{
 "id": "SustcZhangYX/EnvBench",
 "owner": "SustcZhangYX",
 "name": "EnvBench",
 "downloads": 47,
 "likes": 0,
 "last_modified": "2025-07-31T14:33:57.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [],
 "citation": "@article{ZHANG2025100608,\ntitle = {Fine-Tuning Large Language Models for Interdisciplinary Environmental Challenges},\njournal = {Environmental Science and Ecotechnology},\npages = {100608},\nyear = {2025},\nissn = {2666-4984},\ndoi = {https://doi.org/10.1016/j.ese.2025.100608},\nurl = {https://www.sciencedirect.com/science/article/pii/S2666498425000869},\nauthor = {Yuanxin Zhang and Sijie Lin and Yaxin Xiong and Nan Li and Lijin Zhong and Longzhen Ding and Qing Hu}\n}",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "9b267f1446812b9e34611e955a8a560c3e3d8fea",
 "stated_evidence": "https://huggingface.co/datasets/SustcZhangYX/EnvBench/blob/9b267f1446812b9e34611e955a8a560c3e3d8fea/README.md",
 "file_evidence": "https://huggingface.co/datasets/SustcZhangYX/EnvBench/tree/9b267f1446812b9e34611e955a8a560c3e3d8fea",
 "card_nc": false
}