Evidence map›Paper›PMID 41381535›Full record

ArticleScientific data2025

Mechanical and Electrical Properties dataset of Cu-Cr-X alloys generated with automated figure data extraction.

Peiwen Yun, Huadong Fu, Hongtao Zhang, Lei Jiang, Xingyu Xiao, Shuaicheng Zhu, Yujiao Jia, Changtai Li, Wenjin Yang, Jianxin Xie

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Peiwen YunBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing, 100083, China.
Huadong FuBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing, 100083, China. hdfu@ustb.edu.cn.
Hongtao ZhangBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing, 100083, China.
Lei JiangBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing, 100083, China.
Xingyu XiaoBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing, 100083, China.
Shuaicheng ZhuBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing, 100083, China.
Yujiao JiaBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing, 100083, China.
Changtai LiBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing, 100083, China.
Wenjin YangSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Jianxin XieBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing, 100083, China. jxxie@mater.ustb.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52374379National Natural Science Foundation of China (National Science Foundation of China) 52425409National Natural Science Foundation of China (National Science Foundation of China) U24A2029
6 · The paper itself

Abstract

Extracting data from scientific literature and constructing machine learning-ready (ML-ready) datasets is crucial for data-driven materials discovery. However, material properties under various compositions and processing conditions are often summarized in figures, making manual data extraction time-consuming and labor-intensive. In this study, we propose an automated figure data extraction tool to rapidly generate datasets from figures in materials science literature on Cu-Cr-X alloys used in high-end lead frames, aiming to support the development of next-generation high-strength, high-conductivity copper alloys. We collected 251 figures containing mechanical and electrical performance data from 146 papers and automatically extracted 3,018 high-quality Cu-Cr-X data records. Each record includes article metadata and composition/processing information manually extracted from text, enabling construction of a domain-specific dataset linking alloy composition, processing, and properties. The dataset summarizes 20 microalloying elements and mechanical and electrical performance under different processing conditions. This tool can significantly improve the efficiency of large-scale data accumulation in materials science.

Identifiers

PMID41381535
PMCPMC12748678

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.