Jan Trienes
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- Explainable Artificial Intelligence (XAI)
- Machine Learning in Healthcare
- Topic Modeling
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
Papers in
-
- Topic Modeling 4
- Natural Language Processing Techniques 2
- Explainable Artificial Intelligence (XAI) 2
- Text Readability and Simplification 2
- Privacy-Preserving Technologies in Data 1
- Machine Learning and Data Classification 1
- Adversarial Robustness in Machine Learning 1
- Co-authors
- Christin Seifert (5 shared papers)Jörg Schlötterer (4 shared papers)Maurice van Keulen (2 shared papers)Meike Nauta (2 shared papers)Shreyasi Pathak (2 shared papers)Elisa Nguyen (2 shared papers)Dolf Trieschnigg (1 shared paper)Hans‐Ulrich Schildhaus (1 shared paper)
- Journals
- Future Internet (1 paper)ACM Computing Surveys (1 paper)Universitätsbibliographie, Universität Duisburg-Essen (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- GermanyNetherlands
In The Last Decade
Jan Trienes
5 papers receiving 240 citations
Jan Trienes's Hit Papers
Peers
Comparison fields: 5 of 73
- Health Informatics 44
- Artificial Intelligence 190
- Information Systems and Management 23
- Health Information Management 14
- Safety Research 18
Countries citing papers authored by Jan Trienes
This map shows the geographic impact of Jan Trienes'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 Jan Trienes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Trienes more than expected).
Fields of papers citing papers by Jan Trienes
This network shows the impact of papers produced by Jan Trienes. 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 Jan Trienes. The network helps show where Jan Trienes may publish in the future.
Co-authors
The 13 scholars most cited alongside Jan Trienes, 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 | From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Hit paper breakdown → | 2023 | 205 |
| 2 | 2021 | 21 | |
| 3 | 2022 | 14 | |
| 4 | 2022 | 7 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 0 |
About Jan Trienes
Jan Trienes is a scholar working on Artificial Intelligence, Molecular Biology, General Social Sciences, Infectious Diseases and Organic Chemistry, having authored 6 papers that have together received 250 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Natural Language Processing Techniques (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Text Readability and Simplification (2 papers), Privacy-Preserving Technologies in Data (1 paper), Machine Learning and Data Classification (1 paper), Computational and Text Analysis Methods (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Health Informatics (44 citations), Artificial Intelligence (190 citations), Information Systems and Management (23 citations), Health Information Management (14 citations) and Safety Research (18 citations). Jan Trienes has collaborated with scholars based in Germany and Netherlands. Frequent co-authors include Christin Seifert, Jörg Schlötterer, Maurice van Keulen, Meike Nauta, Shreyasi Pathak, Elisa Nguyen, Dolf Trieschnigg, Hans‐Ulrich Schildhaus, Junyi Jessy Li and Byron Wallace. Their work appears in journals such as Future Internet, ACM Computing Surveys, Universitätsbibliographie, Universität Duisburg-Essen and arXiv (Cornell University).
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.