Tracing Scientific Evolution: A 30-Year Cross-disciplinary Analysis

Yiqiao Jin, Yijia Xiao, Yiyang Wang, Jindong Wang

Keywords: Dataset, data curation, NLP tools for social analysis, ethical considerations in NLP applications, corpus creation, language resources, reflections and critiques

TL;DR: We conduct a 30-year longitudinal scientometric analysis on academic literature, revealing phenomena such as bias and citation amnesia.

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. To address the lack of comprehensive datasets for such analyses, 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.

Submission Number: 2