产品(数学)
数据集
成分
集合(抽象数据类型)
暴露评估
计算机科学
化学工业
数据库
资源(消歧)
风险评估
CAS注册表号
数据挖掘
化学毒性
风险分析(工程)
数据科学
生化工程
业务
化学
统计
数学
环境化学
食品科学
工程类
计算机网络
几何学
有机化学
计算机安全
人工智能
水污染物
程序设计语言
毒性
作者
Kathie L. Dionisio,Katherine A. Phillips,Paul S. Price,Chris Grulke,Antony Williams,Derya Biryol,Hong Tao,Kristin Isaacs
出处
期刊:Scientific Data
[Springer Nature]
日期:2018-07-10
卷期号:5 (1): 180125-180125
被引量:238
标识
DOI:10.1038/sdata.2018.125
摘要
Abstract Quantitative data on product chemical composition is a necessary parameter for characterizing near-field exposure. This data set comprises reported and predicted information on more than 75,000 chemicals and more than 15,000 consumer products. The data’s primary intended use is for exposure, risk, and safety assessments. The data set includes specific products with quantitative or qualitative ingredient information, which has been publicly disclosed through material safety data sheets (MSDS) and ingredient lists. A single product category from a refined and harmonized set of categories has been assigned to each product. The data set also contains information on the functional role of chemicals in products, which can inform predictions of the concentrations in which they occur. These data will be useful to exposure and risk assessors evaluating chemical and product safety.
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