Abstract
UW-Madison has a thriving community of support around research data management and sharing, computational skills, and research reproducibility which are all building blocks for supporting campus data science needs. Support is provided through many campus organizations such as the Center for High Throughput Computing, the Research Cyberinfrastructure Initiative, Research Data Services, the Data Science Hub, and more. However, informal and formal interviews with researchers at UW-Madison have shown that as the campus invests in technological solutions and infrastructure to address common research bottlenecks, researchers are less limited by access to data and computing resources, but rather the knowledge to find, understand, and use those tools effectively. Specifically, researchers need “data people,” or people who can help them address these knowledge-based needs, who can fill in gaps in research support, who can advocate for their needs across campus silos, and who can build broad networks of relationships across campus to facilitate a better support experience.
TopIntroduction
The University of Wisconsin–Madison (UW–Madison) has a thriving community of support around research data management and sharing, computational skills, and research reproducibility, which are all building blocks for supporting campus data science needs. Support is provided through many campus organizations such as the Center for High Throughput Computing (https://datascience.wisc.edu/hub), and more. However, informal and formal interviews with researchers at UW–Madison have shown that as the campus invests in technological solutions and infrastructure to address common research bottlenecks, researchers are limited not by access to data and computing resources but rather the knowledge to be able to find, understand, and use those tools effectively (Bloom et al., 2018; Cook et al., 2021). Specifically, researchers need “data people,” or people who can help them address these knowledge-based needs, who can fill in gaps in research support, who can advocate for their needs across campus silos, and who can build broad networks of relationships across campus to facilitate a better support experience.
This case study focuses on the work of librarians with RDS and the facilitators in the Data Science Hub to fill these needs. RDS is an interdisciplinary group made up of librarians, information technology staff, and research staff, who are committed to advancing research data management and sharing best practices on the UW–Madison campus. The Data Science Hub, with a staff of three facilitators, carries out a mission of community engagement and learning opportunities around data science and computing for campus researchers through a variety of services.
This chapter begins with a framing and history of the current landscape of research data and data science support on the UW–Madison campus, including a history of research IT support, the development of both groups, and how the units have worked together and leveraged each other’s expertise to provide vital programs and resources at a large, decentralized institution. Important to this discussion is the value of interpersonal skills such as communication, active listening, and creativity that have been found critical in these support roles. The “The Need for Data People” section explores the library literature pulling from surveys, job descriptions, and essays of data services in libraries showcasing how interpersonal skills are crucial in addressing researchers’ data needs.
The authors believe that the efforts outlined in the chapter illustrate that human infrastructure is necessary alongside technical infrastructures to develop robust and sustainable data science support within libraries and on academic campuses in general. Support services that provide people with interconnected infrastructure are vital in the growth of any new area of research, especially as it becomes increasingly data-intensive and requires coding skills (Baker, 2017).
Key Terms in this Chapter
Interpersonal Skills: A suite of person-facing and empathetic skills, such as communication, instruction, emotional intelligence, leadership, teamwork, and collaboration.
Computational Research: The use and development of software tools for data analysis.
Technical Skills: A suite of technology-facing skills, such as software engineering, data management, system administration, and database administration.
Holistic Research Infrastructure: A research infrastructure that leverages both technical resources to enable the day-to-day functions of the research process and human resources to enable the human understanding and use of those technical resources.
Carpentries: A global, inclusive community of learners and instructors to cultivate data and computing skills in research.
Grassroots: An effort built from the ground up, often by people who are invested in the purpose of the effort. Instead of being driven by those with significant power or formal leadership positions in an institution, these efforts are initiated and driven by individuals working in a particular field.
Research Data Management: The organization, description, and stewardship of research data throughout the research lifecycle.
Data People: Research staff with technical and interpersonal skills who assist researchers in developing computational research skills, advocate for researcher needs across campus silos, and build broad networks of support throughout the research lifecycle.
Research Data Services (RDS): A term typically used to denote a suite of research services focused on research data management and sharing typically provided by an academic library. In this chapter, RDS is both the name of the UW–Madison service and used as a descriptor for similar services broadly.
Data Science Hub: A team of facilitators with a mission of community engagement and learning opportunities around data science and computing for UW–Madison campus researchers through a variety of services.