A libraries reproducibility hackathon: connecting students to university research and testing the longevity of published code [version 2; peer review: 2 approved]

Background Reproducibility is a basis of scientific integrity, yet it remains a significant challenge across disciplines in computational science. This reproducibility crisis is now being met with an Open Science movement, which has risen to prominence within the scientific community and academic libraries especially. At the Carnegie Mellon University Libraries, the Open Science and Data Collaborations (OSDC) Program promotes Open Science practices with resources, services, and events. Hosting hackathons in academic libraries may show promise for furthering such efforts. Methods To address the need for reproducible computational research and promote Open Science within the community, members of the OSDC Program organized a single-day hackathon centered around reproducibility. Partnering with a faculty researcher in English and Digital Humanities, we invited community members to reuse Python code and data from a research publication deposited to Harvard Dataverse. We also published these materials as a compute capsule in Code Ocean that participants could also access. Additionally, we investigated ways to use ChatGPT to troubleshoot errors from rerunning this code. Results Three students from the School of Computer Science participated in this hackathon. Accessing materials from Harvard Dataverse, these students found success reproducing most of the data visualizations, but they required some manual setup and modifications to address depreciated libraries used in the code. Alternatively, we found Code Ocean to be a highly accessible option, free from depreciation risk. Last, ChatGPT also aided in finding and addressing the same roadblocks to successfully reproduce the same figures as the participating students. Conclusions This hackathon allowed several students an opportunity to interact with and evaluate real research outputs, testing the reproducibility of computational data analyses. Partnering with faculty opened opportunities to improve open research materials. This case study outlines one approach for other academic libraries to highlight challenges that face reproducibility in an interactive setting.