Program

Hackathons can sound intimidating to both students and data librarians. When you hear the word "hackathon" you might picture elite computer programmers coding into the night. However, hackathons do not have to be only coding-based and can be designed in a way that is inviting to students from all disciplines, not just computer or data science. In this session we will present: (1) on how our University Library co-hosted a hackathon focused on addressing transportation and mobility issues around our campus; (2) strategies for designing coding-free or coding-lite hackathons; (3) ways data-librarians and libraries can support students before and during a hackathon; and (4) how hackathons can help students build and practice their data skills.

This session is attended for all data librarians of any experience level.

Learning Objectives:

• Examples of hackathons or other data projects for students with diverse or limited data skill levels.
• How library resources such as databases, LibGuides, and even physical spaces can support students during a hackathon.
• Ways hackathons allow students to gain and apply career and data skills.

Services for the use and management of data are a growing part of the work of many academic libraries. In this presentation, we explore the specific ways that these resources and services are structured to support the work of qualitative researchers. The diversity of research paradigms and methodologies in qualitative work, coupled with the complexity and cost of software that facilitates qualitative work means that qualitative researchers may face additional struggles in learning to conduct analysis of qualitative data.

This presentation draws data from two recent research projects: one exploring the process of learning to analyze qualitative data and a second that evaluates the extent of infrastructure for qualitative work at research institutions. We use this data to (1) outline models of support for qualitative research and (2) to describe specific strategies for growing qualitative research infrastructure and conducting outreach to those who use qualitative data and methods.

Learning Objectives:

• Strategies for assessing the attendees own library services.
• Approaches to outreach to qualitative researchers.
• Strategies for building qualitative research support into existing data-related work.

Randall Library's Data Librarian and Research & Innovation's Research Development Specialist roles were newly added to University of North Carolina-Wilmington (UNCW) in 2021, and both positions have been making steps to support the research needs of campus. In efforts to build campus community and highlight various aspects of data, UNCW Randall Library and Research & Innovation co-facilitated UNCW's first Love Data Week (February 14-18, 2022). Love Data Week is an annual international celebration of data organized by the social science data repository ICPSR and hosted through volunteer universities. A series of online workshops, panels, and spotlights about research data occurred at UNCW throughout the week, meant to give researchers a service point to get more research help, in particular by utilizing Randall Library and Research & Innovation's new positions. This session will review planning, partnering, and marketing tips for a weeklong event, and discuss sustainability for future event planning success.

Learning Objectives:

• Apply event planning scheduling and marketing strategies, generally.
• Set up Love Data Week events at your local university.
• Evaluate ideal campus partners for event collaboration.

The Research Data Services (RDS) team at Seton Hall University has implemented a DataLab, which intends to recruit and pair faculty with promising research ideas with students who want to develop data analysis skills. This process entails DataLab faculty and the RDS team brainstorming a project timeline and then putting together a specialized training schedule for the students who will work with the faculty member in charge of the project. The RDS team designs relevant data workshops in alignment with the research project. The desired outcome of the DataLab is to provide students with the opportunity to learn and apply data analysis skills while simultaneously allowing for faculty members to receive assistance with research projects and scholarship.

Learning Objectives:

• Participants will learn about the process of creating the DataLab, funding, and outreach techniques to engage with faculty and students.
• Participants might find it beneficial to learn about classes offered by the DataLab.

With more publishers and funders requiring data management and sharing plans, both novice and experienced information research professionals are looking for tools to help them skillfully manage research data. With the variety of tools available, selecting an affordable, accessible tool can be a barrier to use. Once a tool has been identified there is still a learning curve before it can be usefully employed.

This session seeks to promote awareness of freely available tools for data management, wrangling, and sharing for use in daily work and research projects. The presenters will review five data tools (DMPTool, NIH Common Data Elements Repository, NLM Scrubber, OpenRefine, and Open Science Framework) and discuss features, usability, and training resources. These tools facilitate open and FAIR data practices across the research data lifecycle.

Learning Objectives:

• Researchers will have increased awareness and access to freely available data tools, regardless of budgetary support or restrictions.
• Information research professionals will gain knowledge and resources on open data tools to promote FAIR research and share with colleagues.

Many academic librarians begin their work in research data support by offering reviews of Data Management Plans (DMPs, also called Data Management and Sharing Plans/DMSPs), which are required components of many grant proposals. After a researcher drafts a DMP, they may want someone to review it, assess its fit with best practices, and give feedback. Reviewing DMPs means evaluating and offering advice for improvement. But how does a library get started with reviewing DMPs?

This workshop is tailored for new data librarians and subject librarians starting in data, who want to provide a DMP or DMSP support toolbox. The panel portion will compare real-world practices on how to provide DMP reviews for existing drafts created by researchers in different institutional settings. Next, presenters will compare different approaches, such as contrasting the DART rubric ("DMPs as a Research Tool") for in-depth National Science Foundation (NSF) reviews versus the Caltech NSF checklist for fast reviews; discussing how FASEB's NIH DMSP contest rubric differs from the DART rubric; and summarizing how funder notes in DMPTool can be used for reviewing DMPs from various funders. This discussion will help new DMP evaluators think about how the process might change, and not change, for different funders' DMPs.

Finally, everyone will have guided practice in using the DART rubric to evaluate a simple research proposal and sketch out feedback for improvements in the DMP. At the end, the whole class will be better prepared to evaluate DMPs and offer researcher feedback on how to improve their Plan to make their research data FAIR.

Acronyms & Terms:

DART: Data management plans as A Research Tool. DART was a two-year National Leadership Grant for Libraries project funded by the Institute for Museum and Library Studies. It provided a multi-university study of faculty data management plans conducted by academic librarians. In addition to study outcomes (http://www.ijdc.net/article/view/11.1.53), the primary output of this project was an analytic rubric to standardize the review of data management plans as a means to inform targeted expansion or development of research data services at academic libraries. Principal Investigator: Amanda Whitmire; Co-Principal Investigators: Jake Carlson, Patricia Hswe, Susan Parham, and Brian Westra.

DMP: Data Management Plan. A short document outlining how research data will be handled during and following completion of a project. DMPs are a common component of federal research grant proposals. DMSP: Data Management and Sharing Plan. A DMP with a focus on how researchers will share final research data. Required by NIH.

DMPTool: Data Management Plan Tool. The DMPTool is a freely available online application that helps researchers create data management plans by providing a click-through wizard compiling funder requirements. It contains templates and guidance for a wide variety of funding agencies.

FAIR: Findable, Accessible, Interoperable, Reusable. The FAIR principles are commonly referenced by funding agencies, researchers, and librarians as a framework to optimize the reuse of research data.

FASEB: Federation of American Societies for Experimental Biology. FASEB promotes research and education in biological and biomedical sciences.

NIH: National Institutes of Health. The NIH is a federal funding agency in support of medical research.

NSF: National Science Foundation. The NSF is a federal funding agency is support of science research.

Learning Objectives:

• Practical tips for reviewing DMPs.
• Practice applying DART to evaluate a DMP.

This presentation discusses ways to incorporate data instruction in first year information literacy instruction for the humanities. Two instruction librarians and an English instructor will share their scaffolded approach in a first-year general studies writing course. Multiple library sessions focused on the development of library research strategies on how to locate, interpret, and use qualitative and quantitative data. Throughout the course, students practiced how to effectively use and represent data in building a persuasive argument. By the end of the semester students demonstrated the ability to put data sources in conversation with one another as well as with their argument, through writing and infographics. Specifically, this presentation will detail how librarians and the instructor collaborated to teach students to locate, interpret, and integrate data to support their arguments in non-scientific writing.

Learning Objectives:

• Attendees will discover how librarians and writing instructors can collaborate in teaching data literacy concepts to first-year students.
• Attendees will identify specific approaches for teaching students how to locate, interpret and use data.
• Attendees will consider strategies to incorporate data literacy in library instruction for the humanities.

AUDUBON IN ACTION is the result of a spring 2022 workshop for Earth Day 2022 explores creative approaches to data communication and dissemination. Working with plates from John James Audubon's THE BIRDS OF NORTH AMERICA in the libraries' collections, this workshop discussed how artistic depictions, photomosaic, and sonification represent different affordances of data exploration and key concepts of data librarianship. Participants explored key technologies and digital methods as they interpreted collected data results. How can creative projects like this one help interpret wildlife data differently than traditional data visualizations? As we explore these different interpretations of observation data - a painting, a photomosaic, and a sound recording - what new insights does it bring about the data? How might science communicators use these creative approaches to understand and communicate important information about the world around us?

Learning Objectives:

• Examine the affordances of data exploration types, and introduce key concepts of data librarianship through creative approaches.
• Use art historical techniques to explore how Audubon collected and depicted his data.
• Use Python scripts developed by Data Dolittle, to introduce photomosaic as art and technique for data exploration.
• Learn how to load data in PyDub to create a sonified file.

The National Institutes of Health new policy for data management and sharing will be effective on January 25, 2023. These requirements will apply to all research, whether funded or conducted in whole or in part by NIH, that results in the generation of scientific data. Therefore, this short talk will provide general tips on how to prepare for the NIH's updated requirements. Presenters will highlight areas where information professionals can assist researchers with planning and engaging best practices in data management and sharing. Topics will include approaches to research data management, outreach, and education.

Learning Objectives:

• Participants will be able to describe best practices in management of biomedical data.
• Participants will be able to explain key components of the 2020 NIH Data Management and Sharing Policy.
• Participants will be able to determine approaches they can take to address the NIH Data Management and Sharing Policy update at their institutions.

While collaboration with other library colleagues within our own institutions has become a common practice, cross-institutional partnerships occur less frequently due to each institution's unique complexities. Over the past year, two labor economics liaison librarians at peer institutions have collaborated to create data-literacy-focused instructional resources that satisfy the needs of both of their constituents. Challenges faced included different curricular requirements, despite the similar subject matter, differing database subscriptions at each institution, and dissimilar accessibility requirements for online instructional resources such as LibGuides. This poster will discuss the steps taken to create a resource that was useful for both libraries, and how those resources addressed the learning objectives at each institution, as well as provide attendees with suggestions of considerations when starting their own cross-institutional data-focused collaborative projects.

Learning Objectives:

• Attendees will be able to identify potential ways to start a cross-library collaboration at peer institutions.
• Attendees will be able to identify solutions to challenges within cross-institutional collaboration.
• Attendees will be able to describe ways in which co-created resources may be adapted to suit learning objectives at different institutions.

Since Spring 2021, our library has offered an R workshop for beginners. We saw a comparatively higher registration; People who registered for the workshop came from various academic background such as education, engineering, and business; Most attendees were graduate students. How are data skills taught on campus? How can our workshops better support the teaching and learning process? To answer these questions, I searched the keyword "data" in the course title and description of the Course Catalog 2022-2023 and identified over 200 classes as data-related courses. I reviewed the course description and curriculum to find data skills and tools covered in these courses. This presentation aims to present the distributions of data-related courses at our university and analyze the data skills and tools used in these classes.

Learning Objectives:

• Learn how data-related courses are distributed by subject area and academic classification.
• Learn what data skills and tools are covered in these courses.
• Be able to apply the similar research at their own institution if interested.

Job advertisements serve multiple functions; in addition to inviting applications, they also communicate a library's organizational goals, values, and understanding of its own role in the broader campus and scholarly environment. In a call for a data librarian, we can find language that defines how the library sees data and data services as they describe the organization, the need for the position, and the hoped-for qualifications of the successful applicant.

In this presentation, we share the results of a mixed-methods analysis of data-related positions on the ALA JobLIST from 2008-2019. Our analysis shows trends in the number and location of positions over time, as well as themes in how data and data services are defined. We will invite attendees — particularly those involved in hiring data librarians — to assess example advertisements to consider how the choice of language may ultimately shape decisions by potential applicants.

Learning Objectives:

• Knowledge of trends in data-related job advertisements in libraries.
• Strategies for assessing language in job advertisements to assess how language is used to define data and data services to potential applicants.

In Spring 2022, Georgia State University Library's Research Data Services Department launched the GSU Data Ready! Badges data literacy micro-credentialing initiative: lib.gsu.edu/data-ready. In this poster session, we will present: (1) content of our two badges tracks — data literacy foundations and software/coding training [14 separate badges]; (2) online platforms [Canvas and Badgr] used for automated badge earning and distribution; (3) assessment of program success; and (4) ways in which other institutions might develop similar initiatives.

The poster session format will optimize attendee engagement via extended Q&A and demonstration opportunities.

This session should appeal to all data librarians, from beginning to experienced.

Learning Objectives:

• Insight into how we structured our workshop content and learning assessments for badge earning.
• A behind-the-scenes look at the online platforms used to create our badge initiative.
• Successes and lessons learned from our implementation to inform improvements and potential models for other institutions.

This poster proposal seeks to address data sharing, positionality, and diversity in research.

Researchers create data and share that data in university repositories. Once data is accessed, downloaded, and used in other research it loses context. Data is not neutral. To understand data, it's important to understand the standpoint from which it was collected.

Standpoint Theory tells us that research perspectives are shaped by social position, and that knowledge (and data) is not objective but contextual. Positionality statements aim to place the research in the context of the researcher's social position.

A data positionality statement written from the standpoint of the data would give context to the data itself — the who, what, where, and why. We can use the positionality statements to make this information more accessible (metadata). This context matters. We all want more diversity in research materials, but how do we get it and how do we find it? We have to label it. This work has implications for the reuse of data, machine learning/data science, and a bigger picture look at the context of all this data.

Learning Objectives:

• How Standpoint Theory and positionality statements can impact research data.
• Stir some thoughts about context, intersectionality, and diversity in research data and why it matters.
• Ask questions about how we can/if we should label the diversity in research data this way.

In "Developing Data Services Skills in Academic Libraries," Justin Fuhr investigates the data skills proficiency of librarians currently providing data services or involved in data workflows (2022). One of his most intriguing findings was that data-involved librarians are not as well versed in the programming and technical skills needed to support large-scale data services at their institutions. Whether due to limited professional resources or limited time, data-involved librarians often put their desire to learn programming skills in the back of their mind as they maintain their more time-sensitive duties. This workshop will introduce participants to Python, one of the most popular coding languages in use, and provide them with a roadmap to becoming more comfortable with programming.

Learning Objectives:

• Comfort with the basic principles and operations in Python.
• Identify next steps for continuing their programming journey.

At University of North Carolina-Wilmington, the Watson College of Education's Educational Leadership program offers a masters & EdD degree. Students from this program are typically adults returning to school while holding full-time jobs. For years, the education liaison librarian has spent a large proportion of time reviewing information literacy concepts individually with students at various levels of comfort re-orienting to research processes. In 2022, she created a self-enrollable instruction module connected to students' Canvas learning shells, and when a data librarian position was added into the library, the two librarians collaborated to integrate data literacy pages to the course. This session will introduce the topics covered in the module, review the process of integrating the Canvas module into the Educational Leadership program, provide some early assessment of how students took to the module, and suggest future steps for embedding this module for improving adult student success.

Learning Objectives:

• Apply ideas for how to create an asynchronous learning guide covering data literacy for graduate students.
• Reproduce the outreach methods used to embed a module into a program or course.
• Associate what sort of data and information literacy concepts education (or other social sciences) graduate students need help with.

The VCU Library system hosts regularly-scheduled webinars (called Online Workshops), through which all interested parties can register to learn how to use library resources for various research needs. Lately, the feedback from faculty was that graduate students could benefit from more pointed instruction on data literacy topics, including how to deconstruct a secondary data set record.

In response to this feedback and the desire to create new workshops, the Health Sciences Library (HSL) created two new webinars to introduce the university's community to library resources that supported data and statistics queries. A "Finding Health Statistics Online" webinar was first piloted to call attention to library guides on online statistical resources and how to use them. Due to the success of this course, the HSL proposed a new webinar on finding secondary data resources and reviewing the structure of a sample dataset record. This webinar is scheduled to be presented in the fall and, so far, response to this offering has been positive, with a total number of 6 people registered.

This presentation will discuss how the HSL developed these webinars and how these will be used to help fill a data literacy gap for graduate students and beginning researchers. Once the webinar has been offered, participant feedback will be applied to determine how it can be further adapted to suit the audience's needs.

Learning Objectives:

• Identifying data literacy needs within a health sciences university population.
• How to guide graduate students (and beginning researchers) to statistics and data resources.
• Using an existing teaching infrastructure (an existing webinar series) to help the target audience effectively use these resources for their own research.

How do you create a data-focused reference and instruction program that supports social science programs in a specific school within the larger university while also aligning your work with existing library and data services? In 2019, a liaison librarian at a R1 institution was tasked with creating a data reference and instruction program from scratch in order to support specific social science programs on campus. While there were existing data support services at other campus libraries, most were focused on the physical sciences, and the librarian's unit had never provided focused data services to its constituents. This talk will provide both new and established data librarians with an overview of the unique, ongoing challenges and opportunities in creating a social science-focused data reference and instruction program from scratch during the pandemic while also fitting it into pre-existing library offerings. Attendees can learn about creating services that are meaningful to your constituents while not reinventing the wheel of existing library services on campus.

Learning Objectives:

• Attendees will be able to identify opportunities for additional librarian-led data support at their own institutions.
• Attendees will be able to understand how to create new data-focused services to serve undersupported programs at their own institutions.
• Attendees will be able to describe ways in which existing library services can be adapted to support the data needs of different schools and departments.

"Tools for RDS" is an open educational resource created to help librarians implement research data services (RDS) that directly address the needs of their researchers. Librarians in small or under-resourced institutions often face challenges such as budget and personnel limitations or resistance from faculty as they work to create services. "Tools for RDS" was launched as a way to provide ideas and solutions in focused, interactive modules. It is intended to be an evergreen resource with inclusion of case studies, templates, tips and tricks provided by those who have had both failures and successes in their RDS journeys.

The short talk will introduce participants to the Tools for RDS project. We will discuss our goals for this Open Resource, share our collaboration and creation process, and show participants the work that has been completed thus far in the Pressbooks platform.

Additionally, following the short talk we will have time to solicit input and contributions from both novice and veteran data services librarians who might be interested in adding their voice to the project.

Short Talk Activities: (1) discuss the need for this Tools for RDS project, and how we saw it as an opportunity to fill a gap in the available resources, (2) share information about the collaborative process we have used thus far to create Tools for RDS, (3) show the importance of illustrating principles and practices through the use of real and fictional case studies, (4) walk through the Pressbooks online publishing platform we are using to create Tools for RDS to participants, and (5) talk about our coming goals for the Tools for RDS project: how we would like to build it from here, what additional components would be most helpful, and how interested parties can contribute to it.

Audience Input: (1) discuss three of the biggest challenges for launching or growing RDS programs, (2) brainstorm innovative approaches to the identified challenges using a variety of techniques such as mind mapping and popcorn ideation, (3) create and write up concrete actions that address the identified challenges for inclusion in "Tools for RDS", and (4) share information with attendees about how they can contribute to the Tools for RDS project.

Learning Objectives:

• Attendees will see how the Tools for RDS project has been created, and gain understanding of how this collaborative process has been used to produce a deliverable that fills a gap and addresses current needs.
• Attendees will share their experiences launching research data services with colleagues. They will collaborate to build successful strategies that can be used at their own institutions.
• Attendees will contribute to an open, online resource that will be used by librarians in the future as they implement research data services.
• Attendees will have the opportunity to identify collaborators that they can work with in the future to create and implement programs or develop areas of research interest.

This workshop will focus on incorporating maps into your Power BI reports.

You will learn about maps in general as well as some tricks that might be helpful for building maps in a data analytics and visualization tool - Power BI. You will discover mapping options included by default with Power BI and those available from the Marketplace app.

In Power BI we will also demonstrate how to incorporate and analyze layers that were created in ArcGIS Online, a cloud-based platform to use for visualization of spatial information.

We will focus on issues related to map visualizations received through Power BI consultations at Penn State University Libraries. This session is suitable for those building their own reports as well as those supporting others with map visualizations. It is recommended, not required, that participants have a basic knowledge of how to load data and create visuals in Power BI and ArcGIS Online.

Learning Objectives:

• Participants will gain a better understanding of the use of maps within Power BI to assist users in the visualization of geographic content.
• Participants will learn how to handle problems with geographic field types in Power BI.
• Participants will incorporate ArcGIS Online content within Power BI map visualizations.