2026-10-07T07:36:02Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34018209 2026-09-28T18:56:40Z category_29182 item_type_7 month_year_09_2026

An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process Nicholas Synovic (11655277) Karolina Ryzka (20591594) Alessandra Vellucci Solari (20591690) Kenny Lyons (23549448) James C. Davis (14565206) George K. Thiruvathukal (470175) Empirical software engineering Pre-trained Models Software Reuse Empirical Software Engineering AI for Science and Engineering Scientific Process Large Language Models Automated Literature Review Research Productivity <p dir="ltr">Deep learning has achieved recognition for its impact within natural sciences, yet the prohibitive financial and technical cost of training models from scratch inhibits adoption. Following software engineering community guidance, natural scientists are reusing pre-trained deep learning models (PTMs) to amortize these costs. While prior works recommend PTM reuse patterns, we present the first empirical study of these patterns in the natural sciences, quantifying the utilization and impact of PTM reuse within the scientific process across 17,718 peer reviewed, open access papers. Our results show that “Biochemistry, Genetics and Molecular Biology” has outpaced other natural scientific fields in PTM reuse, “adaptation” reuse is the most prevalent PTM reuse pattern identified across all natural science fields, and the “testing” stage of the scientific process has been most impacted by PTM integration.</p> 2026-09-28T18:56:40Z Text Presentation 10.6084/m9.figshare.34018209.v1 https://figshare.com/articles/presentation/An_Empirical_Investigation_of_Pre-Trained_Deep_Learning_Model_Reuse_in_the_Scientific_Process/34018209 CC BY 4.0