E. Seibold

464 citations
14 papers · 344 · h-index 9

Impact in

Papers in

    • Bacillus and Francisella bacterial research 7
    • Machine Learning in Bioinformatics 1
    • Yersinia bacterium, plague, ectoparasites research 6

E. Seibold

13 papers receiving 327 citations

Peers

E. Seibold
Comparison fields: 5 of 63
  • Virology 77
  • Clinical Biochemistry 84
  • Genetics 187
  • Microbiology 35
  • Health Informatics 6
Replace Aurélie Hennebique with:
Aurélie Hennebique France
W. Splettstößer Germany
Lennart Berglund Sweden
Aditya Kumar Lankapalli Germany
LU Zhong-xin China
Janika Möller Germany
Saibal K. Poddar United States
Fabien Dorange France
Kimberly E. Walker United States
Belinda B. Oude Essink Netherlands
E. Seibold relative to Aurélie Hennebique France Aurélie Hennebique's profile →
Citations per field
00.5×1.5×2.5×
Aurélie Hennebique · 1×
Citations per year

Countries citing papers authored by E. Seibold

Since Specialization
Citations

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

Fields of papers citing papers by E. Seibold

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

14 of 14 papers shown
#Work
1 201098
2 200852
3 200847
4 200733
5 200732
6 200726
7 201016
8 200015
9 20099
10 20248
11 20245
12 20232
13 20241
14 20250

About E. Seibold

E. Seibold is a scholar working on Molecular Biology, Genetics, Virology, Anesthesiology and Pain Medicine and Surgery, having authored 14 papers that have together received 344 indexed citations. Recurring topics across this work include Bacillus and Francisella bacterial research (7 papers), Yersinia bacterium, plague, ectoparasites research (6 papers), Poxvirus research and outbreaks (5 papers), Cardiac, Anesthesia and Surgical Outcomes (2 papers), Anesthesia and Sedative Agents (2 papers), Machine Learning in Bioinformatics (1 paper), COVID-19 diagnosis using AI (1 paper) and Artificial Intelligence in Healthcare and Education (1 paper). The work is most often cited by research in Virology (77 citations), Clinical Biochemistry (84 citations), Genetics (187 citations), Microbiology (35 citations) and Health Informatics (6 citations). E. Seibold has collaborated with scholars based in Germany, United States and France. Frequent co-authors include Wolf D. Splettstoesser, Markus Kostrzewa, T. Maier, E.-J. Finke, Roland Grunow, Philippe Thullier, Kerstin Mätz‐Rensing, Herbert Tomaso, Heinrich Neubauer and Meliha Meriç Koç. Their work appears in journals such as British Journal of Anaesthesia, Journal of Clinical Microbiology, Epidemiology and Infection, Veterinary Pathology and Anesthesiology.

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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