Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?
2023105 citationsHarrisen Scells, Bevan Koopman et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Bevan Koopman'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 Bevan Koopman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bevan Koopman more than expected).
This network shows the impact of papers produced by Bevan Koopman. 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 Bevan Koopman. The network helps show where Bevan Koopman may publish in the future.
Co-authorship network of co-authors of Bevan Koopman
This figure shows the co-authorship network connecting the top 25 collaborators of Bevan Koopman.
A scholar is included among the top collaborators of Bevan Koopman based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Bevan Koopman. Bevan Koopman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zuccon, Guido, et al.. (2019). Health Cards to Assist Decision Making in Consumer Health Search.. PubMed Central. 2019. 1091–1100.2 indexed citations
8.
Nguyen, Anthony, Madonna Kemp, Bevan Koopman, et al.. (2018). Computer-Assisted Diagnostic Coding: Effectiveness of an NLP-based approach using SNOMED CT to ICD-10 mappings.. Europe PMC (PubMed Central). 2018. 807–816.28 indexed citations
9.
Scells, Harrisen, et al.. (2017). QUT ielab at CLEF eHealth 2017 Technology Assisted Reviews Track: Initial experiments with learning to rank. QUT ePrints (Queensland University of Technology).3 indexed citations
10.
Zuccon, Guido, et al.. (2017). QUT ielab at CLEF 2017 e-Health IR Task: Knowledge Base Retrieval for Consumer Health Search.. QUT ePrints (Queensland University of Technology). 1866.2 indexed citations
11.
Koopman, Bevan, Guido Zuccon, Peter Bruza, Laurianne Sitbon, & Michael Lawley. (2016). Information retrieval as semantic inference: A graph inference model applied to medical search. QUT ePrints (Queensland University of Technology).1 indexed citations
12.
Zuccon, Guido, Bevan Koopman, & João Palotti. (2015). Diagnose this if you can: On the effectiveness of search engines in finding medical self-diagnosis information. QUT ePrints (Queensland University of Technology).4 indexed citations
13.
Zuccon, Guido, Bevan Koopman, Peter Bruza, & Leif Azzopardi. (2015). Integrating and evaluating neural word embeddings in information retrieval. QUT ePrints (Queensland University of Technology).1 indexed citations
14.
Koopman, Bevan & Guido Zuccon. (2014). Relevation! : an open source system for information retrieval relevance assessment. QUT ePrints (Queensland University of Technology).3 indexed citations
15.
Zuccon, Guido, Alexander Holloway, Bevan Koopman, & Anthony Nguyen. (2013). Identify disorders in health records using Conditional Random Fields and Metamap: AEHRC at ShARe/CLEF 2013 eHealth Evaluation Lab Task 1. QUT ePrints (Queensland University of Technology).6 indexed citations
16.
Koopman, Bevan, et al.. (2012). Exploiting SNOMED CT Concepts & Relationships for Clinical Information Retrieval: Australian e-Health Research Centre and Queensland University of Technology at the TREC 2012 Medical Track. Text REtrieval Conference.7 indexed citations
17.
Symonds, Michael, Guido Zuccon, Bevan Koopman, Peter Bruza, & Anthony Nguyen. (2012). Semantic Judgement of Medical Concepts: Combining Syntagmatic and Paradigmatic Information with the Tensor Encoding Model. QUT ePrints (Queensland University of Technology). 15–22.6 indexed citations
18.
Symonds, Michael, Guido Zuccon, Bevan Koopman, & Peter Bruza. (2012). QUT Para at TREC 2012 Web Track: Word Associations for Retrieving Web Documents. QUT ePrints (Queensland University of Technology).2 indexed citations
19.
Koopman, Bevan, Peter Bruza, Laurianne Sitbon, & Michael Lawley. (2011). AEHRC & QUT at TREC 2011 Medical Track: a concept-based information retrieval approach. Text REtrieval Conference.9 indexed citations
20.
Koopman, Bevan, Peter Bruza, Laurianne Sitbon, & Michael Lawley. (2010). Analysis of the effect of negation on information retrieval of medical data. QUT ePrints (Queensland University of Technology).9 indexed citations
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.