构建跨学科科学计量数据集，揭示30年科研演化规律
SciEvo: A 2 Million, 30-Year Cross-disciplinary Dataset for Temporal Scientometric Analysis
- 收集200万篇论文，构建含引用图谱的纵向数据集
- 发现应用型领域引文年龄仅2.48年，理论领域达9.71年
- 适合研究科学演化、跨学科合作与知识传播的学者
理解科学知识的生成、演进与传播对弥合学科鸿沟、应对疫情、气候变化和伦理人工智能等全球挑战至关重要。科学计量学通过量化与定性分析学术文献，为这些过程提供洞见。本文推出SciEvo，一个包含超过两百万篇学术论文的纵向科学计量数据集，涵盖完整内容信息与引用图谱，支持跨学科分析。SciEvo易用且可在GitHub、Kaggle和HuggingFace平台获取。基于该数据集，我们开展跨越30年的时序研究，探讨术语演变、引用模式与跨学科知识交流等核心问题。结果揭示了认知文化差异、知识生产模式及引用实践的显著分异：如快速发展的应用型领域（如大语言模型）引文平均年龄仅为2.48年，而传统理论学科（如口述历史）则高达9.71年。相关数据与分析工具可访问https://github.com/Ahren09/SciEvo。
原文摘要 · Abstract (English)
Understanding the creation, evolution, and dissemination of scientific knowledge is crucial for bridging diverse subject areas and addressing complex global challenges such as pandemics, climate change, and ethical AI. Scientometrics, the quantitative and qualitative study of scientific literature, provides valuable insights into these processes. We introduce SciEvo, a longitudinal scientometric dataset with over two million academic publications, providing comprehensive contents information and citation graphs to support cross-disciplinary analyses. SciEvo is easy to use and available across platforms, including GitHub, Kaggle, and HuggingFace. Using SciEvo, we conduct a temporal study spanning over 30 years to explore key questions in scientometrics: the evolution of academic terminology, citation patterns, and interdisciplinary knowledge exchange. Our findings reveal critical insights, such as disparities in epistemic cultures, knowledge production modes, and citation practices. For example, rapidly developing, application-driven fields like LLMs exhibit significantly shorter citation age (2.48 years) compared to traditional theoretical disciplines like oral history (9.71 years). Our data and analytic tools can be accessed at https://github.com/Ahren09/SciEvo.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。