Daniel Barth-Jones

1.1k citations
25 papers · 777 · h-index 13

Impact in

  • Virology top 5%
    • HIV Research and Treatment
    • Antifungal resistance and susceptibility
    • HIV/AIDS Research and Interventions

Papers in

Daniel Barth-Jones

24 papers receiving 738 citations

Peers

Daniel Barth-Jones
Comparison fields: 5 of 106
  • Virology 96
  • Infectious Diseases 302
  • Computational Theory and Mathematics 173
  • Modeling and Simulation 29
  • Epidemiology 182
Replace David Craft with:
David Craft United States
Jennifer M. Anderson United States
Lawrence R. Crane United States
Chris Sherlock United Kingdom
Dragan Janković Serbia
Steve Bennett United Kingdom
Sandeep Juneja India
Mark Boyd Australia
Ioana Bica United States
Rong Zhao China
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Citations per field
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Citations per year

Countries citing papers authored by Daniel Barth-Jones

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Barth-Jones

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1997184
2 1993108
3 199281
4 200778
5 200656
6 200748
7 201238
8 199730
9 200419
10 199818
11 200317
12 200614
13 200514
14 200411
15 200711
16 200510
17 20018
18 20057
19 20046
20 20005

About Daniel Barth-Jones

Daniel Barth-Jones is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Infectious Diseases, Computer Networks and Communications and Virology, having authored 25 papers that have together received 777 indexed citations. Recurring topics across this work include Formal Methods in Verification (6 papers), Petri Nets in System Modeling (5 papers), Distributed systems and fault tolerance (5 papers), Privacy-Preserving Technologies in Data (5 papers), HIV/AIDS Research and Interventions (4 papers), HIV Research and Treatment (4 papers), Data Quality and Management (3 papers) and Machine Learning and Algorithms (3 papers). The work is most often cited by research in Virology (96 citations), Infectious Diseases (302 citations), Computational Theory and Mathematics (173 citations), Modeling and Simulation (29 citations) and Epidemiology (182 citations). Daniel Barth-Jones has collaborated with scholars based in United States, China and Egypt. Frequent co-authors include Hao Ying, Lawrence R. Crane, Rodger D. MacArthur, Jonathan Cohn, Marcus Zervos, José A. Vázquez, Verónica Sánchez, Jack D. Sobel, James S. Koopman and Andrew L. Adams. Their work appears in journals such as Journal of Clinical Microbiology, Information Sciences, IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), Statistics in Medicine and American Journal of Roentgenology.

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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