Pascal Chappert

1.7k citations
31 papers · 1.1k · h-index 17

Impact in

  • Immunology top 5%
    • Immune Cell Function and Interaction
    • Immunotherapy and Immune Responses
    • T-cell and B-cell Immunology
    • SARS-CoV-2 and COVID-19 Research
    • COVID-19 Clinical Research Studies

Papers in

    • T-cell and B-cell Immunology 15
    • Immunotherapy and Immune Responses 12
    • Immune Cell Function and Interaction 12
    • SARS-CoV-2 and COVID-19 Research 7
    • COVID-19 Clinical Research Studies 3

Pascal Chappert

30 papers receiving 1.1k citations

Peers

Pascal Chappert
Comparison fields: 5 of 82
  • Immunology 514
  • Infectious Diseases 446
  • Modeling and Simulation 32
  • Biological Psychiatry 16
  • Virology 27
Replace Aisha Souquette with:
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Citations per field
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Citations per year

Countries citing papers authored by Pascal Chappert

Since Specialization
Citations

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

Fields of papers citing papers by Pascal Chappert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021249
2 2013185
3 2021107
4 201098
5 202159
6 202242
7 201338
8 202035
9 202233
10 201328
11 202227
12 200924
13 202323
14 201823
15 201921
16 202320
17 201418
18 202116
19 202214
20 201613

About Pascal Chappert

Pascal Chappert is a scholar working on Immunology, Infectious Diseases, Genetics, Oncology and Epidemiology, having authored 31 papers that have together received 1.1k indexed citations. Recurring topics across this work include T-cell and B-cell Immunology (15 papers), Immunotherapy and Immune Responses (12 papers), Immune Cell Function and Interaction (12 papers), SARS-CoV-2 and COVID-19 Research (7 papers), Virus-based gene therapy research (5 papers), COVID-19 Clinical Research Studies (3 papers), CAR-T cell therapy research (3 papers) and Diabetes and associated disorders (2 papers). The work is most often cited by research in Immunology (514 citations), Infectious Diseases (446 citations), Modeling and Simulation (32 citations), Biological Psychiatry (16 citations) and Virology (27 citations). Pascal Chappert has collaborated with scholars based in France, United States and United Kingdom. Frequent co-authors include Ronald H. Schwartz, Jean M. Davoust, David‐Alexandre Gross, David S. Weiss, Rajesh Ravindran, Helder Imoto Nakaya, David Ron, Bali Pulendran, Youngja H. Park and Nooruddin Khan. Their work appears in journals such as Immunity, Cell, Science Translational Medicine, Molecular Therapy and JCI Insight.

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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