Jan Trienes

593 total citations · 1 hit paper
6 papers, 250 citations indexed

About

Jan Trienes is a scholar working on Artificial Intelligence, Molecular Biology and General Social Sciences. According to data from OpenAlex, Jan Trienes has authored 6 papers receiving a total of 250 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 1 paper in Molecular Biology and 1 paper in General Social Sciences. Recurrent topics in Jan Trienes's work include Topic Modeling (4 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Natural Language Processing Techniques (2 papers). Jan Trienes is often cited by papers focused on Topic Modeling (4 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Natural Language Processing Techniques (2 papers). Jan Trienes collaborates with scholars based in Germany and Netherlands. Jan Trienes's co-authors include Christin Seifert, Jörg Schlötterer, Meike Nauta, Shreyasi Pathak, Elisa Nguyen, Maurice van Keulen, Dolf Trieschnigg, Hans‐Ulrich Schildhaus, Wei Xu and Kyle Lo and has published in prestigious journals such as ACM Computing Surveys, Future Internet and arXiv (Cornell University).

In The Last Decade

Jan Trienes

5 papers receiving 240 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI

2023 205 citations Meike Nauta, Jan Trienes et al. ACM Computing Surveys profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jan Trienes Germany	4	190	44	23	21	18	6	250
Hanjie Chen United States	6	235 1.2×	39 0.9×	11 0.5×	28 1.3×	13 0.7×	10	347
Bettina Finzel Germany	6	189 1.0×	49 1.1×	27 1.2×	25 1.2×	25 1.4×	14	266
Eoin M. Kenny Ireland	5	222 1.2×	36 0.8×	20 0.9×	33 1.6×	31 1.7×	10	266
Yu-Liang Chou Taiwan	5	157 0.8×	34 0.8×	4 0.2×	18 0.9×	28 1.6×	16	253
Christopher Akiki Germany	4	133 0.7×	25 0.6×	4 0.2×	21 1.0×	8 0.4×	10	201
Imrana Abdullahi Yari Germany	4	250 1.3×	42 1.0×	5 0.2×	27 1.3×	7 0.4×	7	331
Lorenz Kuhn Switzerland	4	88 0.5×	49 1.1×	4 0.2×	7 0.3×	7 0.4×	4	203
Dimitrios P. Panagoulias Greece	9	71 0.4×	39 0.9×	3 0.1×	11 0.5×	10 0.6×	30	190
Daniel Hesslow France	2	139 0.7×	23 0.5×	4 0.2×	24 1.1×	6 0.3×	4	194
Lena Wiese Germany	8	84 0.4×	5 0.1×	12 0.5×	18 0.9×	6 0.3×	40	166

Countries citing papers authored by Jan Trienes

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Jan Trienes

This figure shows the co-authorship network connecting the top 25 collaborators of Jan Trienes. A scholar is included among the top collaborators of Jan Trienes based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jan Trienes. Jan Trienes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Chen, Lily, et al.. (2024). FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence. 8437–8464. 3 indexed citations

2.

Trienes, Jan, Jörg Schlötterer, Christin Seifert, et al.. (2024). InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification. Universitätsbibliographie, Universität Duisburg-Essen. 4263–4294.

3.

Nauta, Meike, Jan Trienes, Shreyasi Pathak, et al.. (2023). From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ACM Computing Surveys. 55(13s). 1–42. 205 indexed citations breakdown →

4.

Trienes, Jan, Jörg Schlötterer, Hans‐Ulrich Schildhaus, & Christin Seifert. (2022). Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset. 19–27. 7 indexed citations

5.

Nauta, Meike, Jan Trienes, Shreyasi Pathak, et al.. (2022). From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. arXiv (Cornell University). 14 indexed citations

6.

Trienes, Jan, et al.. (2021). Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records. Future Internet. 13(5). 136–136. 21 indexed citations

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.

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