Tammy R. Beckham

635 total citations
16 papers, 489 citations indexed

About

Tammy R. Beckham is a scholar working on Agronomy and Crop Science, Ecology, Evolution, Behavior and Systematics and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Tammy R. Beckham has authored 16 papers receiving a total of 489 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Agronomy and Crop Science, 10 papers in Ecology, Evolution, Behavior and Systematics and 8 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Tammy R. Beckham's work include Animal Disease Management and Epidemiology (16 papers), Vector-Borne Animal Diseases (10 papers) and Viral Infections and Immunology Research (8 papers). Tammy R. Beckham is often cited by papers focused on Animal Disease Management and Epidemiology (16 papers), Vector-Borne Animal Diseases (10 papers) and Viral Infections and Immunology Research (8 papers). Tammy R. Beckham collaborates with scholars based in United States, United Kingdom and France. Tammy R. Beckham's co-authors include Thomas McKenna, N.P. Ferris, Nick J. Knowles, Ming Deng, Gordon B. Ward, He Wang, Donald P. King, Jean‐François Valarcher, G. Hutchings and Youjun Shang and has published in prestigious journals such as Emerging infectious diseases, Veterinary Microbiology and Frontiers in Public Health.

In The Last Decade

Tammy R. Beckham

16 papers receiving 467 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tammy R. Beckham United States 11 336 241 237 127 74 16 489
В. М. Захаров Russia 6 267 0.8× 234 1.0× 234 1.0× 74 0.6× 27 0.4× 10 370
Caroline F. Wright United Kingdom 13 273 0.8× 258 1.1× 193 0.8× 96 0.8× 99 1.3× 18 466
Raphael Sallu Tanzania 11 206 0.6× 150 0.6× 221 0.9× 103 0.8× 16 0.2× 23 321
David Mackay United Kingdom 16 806 2.4× 681 2.8× 635 2.7× 107 0.8× 86 1.2× 29 967
Thi Lan Nguyen Vietnam 12 358 1.1× 209 0.9× 282 1.2× 155 1.2× 40 0.5× 36 501
Caroline Sharpe United Kingdom 8 327 1.0× 132 0.5× 211 0.9× 70 0.6× 18 0.2× 11 532
Sun-Young Sunwoo United States 15 269 0.8× 105 0.4× 252 1.1× 348 2.7× 34 0.5× 30 565
Ferran Jori South Africa 7 360 1.1× 175 0.7× 339 1.4× 144 1.1× 12 0.2× 8 445
Kinga Urbaniak Poland 9 147 0.4× 54 0.2× 114 0.5× 127 1.0× 30 0.4× 25 279
Chananya Patta Italy 11 279 0.8× 80 0.3× 257 1.1× 143 1.1× 18 0.2× 20 382

Countries citing papers authored by Tammy R. Beckham

Since Specialization
Citations

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

Fields of papers citing papers by Tammy R. Beckham

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tammy R. Beckham. 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 Tammy R. Beckham. The network helps show where Tammy R. Beckham may publish in the future.

Co-authorship network of co-authors of Tammy R. Beckham

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

All Works

16 of 16 papers shown
1.
Beckham, Tammy R., David A. Brake, & J Fine. (2018). Strengthening One Health Through Investments in Agricultural Preparedness. Health Security. 16(2). 92–107. 8 indexed citations
2.
Michelotti, Julia, et al.. (2018). The Convergence of High-Consequence Livestock and Human Pathogen Research and Development: A Paradox of Zoonotic Disease. Tropical Medicine and Infectious Disease. 3(2). 55–55. 10 indexed citations
3.
Beckham, Tammy R., et al.. (2017). Technologies for capturing and analysing animal health data in near real time. Revue Scientifique et Technique de l OIE. 36(2). 525–538. 10 indexed citations
4.
Wall, James, et al.. (2016). Improving Animal Disease Detection Through an Enhanced Passive Surveillance Platform. Health Security. 14(4). 264–271. 6 indexed citations
5.
6.
Sun, Feng, et al.. (2014). Molecular Typing of Epizootic Hemorrhagic Disease Virus Serotypes by One-Step Multiplex RT-PCR. Journal of Wildlife Diseases. 50(3). 639–644. 13 indexed citations
7.
Xu, Lizhe, William Hurtle, Jessica Rowland, et al.. (2013). Development of a universal RT-PCR for amplifying and sequencing the leader and capsid-coding region of foot-and-mouth disease virus. Journal of Virological Methods. 189(1). 70–76. 36 indexed citations
8.
Clavijo, Alfonso, Amir Nikooienejad, Mohammad Shahrokh Esfahani, et al.. (2012). Identification and Analysis of the First 2009 Pandemic H1N1 Influenza Virus from U.S. Feral Swine. Zoonoses and Public Health. 60(5). 327–335. 16 indexed citations
9.
Das, Amaresh, Tammy R. Beckham, & Michael T. McIntosh. (2011). Comparison of methods for improved RNA extraction from blood for early detection of Classical swine fever virus by real-time reverse transcription polymerase chain reaction. Journal of Veterinary Diagnostic Investigation. 23(4). 727–735. 10 indexed citations
10.
Martin, Barbara, et al.. (2011). Development, optimization, and validation of a Classical swine fever virus real-time reverse transcription polymerase chain reaction assay. Journal of Veterinary Diagnostic Investigation. 23(5). 994–998. 8 indexed citations
11.
Valarcher, Jean‐François, Nick J. Knowles, В. М. Захаров, et al.. (2009). Multiple Origins of Foot-and-Mouth Disease Virus Serotype Asia 1 Outbreaks, 2003–2007. Emerging infectious diseases. 15(7). 1046–1051. 143 indexed citations
12.
Knowles, Nick J., Paul R. Davies, Jean‐François Valarcher, et al.. (2008). Genetic characterization and molecular epidemiology of foot-and-mouth disease viruses isolated from Afghanistan in 2003–2005. Virus Genes. 36(2). 401–413. 32 indexed citations
13.
Fosgate, Geoffrey T., Saraya Tavornpanich, Tammy R. Beckham, et al.. (2008). Diagnostic specificity of a real-time RT-PCR in cattle for foot-and-mouth disease and swine for foot-and-mouth disease and classical swine fever based on non-invasive specimen collection. Veterinary Microbiology. 132(1-2). 158–164. 6 indexed citations
14.
Paíxão, Tatiane A., Alcina Vieira Carvalho Neta, Tammy R. Beckham, et al.. (2008). Diagnosis of foot-and mouth disease by real time reverse transcription polymerase chain reaction under field conditions in Brazil. BMC Veterinary Research. 4(1). 53–53. 32 indexed citations
15.
King, Donald P., N.P. Ferris, Andrew E. Shaw, et al.. (2006). Detection of Foot-and-Mouth Disease Virus: Comparative Diagnostic Sensitivity of Two Independent Real-Time Reverse Transcription-Polymerase Chain Reaction Assays. Journal of Veterinary Diagnostic Investigation. 18(1). 93–97. 75 indexed citations
16.
Deng, Ming, He Wang, Gordon B. Ward, Tammy R. Beckham, & Thomas McKenna. (2005). Comparison of Six RNA Extraction Methods for the Detection of Classical Swine Fever Virus by Real-Time and Conventional Reverse Transcription–PCR. Journal of Veterinary Diagnostic Investigation. 17(6). 574–578. 60 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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