Tim Pearce

2.9k total citations
60 papers, 1.8k citations indexed

About

Tim Pearce is a scholar working on Biomedical Engineering, Sensory Systems and Cellular and Molecular Neuroscience. According to data from OpenAlex, Tim Pearce has authored 60 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Biomedical Engineering, 28 papers in Sensory Systems and 25 papers in Cellular and Molecular Neuroscience. Recurrent topics in Tim Pearce's work include Advanced Chemical Sensor Technologies (28 papers), Olfactory and Sensory Function Studies (28 papers) and Neurobiology and Insect Physiology Research (24 papers). Tim Pearce is often cited by papers focused on Advanced Chemical Sensor Technologies (28 papers), Olfactory and Sensory Function Studies (28 papers) and Neurobiology and Insect Physiology Research (24 papers). Tim Pearce collaborates with scholars based in United Kingdom, Spain and Germany. Tim Pearce's co-authors include Julian W. Gardner, H. Troy Nagle, Susan S. Schiffman, Philip N. Bartlett, Alexandra Brintrup, James A. Covington, Thomas Jacob Koickal, Alister Hamilton, Keith J. Albert and David R. Walt and has published in prestigious journals such as PLoS ONE, Analytical Chemistry and Trends in Neurosciences.

In The Last Decade

Tim Pearce

60 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tim Pearce United Kingdom	18	1.1k	564	419	346	321	60	1.8k
Fabrizio Davide Italy	25	1.3k 1.1×	606 1.1×	230 0.5×	165 0.5×	556 1.7×	63	2.0k
Jordi Fonollosa Spain	25	1.1k 0.9×	785 1.4×	379 0.9×	93 0.3×	298 0.9×	66	2.1k
A. Gutiérrez-Gálvez Spain	14	739 0.7×	410 0.7×	281 0.7×	194 0.6×	214 0.7×	32	921
Yan Shi China	23	847 0.8×	354 0.6×	189 0.5×	160 0.5×	85 0.3×	93	1.5k
Alexander Vergara United States	22	1.3k 1.2×	833 1.5×	473 1.1×	127 0.4×	455 1.4×	44	1.8k
Guang Li China	27	925 0.8×	1.0k 1.9×	84 0.2×	72 0.2×	361 1.1×	194	2.9k
Manabendra Bhuyan India	16	814 0.7×	206 0.4×	113 0.3×	83 0.2×	119 0.4×	67	1.2k
Shih-Wen Chiu Taiwan	14	572 0.5×	445 0.8×	129 0.3×	81 0.2×	198 0.6×	48	808
Fengchun Tian China	27	1.4k 1.2×	936 1.7×	569 1.4×	121 0.3×	282 0.9×	128	2.1k
Jong Hyun Lim South Korea	12	429 0.4×	145 0.3×	64 0.2×	225 0.7×	54 0.2×	18	719

Countries citing papers authored by Tim Pearce

Since Specialization

Citations

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

Fields of papers citing papers by Tim Pearce

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tim Pearce

This figure shows the co-authorship network connecting the top 25 collaborators of Tim Pearce. A scholar is included among the top collaborators of Tim Pearce 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 Tim Pearce. Tim Pearce 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

Pearce, Tim, et al.. (2024). Virtual screening of drug materials for pharmaceutical tablet manufacturability with reference to sticking. International Journal of Pharmaceutics. 667(Pt A). 124722–124722. 2 indexed citations

Yoon, Kyunghee, Alexander Kogan, Miklos A. Vasarhelyi, & Tim Pearce. (2024). External Nonfinancial Measures in Substantive Analytical Procedures: Contributions of Weather Information. Journal of Information Systems. 38(2). 143–162. 3 indexed citations

Mäkynen, Marko, et al.. (2024). Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention. Sensors. 24(15). 4787–4787. 2 indexed citations

Pearce, Tim, et al.. (2019). Expressive priors in Bayesian neural networks: Kernel combinations and periodic functions. Queensland's institutional digital repository (The University of Queensland). 134–144. 6 indexed citations

Pearce, Tim, Alexandra Brintrup, Mohamed Zaki, & Andy Neely. (2018). High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach.. Cambridge University Engineering Department Publications Database. 4075–4084. 10 indexed citations

Pearce, Tim, Mohamed Zaki, Alexandra Brintrup, & Andy Neely. (2018). Uncertainty in Neural Networks: Bayesian Ensembling.. arXiv (Cornell University). 25 indexed citations

Olsson, Shannon B., R. A. John Challiss, Marina Cole, et al.. (2015). Biosynthetic infochemical communication. Bioinspiration & Biomimetics. 10(4). 43001–43001. 6 indexed citations

Kuebler, Linda S., Zsolt Kárpáti, Teun Dekker, et al.. (2014). Temporal Features of Spike Trains in the Moth Antennal Lobe Revealed by a Comparative Time-Frequency Analysis. PLoS ONE. 9(1). e84037–e84037. 2 indexed citations

Karout, Salah, et al.. (2012). Stimulus and Network Dynamics Collide in a Ratiometric Model of the Antennal Lobe Macroglomerular Complex. PLoS ONE. 7(1). e29602–e29602. 6 indexed citations

10.

Olsson, Shannon B., et al.. (2012). Non-linear blend coding in the moth antennal lobe emerges from random glomerular networks. PubMed. 5. 6–6. 11 indexed citations

11.

Carlsson, Mikael A., et al.. (2007). Component Information Is Preserved in Glomerular Responses to Binary Odor Mixtures in the Moth Spodoptera littoralis. Chemical Senses. 32(5). 433–443. 32 indexed citations

12.

Pyk, Pawel, Sergi Bermúdez i Badia, Ulysses Bernardet, et al.. (2006). An artificial moth: Chemical source localization using a robot based neuronal model of moth optomotor anemotactic search. Autonomous Robots. 20(3). 197–213. 89 indexed citations

13.

Pearce, Tim, et al.. (2003). Wiring the Olfactory Bulb - Activity-dependent Models of Axonal Targeting in the Developing Olfactory Pathway. Reviews in the Neurosciences. 14(1-2). 63–72. 6 indexed citations

14.

Albert, Keith J., et al.. (2002). Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays. Analytical and Bioanalytical Chemistry. 373(8). 792–802. 22 indexed citations

15.

Sánchez-Montañés, Manuel & Tim Pearce. (2002). Why do olfactory neurons have unspecific receptive fields?. Biosystems. 67(1-3). 229–238. 12 indexed citations

16.

Albert, Keith J., et al.. (2001). Optical Multibead Arrays for Simple and Complex Odor Discrimination. Analytical Chemistry. 73(11). 2501–2508. 72 indexed citations

17.

Alkasab, Tarik K., Thomas Bozza, Thomas A. Cleland, et al.. (1999). Characterizing complex chemosensors: information-theoretic analysis of olfactory systems. Trends in Neurosciences. 22(3). 102–108. 18 indexed citations

18.

Pearce, Tim & Julian W. Gardner. (1998). Predicting organoleptic scores of sub-ppm flavour notes.Part 1. Theoretical and experimental details. The Analyst. 123(10). 2047–2055. 16 indexed citations

19.

Pearce, Tim. (1997). Computational parallels between the biological olfactory pathway and its analogue `The Electronic Nose':. Biosystems. 41(1). 43–67. 83 indexed citations

20.

Pearce, Tim. (1997). Computational parallels between the biological olfactory pathway and its analogue `The Electronic Nose': Part II. Sensor-based machine olfaction. Biosystems. 41(2). 69–90. 90 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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