David M. Haig

3.4k total citations
94 papers, 2.6k citations indexed

About

David M. Haig is a scholar working on Epidemiology, Immunology and Genetics. According to data from OpenAlex, David M. Haig has authored 94 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Epidemiology, 40 papers in Immunology and 27 papers in Genetics. Recurrent topics in David M. Haig's work include Herpesvirus Infections and Treatments (43 papers), Virus-based gene therapy research (23 papers) and Poxvirus research and outbreaks (18 papers). David M. Haig is often cited by papers focused on Herpesvirus Infections and Treatments (43 papers), Virus-based gene therapy research (23 papers) and Poxvirus research and outbreaks (18 papers). David M. Haig collaborates with scholars based in United Kingdom, United States and New Zealand. David M. Haig's co-authors include Colin J. McInnes, George C. Russell, Andrew A. Mercer, James P. Stewart, Jackie Thomson, Ellen E. E. Jarrett, Stephen B. Fleming, H.W. Reid, David Deane and H. R. P. Miller and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Blood.

In The Last Decade

David M. Haig

94 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David M. Haig United Kingdom 32 1.2k 969 673 609 535 94 2.6k
R. Eberle United States 29 2.5k 2.0× 724 0.7× 467 0.7× 471 0.8× 272 0.5× 112 3.2k
Hiroshi Sentsui Japan 26 547 0.4× 761 0.8× 500 0.7× 442 0.7× 954 1.8× 111 2.1k
Barbara E.H. Coupar Australia 35 1.4k 1.1× 1.4k 1.4× 1.2k 1.8× 970 1.6× 416 0.8× 75 3.6k
William P. Cheevers United States 29 1.3k 1.1× 662 0.7× 1.1k 1.7× 558 0.9× 669 1.3× 92 2.5k
Yasuo Inoshima Japan 27 940 0.8× 350 0.4× 762 1.1× 628 1.0× 392 0.7× 174 2.7k
Timothy J. Zamb United States 23 1.0k 0.8× 771 0.8× 241 0.4× 401 0.7× 221 0.4× 45 2.1k
Günther M. Keil Germany 39 2.8k 2.2× 1.0k 1.0× 257 0.4× 805 1.3× 950 1.8× 120 4.6k
Gustavo Delhon United States 23 768 0.6× 365 0.4× 627 0.9× 344 0.6× 896 1.7× 40 2.2k
Rolf M. Flügel Germany 33 963 0.8× 651 0.7× 1.1k 1.6× 1.1k 1.7× 357 0.7× 99 2.8k
M. J. Van Der Maaten United States 24 601 0.5× 1.1k 1.1× 286 0.4× 363 0.6× 1.1k 2.0× 67 2.0k

Countries citing papers authored by David M. Haig

Since Specialization
Citations

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

Fields of papers citing papers by David M. Haig

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David M. Haig

This figure shows the co-authorship network connecting the top 25 collaborators of David M. Haig. A scholar is included among the top collaborators of David M. Haig 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 David M. Haig. David M. Haig 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.
2.
Jaafar, Fauziah Mohd, Carrie Batten, Houssam Attoui, et al.. (2021). Serological Cross-Reactions between Expressed VP2 Proteins from Different Bluetongue Virus Serotypes. Viruses. 13(8). 1455–1455. 15 indexed citations
3.
Bartley, Kathryn, David Deane, Ann Percival, et al.. (2014). Identification of immuno-reactive capsid proteins of malignant catarrhal fever viruses. Veterinary Microbiology. 173(1-2). 17–26. 6 indexed citations
4.
Parsons, Richard, Robert Patterson, Tracey J. Coffey, et al.. (2010). Characterisation of antibodies to bovine toll-like receptor (TLR)-2 and cross-reactivity with ovine TLR2. Veterinary Immunology and Immunopathology. 139(2-4). 313–318. 14 indexed citations
5.
Anderson, I.E., David Deane, Jackie Thomson, et al.. (2008). Production and Utilization of Interleukin-15 in Malignant Catarrhal Fever. Journal of Comparative Pathology. 138(2-3). 131–144. 6 indexed citations
6.
Jann, O., Dirk Werling, Jung‐Su Chang, David M. Haig, & Elizabeth Glass. (2008). Molecular evolution of bovine Toll-like receptor 2 suggests substitutions of functional relevance. BMC Evolutionary Biology. 8(1). 288–288. 69 indexed citations
7.
Anderson, I.E., D. Buxton, Iain Campbell, et al.. (2007). Immunohistochemical Study of Experimental Malignant Catarrhal Fever in Rabbits. Journal of Comparative Pathology. 136(2-3). 156–166. 33 indexed citations
8.
Haig, David M.. (2006). Orf virus infection and host immunity. Current Opinion in Infectious Diseases. 19(2). 127–131. 59 indexed citations
9.
Wright, Harry W., James P. Stewart, Iain Campbell, et al.. (2003). Genome re-arrangements associated with loss of pathogenicity of the γ-herpesvirus alcelaphine herpesvirus-1. Research in Veterinary Science. 75(2). 163–168. 22 indexed citations
10.
Haig, David M., David Deane, Nyree Myatt, et al.. (1996). The activation status of ovine CD45R+ and CD45R− efferent lymph T cells after orf virus reinfection. Journal of Comparative Pathology. 115(2). 163–174. 18 indexed citations
11.
12.
McInnes, Colin J., Mary A. Logan, David M. Haig, & Frank Wright. (1994). Cloning of a cDNA encoding ovine interleukin-3. Gene. 139(2). 289–290. 5 indexed citations
13.
Haig, David M.. (1993). Ovine bone-marrow stromal cell-dependent myelopoiesis. Journal of Comparative Pathology. 109(3). 259–270. 5 indexed citations
14.
Huntley, John F., et al.. (1992). Characterisation of ovine mast cells derived from in vitro culture of haemopoietic tissue. Veterinary Immunology and Immunopathology. 32(1-2). 47–64. 8 indexed citations
15.
Ballingall, Keith T., Harry W. Wright, Bernadette M. Dutia, et al.. (1992). Expression and characterization of ovine major histocompatibility complex class II (OLA‐DR) genes. Animal Genetics. 23(4). 347–359. 38 indexed citations
16.
Haig, David M., Jackie Thomson, & Ann Percival. (1992). Purification and adhesion receptor phenotype of ovine bone marrow-derived haemopoietic colony-forming cells. Veterinary Immunology and Immunopathology. 33(3). 223–236. 9 indexed citations
17.
Haig, David M., Jackie Thomson, & Angela Dawson. (1991). Reactivity of the workshop monoclonal antibodies with ovine bone marrow cells and bone marrow-derived monocyte/macrophage and mast cell lines. Veterinary Immunology and Immunopathology. 27(1-3). 135–145. 12 indexed citations
18.
Haig, David M., Dennis Brown, & A. Mackellar. (1990). Ovine haemopoiesis: the development of bone marrow-derived colony-forming cells in vitro in the presence of factors derived from lymphoid cells and helper T-cells. Veterinary Immunology and Immunopathology. 25(2). 125–137. 25 indexed citations
19.
Haig, David M., et al.. (1989). Parasite–specific T cell responses of sheep following live infection with the gastric nematode Haemonchus contortus. Parasite Immunology. 11(5). 463–477. 29 indexed citations
20.
Jarrett, Ellen E. E. & David M. Haig. (1984). Mucosal mast cells in vivo and in vitro. Immunology Today. 5(4). 115–119. 53 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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