Leveraging Data Science Research to Enrich Information Literacy Instructions

Librarians play a vital role in helping students and researchers navigate today’s complex scholarly information landscape. In chemical, biomedical, and related scientific fields, information literacy instruction often includes topics such as disciplinary databases, scholarly publishing venues, open access, research impact, data management, publication ethics, and responsible research practices. This project explores how data science and scientometric research methods can be used to enrich library instruction and make information literacy teaching more evidence-based, timely, and relevant to real-world research practices. Students will investigate scholarly communication trends using publication data, citation patterns, metadata, and other research indicators to better understand how knowledge is produced, shared, evaluated, and sometimes corrected within scientific communities. Building on previous student projects including the assessment of open access publishing, pre- and post-publication peer review, retractions, and self-plagiarism, this project may examine a range of topics related to research integrity and scholarly communication. Potential areas of inquiry include the influence of scientific publications on career trajectories, emerging research trends in chemical and biomedical fields, ethical challenges in publishing, responsible use of scholarly metrics, and the AI-literacy skills researchers need in evolving research practices. Through this work, students will gain experience with data-driven research methods while contributing to the development of more engaging and research-informed library instruction. The project will culminate in a comprehensive report, presentation, and instructional resource that will enrich information literacy instructions in science disciplines.

Students will also need to complete this library specific application.

Required Qualifications:

• U-M PhD student or Master’s degree student in good standing.

• Conversant in Python and use of Jupyter Notebook;

• Previous experience with pandas, seaborn, numpy, matplotlib, bokeh;

• Experience with API requests.

Position-Specific Desired Qualifications: 

• Ability to learn quickly, and to work independently;

• Attention to detail;

• Ability to meet deadlines; Good work ethics.

Expectations:

• Adhere to agreed-upon schedule and project deadlines, communicating any challenges or barriers to the mentors.

• Periodically attend Engagement Fellow training sessions and cohort meetings. 

• Publically share the project process and results with the library community near the end of the academic year.