I am very excited to say that I will be a LEADS-4-NDP 2019 Fellow!
Data science and information science have many similarities, so close that researchers have used information science’s trajectory to understand the development of data science (Mayrnik, M., 2018). As a LIS doctoral student who works as the research associate on a data science initiative, I am intrigued both professionally and scholastically by the overlaps between LIS and data science. In particular, I am interested in the way data science can be used to analyze, organize, and make available large quantities of previously hidden information within libraries, archives, and other information centers. When I started my career as a librarian, I worked in a small marine science laboratory that had a lot of information to share and needed to acquire a lot of information to operate. Budgetary concerns made it impossible to complete all of the necessary library projects to support these needs. This underfunding is a common story. Data science can be used to create solutions for some of these problems by providing free access to previously unfindable information and discovering better links among that material which already exists in the library.
Also prior to my doctoral studies, when I was working as a science librarian at a small college, I became involved with the data science graduate program being developed at the school. Along with helping experienced students and faculty members, I found that the new program created excitement around data science among the undergraduates. Students from the humanities and social sciences began to request training in programming languages, with themselves and their faculty members turning to the library for assistance. It became apparent to me that, as an educator, I would need to acquire and continually refresh data science skills. With this in mind, I began seeking training for myself in multiple languages and software. The computer science department covered most of what students needed for programming, but I supplemented this by teaching workshops for students, faculty, and staff on different open source tools.
Ultimately, I believe being involved in the LEADS Fellowship will provide me with a chance to further develop my data science skills while putting these lessons to use for a broad audience. For example, my research associate position focuses on publishing in data science, while my doctoral research focuses on information behavior among an underrepresented group. Both of these endeavors could benefit from the lessons learned at the intersection of data science and library science, as well as application of techniques developed during my work at my fellowship site on their projects.
My fellowship site is The Historical Society of Pennsylvania (HSP). I’ll be working to normalize location data from public school admission records in order to provide visualizations in the HSP’s Encounters platform. The work performed at the HSP can also be used within other organizations, particularly for making historical data more meaningfully interpreted for the general public. Also, I understood the work performed by the Fellow who was assigned to the HSP last year and am interested to participate in the next steps identified for this project. The opportunity to develop data science skills and put them to use for the benefit of a broad audience is what excites me most about being a part of LEADS-4-NDP.
Mayrnik, M. (2018). Data science as an interdiscipline: historical lessons from
information science. Draft copy.