Mathematics, Computing, and Information Processing
Mathematics, Computing, and Information Processing is a research topic within Computational Theory and Mathematics. Science Explorer counts 10k research works in it since 1950. 7.4% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the development and improvement of information retrieval systems specifically tailored for mathematical content. It includes topics such as semantic representation of mathematical expressions, search interfaces for mathematicians, retrieval of scientific documents with mathematical content, and math question answering systems.
- Mathematical Information Retrieval
- Math Search
- Math Formulae
- Semantic Representation
- Digital Libraries
- Math Knowledge
- Mathematical Expressions
- Information Retrieval System
- Textual Context
- Math Question Answering
- Research works
- 10k fractional, since 1950
- In the world top 10%
- 736 per year above
- Top-10% rate
- 7.4% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- -22% the tick is no change
Which countries lead Mathematics, Computing, and Information Processing research?
By volume, the United States and Germany publish the most (235 and 106 works in 2022–2025).
By volume, 2022–2025
- 1 United States 235 works
- 2 Germany 106 works
- 3 China 85 works
- 4 India 67 works
- 5 France 62 works
- 6 United Kingdom 51 works
- 7 Russia 45 works
- 8 Canada 32 works
- 9 Japan 29 works
- 10 Italy 29 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Mathematics, Computing, and Information Processing research?
By volume in 2022–2025, Weatherford College publishes the most Mathematics, Computing, and Information Processing research, followed by University of Vienna and University of Helsinki.
By volume, 2022–2025
- 1 Weatherford College United States 8 works
- 2 University of Vienna Austria 6 works
- 3 University of Helsinki Finland 5 works
- 4 Wirtschaftspsychologische Gesellschaft Germany 5 works
- 5 University of Manchester United Kingdom 4 works
- 6 Central China Normal University China 4 works
- 7 FIZ Karlsruhe – Leibniz Institute for Information Infrastructure Germany 4 works
- 8 Tsinghua University China 4 works
- 9 Hebei University China 4 works
- 10 Centre National de la Recherche Scientifique France 4 works
Who are the leading researchers in Mathematics, Computing, and Information Processing?
The most-cited researchers publishing on Mathematics, Computing, and Information Processing include Donald E. Knuth.
- 1 Donald E. Knuth 4k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Mathematics, Computing, and Information Processing research done?
The largest centres of Mathematics, Computing, and Information Processing research in 2022–2025 are Moscow (Russia), Beijing (China), Paris (France) and London (United Kingdom).
Where is the best place to study Mathematics, Computing, and Information Processing?
Among universities, judged by research, Weatherford College score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Weatherford College United States | 0.0 | 1.3% | 5.8× | 8 | -95.8% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Mathematics, Computing, and Information Processing research growing?
Output in 2018–2022 was 22% lower than in 2013–2017, peaking in 2025. The fastest-growing topics are Mathematics, Computing, and Information Processing.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.