A Database of Stress-Strain Properties Auto-generated from the Scientific Literature using ChemDataExtractor.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-11-23 DOI:10.1038/s41597-024-03979-6

Pankaj Kumar, Saurabh Kabra, Jacqueline M Cole

引用次数: 0

Abstract

There has been an ongoing need for information-rich databases in the mechanical-engineering domain to aid in data-driven materials science. To address the lack of suitable property databases, this study employs the latest version of the chemistry-aware natural-language-processing (NLP) toolkit, ChemDataExtractor, to automatically curate a comprehensive materials database of key stress-strain properties. The database contains information about materials and their cognate properties: ultimate tensile strength, yield strength, fracture strength, Young's modulus, and ductility values. 720,308 data records were extracted from the scientific literature and organized into machine-readable databases formats. The extracted data have an overall precision, recall and F-score of 82.03%, 92.13% and 86.79%, respectively. The resulting database has been made publicly available, aiming to facilitate data-driven research and accelerate advancements within the mechanical-engineering domain.

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利用 ChemDataExtractor 从科学文献中自动生成的应力-应变特性数据库。

机械工程领域一直需要信息丰富的数据库来帮助数据驱动的材料科学。为了解决缺乏合适的属性数据库的问题，本研究采用了最新版本的化学感知自然语言处理（NLP）工具包 ChemDataExtractor，自动整理出一个包含关键应力应变属性的综合材料数据库。该数据库包含材料及其相关属性的信息：极限拉伸强度、屈服强度、断裂强度、杨氏模量和延展性值。从科学文献中提取了 720,308 条数据记录，并整理成机器可读的数据库格式。提取数据的总体精确度、召回率和 F 分数分别为 82.03%、92.13% 和 86.79%。由此产生的数据库已向公众开放，旨在促进数据驱动的研究，加快机械工程领域的进步。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.