# An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process

arXiv: https://arxiv.org/abs/2603.13584 · HTML: https://mlsanity.com/paper/2603.13584/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 119 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions](https://mlsanity.com/paper/2306.02224/) (2023)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry](https://mlsanity.com/paper/2303.02552/) (2023)
- [PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models](https://mlsanity.com/paper/2504.14117/) (2025)
- [Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision](https://mlsanity.com/paper/2303.07476/) (2023)
- [Reusing Deep Learning Models: Challenges and Directions in Software Engineering](https://mlsanity.com/paper/2404.16688/) (2024)
- [PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2402.00699/) (2024)
- [Oil & Water? Diffusion of AI Within and Across Scientific Fields](https://mlsanity.com/paper/2405.15828/) (2024)
- [Influence of agent's self-disclosure on human empathy](https://mlsanity.com/paper/2106.09906/) (2021)
- [SciCat: A Curated Dataset of Scientific Software Repositories](https://mlsanity.com/paper/2312.06382/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Models](https://mlsanity.com/paper/2607.17242/) (2026)

## Abstract (arXiv)

Deep learning has achieved recognition for its impact within natural sciences, yet the prohibitive financial and technical cost of training models from scratch inhibit 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 PTM reuse 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.