Daniel Kayser

804 citations
60 papers · 449 · h-index 11

Impact in

Papers in

Daniel Kayser

52 papers receiving 421 citations

Peers

Daniel Kayser
Comparison fields: 5 of 106
  • Transplantation 131
  • Nephrology 38
  • Computer Vision and Pattern Recognition 57
  • Media Technology 21
  • Surgery 88
Replace Mohamed Shehata with:
Mohamed Shehata Egypt
Charnchai Pluempitiwiriyawej Thailand
B. Cohen Netherlands
Young‐Taek Oh South Korea
Louis Thibault Canada
Warren Chan United States
Chang Ho Yoon South Korea
Yuzhou Zhang China
Joshua Levy United States
Chelvin C. A. Sng Singapore
Daniel Kayser relative to Mohamed Shehata Egypt Mohamed Shehata's profile →
Citations per field
00.5×10×12.7×
Mohamed Shehata · 1×
Citations per year

Countries citing papers authored by Daniel Kayser

Since Specialization
Citations

This map shows the geographic impact of Daniel Kayser'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 Daniel Kayser with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Kayser more than expected).

Fields of papers citing papers by Daniel Kayser

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Kayser. 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 Daniel Kayser. The network helps show where Daniel Kayser may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Kayser, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Kayser Line = papers co-authored together Daniel Kayser links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 60 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201074
2 200956
3 201032
4 200630
5 200424
6 201020
7 200413
8 201112
9 198712
10 199911
11 201710
12 20029
13 19558
14 20098
15 20088
16 20058
17 19918
18 19887
19 20007
20
Fish, flesh and a good red herring: a case of ascending upper limb infection in a renal transplant patient.
20096

About Daniel Kayser

Daniel Kayser is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Mechanical Engineering, Computational Mechanics and Philosophy, having authored 60 papers that have together received 449 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (8 papers), Natural Language Processing Techniques (6 papers), Advanced Measurement and Metrology Techniques (6 papers), Optical measurement and interference techniques (6 papers), Logic, Reasoning, and Knowledge (6 papers), Surface Roughness and Optical Measurements (5 papers), Linguistics and Discourse Analysis (5 papers) and Renal Transplantation Outcomes and Treatments (4 papers). The work is most often cited by research in Transplantation (131 citations), Nephrology (38 citations), Computer Vision and Pattern Recognition (57 citations), Media Technology (21 citations) and Surgery (88 citations). Daniel Kayser has collaborated with scholars based in Germany, France and United States. Frequent co-authors include B. Sis, Gunilla Einecke, Philip F. Halloran, Michael Mengel, Konrad S. Famulski, Wolfgang Osten, J. Reeve, Wilfried Gwinner, Jessica Chang and D.G. de Freitas. Their work appears in journals such as American Journal of Transplantation, Logica Universalis, Journal of Lipid Research, Nephrology Dialysis Transplantation and Journal of the American Society of Nephrology.

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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