Jennifer M. Thomson

1.6k total citations
42 papers, 1.2k citations indexed

About

Jennifer M. Thomson is a scholar working on Genetics, Animal Science and Zoology and Agronomy and Crop Science. According to data from OpenAlex, Jennifer M. Thomson has authored 42 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Genetics, 11 papers in Animal Science and Zoology and 8 papers in Agronomy and Crop Science. Recurrent topics in Jennifer M. Thomson's work include Genetic and phenotypic traits in livestock (18 papers), Meat and Animal Product Quality (8 papers) and Ruminant Nutrition and Digestive Physiology (7 papers). Jennifer M. Thomson is often cited by papers focused on Genetic and phenotypic traits in livestock (18 papers), Meat and Animal Product Quality (8 papers) and Ruminant Nutrition and Digestive Physiology (7 papers). Jennifer M. Thomson collaborates with scholars based in United States, Australia and Canada. Jennifer M. Thomson's co-authors include S. S. Moore, J.P. McNamara, J.A. Boles, Paul Stothard, Urmila Basu, Jung-Woo Choi, I.J. Lean, Xiaoping Liao, Yan Meng and Douglas P. Dyer and has published in prestigious journals such as The Journal of Immunology, Journal of Molecular Biology and Scientific Reports.

In The Last Decade

Jennifer M. Thomson

36 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jennifer M. Thomson United States 17 472 406 348 228 98 42 1.2k
Heather J. Huson United States 17 794 1.7× 312 0.8× 202 0.6× 225 1.0× 126 1.3× 56 1.2k
Dominik Burger Switzerland 19 512 1.1× 397 1.0× 223 0.6× 174 0.8× 57 0.6× 101 1.6k
Ikhide G. Imumorin United States 20 669 1.4× 215 0.5× 253 0.7× 277 1.2× 109 1.1× 63 1.2k
C. A. Gill United States 18 891 1.9× 257 0.6× 234 0.7× 406 1.8× 172 1.8× 66 1.4k
Andrea Verini Supplizi Italy 19 305 0.6× 289 0.7× 338 1.0× 290 1.3× 55 0.6× 54 1.1k
Monika Sodhi India 20 685 1.5× 294 0.7× 383 1.1× 289 1.3× 149 1.5× 93 1.2k
B. M. Burns Australia 20 675 1.4× 404 1.0× 175 0.5× 135 0.6× 76 0.8× 60 1.1k
Mihir Sarkar India 28 490 1.0× 650 1.6× 652 1.9× 700 3.1× 81 0.8× 130 2.0k
S. Modina Italy 31 461 1.0× 367 0.9× 1.1k 3.0× 144 0.6× 67 0.7× 105 2.8k
Mario Van Poucke Belgium 23 600 1.3× 275 0.7× 949 2.7× 236 1.0× 193 2.0× 120 2.0k

Countries citing papers authored by Jennifer M. Thomson

Since Specialization
Citations

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

Fields of papers citing papers by Jennifer M. Thomson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jennifer M. Thomson. 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 Jennifer M. Thomson. The network helps show where Jennifer M. Thomson may publish in the future.

Co-authorship network of co-authors of Jennifer M. Thomson

This figure shows the co-authorship network connecting the top 25 collaborators of Jennifer M. Thomson. A scholar is included among the top collaborators of Jennifer M. Thomson 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 Jennifer M. Thomson. Jennifer M. Thomson 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.
Drummond, Sheona P., Eckart Bartnik, Nikolaos Kouvatsos, et al.. (2023). The recombinant Link module of human TSG-6 suppresses cartilage damage in models of osteoarthritis: A potential disease-modifying OA drug. Osteoarthritis and Cartilage. 31(10). 1353–1364. 5 indexed citations
2.
Golder, H.M., et al.. (2023). Associations among the genome, rumen metabolome, ruminal bacteria, and milk production in early-lactation Holsteins. Journal of Dairy Science. 106(5). 3176–3191. 10 indexed citations
3.
Golder, H.M., S.J. LeBlanc, T.F. Duffield, et al.. (2023). Characterizing ruminal acidosis risk: A multiherd, multicountry study. Journal of Dairy Science. 106(5). 3155–3175. 8 indexed citations
4.
Tripet, Brian, et al.. (2021). 1H NMR based metabolic profiling distinguishes the differential impact of capture techniques on wild bighorn sheep. Scientific Reports. 11(1). 11308–11308. 6 indexed citations
5.
Murphy, Thomas W, et al.. (2020). Phenotypic and genetic differences in Rambouillet lines divergently selected for reproductive rate over 50 years1,2. Translational Animal Science. 4(Supplement_1). S90–S93. 1 indexed citations
6.
Rotella, Jay J., et al.. (2018). Evaluating sample size to estimate genetic management metrics in the genomics era. Molecular Ecology Resources. 18(5). 1077–1091. 22 indexed citations
7.
Golder, H.M., Jennifer M. Thomson, Stuart E. Denman, Christopher S. McSweeney, & I.J. Lean. (2018). Genetic Markers Are Associated with the Ruminal Microbiome and Metabolome in Grain and Sugar Challenged Dairy Heifers. Frontiers in Genetics. 9. 62–62. 23 indexed citations
8.
Ishaq, Suzanne L., et al.. (2017). Feed efficiency phenotypes in lambs involve changes in ruminal, colonic, and small-intestine-located microbiota1. Journal of Animal Science. 95(6). 2585–2592. 75 indexed citations
9.
Miller, Michael, G. E. Carstens, Jennifer M. Thomson, et al.. (2016). 1491 Associations between residual feed intake and metabolite profiles and feeding behavior traits in feedlot cattle. Journal of Animal Science. 94(suppl_5). 723–724. 1 indexed citations
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12.
Geary, T. W., R. W. Kott, J. G. Berardinelli, et al.. (2014). Characterization of the Vaginal Microbiota of Ewes and Cows Reveals a Unique Microbiota with Low Levels of Lactobacilli and Near-Neutral pH. Frontiers in Veterinary Science. 1. 19–19. 133 indexed citations
13.
Dyer, Douglas P., Jennifer M. Thomson, Aurélie Hermant, et al.. (2014). TSG-6 Inhibits Neutrophil Migration via Direct Interaction with the Chemokine CXCL8. The Journal of Immunology. 192(5). 2177–2185. 145 indexed citations
14.
Karisa, Brian, et al.. (2013). Candidate genes and biological pathways associated with carcass quality traits in beef cattle. Canadian Journal of Animal Science. 93(3). 295–306. 16 indexed citations
15.
Baldwin, R.L., Robert W. Li, Congjun Li, Jennifer M. Thomson, & B.J. Bequette. (2012). Characterization of the longissimus lumborum transcriptome response to adding propionate to the diet of growing Angus beef steers. Physiological Genomics. 44(10). 543–550. 16 indexed citations
16.
Thomson, Jennifer M., Jung-Woo Choi, Urmila Basu, et al.. (2012). The identification of candidate genes and SNP markers for classical bovine spongiform encephalopathy susceptibility. Prion. 6(5). 461–469. 2 indexed citations
17.
Stothard, Paul, Jung-Woo Choi, Urmila Basu, et al.. (2011). Whole genome resequencing of black Angus and Holstein cattle for SNP and CNV discovery. BMC Genomics. 12(1). 559–559. 146 indexed citations
18.
Thomson, Jennifer M., J.L. Vierck, & J.P. McNamara. (2010). Differential expression of genes in adipose tissue of first-lactation dairy cattle. Journal of Dairy Science. 94(1). 361–369. 51 indexed citations
19.
Poller, L., et al.. (1969). Coumarin Therapy and Platelet Aggregation. BMJ. 1(5642). 474–476. 15 indexed citations
20.
Robertson, James, et al.. (1966). A comparison of two indoor farrowing systems for sows. Animal Science. 8(2). 171–178. 26 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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