Program

Conversations and scholarship about information design and data visualization often focus on technical aspects —software, techniques, and practices—or on perceptual and cognitive accuracy and effectiveness. In comparison, reflection on their ethics is sparse. This talk is an attempt to start shifting that balance. It outlines a tentative and personal ethical framework grounded in the central question: "What constitutes a good life as an information designer?"

Bio: Alberto Cairo is Professor and Knight Chair in Infographics and Data Visualization at the School of Communication of the University of Miami. He's also director of visualization at UM's Frost Institute for Data Science and Computing. Cairo joined UM in 2012, after a long career as a graphics director for publications in Spain (El Mundo) and Brazil (Editora Globo), where he led award-winning teams widely considered pioneering in the use of interactive news graphics. He's the author of four books, The Functional Art (2012), The Truthful Art (2016), How Charts Lie (2019), and The Art of Insight (2023), and is working on a fifth one about the topics covered in his talk. His latest project is the Open Visualization Academy (https://openvisualizationacademy.org/), launched in January of 2026, a free library of educational resources about information design and data visualization.

Systems Librarians and Library IT are often hidden behind the scenes keeping the digital spaces of the library running. However, the data they generate can be wrangled and shared to support their work publicly and advocate for the library. This presentation will discuss how a new Systems librarian built a free ticketing system using Microsoft infrastructure and leveraged data storytelling to promote Systems work to library administration, across the campus, and through social media. Results have included increased library staff participation in building a knowledge base of common issues, visibility and recognition of the Systems team's accomplishments, and increased understanding of the importance of collecting and maintaining data within the library.

Learning Objectives:

• How to build and utilize a ticketing system to collect and visualize Systems data
• Ideas on what data to collect and how to share it
• Creative avenues for communicating the data across the library and campus to advocate for libraries

Data librarians at the University of South Carolina, Clemson University, and Coastal Carolina University collaborated to develop a coordinated statewide approach to Love Data Week 2026. This presentation will describe how the three institutions planned a shared slate of research data programming, divided responsibilities, promoted events across campuses, and used each librarian's expertise to expand the reach of data services beyond a single institution. Presenters will highlight the programs offered, lessons learned from coordinating statewide outreach, and strategies for making collaborative programming manageable with limited capacity. The session will also discuss next steps for sustaining the partnership, including the development of a statewide data roadshow designed to bring research data professional development programming to South Carolina institutions that do not currently have dedicated data librarians.

Learning Objectives:

Attendees will explore how to:
• 1.Design collaborative data programming across multiple institutions
• 2. Coordinate outreach efficiently with limited staff capacity
• 3. Extend data services to underserved campuses in a larger region

Surveys are a common tool for collecting information, but design choices can significantly affect the quality, accessibility, and interpretation of the data they produce. Grounded in the symposium theme, "The People's Data," this short talk explores how thoughtful survey design can enhance trust, improve data quality, and help ensure diverse community voices are represented. The session will highlight considerations for ADA-compliant surveys, clear and unbiased question construction, and common survey design pitfalls that can contribute to misunderstanding or misuse of data. It will also discuss connections between survey design, data ethics, accessibility, and misinformation. Drawing on examples from library and research contexts, the talk offers practical considerations for creating more equitable and user-centered assessments.

Learning Objectives:

• Identify key elements of accessible and ADA-compliant survey design
• Avoid common survey design pitfalls to write effective survey questions
• Apply basic research-design strategies to plan meaningful assessments

The Integrated Postsecondary Education Data System (IPEDS), maintained by the National Center for Education Statistics (NCES), is the federal government's primary source of data on U.S. postsecondary institutions. Colleges and universities participating in federal student aid programs submit annual data on enrollment, completions, finances, staffing, student outcomes, and other institutional characteristics. Although IPEDS provides an invaluable resource for research and policy analysis, the complexity of its annual survey components, documentation, and changing variable structures can create barriers to effective use.

This presentation describes (and demonstrates) the development of a research-ready longitudinal IPEDS dataset created by compiling and harmonizing multiple years of institutional data. Using examples from analyses of education trends among Southeastern Conference (SEC) institutions, the session demonstrates how curated public data resources can support data literacy, reproducible research, and evidence-based decision-making in higher education.

Learning Objectives:

• Understand how IPEDS data are collected, maintained by NCES, and used in higher education research and policy analysis.
• Learn strategies and techniques for creating research-ready longitudinal datasets from complex federal higher education data sources.
• Identify approaches that promote data literacy, transparency, and reproducible research when working with public higher education data.

Open Government Data (OGD) can be a rich source of information for social science researchers as well as teaching faculty. Traditionally a source of reputable and reusable data, American sources of OGD have been plagued by erratic levels of accessibility in recent years. European OGD, by contrast, is extensive and provides these benefits to researchers without the uncertainty of politically charged data removal. This short talk will present the preliminary findings of a mixed methods study conducted with French researchers (including doctoral students, teaching faculty, and government agency employees) who utilize OGD in their work. Their insights can help data librarians identify opportunities for instruction as well as enhanced researcher productivity on campus.

Learning Objectives:

• Participants will gain insights into the use of OGD as both a teaching tool with graduate students, as well as a source of research data for students and faculty.
• Participants will learn strategies for promoting use of OGD by campus researchers.
• Participants will be directed to resources to learn more about sources of European and American OGD.

In February of 2013, the White House's Office of Science and Technology Policy released a memo titled "Increasing Access to the Results of Federally Funded Scientific Research". As part of the Obama Administration's effort to promote the values of transparency and accountability this memo, colloquially called the "Holdren Memo," required US federal research agencies with budgets over $100 million dollars to provide a plan which described: how the agency would give public access to results of research funding, both publications and research data, and required research projects to have research data management plans. Through thematic analysis this paper reviews works which have evaluated the OSTP memo since 2013. By answering the research question "what assessment of the 2013 OSTP memo has occurred?" researchers in the field of information and public policy analysis have a snapshot for what has been done and identified gaps that need to be filled to have the best idea of the impact of these U.S. federal government-wide open science mandates. This presentation will review what evaluation has occurred to date, and what it all means in light of current changes in federal policy including the Nelson Memo of 2022 and the attempts to hollow out the US's open data policies via rule changes at OMB.

Learning Objectives:

• Attendees will be able to see how the 2013 Holdren Memo changed research outputs since its implementation.
• They will understand what evaluation of the Memo's outputs has already occurred and what gaps remain.
• Finally, an appreciation of what will be lost if the Holdren and Nelson memos are rescinded through proposed changes.

Fundamentally, map making is a form of data visualization. As a librarian trying to provide learning materials for both of these topics, they present similar dilemmas; they are skills missing at the college level, they are needed across disciplines, and while librarians are equipped to do this, there is no class time to teach them. Several years ago, Canvas modules on data literacy were created to support the campus community, and the data literacy module had extended success with a YouTube video series. Hoping to build on this success, while filling a newer need on campus, geospatial data literacy was added to the mix. Given the natural parallels, new initiatives were planned to create open educational resources, LibGuides and videos, on both topics. All of these materials are now fully open access, ADA compliant, and support learners in a time where those fundamental skills are essential for success.

Learning Objectives:

• Attendees will gain a perspective of how much trial and error there can be in smoothing out asynchronous data literacy instruction over time
• Attendees will explore different literacies related to data literacy that can easily use the same instruction methods
• Attendees will learn about different learning platforms and formats for asynchronous instruction, their benefits and drawbacks.

Spatial literacy is an important component of K-12 curriculum standards, but it can be difficult to teach without accessible tools and examples. GIS and spatial data offer powerful educational resources for supporting geographic inquiry, place-based learning, and student engagement. Academic libraries increasingly support data literacy initiatives, instructional technologies, and educator preparation programs, yet librarians may not always have a clear model for introducing spatial tools to educators with limited GIS experience. As spatial data becomes increasingly common in news and social media, foundational spatial literacy also supports learners' ability to evaluate geographic information, assess the credibility of spatial data sources, and recognize when maps or visualizations may communicate misleading conclusions.

The ArcGIS for Teachers workshop was delivered as an introductory session on GIS tools as part of a larger multi-day seminar on spatial learning for K-12 educators. The workshop focused on accessible, low-barrier tools: National Geographic MapMaker and ArcGIS StoryMaps as participants had limited time and varying levels of prior GIS knowledge. Rather than emphasizing advanced GIS workflows, the session centered on classroom applications, curriculum standards, and the creation of materials teachers could adapt for instruction. Participant feedback was collected post-workshop on understood curriculum standard alignment, intended classroom use, barriers to adoption, and likelihood of future use. This assessment provides insight into how educators understand the classroom value of GIS tools and what challenges remain after an introductory professional development session. While the workshop was designed for K-12 educators, the findings have broader implications for academic librarians working with education faculty, future teachers, and instructors interested in incorporating spatial data and mapping tools into their teaching. This presentation will discuss strategies for "teaching the teachers" in GIS and spatial data contexts, including what participants found most useful, what barriers remained, and what librarians can learn from supporting educator adoption of geospatial technologies. Attendees will leave with practical strategies for introducing geospatial technologies to educators, designing low-barrier GIS instruction, and identifying common barriers to adoption that may influence future outreach and instructional efforts. These approaches also contribute to broader data literacy goals by equipping educators to help students critically interpret, evaluate, and communicate spatial information.

Learning Objectives:

• Design low-barrier GIS workshops for educators
• identify barriers to geospatial technology adoption
• adapt GIS instruction for faculty and teacher preparation

Data literacy is an increasingly important component of research support and instruction, yet many librarians and educators are asked to teach data concepts without a clear instructional framework. This hands-on workshop demonstrates how Python can be used to build data literacy skills through accessible data exploration activities. Participants will work with the Palmer Penguins dataset to examine data structure, identify missing data, interpret summary statistics, and evaluate visualizations. Throughout the session, the focus is on using Python as a tool to promote critical thinking about data rather than on developing advanced programming skills. Participants will engage with activities in a browser-based coding environment and leave with a reusable notebook, discussion prompts, and practical strategies for incorporating data literacy into workshops, classroom instruction, and research consultations.

Learning Objectives:

• Use Python to teach foundational data literacy concepts, including data context, quality, interpretation, and visualization.
• Adapt and reuse a hands-on Python notebook for library instruction, workshops, classroom teaching, and research consultations.
• Design discussion prompts and learning activities that help learners critically evaluate data and data-driven claims.

Our local Carpentries community — consisting of an R1 land-grant university and a mid-size regional university — has been offering synchronous, collaborative workshops since 2023. As a result of attendee feedback, we began exploring how to help learners reinforce concepts introduced during workshop sessions and more formally recognize workshop completion. We created practice problems for the Introduction to R and RStudio series that allowed learners to apply coding concepts from each week's workshop to new datasets. After debuting these practice problems in Fall 2025, we developed a micro-credential which we offered in Spring 2026. Learners who attended all three sessions and completed all practice problems and reflections received a "Basics of R and RStudio" micro-credential through Credly. This presentation will overview our process of developing practice problems and reflection questions, which will be especially applicable for data librarians teaching skill-focused workshops. We will also share insights gained from learner micro-credential submissions.

Learning Objectives:

Individuals who attend this session will be able to:
• 1) Understand the impact of the micro-credential components on learner engagement and information retention;
• 2) Utilize our framework to develop micro-credentials for Carpentries-style and other skill-focused workshops; and
• 3) Adapt our openly available practice problem sets for use in their own coding workshops.

We will give an overview of our Library Research and Data Services workshop series that positions basic programming and data literacy as prerequisites for using AI responsibly in data analysis and visualization. We will present our scaffolded programming instruction approach and apply critical data literacy principles to AI

workflows. Weekly 60-minute Python and R sessions introduce essential concepts for working with datasets, such as tidy data, reproducible workflows, and coding for accessible chart types, before presenting a capstone workshop on using AI tools (ChatGPT and Julius.ai) for coding assistance and data visualizations. Rather than treating AI as a shortcut to programming skills, we frame it as a tool that must be interrogated using data literacy skills, and building upon core programming knowledge. Workshop participants follow concrete coding examples and discussions on how the deep learning curve of programming can be complemented with AI tools while using them responsibly and critically.

Learning Objectives:

• Participants will be able to evaluate a scaffolded library workshop model that introduces Python and R programming concepts
• then adds AI visualization in ways that reinforce (not replace) core data literacy concepts. Participants will also be able to adapt concrete teaching activities and strategies to help their own learners compare code-based and AI-generated data analyses and visualizations. Participants will also be able to address key data ethics and literacy issues in AI workflows
• including misinformation
• algorithmic bias
• inaccessible design
• and the need for transparent documentation.

For the Tuscarora Nation of North Carolina, recovering knowledge is not simply a matter of collecting data, but of restoring relationships between communities, ancestors, places, and future generations. This presentation discusses community-led efforts by the Tuscarora Historical & Preservation Society (SUN) to recover and steward Tuscarora knowledge, including locating a nearly forgotten history preserved in Ireland, documenting lineal descent from Tuscarora chiefs, navigating NAGPRA repatriation, recovering historical documents from archives in England and Spain, and developing language-learning resources. Presenters will explore Indigenous approaches to trust, access, and stewardship, arguing that many forms of Indigenous knowledge are better understood as relationships rather than data, and that community authority is essential to ethical archival recovery and preservation.

Learning Objectives:

• Participants will learn strategies for locating and recovering Indigenous historical materials from international archives and repositories.
• Participants will examine Indigenous perspectives on data sovereignty and consider how cultural knowledge, genealogy, language, and ancestral histories may be stewarded as relationships rather than simply as datasets.
• Participants will identify approaches libraries, archives, and researchers can use to support community-led preservation, repatriation, and ethical access to Indigenous knowledge.

This poster presents a student-led, librarian-facilitated model for academic library data workshops that strengthens both campus data literacy and student professional development. Grounded in constructivist learning theory, the Gradual Release of Responsibility framework, peer-assisted learning, and critical pedagogy, the model supports student workers as they progress from observation to co-facilitation and eventually independent workshop leadership. The program was developed in a university Data Visualization Lab to extend instructional capacity after staffing changes while maintaining high-quality, supervised support for campus users. Student facilitators gain technical, communication, and presentation skills through structured mentorship and repeated practice, while participants benefit from peer-informed instruction in data literacy, Python, Tableau, ArcGIS, and related tools. The poster will describe the training model, workshop progression, and reflections from student staff and participants. It will also highlight how libraries can leverage student expertise to create collaborative learning environments and expand meaningful professional opportunities.

Learning Objectives:

• Attendees will leave with a practical model for training student workers to facilitate academic library data workshops
• including a phased approach from observation to independent leadership. They will also gain strategies for aligning student strengths and interests with workshop topics
• so training is manageable and sustainable for library staff. Finally
• attendees will learn how this model can expand instructional capacity while supporting student professional development and creating more collaborative learning environments.

Given the rapid growth of data creation and increasing automation and AI, our collective need to increase data literacy is vital. The demand for a data literate workforce, combined with the perception of low data literacy skills, has fueled a data literacy gap. Drawing on findings from two recent studies of employee perceptions of workplace data literacy, this session explores how the concept of the "data literacy gap" manifests in everyday work practices. Rather than a single gap, our research suggests multiple gaps, including misalignment between employer expectations and employee preparation, inconsistencies in foundational data literacy skills, and disconnects between educational experiences and workplace demands. While our research focused on the supply chain and logistics industry, the findings offer insights that extend to other organizational sectors. This session will present key findings from the studies that inform the multiple ways in which we can characterize the data literacy gap and help data librarians of all levels align their practice with workplace data literacy needs.

Learning Objectives:

• (1) Attendees will gain an understanding of workplace data literacy needs and practical ideas for aligning library instruction with evolving workforce expectations.
• (2) Participants will be able to describe how the data literacy gap manifests across workplace contexts including skill development and workforce expectations.
• (3) Attendees will identify areas of misalignment between employer expectations, employee skills, and educational preparation.

Following a sudden announcement in early 2026, the NIH upended years of preparation for their Data Management and Sharing Policy by drastically changing the elements researchers were required to address in their Data Management and Sharing (DMS) Plans. The changes were purportedly to "minimize applicant burden," but the NIH provided little to no guidance or supporting documentation on how researchers are meant to comply with these format changes. This poster will outline how librarians from institutions across the United States reconvened a working group to interpret the overhauled elements, update existing guidance, and create new educational materials to reflect these changes. The presenters will discuss challenges the working group faced along the way.

Learning Objectives:

• 1) Discover resources available to aid data services providers and researchers to adhere to the 2026 NIH DMS plan format.
• 2) Converse with colleagues about common issues surrounding the new DMS plan format and its implementation.

Libraries play an important role in supplying people with reliable information from trusted sources. Our research library provides access to Zotero, an open-source citation manager. Zotero serves as a pipeline from our library's peer-reviewed publication subscriptions to a researcher's personal reference database. Zotero's features offer value to institutions, libraries, and researchers by connecting authors to trusted library resources. We will present two in-house projects combining the Zotero API and Microsoft Power Automate to extend Zotero into new applications for our research organization. We will discuss lessons learned from vibe coding with Microsoft Copilot and share findings from a library workshop created to demonstrate Zotero's functionality for enabling research integrity by steering clear of hallucinated citations and some retractions.

Learning Objectives:

• How APIs and Power Automate can be leveraged to extend the uses of open-source tools like Zotero;
• How to apply generative AI and vibe coding techniques to expand development capabilities and build solutions;
• Why authors may want to consider using Zotero to validate their reference list.

In 2024, the George Washington University (GW) convened the Research Data Management (RDM) Task Force charged with assessing institutional research data management needs. Early discussions revealed a need for data literacy and a shared language around research data, including clearer distinctions between research data and other types of institutional data, as well as increased awareness of existing support services. Analysis of the resulting needs assessment survey further identified diverse data needs and widespread uncertainty around data sharing practices and data infrastructure. While respondents expressed an interest in a centralized research data management response, they also raised concerns about sustainability, long-term institutional commitment, cost and other critical themes. These findings suggest that successful RDM programs require not only technical infrastructure, but also researcher education and trust in the systems and services that support data stewardship and sharing.

Learning Objectives:

• 1. Building research data infrastructure requires shared language. Establishing common understanding among libraries, IT, research administration, and researchers is a critical first step in developing effective RDM services
• 2. Data literacy and trust are essential components of successful data-sharing programs.Researchers need clear guidance on concepts such as data sharing, repositories, and metadata in order to confidently engage with institutional services
• 3. Sustainable research data support depends on coordinated campus partnerships. Distributed expertise can be leveraged to create support networks that connect researchers with trusted resources, services, and infrastructure

Our department provides three pillars of services in support of data science at our university. Across these services, the largest user group is graduate students whose disciplinary focus is represented by 12 colleges across our university. Students regularly request assistance on topics that span the research data science lifecycle. Different aspects of the data science lifecycle may be addressed in individual academic programs, however, our experience suggests gaps exist that are difficult to fill on a college by college basis. Libraries provide a centralized resource with appropriate skills to fill these gaps. In this session, we will discuss the development and implementation of a Data Science Training Intensive for graduate students at our institution. We will share the impetus for developing the training and describe our journey assessing the potential scope of, creating and providing the training in order to better support the needs of our graduate student patrons.

Learning Objectives:

• Understanding of graduate students needs for support to gain data science skills;
• Insight into the opportunity for libraries to represent a centralized resource positioned to provide data science training to a cross-disciplinary audience;
• A template for designing a data science training that could be adapted to local setting

As the first Data Services Librarian at ZSR, I conducted a needs assessment to design our brand-new services for faculty support across many disciplines. With input from my department, I designed and ran a mixed-methods faculty research services survey delivered to campus faculty in Summer 2025. The survey provided us with a rich illustration of where faculty need support, and kept service scoping and prioritization responsive to demonstrated needs. I created reports to share the results with my department, library directors, and the library as a whole. In this talk, I will discuss how we determined the survey questions and the tools we chose (Qualtrics, MaxQDA, and R), and how the assessment results informed our strategy for the following year. I will also share some of the materials from the process to help illustrate how to conduct a similar assessment at other institutions.

Learning Objectives:

• -Conducting a needs assessment survey helps data librarians strategize and establish goals for their service areas by reaching out directly to users (i.e. faculty).
• -The results can be shared with library leadership to illustrate the need for data services and faculty support, and across the library to increase awareness of data services.
• -Librarians at other institutions could benefit from conducting a similar survey, and I would be excited to answer questions about how to implement an assessment and effectively share the results.

North Carolina A&T State University, the nation's largest historically Black college or university and an 1890 land-grant institution, is expanding its research capacity as it approaches R1 Carnegie classification. Supporting this growth requires effective, well-coordinated cyberinfrastructure (CI). In this research, we are examining the effectiveness of existing CI through a comprehensive user-needs assessment led by the F.D. Bluford Library by documenting current research capabilities, identifying barriers to research productivity, and highlighting opportunities for improved coordination and support. Preliminary findings (n=84) reveal gaps in awareness, access, and integration of CI resources, particularly for humanities researchers. The project advances an evidence-based, scalable model that positions our library as a central hub for research support (e.g., data, software, training, etc.) and offers a framework for strengthening CI at minority-serving institutions.

Learning Objectives:

• Faculty have a strong interest in various data-science workshops; libraries could be a good fit for a cyberinfrastructure facilitator

As libraries play a central role in democratizing access to information, this session highlights how librarians can integrate a particular AI tool, Gemini Notebook, into data instruction in ways that foreground accessibility, critical engagement, and ethical awareness. Gemini Notebook, formerly called NotebookLM, is a Google Workspace tool, and may be made available with additional data protection through a university campus license, though highly sensitive data should not be used. Gemini Notebook's summarization, question answering, and synthesis features can reduce cognitive barriers while also raising important questions about bias, transparency, and representation. Teaching examples and applied use cases will be presented to show how this tool can support these approaches when covering data literacy concepts.

Learning Objectives:

• Apply Gemini Notebook in instruction: Attendees will learn practical ways to integrate Gemini Notebook into data literacy teaching including activities that support source-based reasoning and AI-assisted synthesis
• Design for accessibility and inclusion: Attendees will gain strategies for using AI tools to reduce barriers to complex research materials while remaining attentive to issues of bias/representation and uneven access
• Critically evaluate AI in data contexts: Attendees will develop approaches to teaching students how to engage with AI tools responsibly with attention to transparency trust and ethical use in research workflows.

The student-staffed Data Science Consulting Program within North Carolina State University Libraries' Data Science Services Department supports researchers, students, and faculty across the university throughout the data science lifecycle. Because the program works with patrons across a broad range of experience levels, tools, and research workflows, we have seen firsthand how rapidly generative AI is changing research support.

Increasingly, patrons arrive with AI-generated scripts, workflows, and analyses that appear sophisticated but contain hidden methodological, computational, or interpretive issues. This student consultant-led presentation examines emerging patterns in AI-mediated consultations, including challenges around prompt specificity, workflow validation, troubleshooting generated code, and evaluating AI-produced outputs. We also discuss how consultants use AI for instructional support, workflow development, and administrative efficiency.

As plausible outputs become easier to produce, the ability to determine whether those outputs are trustworthy becomes increasingly important, reinforcing the library's longstanding role as a trusted guide in the research process.

Learning Objectives:

• 1) Insight into a student-staffed, library-based data science consultation model and how this model can support scalable, peer-informed academic support in the new AI-assisted realm of research and higher education.
• 2) Examples of how consultations continue to evolve from isolated technical questions toward helping patrons critically assess the assumptions, methods, and results embedded within AI-assisted workflows.
• 3) Practical strategies for helping patrons evaluate AI-generated code, workflows, and analyses through validation, troubleshooting, and data literacy instruction.

This short talk explores the value of modeling AI literacy during data research consultations. Behind the scenes, librarians may use AI tools to help identify the best data sources for an unfamiliar topic or guide them to the location of a specific dataset in a database. While we may be loath to do this in front of our students, keeping AI use "behind the curtain" may be a missed opportunity to model how a professional navigates the world of AI research to find and interrogate information. Further, AI chatbots can serve as "translators" among the three "voices" in the room: the student's, the librarian's, and the faculty member's (via the assignment). Attendees will leave with ideas for how to bring AI into the research conversation so that students not only find the answers they need, but gain insight into how to use AI tools safely and effectively.

Learning Objectives:

• How to participate in AI literacy with students, not just teach it;
• How to model finding and interrogating AI-produced information;
• Replacing AI-stigma with AI-literacy

As more researchers adopt generative AI, librarians face a dual challenge: driving meaningful tool adoption while mitigating data privacy and compliance risks. This session presents a case study of how a research library led pilots for three different AI research tools (Elicit, Scite, and Consensus) for over 30 staff members to better understand researcher workflows, tool needs, and concerns, as well as evidence a business case for institutional licensing. These pilots successfully proved institutional adoption, identified hesitations and risks, and secured buy-in for enterprise licensing. The session will detail how the library partnered with IT and security teams to directly address user and institute concerns, shape future AI governance, and launch both a resource-focused internal website and a custom 6-part AI literacy training series. Attendees will leave with a practical framework for building impactful cross-functional relationships and driving responsible, proactive AI adoption.

Learning Objectives:

• 1. A framework for multi-vendor AI evaluation: Attendees will learn how to execute and gather user feedback from AI pilots to understand researcher workflows, learn of data privacy concerns, and build a data-backed business case for enterprise licensing.
• 2. A roadmap for targeted AI literacy education: Attendees will obtain a framework to build a centralized, AI-focused resource and a structured, multi-part training series that helps patrons evolve from casual AI users to responsible, proactive researchers.

Genomic (DNA) and transcriptomic (RNA) sequencing data are among the most commonly shared types of research data. Well-established community standards, along with journal and funder requirements, have made the sharing of these data both expected and highly standardized through established repositories and workflows. Data librarians are called upon to support researchers working with sequencing data, yet the technical jargon and domain-specific concepts can present barriers to effective assistance.

This session will provide an accessible primer (pun intended) on the biological foundations of DNA and RNA sequencing, including an overview of why researchers conduct sequencing experiments and the types of questions these data are used to answer. Building on this foundation, the workshop will introduce practical decision-making frameworks to help data librarians navigate common data management and sharing challenges. Particular attention will be given to publisher expectations and federal requirements, including the NIH Genomic Data Sharing Policy and broader public access mandates.

Learning Objectives:

• Participants will engage in hands-on activities using case studies to apply these concepts in realistic scenarios. By the end of the session, attendees will be better equipped to interpret sequencing data workflows, communicate effectively with researchers, and provide informed guidance on data management and sharing practices.

Researchers often face complex decisions about whether, when, and how to share sensitive data. Often, they work with data involving human participants, confidential information, Indigenous or community-governed knowledge, proprietary materials, or other data requiring restricted access. Drawing on researchers' firsthand experiences, this panel will explore the practical, ethical, and discipline-specific considerations that shape those decisions. Panelists will discuss how they balance transparency, reproducibility, funder and publisher expectations, participant protections, legal requirements, and community responsibilities. Attendees will gain insight into the real-world challenges researchers face in managing sensitive data and learn strategies from librarians who support responsible data-sharing practices that recognize both the value of openness and the importance of care.

Learning Objectives:

After the panel session, participants will be able to:
• Identify key ethical, legal, and practical considerations that influence decisions about sharing sensitive research data.
• Explain how disciplinary norms, participant consent, community expectations, funder requirements, and institutional policies shape responsible data-sharing practices.
• Describe strategies for planning and supporting responsible, sensitive data sharing, including de-identification, access controls, documentation, and alternatives to full public sharing.

Research data management (RDM) and data services share similar goals, but without clear scoping, responsibilities can overlap, creating confusion, duplication of effort, and service gaps. Following up on the capstone project for the ICPSR's topical workshop Providing Data Services and Support: From Surviving to Succeeding, this poster will explore how academic libraries can strategically define and align data services and RDM services so they work in tandem. Through an environmental scan and service analysis, the poster will present points of intersection and practical approaches to help define a clear scope of service that helps researchers discover, manage, share, and preserve data while maximizing institutional expertise and resources.

Learning Objectives:

• Clarify the roles of data services and RDM.
• Define service boundaries to reduce overlap.
• Build complementary, researcher-centered support models

After participating in ICPSR's Providing Data Services: From Surviving to Succeeding during the summer of 2026, I completed a Capstone project examining the data support landscape at my university. I mapped out the support levels that my colleagues and I provide at a regional R2 university. I used R to create heatmap visualizations to depict the distribution of data support services. This exercise allowed me to assess the type of services available as someone brand new to my current institution. However, I surmised that data support makes up different percentages of individuals' jobs and can be housed in units outside the library.

Learning Objectives:

• - Data support can be incorporated into various liaison roles.
• - Data support can exist outside the library.

"What data does the library provide access to?" In the spring of 2026, it became clear that there was not a singular library resource that could answer this question. Inspired by the Making Data Collections Discoverable capstone project from the ICPSR Providing Data Services summer course and requests from my supervisor, I spent the summer working to create resources to make the library's data collections more discoverable.

Beginning with an environmental scan of ten peer institutions' methods for showcasing data collections, I then worked with my data librarian colleagues to assess popular data sources before finally creating and publishing the web-based resources to showcase the data collections. This poster will provide an overview and reflection on my experience in creating library resources to highlight the library's data collections consisting of licensed data, subscription-based data sources, and selected popular open datasets.

Learning Objectives:

• Attendees can expect to learn strategies for making library data collections discoverable online, how to collaborate with colleagues on increasing data collections visibility, and the ICPSR Providing Data Services capstone process.

Designing a bilingual Libguide profiling the use of Sage Research Methods datasets serves both faculty needs when teaching data literacy and student needs to practice working with data sets. In addition to promoting the use of an existing library subscription, this offers students a practical resource to begin to build or bolster their data analysis skills. The data life cycle can take many forms as the author learned in the 2026 edition of the "ICPSR Providing Data Services" course and each academic environment supports varying stages in this cycle. Libguides can therefore serve as an entry point to learning about the people, services, software and spaces that can help students at many stages of their data research adventure. (118 words)

Learning Objectives:

• Provide an easy entry point to introducing the concepts of data literacy;
• Fill a gap in the data analysis services offered at your library;
• Simplify acquisition of analysis skills using an existing library subscription.

Since data sharing policies were first required in the US in the early 2000s, the expectations for sharing scientific data have only increased with time. Data Management Plans (DMPs) are a vital part of the research data management (RDM) ecosystem, but as information resources they are not readily findable, accessible and reusable. As part of the 2026 Network of the National Library of Medicine's National Center for Data Services (NCDS) internship, we developed recommendations for a metadata schema to improve the storage, discovery, and retrieval of DMPs at Memorial Sloan Kettering Cancer Center (MSK). In this poster we map our project and its methodology, including learning the policy landscape, understanding the kinds of information in DMPs and ultimately choosing a schema that we hope will make them more useful to researchers and enhance their value as institutional assets.

Learning Objectives:

• Provide an example of how data librarians can use metadata to promote FAIR compliance;
• Learn more about RDM within a health sciences context (using data management plans);
• Learn a potentially transferable methodology for improving metadata schema in data librarianship

The Library Data Services (LIDS) dataset documents the web presence of research data services at R1 and R2 institutions, providing a valuable resource for understanding how academic libraries support data-related research and learning. As a National Center for Data Services (NCDS) intern, my project group and I conducted a secondary analysis of the LIDS dataset. We evaluated data quality using the Data Curation Network's CURATE(D) workflow, identified patterns in institutional offerings, and used Tableau to create visualizations to support analysis. This presentation will discuss challenges and opportunities encountered while analyzing the dataset, and share lessons learned as a secondary data user, highlighting how data curation, documentation, and accessibility influence the successful reuse of research data.

Learning Objectives:

• 1) Learn practical strategies for secondary data analysis, including using data curation frameworks, data quality review methods, and different visualization tools to assess and interpret existing datasets.
• 2) Identify common challenges secondary data users encounter when working with research datasets
• 3) Recognize how data curation, documentation, and accessibility practices affect data reuse.

This presentation introduces the use of a reparative assessment tool to identify potentially harmful language and description items in the metadata and to further develop a custom lexicon for remediation through reparative description. The MaRMAT tool [https://www.marmatproject.org] was employed to create a custom lexicon list by identifying derogatory terms, offensive content, and descriptions from various governmental and non-profit sources, as well as pre-curated lexicon lists. The concept of reparative description was used to create terms related to Indigenous communities. As a result, during a 2026 Network of the National Library of Medicine's National Center for Data Services (NCDS) internship, 185 instances of problematic content were identified, including those that unintentionally spread Indigenous communities' Traditional Knowledge, locations of endangered species, and other sensitive information.

The custom lexicon list will facilitate nuanced discussion of content censorship, historical context, and further engagement with the CARE principles. Additionally, the list will support contextual and data analysis of the collection. Next steps will be discussed for the remediation process.

Learning Objectives:

The audience will takeaway from the session:
• explain the concept of reparative description
• understand the MaRMAT tool for developing custom lexicons
• consider remediation practices for sensitive information
• contexts
• and CARE principles.

The University of Michigan Library is migrating research datasets from the Deep Blue Docs repository to Deep Blue Data to improve long-term stewardship and access. As of May 13, 2026, 1,624 datasets were identified as eligible for migration. As part of a 2026 Network of the National Library of Medicine's National Center for Data Services (NCDS) internship, preparing these datasets for migration revealed metadata and curation issues, including missing license information, incorrectly classified research datasets, and a large collection of HeLa cell microscopy images deposited as 1,344 individual works in need of consolidation. This poster presents a practical workflow for migrating research datasets while preserving metadata and remediating missing license information. It also proposes repository best practices for the ethical stewardship of HeLa microscopy image collections, emphasizing transparency, provenance, and historical context for non-genomic research data.

Learning Objectives:

• Learn a practical workflow for migrating research datasets between institutional repositories
• Learn recommendations for repository best practices for the ethical stewardship of HeLa microscopy image collections

This poster session presents a method for creating, preserving and storing environmental research dataset metadata in an academic institutional generalist data repository. Using the Environmental Data Initiative's (EDI) ezEML tool (https://ezeml.edirepository.org), users with minimal metadata knowledge can create rich metadata written in Ecological Metadata Language (EML), a common ecological-specific metadata schema. The presenter will share about their work during a 2026 Network of the National Library of Medicine's National Center for Data Services (NCDS) internship creating and adapting ezEML generated metadata for deposit in a generalist institutional data repository, as well as highlight considerations when using externally funded and managed tools. In light of funding uncertainty for many disciplinary-specific data repositories, utilizing existing tools like EDI's ezEML provides short-term options, with long-term potentials, for data preserving and providing access to ecological datasets without losing crucial ecology metadata. This presentation is intended for all data librarians, especially those working with institutional data repositories. No knowledge of ecological research or EML metadata is needed.

Learning Objectives:

In this session, attendees will (1) learn about the Environmental Data Initiative's ezEML metadata tool and understand its role in creating Ecological Metadata Language (EML) metadata
• (2) learn how to adapt and prepare ezEML generated metadata for deposit in a generalist data repository
• (3) identify areas of concern in using an external tool to generate metadata.

I will discuss the beneficial outcomes of the 2026 National Center for Data Services (NCDS) Data Internship for both myself as an intern and librarian-in-training and the Research Data Services director who conceptualized and oversaw the project. During the internship, our team used the LIbrary Data Services (LIDS) datasets to complete an environmental scan to better inform the data services offerings at our mentor's home institution. We curated and cleaned the open access datasets before conducting analyses and creating visualizations to better understand the data. As a result, our mentor received evidence-based data services recommendations and I gained valuable data-related skills as well as a more thorough understanding of the current landscape of data services in academic libraries. Overall, hands-on experiential learning opportunities are important for LIS students and early career librarians, and there is high potential for a mutually beneficial relationship among internship participants at all levels.

Learning Objectives:

• (1) Discover the potential benefits of completing special projects with library school students or early career interns.
• (2) Learn about important considerations when sharing and using open access datasets.

Within a cohort internship structure with the National Center for Data Services, I analyzed the 2024 LIbrary Data Services (LIDS) Dataset, which records the web presence of library data services at R1 and R2 academic institutions. Over ten weeks, I applied the Data Curation Network's CURATE(D) workflow, OpenRefine for data cleaning, and Tableau Public for data visualization to improve the dataset's FAIRness.

This poster highlights how collaborative, people-centered curation can strengthen both dataset quality and data librarianship skill development. While digital tools facilitated data cleaning, analysis, and visualization, meaningful curation relied on information exchange and collective knowledge of internship participants. Through this project, I developed practical skills in data exploration methods, FAIR data practices, and data stewardship while developing my own data literacy skills.

Learning Objectives:

• Attendees will learn how data reuse can improve data literacy while building data analysis competencies among information professionals.

This proposed talk introduces the ETHICS Framework, an applied ethical decision-making tool for transforming archival collections into data. Designed for librarians and archivists, the framework is adaptable across institutional contexts and provides a practical, replicable approach for navigating the increasing use of AI and data-driven methods. It begins by asking "Should we do this work?", guiding practitioners through consideration of the project purpose, scope, goals, and institutional capacity and alignment. It also addresses questions such as "What should be included?", focusing on data selection, exclusion, sensitivity, and potential harms. Finally, it considers who is involved and affected by the work, emphasizing stakeholder engagement, community perspectives, and inclusive description. By foregrounding transparency, care, and responsibility, the framework positions libraries and archives as leaders in developing equitable and sustainable data practices.

Learning Objectives:

• Attendees gain access to a practical framework for evaluating ethical considerations throughout the lifecycle of archival data projects, from planning and data selection to access and reuse.
• Attendees gain an understanding of how the ETHICS Framework can support consistent, transparent, and accountable decision-making in archival data projects.

With the growth of open data sharing practices, some researchers question what to do with existing data that was not shared at time of collection. The Roll's Collection is a compelling case study: an internationally renowned researcher in human ingestive behavior wants to share all her digitized data, spanning from 1988-2025 and encompassing ~80 distinct studies. Curating a collection of data like this presents a few unique challenges: 1) each dataset needs a persistent identifier and unique metadata; and 2) collection-level metadata is needed to help researchers identify opportunities for cross-study comparisons, meta-analyses, harmonize, or synthesis. An interactive dashboard was used to increase opportunities for engagement with the Roll's Collection by enabling potential users to explore collection- and study-level metadata and descriptive statistics. Interactive dashboards can help increase the reusability of data collections, connecting foundational scientific research data to new lines of scientific inquiry.

Learning Objectives:

• Evaluate ethical considerations for sharing legacy data, particularly human subjects legacy data;
• Explore collection-level metadata that support reuse through harmonization or multi-study synthesis of related datasets;
• Practical examples of how interactive dashboards can support discovery and reuse across multiple datasets.

Social activities, community building, and more.