Program

As data analysis and visualization become increasingly important in business and academic research, it's essential for students to be adept at working with data. Librarians have a unique opportunity to support learning across their campuses through instruction in data literacy, data visualization, and responsible use of AI tools when working with data. A tangible contribution made by two librarians from a satellite campus of Georgetown University in Qatar (GU-Q) was to offer a one-credit course on data visualization that taught students important concepts of data retrieval, organization, analysis and representation. The course was open to students of all majors and levels of technical expertise. This presentation will showcase how GU-Q librarians were able to extend their current practice to offering a course on data visualization and what direction it could take in the future.

The session is intended for all data librarians interested in developing students' data literacy skills.

Learning Objectives:

• How librarians can engage in teaching data literacy skills and help students advance their academic work and research.
• How librarians can provide leadership on campus for data literacy.
• How librarians can use AI to teach students data analysis and visualization skills.

The CARE data principles (Collective benefit, Authority to control, Responsibility, and Ethics) are a conceptual framework meant to ensure ethical collection, sharing, and stewardship of Indigenous data. As part of a workshop hosted by the Data Curation Network in 2022, librarians created a foundational primer on the CARE data principles and how they apply to data management, curation, and sharing. The primer touches on the cultural context regarding the CARE data principles, the historical misuse of Indigenous data, tribal sovereignty, and Indigenous Peoples' right to governance of their data. Using the CURATE(D) checklist, the primer walks information professionals, researchers, and data curators through key questions and steps to ensure ethical use, sharing, and preservation of data.

Learning Objectives:

• Discuss the CARE data principles and how they relate to Indigenous Peoples' rights, sovereignty, and cultural traditions.
• Understand how CARE data principles can be used to ensure the ethical stewardship of data.
• Use the CURATE(D) checklist to review if datasets and metadata documentation adhere to the CARE data principles.

The Library's Research Data Services team occupies a unique position as a bridge between the university community's instruction and research and IT priorities and technical support when it comes to statistical software tools. These tools play an important role in quantitative research methods, helping faculty and students to conduct rigorous data analysis in research and coursework. In an evolving landscape, the tools and software in demand shift over time and consequently, it is our role to facilitate the continuous updating of the arsenal.

This poster illustrates our experience in transitioning faculty members from SPSS to Stata as their primary statistical software tool in teaching and research. Through targeted outreach, a flexible suite of training options, and close collaboration with faculty, Research Data Services offers comprehensive support to enhance both teaching and research experiences. These same principles and experiences go beyond software support and can inform other potential collaborations where library and data experience can be brought to bear.

Learning Objectives:

• Participants will learn effective approaches to inform faculty and get their buy-in for changes in teaching and research support.
• Participants will learn how to adopt a supportive environment to build faculty members' confidence in trying new tools and techniques effectively.
• Participants will learn practices for fostering productive collaborations with varied institutional stakeholders.

Citation analysis can be used to track journal usage in dissertations, aiding in collection development. Existing studies usually involve creating tabular data to analyze in Excel, and sometimes incorporate light scripting with R. In this talk, we share a new methodology in which PDF chemistry doctoral dissertations were converted to HTML to employ text-scraping techniques. Using Python, we extracted the journal and book titles from each citation and then arranged them into Pandas Dataframes for further analysis. This format provides numerous possibilities for fine-grained analysis of large datasets.

Learning Objectives:

• Learning a new way to conduct citation analysis
• Using Python and Pandas Dataframes to analyze a large dataset

Lawyers regularly engage with commercial legal research and intelligence platforms that boast of features based on data visualization, natural language processing, and machine learning; additionally, lawyers who operate within the criminal legal system must contend with the instrumentalization of algorithmic technologies in the public sphere. Given this situation, lawyers must possess data literacy in order to meet not only their obligations to their clients, but also what the American Bar Association characterizes as the profession's "special responsibility for the quality of justice." In spite of this, law school curricula are still inconsistent in equipping students with data literacy skills. This poster will describe my approach to incorporating data literacy into various instructional contexts as a data librarian, as well as challenges that I have encountered. While the topic will be suitable for all data librarians, it will be especially useful for librarians who work with students pursuing professional degrees.

Learning Objectives:

• Identify the opportunities and challenges associated with incorporating data literacy into a law school curriculum.
• Relate law library experiences to other curricular contexts for data literacy instruction especially other forms of professional education.

As part of a top-tier research library, the Penn State University Libraries partners with students, faculty, and staff on all campuses to consult, provide training, and support projects in the areas of research data management and sharing, data discovery and reuse, data analysis and visualization as well as open publishing. We collect data related to providing services and use it for building reports that support decision making, service improvement, and forecasting future needs for workshops and consultations. In this poster, we will review the workflow related to tracking consultation progress from the moment the consultation is scheduled by the patron with our department. We will discuss the use of MS Bookings, MS Planner and Power Automate in the process and the development of fully automated Power BI reports and how they can be used for departmental and team analytics. We will share experiences and challenges that we encountered.

Learning Objectives:

• Learning about experiences and challenges in the development of fully automated Power BI reports
• Learning about tools available for building automated reporting processes

Many academic libraries now have entire departments dedicated to research data curation and other research methods that incorporate working with data. However, many librarians working in these data-centric roles often discover that their MLIS coursework has left them ill-prepared for the practical data science skills they need in their jobs. While researchers have investigated the role of academic librarians in the data science landscape and the development of data curation curriculum, less attention has been paid to data education across the entire data lifecycle. Based on research conducted between summer 2022 and summer 2023, this poster maps the data lifecycle to current MLIS courses offered during this period in data science from ALA-accredited programs. Visualizing these trends can help attendees identify and advocate for future areas of curriculum growth to support future data librarians.

Learning Objectives:

• Understanding of current data science/literacy related coursework offered through MLIS programs
• Understanding of potential knowledge gaps for incoming and current data librarians

Recent academic and business literature reiterates the value of data literacy in educational and professional business contexts. Academic librarians have a distinct role in teaching data literacy to prepare students for the workforce. While student self-perception of their data literacy has been studied, limited research has investigated professionals with regards to using data literacy skills in practice. This poster shares initial findings from a research survey examining how business data literacy competencies align with professional workplace practices. The presenters surveyed professionals within the supply chain and logistics industries, seeking information on their levels of data literacy, in what ways data literacy competencies are reflected in their work, and how they developed the data literacy skills required for their job. The findings help demonstrate how business data literacy competencies resonate from classroom to workplace and provide further context for teaching data literacy along with information literacy.

Learning Objectives:

• Attendees will be able to recognize the business data literacy competencies.
• Attendees will be able to describe how surveyed workers rate their data literacy skills.
• Attendees will be able to explain the value of librarians' role in teaching data literacy for workforce preparation.

As data analysis is very important for undergraduate students, staff, and faculty, many institutions are putting efforts into teaching data literacy and coding literacy. Based on the Python workshop series held at the DataLab at Seton Hall University, this workshop will give an introduction to the basic skills needed for analyzing data within a Python environment. Participants will learn how to extract relevant data from a spreadsheet, clean it, and create a model for analysis. No prior experience with Python is needed in order to attend.

Learning Objectives:

• Participants will gain an understanding of what Python is and how to use Python libraries.
• Participants will experience how Python can be used to clean invalid incomplete or irrelevant data.
• Participants will learn how to analyze data using the six steps of CRISP-DM which is a standard process for data mining.

Data and data skills can help us better understand and address social issues. How data are used can also become its own social problem (e.g. misinformation or privacy issues). In 2022, I taught a one-credit-hour course for first-year students that introduced a variety of data literacy concepts and skills including basic data analysis, ethics, collection, and storytelling — with the goal of empowering students to use their data literacy skills for social change. Class activities, discussions, assignments, and projects focused on social issues — giving students the opportunity to practice their data literacy skills using meaningful and real-world issues. During this presentation I will highlight: (1) ways the course connected students to library resources, (2) assignments and activities from the course that can be used during library instruction, (3) and student feedback on the course. For all data librarians.

Learning Objectives:

• (1) examples of data literacy related assignments and discussions that can be completed during a 50-minute class period.
• (2) a reading/media list used in the course with materials that are both relatively short and engaging.
• (3) a strategy to appeal to students' interests in social issues as a way to foster data skills.

Archival textually-rich materials--such as warranty deeds, mortgages, legal documents, and letter correspondence--can provide valuable historical insights, and if transcribed and analyzed, can produce data points in the form of unstructured text, tabular data, and geospatial assets. This presentation will provide an overview of the process Florida International University librarians went through to turn the papers of Dana A. Dorsey, Miami's first Black Millionaire, into data. Their work is guided by the concept of "collections as data" as a form of reparative archival practice, enabling the elevation of marginalized individuals' histories. The goal of reparative archival practice is to create a reflective practice that informs and challenges librarians and archivists to engage with different types of historical data to provide a more inclusive and comprehensive understanding of the past. The presentation will cover the ways in which ethical practices were implemented in each step of the initiative, including project planning, stakeholder involvement, data selection, and access.

Learning Objectives:

• Deriving Data from Historical Resources: Participants will gain technical insights into how data can be derived from text-rich historical resources.
• Engaging in Reparative Archival Practice through "Collections as Data": The presentation will highlight the concept of "collections as data" as a form of reparative archival practice where the histories of marginalized individuals can be elevated.
• Striving for Ethical Practices in Data Collection: Participants will gain insights into the significance of integrating ethical practices across the entire data collection process encompassing project planning stakeholder involvement data selection and access considerations.

In a first-of-its-kind approach to facilitating digital humanities (DH) research at scale, the University of Houston's Digital Humanities Core facility (DHC) consolidates technological infrastructure, training programs, and interdisciplinary expertise to establish an enterprise-wide resource for digital scholarship production and publication. At the heart of the DHC is a three-tiered, globalized micro-credentialing program designed to train cohorts of faculty and students on successful strategies for project planning, development, and funding. This credentialing produces autonomous communities of scholars capable of growing and sustaining a culture of interdisciplinary research. This presentation discusses our approach to providing scalable DH training for faculty and students, with a focus on its strong data literacy curriculum. We will demonstrate how this instructional approach anticipates and supports more advanced stages of digital project development as well as the DHC's programmatic team-building, ideation forums, and elements of its technological infrastructure.

Learning Objectives:

• 1) How to establish a scalable globalized Micro-credentialing program.
• 2) How to create a globalized data literacy curriculum for the digital humanities.

Dr. Exner will share thoughts on where data librarianship has been, how we got here, and where we're heading as big data morphs into smart devices and end-user-focused AI.

We, as librarians who do things with data, have gathered such a vast variety of skills from data-focused instruction and reference interviews to digital preservation and subject description. The contributions of librarians to research data have been recognized beyond the library for our impact on topics like open science, reproducibility, bias and source critique, and data literacies.

The end-user-driven AI era is pulling all manner of data and texts into their models, including existing description and preservation practices. As a result, the skills to use and critique datasets will also help critique and use AI.

Dr. Exner will share examples of how data description and data instruction feed into a cycle that affects design and use of AI-driven systems. Building on those insights, attendees will envision how data-adjacent description and instruction and all of library data services are already tomorrow's AI services.

About the Speaker:
Dr. Nina Exner is the research data librarian at Virginia Commonwealth University in Richmond, VA. They (or she) serves both the medical and nonmedical campuses of VCU. In that capacity, Nina advocates for data "as closed as it needs to be, and as open as it can be." Nina works closely with the national Research Development community, helping grant writers and faculty development professionals to incorporate library services into the research enterprise. In addition, Nina has occupied national service roles in the NIH DMS Plan implementation period. These roles have included being the inaugural NNLM National Center for Data Services Ambassador and co-chairing the DMPTool working group on the NIH DMS Plan. They have spoken to librarians, medical faculty, academic computing, and research leadership audiences about the value of interprofessional collaboration to the wider academic research enterprise.

This workshop proposal aims to introduce participants to logistic regression analysis using Stata, a widely used proprietary statistical software package that most universities offer to students, faculty, and staff. Logistic regression is a powerful statistical technique used to model and analyze binary or categorical outcome variables. Understanding logistic regression is essential for researchers and data analysts in various fields, including social sciences, health sciences, business, and economics. The workshop will provide a comprehensive overview of logistic regression, covering the theoretical foundations, model interpretation, and practical implementation in Stata.

Learning Objectives:

• Participants will learn how to effectively apply logistic regression techniques to analyze binary outcome variables in their own research or professional projects. Participants will gain a solid understanding of how to interpret logistic regression models and effectively communicate the results. Participants will learn how to navigate Stata's syntax and execute logistic regression commands.

It can be a challenge to go from an introductory workshop to working independently in a programming language. This talk will describe a pilot program for participants who had previously taken a Library Carpentry workshop to develop confidence and skills in R programming. In this session, we will discuss the considerations that went into developing the structure of the program, lessons learned from the experience, and plans for continuation and future directions.

This experience was designed to cultivate a sense of community for data-minded information professionals around developing new skills and their practical application. Over 6 live sessions, we explored new skills in R, for example, data visualization, restructuring datasets, and communicating with Quarto. Participants were also encouraged to work on their own data throughout the class. Time was allocated in each session for participants to ask questions and get feedback in a supportive, collaborative learning environment.

Learning Objectives:

• Explore the challenges of transferring skills from one-shot Carpentries-type training to using programming day to day
• How to increase learner confidence in programming skills through practical application
• Demonstrate relevance of R programming to librarianship

During this 15 minute session, Michael Stamper, Data Visualization Designer and Consultant for the Digital Arts at Virginia Tech Libraries, will discuss the fundamentals of good design and the importance visual graphics play when conveying information that needs to be seen and easily understood by different audience types.

Learning Objectives:

• (1) Gain insights on transforming "raw" renders of data and information into meaningful, insightful, and engaging visualizations suitable for public consumption (i.e. print, Web presentations, publications, etc.).
• (2) Gain an understanding of Design Thinking processes and tools/software when creating various types of visualizations.
• (3) Learn the importance of creating modifiable visualizations to fit specific target audiences and planning for how these visualizations will be seen and/or interacted with.

As researchers gain more proficiency with using browser-based mapping presentation software such as ESRI StoryMaps, it is important libraries provide data literacy training on the long-term preservation and publication challenges of creating and storing data in ephemeral and unreliable proprietary software. We will discuss data literacy outreach with students and researchers on this topic.

Learning Objectives:

• 1. the benefits and limitations researchers face in using popular online GIS tools to capture and display their research data
• 2. which competencies researchers are currently struggling with as it relates to managing their GIS data in open access contexts
• 3. strategies for working with researchers to account for these challenges in their workflows

One of many challenges for data librarians is bridging the gap (often as much physical as philosophical) between researchers and the providers of research-oriented services. Data librarians possess much of the knowledge and skills necessary to assist researchers throughout the lifecycle of their projects, providing assistance on everything from data management and sharing plans to the eventual archival of various research products. However, librarians and researchers do not always speak the same language or prioritize the same outcomes for research products. As someone nearing the completion of a Ph.D. in biology and serving as a Research Data Specialist in an academic library, I can contribute perspectives as both a researcher and research support staff. Using my own personal experience as well as evidence from empirical research, I will discuss problematic divides and promising overlaps between researchers and data librarians and recommend strategies for more effective cooperation between these two groups.

Learning Objectives:

• Attendees will be able to assess the relationship between librarians and researchers at their institution and implement manageable changes to improve that relationship.
• Attendees will be able to evaluate how their current research support services and priorities might align or conflict with the needs and wants of researchers.

Recognizing the importance of diversifying the data librarian profession, the National Center for Data Services (NCDS) within the Network of the National Library of Medicine (NNLM) has taken a proactive step by offering the NCDS Data Internship program to individuals from historically underrepresented racial and ethnic groups. In this talk, I will delve into the successes and challenges encountered in this BIPOC-centered data services training. By thoroughly examining the experiences of this internship and the program's impact, I will explore its contributions to fostering diversity within the data services profession. I will discuss interns' experiences, including their acquisition of valuable skills and introduction into the data librarian community. Nevertheless, I will also candidly acknowledge that no program is without its challenges. Therefore, I will address the obstacles faced within the program, such as building a sense of community in remote work settings, managing limited time, and addressing site onboarding complexities.

Learning Objectives:

• By sharing insights, experiences, and lessons learned the talk aims to inspire continued efforts in creating inclusive and equitable opportunities for aspiring data professionals from underrepresented backgrounds particularly in the context of recent scrutiny of diversity, equity, and inclusion initiatives nationally. This talk also aims to be informative for those interested in participating in such a program or creating similar programs.

We report results from a study that explores how researchers who have initiated data deposits with the Qualitative Data Repository perceive the human curation services provided by QDR. We queried two user groups: researchers who have completed a data publication via QDR and researchers who have started deposits, but whose project has not yet been published. With the help of both groups' answers, we aim to address two questions: 1) how do researchers judge the value of services provided by QDR and 2) what are the major obstacles and pain points experienced by researchers attempting to share qualitative data. We base our questionnaire in large part on a protocol developed by the Data Curation Network, and so add to scholarship on the value of curation more generally, with a comparative lens specifically trained on qualitative social scientists. In this short presentation, we discuss our methodological approach and some lessons learned for surveying this researcher population, as well as present summary analyses of the qualitative and quantitative portions of the survey. We draw lessons from the experiences provided by the depositors who achieved data publication, to provide insights that data librarians — a key liaison between researchers and repositories — can apply to assist future candidates for qualitative data publication.

Learning Objectives:

• Data librarians will learn what factors have assisted qualitative social scientists with completing a data publication.
• Attendees will identify what factors have challenged qualitative social scientists when depositing data.
• Attendees will receive general good-practice recommendations about curation services based on researchers’ experience with QDR.

This is a workshop perfect for everyone who would like to learn about analyzing survey data with Power BI to present results. In this session we will connect to a raw data file, create visualizations, and build a report that’s ready to share with others. Attendees will learn about the challenges they might experience when trying to load a data file into Power BI in its original structure generated by MS Forms as well as best practices on designing visualizations for presenting survey results in the Power BI report. No prior experience with Power BI is necessary to participate in this session.

Learning Objectives:

• Participants will learn how to connect to data in Power BI and make necessary data transformations.
• Participants will familiarize themselves with visualizations suitable to present various types of survey questions in a Power BI report.
• Participants will learn how to share a report summarizing survey results.