five

Archived Distributed Structure-Searchable Toxicity (DSSTox) Data

收藏
DataCite Commons2024-04-11 更新2024-07-13 收录
下载链接:
https://epa.figshare.com/articles/dataset/Predicted_EI-MS_Spectra_of_CompTox_Chemicals_Dashboard_Structures/7660859
下载链接
链接失效反馈
官方服务:
资源简介:
The most updated EPA DSSTox data files are available https://doi.org/10.23645/epacomptox.5588566 . EPA’s Distributed Structure-Searchable Toxicity (DSSTox) database contains curated chemical substances mapped to data including chemical identifiers (i.e., chemical synonyms, systematic names, CAS Registry Numbers and others) and, where appropriate, chemical structure representations. The goal for DSSTox is to accurately represent chemical substances, their structures and identifiers, as well as relevant chemical lists which are important to the environmental research and regulatory community.Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25

最新版本的EPA DSSTox数据集文件可通过以下链接获取:https://doi.org/10.23645/epacomptox.5588566。 美国环境保护署(EPA)的分布式结构可检索毒性数据库(Distributed Structure-Searchable Toxicity, DSSTox)收录经专业审核整理的化学物质,将其与各类数据关联映射,其中包括化学标识符(如化学同义词、系统命名名称、化学文摘社登记号(CAS Registry Numbers)等),以及适配场景下的化学结构表征信息。 DSSTox的构建目标是精准呈现化学物质本身、其分子结构与相关标识符,同时收录对环境研究及监管领域至关重要的相关化学名录。 科学清单(Science Inventory)、CCTE相关成果:https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25
提供机构:
The United States Environmental Protection Agency’s Center for Computational Toxicology and Exposure
创建时间:
2019-02-01
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作