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数据图:朝着数据集发现的系统方法.
Pascal Petit1, Nicolas Vuillerme2,3
1Univ. Grenoble Alpes, AGEIS, 38000 Grenoble, France.
GigaScience
|October 22, 2025
概括
数据图提供了一个结构化的方法来查找和评估数据集,提高研究透明度和可重复性. 这种方法使数据集搜索正式化,解决大数据生态系统中面临的挑战,以便更有效地重复使用数据.
科学领域:
- 数据科学数据科学数据科学
- 研究方法研究方法研究方法学
- 信息科学 信息科学 信息科学
背景情况:
- 科学发现越来越依赖于数据,但重复使用现有数据集受到碎片化和异质性的阻碍.
- 目前的数据集发现方法缺乏标准化,导致效率低下和潜在的偏见.
- 需要一个正式的方法来选择数据集,类似于文献学研究.
研究的目的:
- 介绍数据图,一种用于系统数据集识别和评估的结构化方法.
- 将数据集搜索正式化为研究实践,以提高透明度,可复制性和协作.
- 在大数据生态系统中解决数据集发现和重用方面的挑战.
主要方法:
- 开发了一个九步框架来实现数据图形的运行.
- 通过以暴露组为重点的数据图形搜索应用框架.
- 分析了包括元数据可用性,存储库异质性和数据集质量在内的挑战.
主要成果:
- 数据图提供了一个系统的基础,用于识别和合成可重复使用的数据集.
- 该框架可以提高研究人员层面的透明度,可复制性和效率.
- 确定了对元数据,存储库,可访问性和质量影响数据的关键挑战.
结论:
- 数据图表通过标准化研究人员级实践来补充存储库的改进.
- 将数据图形与FAIR原则和技术进步相结合,可以实现自动发现和可持续的数据再利用.
- 这种结构化的方法提供了一个可扩展的方法,用于跨学科的FAIR调整,数据驱动的研究.
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