基于专利数据的文学协会分析与不断变化的创新趋势
Adrian Sven Geissler1, Jan Gorodkin1, Stefan Ernst Seemann1
1Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Frederiksberg, Denmark.
Frontiers in research metrics and analytics
|August 16, 2024
概括
这项研究引入了一种新的方法来识别推动专利创新的关键科学出版物,超越传统的引用数量. 这种数据驱动的方法揭示了生物技术新兴技术背后的关键研究.
科学领域:
- 生物技术是生物技术.
- 知识产权知识产权知识产权
- 图书统计学 图书统计学
背景情况:
- 专利对于将科学发现转化为社会利益至关重要.
- 传统的学术指标,如引用数,与可专利的创新相关性没有相关性.
- 现有的方法没有办法量化地将基础研究与特定的创新趋势联系起来.
研究的目的:
- 开发和应用数据驱动的工作流程,以确定影响可专利创新的学术作品.
- 探索专利与科学文献之间的关联,使用从基因疾病关联研究中调整的统计方法.
- 分析与CRISPR基因编辑和蓝菌生物技术相关的专利数据,以确定创新趋势.
主要方法:
- 利用统计方法,类似于生物学的基因疾病关联方法,分析专利和出版数据.
- 对专利数量进行时间序列分析,以确定显示创新趋势的重大变化.
- 识别的学术作品在与不断变化的创新趋势相关的专利参考文献中统计表现过高.
主要成果:
- 确定了大约1000个与免疫学,农业植物基因组学和生物技术工程的创新趋势有显著联系的学术作品.
- 检测到特定出版物与CRISPR基因编辑和蓝生物技术中的专利趋势之间的关联.
- 发现识别的学术作品与各自创新的技术要求保持一致.
结论:
- 拟议的数据驱动工作流有效地确定了对于创新趋势的转变至关重要的基础研究.
- 这种方法为评估科学出版物的影响和相关性提供了宝贵的工具,超出了引用指标.
- 这些发现对研究人员,政策制定者和对跟踪和理解技术进步感兴趣的行业利益相关者来说尤其重要.
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