provided a successful pipeline for supporting domain experts to expand the metadata schema with additional descriptors.&nbsp; Enhancing the metadata schema with additional descriptors improves its usability for fashion collection managers and allows for a more precise description of the artifacts. As a result, many new terms that were expertly chosen were added to the metadata schema. 2024-03-06T10:15:00Z info:eu-repo/semantics/article 13 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.845 https://doi.org/10.7191/jeslib.845 https://publishing.escholarship.umassmed.edu/jeslib/article/id/845/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e845

oai:jeslib:id:846 2024-03-06T10:15:00Z

Ethical Considerations in Integrating AI in Research Consultations: Assessing the Possibilities and Limits of GPT-based Chatbots Ethical Considerations in Integrating AI in Research Consultations: Assessing the Possibilities and Limits of GPT-based Chatbots Feng, Yali Wang, Jun Anderson, Steven G. Objective: This case study sought to provide early information on the accuracy and relevance of selected GPT-based product responses to basic information queries, such as might be asked in librarian research consultations. We intended to identify positive possibilities, limitations, and ethical issues associated with using these tools in research consultations and teaching.Methods: A case simulation examined the responses of GPT-based products to a basic set of questions on a topic relevant to social work students. The four chatbots (ChatGPT-3.5, ChatGPT-4, Bard, and Perplexity) were given identical question prompts, and responses were assessed for relevance and accuracy. The simulation was supplemented by reviewing actual user exchanges with ChatGPT-3.5 using a ShareGPT file containing conversations with early users.Results: Each product provided relevant information to queries, but the nature and quality of information and the formatting sophistication varied substantially. There were troubling accuracy issues with some responses, including inaccurate or non-existent references. The only paid product examined (ChatGPT-4), generally provided the highest quality information, which raises equitable access to quality technology concerns. Examination of ShareGPT conversations also raised issues regarding ethical use of chatbots to complete course assignments, dissertation designs, and other research products.Conclusions: We conclude that these new tools offer significant potential to enhance learning if well-employed. However, their use is fraught with ethical challenges. Librarians must work closely with instructors, patrons, and administrators to assure that the potential is realized while ethical values are safeguarded. 2024-03-06T10:15:00Z info:eu-repo/semantics/article 13 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.846 https://doi.org/10.7191/jeslib.846 https://publishing.escholarship.umassmed.edu/jeslib/article/id/846/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e846

oai:jeslib:id:860 2024-03-06T10:15:00Z

Introduction to the Special Issue: Responsible AI in Libraries and Archives Introduction to the Special Issue: Responsible AI in Libraries and Archives Mannheimer, Sara Rossmann, Doralyn Clark, Jason Shorish, Yasmeen Bond, Natalie Scates Kettler, Hannah Sheehey, Bonnie Young, Scott W. H. Librarians and archivists are often early adopters and experimenters with new technologies. Our field is also interested in critically engaging with technology, and we are well-positioned to be leaders in the slow and careful consideration of new technologies. Therefore, as librarians and archivists have begun using artificial intelligence (AI) to enhance library services, we also aim to interrogate the ethical issues that arise while using AI to enhance collection description and discovery and streamline reference services and teaching. The IMLS-funded Responsible AI in Libraries and Archives project aims to create resources that will help practitioners make ethical decisions when implementing AI in their work. The case studies in this special issue are one such resource. Seven overarching ethical issues come to light in these case studies—privacy, consent, accuracy, labor considerations, the digital divide, bias, and transparency. This introduction reviews each issue and describes strategies suggested by case study authors to reduce harms and mitigate these issues. 2024-03-06T10:15:00Z info:eu-repo/semantics/article 13 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.860 https://doi.org/10.7191/jeslib.860 https://publishing.escholarship.umassmed.edu/jeslib/article/id/860/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e860

oai:jeslib:id:849 2024-03-06T10:15:00Z

"I’ve Got a Feeling": Performing Sentiment Analysis on Critical Moments in Beatles History "I’ve Got a Feeling": Performing Sentiment Analysis on Critical Moments in Beatles History Wolff, Milana Sergeevna Mainzer, Liudmila Drummond, Kent Our project involved the use of optical character recognition (OCR) and sentiment analysis tools to assess popular feelings regarding the Beatles and to determine how aggregated sentiment measurements changed over time in response to pivotal events during the height of their musical career. We used Tesseract to perform optical character recognition on historical newspaper documents sourced from the New York Times and smaller publications, leveraging advances in computer vision to circumvent the need for manual transcription. We employed state-of-the-art sentiment analysis models, including VADER, TextBlob, and SentiWordNet to obtain sentiment analysis scores for individual articles (Hutto and Gilbert 2014; TextBlob, n.d.; Baccianella, Esuli, and Sebastiani 2010). After selecting articles mentioning the group, we examined the changes in average sentiments displayed in articles corresponding to critical moments in the Beatles’ musical career to determine the impact of these events. 2024-03-06T10:15:00Z info:eu-repo/semantics/article 13 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.849 https://doi.org/10.7191/jeslib.849 https://publishing.escholarship.umassmed.edu/jeslib/article/id/849/ 2161-3974 https://creativecommons.org/licenses/by-nc-sa/4.0/ e849

oai:jeslib:id:850 2024-03-06T10:15:00Z

Using AI/Machine Learning to Extract Data from Japanese American Confinement Records Using AI/Machine Learning to Extract Data from Japanese American Confinement Records Elings, Mary Friedman, Marissa Singh, Vijay Purpose: This paper examines the use of Artificial Intelligence/Machine Learning to extract a more comprehensive data set from a structured “standardized” form used to document Japanese American incarcerees during World War II.Setting/Participants/Resources: The Bancroft Library partnered with Densho, a community memory organization, and Doxie.AI to complete this work.&nbsp;Brief Description: The project digitized the complete set of Form WRA-26 “individual records'' for more than 110,000 Japanese Americans incarcerated in War Relocation Authority camps during WWII. The library utilized AI/machine learning to automate text extraction from over 220,000 images of a structured “standardized” form; our goal was to improve upon and collect information not previously recorded in the Japanese American Internee Data file held by the National Archives and Records Administration. The project team worked with technical, academic, legal, and community partners to address ethical and logistical issues raised by the data extraction process, and to assess appropriate access options for the dataset(s) and digitized records.&nbsp;Results/Outcome: Using AI/machine learning increased the quality of the data extracted from the digitized WWII era forms.&nbsp;Evaluation Method: A comparison of the earlier dataset extracted from the 1940s’s computer punch cards to the current data set extracted using AI/machine learning, the use of AI/machine learning showed marked improvement. 2024-03-06T10:15:00Z info:eu-repo/semantics/article 13 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.850 https://doi.org/10.7191/jeslib.850 https://publishing.escholarship.umassmed.edu/jeslib/article/id/850/ 2161-3974 https://creativecommons.org/licenses/by-nc-sa/4.0/ e850

oai:jeslib:id:811 2024-03-05T10:45:00Z

Ethical considerations in utilizing artificial intelligence for analyzing the NHGRI’s History of Genomics and Human Genome Project archives Ethical considerations in utilizing artificial intelligence for analyzing the NHGRI’s History of Genomics and Human Genome Project archives Hosseini, Mohammad Hong, Spencer Holmes, Kristi Wetterstrand, Kris Donohue, Christopher Amaral, Luis A. Nunes Stoeger, Thomas Understanding “how to optimize the production of scientific knowledge” is paramount to those who support scientific research—funders as well as research institutions—to the communities served, and to researchers. Structured archives can help all involved to learn what decisions and processes help or hinder the production of new knowledge. Using artificial intelligence (AI) and large language models (LLMs), we recently created the first structured digital representation of the historic archives of the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health. This work yielded a digital knowledge base of entities, topics, and documents that can be used to probe the inner workings of the Human Genome Project, a massive international public-private effort to sequence the human genome, and several of its offshoots like The Cancer Genome Atlas (TCGA) and the Encyclopedia of DNA Elements (ENCODE). The resulting knowledge base will be instrumental in understanding not only how the Human Genome Project and genomics research developed collaboratively, but also how scientific goals come to be formulated and evolve. Given the diverse and rich data used in this project, we evaluated the ethical implications of employing AI and LLMs to process and analyze this valuable archive. As the first computational investigation of the internal archives of a massive collaborative project with multiple funders and institutions, this study will inform future efforts to conduct similar investigations while also considering and minimizing ethical challenges. Our methodology and risk-mitigating measures could also inform future initiatives in developing standards for project planning, policymaking, enhancing transparency, and ensuring ethical utilization of artificial intelligence technologies and large language models in archive exploration.Author Contributions:&nbsp;Mohammad Hosseini: Investigation; Project Administration; Writing – original draft; Writing – review &amp; editing.&nbsp;Spencer Hong: Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review &amp; editing.&nbsp;Thomas Stoeger: Conceptualization; Investigation; Project Administration; Supervision; Writing – original draft; Writing – review &amp; editing.&nbsp;Kristi Holmes: Funding acquisition, Supervision, Writing – review &amp; editing.&nbsp;Luis A. Nunes Amaral: Funding acquisition, Supervision, Writing – review &amp; editing.&nbsp;Christopher Donohue: Conceptualization, Project administration, Resources, Supervision, Writing – original draft, Writing – review &amp; editing.&nbsp;Kris Wetterstrand: Conceptualization, Funding acquisition, Project administration. 2024-03-05T10:45:00Z info:eu-repo/semantics/article 13 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.811 https://doi.org/10.7191/jeslib.811 https://publishing.escholarship.umassmed.edu/jeslib/article/id/811/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e811

oai:jeslib:id:804 2024-03-05T10:45:00Z

Open Science Recommendation Systems for Academic Libraries Open Science Recommendation Systems for Academic Libraries Beltran, Lencia Griego, Chasz Herckis, Lauren An interdisciplinary academic team offers a comprehensive case study describing the development of a predictive model as the cornerstone for an open science recommendation system tailored to the Carnegie Mellon University community. This initiative will empower users in choosing open science services that align with their academic requirements, introduce academics to resources they find valuable, and bridge gaps within academic library service offerings.As an institution with a longstanding commitment to a science-informed approach and a focus on computer science, engineering, and artificial intelligence, Carnegie Mellon University has enthusiastically embraced open science practices. The Carnegie Mellon University’s Libraries has been instrumental in bringing these practices into our academic landscape.The authors strive to develop a predictive model which will evolve into a recommendation system. The pursuit of this endeavor has led the authors through several ethical considerations, such as data privacy, the involvement of student contributors, and the design of a persuasive recommendation system. We are committed to exploring ethical approaches for delivering user-centered recommendations and to preserving individual autonomy.The authors have actively engaged with diverse academic departments, students, and faculty, embarking on data exploration, and applying open science principles throughout the process. The resulting system will raise awareness of library services and deliver tailored recommendations for the adoption of proven research tools and practices.This case study serves as an exemplar of how universities can enact open science principles and develop systems that prioritize the user's interests, navigate institutional complexities to forge interdisciplinary collaboration, and muster resources to support innovative, multi-disciplinary efforts.&nbsp; 2024-03-05T10:45:00Z info:eu-repo/semantics/article 13 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.804 https://doi.org/10.7191/jeslib.804 https://publishing.escholarship.umassmed.edu/jeslib/article/id/804/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e804

oai:jeslib:id:805 2024-03-05T10:45:00Z

Responsible AI at the Vanderbilt Television News Archive: A Case Study Responsible AI at the Vanderbilt Television News Archive: A Case Study Anderson, Clifford Blake Duran, Jim We provide an overview of the use of machine-learning and artificial intelligence at the Vanderbilt Television News Archive (VTNA). After surveying our major initiatives to date, which include the full transcription of the collection using a custom language model deployed on Amazon Web Services (AWS), we address some ethical considerations we encountered, including the possibility of staff downsizing and misidentification of individuals in news recordings. 2024-03-05T10:45:00Z info:eu-repo/semantics/article 13 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.805 https://doi.org/10.7191/jeslib.805 https://publishing.escholarship.umassmed.edu/jeslib/article/id/805/ 2161-3974 https://creativecommons.org/licenses/by-nc/4.0 e805

oai:jeslib:id:800 2024-03-05T10:45:00Z

The Implementation of Keenious at Carnegie Mellon University The Implementation of Keenious at Carnegie Mellon University Pastva, Joelen Jebbia, Dom Reilly, Maranda Werlinich, Ashley In the fall of 2022, the Carnegie Mellon University (CMU) Libraries began investigating Keenious—an artificial intelligence (AI)-based article recommender tool—for a possible trial implementation to improve pathways to resource discovery and assist researchers in more effectively searching for relevant research. This process led to numerous discussions within the library regarding the unique nature of AI-based tools when compared with traditional library resources, including ethical questions surrounding data privacy, algorithmic transparency, and the impact on the research process. This case study explores these topics and how they were negotiated up to and immediately following CMU’s implementation of Keenious in January, 2023, and highlights the need for more frameworks for evaluating AI-based tools in academic settings. 2024-03-05T10:45:00Z info:eu-repo/semantics/article 13 1 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.800 https://doi.org/10.7191/jeslib.800 https://publishing.escholarship.umassmed.edu/jeslib/article/id/800/ 2161-3974 https://creativecommons.org/licenses/by-nc/4.0 e800

oai:jeslib:id:754 2023-12-20T10:00:00Z

A Problem Shared Is a Community Created: Recommendations for Cross-Institutional Collaborations A Problem Shared Is a Community Created: Recommendations for Cross-Institutional Collaborations Hertz, Marla Badger, Kelsey Bohman, Lena Carr Jones, Lucy Nieman Hislop, Christine Smith, Katy Ye, Hao Committee work is a requisite job function for many in academia, yet designing a productive collaborative experience often remains a challenge. In this article, we reflect on our experiences as part of a successful cross-institutional working group and describe strategies to improve leadership structure, group dynamics, accountability, and incentives for collaborative projects.As of January 2023, the National Institutes of Health (NIH) Data Management &amp; Sharing (DMS) Policy requires investigators applying for funding to submit a Data Management and Sharing Plan (DMS Plan) that describes how scientific data will be managed, preserved, and shared. In response to this new policy, a community of more than 30 librarians and other research data professionals convened the Working Group on NIH DMSP Guidance, collaboratively producing comprehensive guidance about the policy for researchers and research support staff. In less than a year, the working group produced glossaries of NIH and data management jargon, an example data management and sharing plan, a directory of existing example plans, checklists for researchers and librarians, and an interactive repository finder.This group was a successful grassroots effort by contributors with diverse expertise and backgrounds. We discuss practical strategies for each stage of activity throughout the lifecycle of the working group; from recruiting members, designing pathways to encourage participation from busy professionals, structuring the meetings to facilitate progress and productivity, and disseminating final products broadly. We invite fellow librarians, data professionals, and academics to apply and build upon these strategies to tackle cross-institutional challenges. 2023-12-20T10:00:00Z info:eu-repo/semantics/article 12 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.754 https://doi.org/10.7191/jeslib.754 https://publishing.escholarship.umassmed.edu/jeslib/article/id/754/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e754

oai:jeslib:id:761 2023-12-20T10:00:00Z

Building a Trustworthy Data Repository: CoreTrustSeal Certification as a Lens for Service Improvements Building a Trustworthy Data Repository: CoreTrustSeal Certification as a Lens for Service Improvements Key, Cara Llebot, Clara Boock, Michael Objective: The university library aims to provide university researchers with a trustworthy institutional repository for sharing data. The library sought CoreTrustSeal certification in order to measure the quality of data services in the institutional repository, and to promote researchers’ confidence when depositing their work.Methods: The authors served on a small team of library staff who collaborated to compose the certification application. They describe the self-assessment process, as they iterated through cycles of compiling information and responding to reviewer feedback.&nbsp;Results: The application team gained understanding of data repository best practices, shared knowledge about the institutional repository, and identified areas of service improvements necessary to meet certification requirements. Based on the application and feedback, the team took measures to enhance preservation strategies, governance, and public-facing policies and documentation for the repository.Conclusions: The university library gained a better understanding of top-notch data services and measurably improved these services by pursuing and obtaining CoreTrustSeal certification. 2023-12-20T10:00:00Z info:eu-repo/semantics/article 12 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.761 https://doi.org/10.7191/jeslib.761 https://publishing.escholarship.umassmed.edu/jeslib/article/id/761/ 2161-3974 https://creativecommons.org/licenses/by-nc/4.0 e761

oai:jeslib:id:739 2023-12-20T10:00:00Z

Connecting Repositories to the Global Research Community: A Re-Curation Process Connecting Repositories to the Global Research Community: A Re-Curation Process Habermann, Ted Over the last decade, significant changes have affected the work that data repositories of all kinds do. First, the emergence of globally unique and persistent identifiers (PIDs) has created new opportunities for repositories to engage with the global research community by connecting existing repository resources to the global research infrastructure. Second, repository use cases have evolved from data discovery to data discovery and reuse, significantly increasing metadata requirements.To respond to these evolving requirements, we need retrospective and on-going curation, i.e. re-curation, processes that 1) find identifiers and add them to existing metadata to connect datasets to a wider range of communities, and 2) add elements that support reuse to globally connected metadata.The goal of this work is to introduce the concept of re-curation with representative examples that are generally applicable to many repositories: 1) increasing completeness of affiliations and identifiers for organizations and funders in the Dryad Repository and 2) measuring and increasing FAIRness of DataCite metadata beyond required fields for institutional repositories.These re-curation efforts are a critical part of reshaping existing metadata and repository processes so they can take advantage of new connections, engage with global research communities, and facilitate data reuse. 2023-12-20T10:00:00Z info:eu-repo/semantics/article 12 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.739 https://doi.org/10.7191/jeslib.739 https://publishing.escholarship.umassmed.edu/jeslib/article/id/739/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e739

oai:jeslib:id:862 2023-12-20T10:00:00Z

Special Issue: 2023 Research Data Access and Preservation (RDAP) Summit Special Issue: 2023 Research Data Access and Preservation (RDAP) Summit Cappello, Alicia Hertz, Marla Griffin, Tina Jones, Jamaica The 2023 Research Data Access and Preservation (RDAP) Summit, Building on Experience: Centering Communities in Data Creation and Access, focused on engagement with and building communities within data environments, including how data is being made more accessible for a wider range of communities. The 2023 RDAP Summit was a natural extension from the prior year’s theme of Envisioning an Inclusive Data Future, which highlighted the ways data service providers tailor their services to address specific needs. A selection of presentations from this year’s Summit were expanded into articles for this special issue covering topics on developing and maintaining communities that address aspects across the research data life cycle. 2023-12-20T10:00:00Z info:eu-repo/semantics/article 12 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.862 https://doi.org/10.7191/jeslib.862 https://publishing.escholarship.umassmed.edu/jeslib/article/id/862/ 2161-3974 https://creativecommons.org/licenses/by/4.0 e862

oai:jeslib:id:757 2023-12-20T10:00:00Z

Teaching by Example: Evidence of Data Literacy Competencies and Practices in Top Economics Journal Articles Teaching by Example: Evidence of Data Literacy Competencies and Practices in Top Economics Journal Articles Marchant, Margaret Objective: Data literacy is the ability to describe, evaluate, use, share, and cite data. It is increasingly important for researchers and college students, including in the field of economics. This study explores the prevalence of data literacy competencies in economics articles. Data literacy competencies displayed in journal articles demonstrate what researchers value and provide opportunities to teach students, helping librarians shape data services and instruction.Methods: Based on close reading of economics and data literacy literature, the author developed a protocol of terms relating to data literacy. A stratified random sample of 100 articles was selected from ten top economics journals. Adobe Acrobat’s index search function was used to conduct automated content analysis coding, with additional manual checking for accuracy and data sharing and sources.Results: The economics research articles in the study sample showed strong coverage of terms relating to describing, evaluating, and using data. Sharing and citing data were identified as areas for improvement as only 36% of articles shared data and 40% included terms related to citation. The analysis verifies previous research about the prevalence of commercial data use in business research and adds insight on frequently used open data sources.&nbsp;Conclusions: There are clear data literacy strengths within economics. Librarians have the skills to partner with economics instructors to reinforce strengths and improve gaps to prepare more data literate students. 2023-12-20T10:00:00Z info:eu-repo/semantics/article 12 3 Lamar Soutter Library, UMass Chan Medical School Journal of eScience Librarianship 10.7191/jeslib.757 https://doi.org/10.7191/jeslib.757 https://publishing.escholarship.umassmed.edu/jeslib/article/id/757/ 2161-3974 https://creativecommons.org/licenses/by-nc/4.0 e757 page%3D2