使用不寻常的数据集组合是否有助于更大的科学影响?
Yulin Yu1, Daniel M Romero1,2,3
1School of Information, University of Michigan, Ann Arbor, MI 48109.
概括
结合不经常配对的科学数据集,推动了具有重大影响力的发现和更广泛的公众参与. 这种未被充分利用的策略提高了研究成果,特别是在社会科学领域,尽管它的应用并不频繁.
科学领域:
- 社会科学 社会科学 社会科学
- 图书统计学 图书统计学
- 研究影响力研究影响力
背景情况:
- 数据驱动的研究依赖于科学数据集来发现新的模式.
- 战略性数据利用是刺激科学进步的关键.
- 再组合理论表明,新的知识组合会产生具有重大影响力的发现.
研究的目的:
- 检查假设,不寻常的数据集组合导致高影响力社会科学研究.
- 调查非典型数据组合的科学和更广泛的影响.
- 分析数据集配对,研究团队特征和出版影响之间的关系.
主要方法:
- 分析了来自政治和社会研究大学间联盟的3万多篇出版物和5000个数据集.
- 应用文献识别分析来评估数据集组合的影响.
- 根据数据集主题的特殊性和研究团队规模/经验进行控制.
主要成果:
- 结合数据集,特别是那些不经常配对的数据集,可以显著提高科学和更广泛的影响.
- 不经常配对的数据集与引用数量有很强的相关性,而不依赖于主题.
- 较小,经验较少的团队更频繁地使用非典型的数据集组合.
- 结合数据集的论文仍然很少出现,尽管有证明的好处.
结论:
- 非传统的数据集组合是提高科学影响和传播的未充分利用的策略.
- 利用不经常配对的数据集为高影响力研究提供了一个强大的途径.
- 进一步探索数据重组策略是有必要的,以最大限度地提高研究成果.
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