Daniela Kugelmann

31 papers receiving 693 citations

Peers

Daniela Kugelmann
Comparison fields: 5 of 110
  • General Dentistry 25
  • Human-Computer Interaction 62
  • Genetics 87
  • Pathology and Forensic Medicine 135
  • Computer Vision and Pattern Recognition 153
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Daniel T. Nagasawa United States
Szabolcs Felszeghy Hungary
Timothy T. Bui United States
Zhongying Liu China
Kei Kato Japan
Sohee Jeon South Korea
Natalie E. Barnette United States
Atsuhiro Kojima Japan
Masayuki Hashimoto Japan
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Citations per field
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Citations per year

Countries citing papers authored by Daniela Kugelmann

Since Specialization
Citations

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

Fields of papers citing papers by Daniela Kugelmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniela Kugelmann, 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 Daniela Kugelmann Line = papers co-authored together Daniela Kugelmann links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019122
2 201793
3 201752
4 202050
5 201848
6 201745
7 201440
8 201431
9 201926
10 201926
11 201925
12 202323
13 201520
14 202015
15 202214
16 201914
17 202211
18 202110
19 20239
20 20248

About Daniela Kugelmann

Daniela Kugelmann is a scholar working on Genetics, Pathology and Forensic Medicine, Molecular Biology, Biomedical Engineering and Cell Biology, having authored 32 papers that have together received 715 indexed citations. Recurring topics across this work include Autoimmune Bullous Skin Diseases (8 papers), Coagulation, Bradykinin, Polyphosphates, and Angioedema (8 papers), Anatomy and Medical Technology (6 papers), Cellular Mechanics and Interactions (5 papers), Barrier Structure and Function Studies (4 papers), Wnt/β-catenin signaling in development and cancer (4 papers), Augmented Reality Applications (4 papers) and Surgical Simulation and Training (3 papers). The work is most often cited by research in General Dentistry (25 citations), Human-Computer Interaction (62 citations), Genetics (87 citations), Pathology and Forensic Medicine (135 citations) and Computer Vision and Pattern Recognition (153 citations). Daniela Kugelmann has collaborated with scholars based in Germany, Switzerland and Japan. Frequent co-authors include Jens Waschke, Felix Bork, Nassir Navab, Mariya Y. Radeva, Ulrich Eck, Franziska Vielmuth, Volker Spindler, Desalegn Tadesse Egu, Elias Walter and Nicolas Schlegel. Their work appears in journals such as Frontiers in Immunology, Anatomical Sciences Education, Cellular and Molecular Life Sciences, PLoS ONE and Annals of Anatomy - Anatomischer Anzeiger.

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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