Liam Abbott

2.8k citations
3 papers · 37 · h-index 3

Impact in

    • Genetic Associations and Epidemiology
    • Genetic Mapping and Diversity in Plants and Animals
    • Genetic and phenotypic traits in livestock
    • Evolution and Genetic Dynamics
    • Tuberculosis Research and Epidemiology

Papers in

    • Genetic Associations and Epidemiology 2
    • Genetic Mapping and Diversity in Plants and Animals 2
    • Genetic and phenotypic traits in livestock 2
    • Tuberculosis Research and Epidemiology 1

Liam Abbott

3 papers receiving 37 citations

Peers

Liam Abbott
Comparison fields: 5 of 27
  • Genetics 20
  • Infectious Diseases 5
  • Rheumatology 4
  • Statistics and Probability 2
  • Experimental and Cognitive Psychology 3
Replace Nikolas Baya with:
Nikolas Baya United Kingdom
Svetlana Turcan Moldova
Ingrid Salvary United Kingdom
Onur Özer Germany
Mattia Cordioli Finland
Hiroya Nishida Australia
И.Ф. Стеценко Russia
Ronaldo da Silva Francisco Brazil
Ranran Zhai China
Jiwoo Lee United States
Liam Abbott relative to Nikolas Baya United Kingdom Nikolas Baya's profile →
Citations per field
00.5×1.5×
Nikolas Baya · 1×
Citations per year

Countries citing papers authored by Liam Abbott

Since Specialization
Citations

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

Fields of papers citing papers by Liam Abbott

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

About Liam Abbott

Liam Abbott is a scholar working on Genetics, Infectious Diseases, Molecular Biology, Epidemiology and Organic Chemistry, having authored 3 papers that have together received 37 indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (2 papers), Genetic Mapping and Diversity in Plants and Animals (2 papers), Genetic and phenotypic traits in livestock (2 papers), Bioinformatics and Genomic Networks (1 paper), Tuberculosis Research and Epidemiology (1 paper) and Mycobacterium research and diagnosis (1 paper). The work is most often cited by research in Genetics (20 citations), Infectious Diseases (5 citations), Rheumatology (4 citations), Statistics and Probability (2 citations) and Experimental and Cognitive Psychology (3 citations). Liam Abbott has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Wei Zhou, Nikolas Baya, Cotton Seed, Emilie M. Wigdor, Daniel King, Alex Bloemendal, Benjamin M. Neale, Masahiro Kanai, Claire Churchhouse and Duncan S. Palmer. Their work appears in journals such as Science, Nature Human Behaviour and PLoS Computational Biology.

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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