SciEvo: A 2 Million, 30-Year Cross-disciplinary Dataset for Temporal Scientometric Analysis

TL;DR

SciEvo is a comprehensive, longitudinal dataset of over two million publications spanning 30 years, enabling detailed analysis of scientific knowledge evolution and interdisciplinary citation patterns.

Contribution

We present SciEvo, a large-scale, accessible scientometric dataset with tools for cross-disciplinary temporal analysis of scientific literature.

Findings

Application-driven fields like LLMs have shorter citation ages.

Traditional disciplines like oral history show longer citation ages.

Disparities exist in epistemic cultures and citation practices across fields.

Abstract

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.