T. Gal
Impact in
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- Parallel Computing and Optimization Techniques
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- stochastic dynamics and bifurcation
Papers in
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- Advanced Data Storage Technologies 3
- Distributed and Parallel Computing Systems 2
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- Particle physics theoretical and experimental studies 2
- Particle Detector Development and Performance 2
- Co-authors
- George Datseris (1 shared paper)Jerry Ling (4 shared papers)P. Gras (2 shared papers)O. Schulz (2 shared papers)G. A. Stewart (2 shared papers)Pere Mato (2 shared papers)Benedikt Hegner (2 shared papers)L. Heinrich (1 shared paper)
- Journals
- The Journal of Open Source Software (2 papers)arXiv (Cornell University) (1 paper)Open Research Europe (2 papers)EPJ Web of Conferences (2 papers)
- Partner nations
- GermanyUnited StatesSwitzerland
In The Last Decade
T. Gal
5 papers receiving 38 citations
Peers
Comparison fields: 5 of 32
- Hardware and Architecture 5
- Statistical and Nonlinear Physics 8
- Nuclear and High Energy Physics 6
- Computer Networks and Communications 11
- Cognitive Neuroscience 8
Countries citing papers authored by T. Gal
This map shows the geographic impact of T. Gal's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by T. Gal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Gal more than expected).
Fields of papers citing papers by T. Gal
This network shows the impact of papers produced by T. Gal. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by T. Gal. The network helps show where T. Gal may publish in the future.
Co-authors
The 23 scholars most cited alongside T. Gal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 25 | |
| 2 | 2023 | 8 | |
| 3 | 2022 | 4 | |
| 4 | 2025 | 1 | |
| 5 | 2023 | 1 | |
| 6 | 2023 | 0 | |
| 7 | 2024 | 0 |
About T. Gal
T. Gal is a scholar working on Computer Networks and Communications, Nuclear and High Energy Physics, Hardware and Architecture, Information Systems and Information Systems and Management, having authored 7 papers that have together received 39 indexed citations. Recurring topics across this work include Advanced Data Storage Technologies (3 papers), Distributed and Parallel Computing Systems (2 papers), Particle physics theoretical and experimental studies (2 papers), Particle Detector Development and Performance (2 papers), Parallel Computing and Optimization Techniques (2 papers), Research Data Management Practices (1 paper), Scientific Computing and Data Management (1 paper) and Particle Accelerators and Free-Electron Lasers (1 paper). The work is most often cited by research in Hardware and Architecture (5 citations), Statistical and Nonlinear Physics (8 citations), Nuclear and High Energy Physics (6 citations), Computer Networks and Communications (11 citations) and Cognitive Neuroscience (8 citations). T. Gal has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include George Datseris, Jerry Ling, P. Gras, O. Schulz, G. A. Stewart, Pere Mato, Benedikt Hegner, L. Heinrich, Mark Kettenis and D. Kresan. Their work appears in journals such as The Journal of Open Source Software, arXiv (Cornell University), Open Research Europe and EPJ Web of Conferences.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.