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What's next? Forecasting scientific research trends.

Dan Ofer1, Hadasah Kaufman1, Michal Linial1

  • 1Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.

Heliyon
|January 15, 2024
PubMed
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Forecasting scientific research trends five years ahead is possible using diverse data sources. This predictive capability aids institutions and funders in strategic planning for emerging topics.

Keywords:
BibliometricsCitation analysisMachine learningMeSHNLPPubMedTime series

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Area of Science:

  • Scientific research trends
  • Biomedical and life sciences
  • Technology and engineering

Background:

  • Evolving scientific interests necessitate methods for trend identification and forecasting.
  • Accurate trend prediction is crucial for stakeholders like educational institutions, practitioners, investors, and funding bodies.

Purpose of the Study:

  • To predict future trends in scientific publications using heterogeneous data sources.
  • To demonstrate the predictability of scientific topic popularity and changes up to five years in advance.

Main Methods:

  • Utilized historical publication time series from PubMed, research/review articles, pre-trained language models, and patents.
  • Analyzed 125 diverse topics across 40 years, including life sciences, biomedical, anatomy, and STEM fields.
  • Investigated the ratio of review to original research articles as an indicator of topic trajectory.

Main Results:

  • Successfully predicted scientific topic trends five years in advance.
  • Identified preceding publications and future patents as leading indicators for emerging topics.
  • Found that an excess of review articles signals declining scientific topics.
  • Language models enhanced insights into temporal dynamics, outperforming historical baselines.

Conclusions:

  • Scientific publication trends are predictable across a wide range of disciplines.
  • The ratio of reviews to research articles and patent data offer valuable predictive signals.
  • Developed SciTrends, a webtool for predicting PubMed-indexed publication trends.