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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated collection of papers, datasets, and resources on Scientific Datasets and Large Language Models (LLMs)
| Date | Stars |
|---|---|
| 2026-07-31 | 458 |
| 2026-08-06 | 458 |
| 2026-08-24 | 459 |
| 2026-08-31 | 460 |
| 2026-09-15 | 461 |
| 2026-09-16 | 461 |
| 2026-09-20 | 461 |
Today
— stars today
This week
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growth rate 0.22%/day
# Awesome-Scientific-Datasets-and-LLMs A curated collection of papers, datasets, and resources on Scientific Datasets and Large Language Models (LLMs), organized in reference to our survey: [**"A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers"**](https://arxiv.org/abs/2508.21148) > If you spot any mistakes or have suggestions, feel free to reach out by email: [email protected] > > (We also recommend CC’ing [email protected] and [email protected] in case of any unsuccessful delivery issue.) > > If you find our survey useful for your research, please cite the following paper: ## 📖 Citation If you find this repository or our survey helpful in your research, please kindly cite our paper: ```bibtex @article{hu2025survey, title={A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers}, author={Hu, Ming and Ma, Chenglong and Li, Wei and Xu, Wanghan and Wu, Jiamin and Hu, Jucheng and Li, Tianbin and Zhuang, Guohang and Liu, Jiaqi and Lu, Yingzhou and others}, journal={arXiv preprint arXiv:2508.21148}, year={2025} } ``` In addition, [**"Awesome-Agent-Scientists"**](https://github.com/AgenticScience/Awesome-Agent-Scientists) highlights the latest advances of AI agents in scientific research, which nicely complements our work. ```bibtex @article{wei2025ai, title={From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery}, author={Wei, Jiaqi and Yang, Yuejin and Zhang, Xiang and Chen, Yuhan and Zhuang, Xiang and Gao, Zhangyang and Zhou, Dongzhan and Wang, Guangshuai and Gao, Zhiqiang and Cao, Juntai and others}, journal={arXiv preprint arXiv:2508.14111}, year={2025} } ``` ## 📈 Trends in Scientific LLM Publications  Cumulative trend of publications on major preprint platforms whose titles or abstracts mention the keyword “language model” or the combination “language model + scientific domain” (e.g., chemistry, physics, multi-omics, medicine, etc.). Left: Results from January 2018 to August 2025, from arXiv and PubMed. For arXiv, the matching includes “language model” in combination with additional science-related keywords; PubMed results are limited to occurrences in titles and abstracts. Both platforms show rapid growth. Right: Results from 2020 to August 2025, from bioRxiv, medRxiv, and ChemRxiv, all based on direct matches of “language model” in titles and abstracts. While the overall volumes are smaller than arXiv and PubMed, all three platforms, especially bioRxiv, show rapid acceleration, reflecting growing interdisciplinary interest in large language models across biomedical, chemical, and computational sciences ## 🔬 Development of Sci-LLMs  Evolution of Sci-LLMs reveals four paradigm shifts from 2018 to 2025, including (1) the progression from transfer learning approaches, (2) through the scaling era marked by knowledge integration in larger models, (3) instruction-following capabilities enabling flexible task adaptation, to (4) the latest paradigm introduces scientific agents—AI systems capable of autonomously conducting scientific research, from hypothesis generation and experimental design to data analysis and discovery. Note: Model positions reflect their release dates (x-axis) rather than strict paradigm classification. The four paradigms represent evolving trends in Sci-LLM development with overlaps and continuities, not mutually exclusive categories. ## 🕑 Timeline of Sci-LLMs  Chronological overview of notable Sci-LLMs categorized by six scientific domains, spanning from 2019 through early 2025. Due to the rapid expansion of the field, this figure presents a selective overview. ## 📑 Table of Contents - [Awesome-Scientific-Datasets-and-LLMs](#awesome-scientific-datasets-and-llms) - [📖 Citation](#-citation) - [📈 Trends in Scientific LLM Publications](#-trends-in-scientific-llm-p
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ea0e42404ec9898e, name:datasets, desc:datasets