Sm-Nd Isotope Data Compilation from Geoscientific Literature Using an Automated Tabular Extraction Method.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-03 DOI:10.1038/s41597-024-04229-5
Zhixin Guo, Tao Wang, Chaoyang Wang, Jianping Zhou, Guanjie Zheng, Xinbing Wang, Chenghu Zhou
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Abstract

The rare earth elements Sm and Nd significantly address fundamental questions about crustal growth, such as its spatiotemporal evolution and the interplay between orogenesis and crustal accretion. Their relative immobility during high-grade metamorphism makes the Sm-Nd isotopic system crucial for inferring crustal formation times. Historically, data have been disseminated sporadically in the scientific literature due to complicated and costly sampling procedures, resulting in a fragmented knowledge base. However, the scattering of critical geoscience data across multiple publications poses significant challenges regarding human capital and time. In response, we present an automated tabular extraction method for harvesting tabular geoscience data. We collect 10,624 Sm-Nd data entries from 9,138 tables in over 20,000 geoscience publications using this method. We manually selected 2,118 data points from it to supplement the previously constructed global Sm-Nd dataset, increasing its sample count by over 20%. Our automatic data collection methodology enhances the efficiency of data acquisition processes spanning various scientific domains.

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用自动表格提取法整理地学文献中的钐钕同位素数据。
稀土元素Sm和Nd对地壳生长的时空演化、造山作用与地壳吸积的相互作用等基本问题具有重要意义。它们在高变质作用期间的相对不动性使得Sm-Nd同位素系统对推断地壳形成时间至关重要。从历史上看,由于采样程序复杂和昂贵,数据在科学文献中零星传播,导致知识库碎片化。然而,关键的地球科学数据分散在多个出版物中,对人力资本和时间构成了重大挑战。为此,我们提出了一种自动表格提取方法,用于获取表格地球科学数据。我们使用这种方法从超过20,000份地球科学出版物的9,138个表中收集了10,624个Sm-Nd数据条目。我们从中手动选择了2118个数据点来补充之前构建的全球Sm-Nd数据集,使其样本数量增加了20%以上。我们的自动数据收集方法提高了跨越各个科学领域的数据采集过程的效率。
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来源期刊
Scientific Data
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.
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