Dan Riesel

414 citations
4 papers · 106 · h-index 3

Impact in

Papers in

    • Influenza Virus Research Studies 2
    • Respiratory viral infections research 2
    • Pneumonia and Respiratory Infections 1
    • COVID-19 Clinical Research Studies 1

Dan Riesel

3 papers receiving 101 citations

Peers

Dan Riesel
Comparison fields: 5 of 47
  • Modeling and Simulation 21
  • Health Informatics 5
  • Infectious Diseases 38
  • Radiology, Nuclear Medicine and Imaging 30
  • Epidemiology 39
Replace Valentin Goutaudier with:
Valentin Goutaudier France
Dakshitha Wickramasinghe Sri Lanka
Ayesha Ahmed India
Leonardo Clemente United States
Sairam Bade United States
Sharon Nirenberg United States
Emma Hannay Switzerland
Thinh Viet Nguyen Vietnam
Sarah Denny United Kingdom
N. A. Lipunova United Kingdom
Dan Riesel relative to Valentin Goutaudier France Valentin Goutaudier's profile →
Citations per field
00.5×4.3×
Valentin Goutaudier · 1×
Citations per year

Countries citing papers authored by Dan Riesel

Since Specialization
Citations

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

Fields of papers citing papers by Dan Riesel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

4 of 4 papers shown
#Work
1 202077
2 201919
3 201910
4 20240

About Dan Riesel

Dan Riesel is a scholar working on Epidemiology, Infectious Diseases, Endocrine and Autonomic Systems, Pulmonary and Respiratory Medicine and Oncology, having authored 4 papers that have together received 106 indexed citations. Recurring topics across this work include Influenza Virus Research Studies (2 papers), Respiratory viral infections research (2 papers), COVID-19 diagnosis using AI (1 paper), Neuroscience of respiration and sleep (1 paper), COVID-19 Clinical Research Studies (1 paper), Pancreatic and Hepatic Oncology Research (1 paper), Machine Learning in Healthcare (1 paper) and Pneumonia and Respiratory Infections (1 paper). The work is most often cited by research in Modeling and Simulation (21 citations), Health Informatics (5 citations), Infectious Diseases (38 citations), Radiology, Nuclear Medicine and Imaging (30 citations) and Epidemiology (39 citations). Dan Riesel has collaborated with scholars based in Israel, United States and Australia. Frequent co-authors include Uri Shalit, Doron Netzer, Eitan Bachmat, Ran D. Balicer, Noam Barda, Guy N. Rothblum, Noa Dagan, Gal Yona, Joseph Levy and Jonathan Somer. Their work appears in journals such as The Journal of Infectious Diseases, Clinical Infectious Diseases, Digestive Diseases and Sciences and Nature Communications.

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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