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

被引:0
|
作者
Guo, Zhixin [1 ,2 ,3 ,4 ]
Wang, Tao [5 ]
Wang, Chaoyang [5 ]
Zhou, Jianping [1 ]
Zheng, Guanjie [6 ]
Wang, Xinbing [1 ]
Zhou, Chenghu [7 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China
[2] Shanghai Satellite Network Res Inst Co Ltd, Shanghai 201210, Peoples R China
[3] Shanghai Key Lab Satellite Network, Shanghai 201210, Peoples R China
[4] State Key Lab Satellite Network, Shanghai 201210, Peoples R China
[5] Chinese Acad Geol Sci, Inst Geol, Beijing 100037, Peoples R China
[6] Shanghai Jiao Tong Univ, John Hopcroft Ctr Comp Sci, Shanghai 200240, Peoples R China
[7] Chinese Acad Sci, Inst Geol Sci & Nat Resources Res, Beijing 100101, Peoples R China
关键词
ELEMENT GEOCHEMISTRY; ACCRETIONARY OROGENS; GRENVILLE PROVINCE; SR; COMPLEX; PETROGENESIS; SYSTEMATICS; ORIGIN; GROWTH; GRADE;
D O I
10.1038/s41597-024-04229-5
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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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页数:13
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