重新思考研究结果的机器可读表达的生产和出版
Markus Stocker1,2, Lauren Snyder3, Matthew Anfuso4
1TIB - Leibniz Information Centre for Science and Technology, 30167, Hannover, Germany. markus.stocker@tib.eu.
Scientific data
|April 30, 2025
概括
这项研究引入了一种名为reborn的出版前方法,从一开始就使科学知识成为机器可读的. 与手动提取相比,这种方法可以提高知识的准确性和可重复性.
科学领域:
- 科学知识表示科学知识表示
- 数据科学是数据科学.
- 学术传播学术交流
背景情况:
- 科学文献至关重要,但往往不是机器可读的.
- 手动从出版物中提取知识是耗时的,容易出错的.
- 自动化出版后提取方法有其局限性.
研究的目的:
- 提出和评估一个出版前的方法 (重生) 创造机器可读的科学知识.
- 加强科学知识的 FAIR (可查找,可访问,可互操作,可重复使用) 组织.
- 提高提取的研究数据的准确性,丰富性和可重复性.
主要方法:
- 在创作过程中开发了"重生"方法,用于在创作过程中产生机器可读的科学知识.
- 利用开放研究知识图基础设施进行知识组织.
- 用土壤科学,计算机科学和农业生态学的统计发现来测试该方法.
主要成果:
- 这种重生的方法在手动和半自动的出版后提取方面表现出优越性.
- 实现了科学知识的更高的准确性,丰富性和可重复性.
- 展示了实施中的技术简单性.
结论:
- 出版前的机器可读性是一个比出版后提取更有效的策略.
- 重生的方法促进了 FAIR 科学知识组织.
- 这种方法在科学数据的再利用和研究完整性方面具有显著的优势.
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