Tim Pearson

2.2k total citations
37 papers, 1.7k citations indexed

About

Tim Pearson is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Ecology. According to data from OpenAlex, Tim Pearson has authored 37 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 10 papers in Cellular and Molecular Neuroscience and 8 papers in Ecology. Recurrent topics in Tim Pearson's work include Adenosine and Purinergic Signaling (8 papers), Neuroscience and Neuropharmacology Research (7 papers) and Muscle Physiology and Disorders (7 papers). Tim Pearson is often cited by papers focused on Adenosine and Purinergic Signaling (8 papers), Neuroscience and Neuropharmacology Research (7 papers) and Muscle Physiology and Disorders (7 papers). Tim Pearson collaborates with scholars based in United Kingdom, United States and Italy. Tim Pearson's co-authors include Bruno G. Frenguelli, Malcolm J. Jackson, Nicholas Dale, Daniela Caporossi, Ivan Dimauro, Anne McArdle, Susan A. Masino, Kevin J. Staley, Chris G. Dulla and Peter Dobelis and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Neuron and Journal of Neuroscience.

In The Last Decade

Tim Pearson

36 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tim Pearson United Kingdom 23 711 392 368 324 198 37 1.7k
Takashi Murayama Japan 34 2.1k 2.9× 339 0.9× 627 1.7× 373 1.2× 153 0.8× 179 3.5k
Claire M. Peppiatt‐Wildman United Kingdom 22 1.5k 2.1× 418 1.1× 756 2.1× 287 0.9× 215 1.1× 38 3.3k
T Furukawa Japan 30 1.2k 1.7× 545 1.4× 821 2.2× 113 0.3× 110 0.6× 155 3.2k
Jason N. Peart Australia 38 1.1k 1.6× 527 1.3× 364 1.0× 486 1.5× 70 0.4× 124 4.1k
José M. Fernández‐Fernández Spain 30 1.5k 2.1× 503 1.3× 690 1.9× 131 0.4× 48 0.2× 58 2.6k
Shinya Ugawa Japan 31 1.4k 1.9× 463 1.2× 476 1.3× 138 0.4× 38 0.2× 80 2.9k
Allan M. Judd United States 29 954 1.3× 327 0.8× 473 1.3× 65 0.2× 109 0.6× 97 2.5k
Michèle Darmon France 28 1.3k 1.8× 397 1.0× 1.1k 2.9× 74 0.2× 115 0.6× 50 3.2k
Koji Shibasaki Japan 30 1.1k 1.5× 556 1.4× 831 2.3× 71 0.2× 45 0.2× 88 3.0k
Bernard Himpens Belgium 35 2.9k 4.0× 900 2.3× 725 2.0× 294 0.9× 111 0.6× 92 3.7k

Countries citing papers authored by Tim Pearson

Since Specialization
Citations

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

Fields of papers citing papers by Tim Pearson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tim Pearson

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

All Works

20 of 20 papers shown
1.
Mo, Matthew, et al.. (2024). “Normalising” flying-foxes: a bold vision for improving the public perceptions of our largest and most conspicuous bats. Australian Zoologist. 43(4). 545–573. 3 indexed citations
2.
Wang, Yingxue, Parul Sharma, Matthew Jefferson, et al.. (2021). Non‐canonical autophagy functions of ATG16L1 in epithelial cells limit lethal infection by influenza A virus. The EMBO Journal. 40(6). e105543–e105543. 46 indexed citations
3.
4.
Sakellariou, Giorgos K., Tim Pearson, Adam P. Lightfoot, et al.. (2016). Mitochondrial ROS regulate oxidative damage and mitophagy but not age-related muscle fiber atrophy. Scientific Reports. 6(1). 33944–33944. 104 indexed citations
5.
Vasilaki, Aphrodite, Natalie Pollock, Katarzyna Goljanek‐Whysall, et al.. (2016). The effect of lengthening contractions on neuromuscular junction structure in adult and old mice. AGE. 38(4). 259–272. 25 indexed citations
6.
McCormick, Rachel, Tim Pearson, & Aphrodite Vasilaki. (2016). Manipulation of environmental oxygen modifies reactive oxygen and nitrogen species generation during myogenesis. Redox Biology. 8. 243–251. 21 indexed citations
7.
8.
Boncompagni, Simona, et al.. (2015). Membrane glucocorticoid receptors are localised in the extracellular matrix and signal through the MAPK pathway in mammalian skeletal muscle fibres. The Journal of Physiology. 593(12). 2679–2692. 23 indexed citations
9.
Pearson, Tim, Anne McArdle, & Malcolm J. Jackson. (2014). Nitric oxide availability is increased in contracting skeletal muscle from aged mice, but does not differentially decrease muscle superoxide. Free Radical Biology and Medicine. 78. 82–88. 24 indexed citations
10.
Pearson, Tim, Tabitha Kabayo, Rainer Ng, et al.. (2014). Skeletal Muscle Contractions Induce Acute Changes in Cytosolic Superoxide, but Slower Responses in Mitochondrial Superoxide and Cellular Hydrogen Peroxide. PLoS ONE. 9(5). e96378–e96378. 85 indexed citations
11.
Dimauro, Ivan, Tim Pearson, Daniela Caporossi, & Malcolm J. Jackson. (2012). A simple protocol for the subcellular fractionation of skeletal muscle cells and tissue. BMC Research Notes. 5(1). 513–513. 256 indexed citations
12.
Pearson, Tim, Jihong Zhang, Averil Y. Warren, et al.. (2010). Measurement of vasoactive metabolites (hydroxyeicosatetraenoic and epoxyeicosatrienoic acids) in uterine tissues of normal and compromised human pregnancy. Journal of Hypertension. 28(12). 2429–2437. 22 indexed citations
13.
Pearson, Tim, Averil Y. Warren, David A. Barrett, & Raheela Khan. (2009). Detection of EETs and HETE-generating cytochromeP-450 enzymes and the effects of their metabolites on myometrial and vascular function. American Journal of Physiology-Endocrinology and Metabolism. 297(3). E647–E656. 24 indexed citations
16.
Dulla, Chris G., Peter Dobelis, Tim Pearson, et al.. (2005). Adenosine and ATP Link PCO2 to Cortical Excitability via pH. Neuron. 48(6). 1011–1023. 166 indexed citations
17.
Pearson, Tim & Bruno G. Frenguelli. (2004). Adrenoceptor subtype‐specific acceleration of the hypoxic depression of excitatory synaptic transmission in area CA1 of the rat hippocampus. European Journal of Neuroscience. 20(6). 1555–1565. 19 indexed citations
18.
Pearson, Tim, Nicholas Dale, Simon A. Hawley, et al.. (2004). AICA riboside both activates AMP‐activated protein kinase and competes with adenosine for the nucleoside transporter in the CA1 region of the rat hippocampus. Journal of Neurochemistry. 88(5). 1272–1282. 122 indexed citations
19.
Pearson, Tim & Bruno G. Frenguelli. (2000). Volume‐regulated anion channels do not contribute extracellular adenosine during the hypoxic depression of excitatory synaptic transmission in area CA1 of rat hippocampus. European Journal of Neuroscience. 12(8). 3064–3066. 10 indexed citations
20.
Dale, Nicholas, Tim Pearson, & Bruno G. Frenguelli. (2000). Direct measurement of adenosine release during hypoxia in the CA1 region of the rat hippocampal slice. The Journal of Physiology. 526(1). 143–155. 156 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026