# Whats next? Forecasting scientific research trends

arXiv: https://arxiv.org/abs/2305.04133 · HTML: https://mlsanity.com/paper/2305.04133/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2023.

## Built on by (most cited first, 5 of 5)

- [Proof of Time: A Benchmark for Evaluating Scientific Idea Judgments](https://mlsanity.com/paper/2601.07606/) (2026)
- [FOS: A Large-Scale Temporal Graph Benchmark for Scientific Interdisciplinary Link Prediction](https://mlsanity.com/paper/2511.18631/) (2025)
- [InterFeat: A Pipeline for Finding Interesting Scientific Features](https://mlsanity.com/paper/2505.13534/) (2025)
- [RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases](https://mlsanity.com/paper/2609.10092/) (2026)
- [BackTrend: Evaluating Scientific Weak-Signal Prediction via Backward Reconstruction](https://mlsanity.com/paper/2609.24921/) (2026)

## Abstract (arXiv)

Scientific research trends and interests evolve over time. The ability to identify and forecast these trends is vital for educational institutions, practitioners, investors, and funding organizations. In this study, we predict future trends in scientific publications using heterogeneous sources, including historical publication time series from PubMed, research and review articles, pre-trained language models, and patents. We demonstrate that scientific topic popularity levels and changes (trends) can be predicted five years in advance across 40 years and 125 diverse topics, including life-science concepts, biomedical, anatomy, and other science, technology, and engineering topics. Preceding publications and future patents are leading indicators for emerging scientific topics. We find the ratio of reviews to original research articles informative for identifying increasing or declining topics, with declining topics having an excess of reviews. We find that language models provide improved insights and predictions into temporal dynamics. In temporal validation, our models substantially outperform the historical baseline. Our findings suggest that similar dynamics apply across other scientific and engineering research topics. We present SciTrends, a user-friendly webtool for predicting scientific topics trends: https://hadasakaufman.shinyapps.io/SciTrend