Ian H. Jarman

1.1k total citations
48 papers, 765 citations indexed

About

Ian H. Jarman is a scholar working on Artificial Intelligence, Molecular Biology and General Health Professions. According to data from OpenAlex, Ian H. Jarman has authored 48 papers receiving a total of 765 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 8 papers in Molecular Biology and 6 papers in General Health Professions. Recurrent topics in Ian H. Jarman's work include Advanced Clustering Algorithms Research (5 papers), Gene expression and cancer classification (4 papers) and Sports Performance and Training (3 papers). Ian H. Jarman is often cited by papers focused on Advanced Clustering Algorithms Research (5 papers), Gene expression and cancer classification (4 papers) and Sports Performance and Training (3 papers). Ian H. Jarman collaborates with scholars based in United Kingdom, Spain and Italy. Ian H. Jarman's co-authors include Paulo Lisböa, Terence A. Etchells, Naomi Datson, Matthew Weston, Barry Drust, Warren Gregson, José D. Martín‐Guerrero, Mark A Bellis, Richard Lowsby and Patrick Née and has published in prestigious journals such as PLoS ONE, The British Journal of Psychiatry and BMC Bioinformatics.

In The Last Decade

Ian H. Jarman

44 papers receiving 736 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ian H. Jarman United Kingdom	15	144	108	93	79	71	48	765
Willy Chou Taiwan	22	27 0.2×	92 0.9×	169 1.8×	132 1.7×	96 1.4×	144	1.6k
Vance W. Berger United States	19	40 0.3×	46 0.4×	77 0.8×	42 0.5×	76 1.1×	76	1.4k
Peter H. Millard United Kingdom	22	69 0.5×	78 0.7×	115 1.2×	71 0.9×	243 3.4×	55	1.5k
Fabrizio Pecoraro Italy	12	84 0.6×	37 0.3×	36 0.4×	26 0.3×	89 1.3×	65	544
Himel Mondal India	18	25 0.2×	307 2.8×	46 0.5×	25 0.3×	137 1.9×	197	1.4k
David Moriña Spain	16	24 0.2×	33 0.3×	87 0.9×	39 0.5×	151 2.1×	61	657
Nickolaj Risbo Kristensen Denmark	9	33 0.2×	64 0.6×	119 1.3×	30 0.4×	61 0.9×	14	838
Steven C. Bagley United States	15	19 0.1×	151 1.4×	143 1.5×	212 2.7×	127 1.8×	21	1.5k
Bryant T. Karras United States	13	203 1.4×	117 1.1×	189 2.0×	228 2.9×	314 4.4×	41	1.3k
William F. Rosenberger United States	37	17 0.1×	222 2.1×	92 1.0×	201 2.5×	58 0.8×	124	4.2k

Countries citing papers authored by Ian H. Jarman

Since Specialization

Citations

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

Fields of papers citing papers by Ian H. Jarman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ian H. Jarman. 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 Ian H. Jarman. The network helps show where Ian H. Jarman may publish in the future.

Co-authorship network of co-authors of Ian H. Jarman

This figure shows the co-authorship network connecting the top 25 collaborators of Ian H. Jarman. A scholar is included among the top collaborators of Ian H. Jarman 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 Ian H. Jarman. Ian H. Jarman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Fernández-Navarro, Javier, et al.. (2024). Finding repeatable progressive pass clusters and application in international football. Liverpool John Moores University. 9(4). 289–303. 2 indexed citations

Taylor, Mark, et al.. (2024). A chaos theory view of accidental dwelling fire injuries. Fire and Materials. 48(7). 715–724.

Jones, Michael, Jens N. Lallensack, Ian H. Jarman, Peter Falkingham, & Ivo Siekmann. (2024). Classification of dinosaur footprints using machine learning. Journal of Vertebrate Paleontology. 44(6).

Fernández-Navarro, Javier, et al.. (2024). Creating an augmented possession framework to evaluate phases of play and application in international football. Liverpool John Moores University. 11.

Llanera, Daniel K, Paulo Lisböa, Ian H. Jarman, et al.. (2022). Clinical Characteristics of COVID-19 Patients in a Regional Population With Diabetes Mellitus: The ACCREDIT Study. Frontiers in Endocrinology. 12. 777130–777130. 5 indexed citations

Llanera, Daniel K, Paulo Lisböa, Ian H. Jarman, et al.. (2021). Clinical Characteristics of COVID-19 Patients in a Regional Population with Diabetes Mellitus: The ACCREDIT Study. SSRN Electronic Journal. 1 indexed citations

Jarman, Ian H., et al.. (2020). Music genre profiling based on Fisher manifolds and Probabilistic Quantum Clustering. Neural Computing and Applications. 33(13). 7521–7539. 2 indexed citations

Carter, Bernie, et al.. (2018). Parent’s experiences of their child’s withdrawal syndrome: a driver for reciprocal nurse-parent partnership in withdrawal assessment. Intensive and Critical Care Nursing. 50. 71–78. 8 indexed citations

Martín‐Guerrero, José D., et al.. (2016). Performance assessment of quantum clustering in non-spherical data distributions.. The European Symposium on Artificial Neural Networks. 2 indexed citations

10.

Kinderman, Peter, Sara Tai, Eleanor Pontin, et al.. (2015). Causal and mediating factors for anxiety, depression and well-being. The British Journal of Psychiatry. 206(6). 456–460. 70 indexed citations

11.

Lowsby, Richard, et al.. (2014). Lymphopenia as a predictor of bacteremia in the emergency department. Critical Care. 18(S1).

12.

Burniston, Jatin G., Eleonora Guadagnin, Ian H. Jarman, et al.. (2014). Conditional independence mapping of DIGE data reveals PDIA3 protein species as key nodes associated with muscle aerobic capacity. Journal of Proteomics. 106. 230–245. 29 indexed citations

13.

Ortega‐Martorell, Sandra, et al.. (2012). Constructing similarity networks using the Fisher information metric.. The European Symposium on Artificial Neural Networks. 2 indexed citations

14.

Jarman, Ian H., et al.. (2011). The role of Fisher information in primary data space for neighbourhood mapping. The European Symposium on Artificial Neural Networks. 5 indexed citations

15.

Cook, Penny A., et al.. (2011). Area effects on health inequalities: The impact of neighbouring deprivation on mortality. Health & Place. 17(6). 1266–1273. 17 indexed citations

16.

Bellis, Mark A, Karen Hughes, Zara Quigg, et al.. (2010). Cross-sectional measures and modelled estimates of blood alcohol levels in UK nightlife and their relationships with drinking behaviours and observed signs of inebriation. Substance Abuse Treatment Prevention and Policy. 5(1). 5–5. 38 indexed citations

17.

Lisböa, Paulo, Terence A. Etchells, Ian H. Jarman, et al.. (2009). Partial Logistic Artificial Neural Network for Competing Risks Regularized With Automatic Relevance Determination. IEEE Transactions on Neural Networks. 20(9). 1403–1416. 31 indexed citations

18.

Rögnvaldsson, Thorsteinn, et al.. (2009). How to find simple and accurate rules for viral protease cleavage specificities. BMC Bioinformatics. 10(1). 149–149. 27 indexed citations

19.

Jarman, Ian H., Terence A. Etchells, José D. Martín‐Guerrero, & Paulo Lisböa. (2008). An integrated framework for risk profiling of breast cancer patients following surgery. Artificial Intelligence in Medicine. 42(3). 165–188. 11 indexed citations

20.

Etchells, Terence A., Ian H. Jarman, Min Hane Aung, et al.. (2007). Development of a Rule Based Prognostic Tool for HER 2 Positive Breast Cancer Patients. Conference proceedings. 2007. 5416–5419. 2 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.

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