I was curious to see if I could identify some patterns of collaboration and research topics in cow health research made in Canada. For this, I’m trying the R package bibliometrix. This package allows quantitative research in scientometrics and bibliometrics by providing different routines for importing bibliographic data from Scopus and ISI Web of Knowledge databases, and performing various bibliometric analyses. The Bibliometrix website provides a good tutorial that I will mainly follow, with the sole objective to satisfy my curiosity and have fun!
As explained in the bibliometrix tutorial, bibliographic data were retrieved from ISI Web of Knowledge, Web of Science Core Collection database. The following descriptors of thematics were used:
TS=(ruminant OR cow OR bovine OR cattle OR calf) AND PY=(2012-2017) AND TS=(coccidiosis OR Pasteurella OR mastitis OR parvovirus OR PPV OR mummification OR stillbirth OR “foot-and-mouth disease” OR FMD “Aphtous fever” OR pneumonia OR streptococci OR “E. coli” OR tuberculosis OR brucellosis OR salmonellosis OR salmonella OR anthrax OR babesiosis OR piroplasmosis OR “tick fever” OR “red water” OR babesia OR sarcocystosis OR sarcosystis OR toxoplasmosis OR toxoplasma OR streptococcus OR clostridium OR influenza OR leptospirosis OR mycoplasma OR rotavirus OR BSE OR prion OR TSE OR “mad cow” OR metritis OR encephalitis OR biotin OR “energy balance” OR “drug resistance” OR mannheimia OR escherichia-coli OR “bovine respiratory disease” OR “subclinical ketosis” OR beta-hydroxybutyrate OR campylobacter OR epidemic OR “viral hemorrhagic fever” OR O157H7 OR lameness OR diarrhea OR “necrotic enteritis” OR “zoonotic pathogen” OR zoonosis OR q-fever OR “animal welfare” OR giardia OR paratuberculosis OR coxiella OR coxiellosis OR “Johne’s disease” OR “uterine disease” OR endometritis OR reproductive-performance OR “west nile virus” OR PrPSc OR cesarean-section OR aureus OR staphylococcus OR epidermidis OR eimeria OR coccidia OR haemonchus OR “metabolic disease” OR ketosis OR “O157:H7” OR foot-and-mouth OR pseudomonas OR “airborne transmission” OR “foodborne transmission” OR “gastrointestinal nematodes” OR ixodes OR “tick-borne disease” OR borreliosis OR “failure of transfer of passive immunity” OR mycotoxins OR “antimicrobial resistance” OR enterococcus OR “foodborne pathogens” OR “somatic cell count” OR agalactiae OR mastitis OR “staphylococcus aureus” OR “MRSA” OR uberis OR “subacute ruminal acidosis” OR “pregnancy loss” OR fertility OR anovulation OR “udder health” OR “bovine spongiform encephalopathy” OR scrapie OR “bulk milk somatic cell count” OR “downer cow syndrome” OR “milk fever” OR mycobacterium OR listeria OR rotavirus OR “intramammary infection” OR parasitism OR borrelia OR bluetongue OR BVD OR “bovine viral diarrhea” OR abortion OR vibriosis OR “cystic ovaries” OR neosporosis OR “repeat breeding syndrome” OR “retained fetal membranes” OR schmallenberg OR acetonaemia OR “fatty liver” OR acidosis OR “calf scour” OR IBR OR “digital dermatitis” OR “foot rot” OR “epizootic hemorrhagic disease” OR lice OR mange OR ringworm OR “sole ulcer” OR “displaced abomasum” OR botulism OR rabies) AND LA=(English OR French) AND DT=(Article OR Review) AND CU=(Canada)
i.e. articles or reviews published between 2012 and now (October 2017), in English or French, under the SCI-EXPANDED citation index, for Canada. The ISI website only allows to export 500 records at a time. So several files were exported in bibtex format, then merged together and their leading white spaces removed thanks to a small bash script:
cat *.bib >> records.bib
sed "s/^[ \t]*//" -i records.bibThen we can load the R package and read the data!
library(bibliometrix)##
## bibliometrix
## A R tool for comprehensive bibliometric analysis of scientific literature
##
## by Massimo Aria & Corrado Cuccurullo
##
## http:\\www.bibliometrix.orgD <- readFiles("records.bib")
M <- convert2df(D, dbsource = "isi", format = "bibtex")
results <- biblioAnalysis(M, sep = ";")So we’ve got 1569 articles and 114 reviews, from 5157 authors.
results$Articles ## number of articles## [1] 1683results$nAuthors ## number of authors## [1] 5157There’s a generic summary function that provides overall results of the bibliographic analysis:
S <- summary(results, k = 10, pause = FALSE)##
##
## Main Information about data
##
## Articles 1683
## Sources (Journals, Books, etc.) 377
## Keywords Plus (ID) 6039
## Author's Keywords (DE) 3718
## Period 2012 - 2017
## Average citations per article 7.185
##
## Authors 5157
## Author Appearances 8680
## Authors of single authored articles 17
## Authors of multi authored articles 5140
##
## Articles per Author 0.326
## Authors per Article 3.06
## Co-Authors per Articles 5.16
## Collaboration Index 3.12
##
##
## Annual Scientific Production
##
## Year Articles
## 2012 293
## 2013 274
## 2014 301
## 2015 298
## 2016 305
## 2017 212
##
## Annual Percentage Growth Rate -6.266756
##
##
## Most Productive Authors
##
## Authors Articles Authors Articles Fractionalized
## 1 MCALLISTER,TA 66 MCALLISTER,TA 12.55
## 2 BARKEMA,HW 63 BARKEMA,HW 11.77
## 3 WEARY,DM 41 WEARY,DM 10.70
## 4 LEBLANC,SJ 40 LEBLANC,SJ 10.36
## 5 ORSEL,K 35 VON,KEYSERLINGKMAG 9.39
## 6 VON,KEYSERLINGKMAG 34 DEVRIES,TJ 8.05
## 7 DE,BUCKJ 32 BUCZINSKI,S 8.01
## 8 DEVRIES,TJ 31 LESLIE,KE 6.72
## 9 LESLIE,KE 30 ORSEL,K 6.50
## 10 DUFFIELD,TF 29 DUFFIELD,TF 6.27
##
##
## Top manuscripts per citations
##
## Paper
## 1 QUINLAN CL;ORR AL;PEREVOSHCHIKOVA JR;ACKRELL BA;BRAND MD,(2012),NA
## 2 LAING R;KIKUCHI T;MARTINELLI A;TSAI RN;REDMAN E;HOLROYD DJ;BEASLEY H;BRITTON C;CURRAN D;DEVANEY E;GILABERT A;HUNT F;JOHNSTON SL;KRYUKOV I;LI AA;REID AJ;SARGISON GI;WASMUTH JD;WOLSTENHOLME M;GILLEARD JS;COTTON JA,(2013),NA
## 3 REID AJ;VERMONT SJ;COTTON JA;HARRIS D;HILL-CAWTHORNE GA;KOENEN-WAISMAN SM;MOURIER T;NORTON R;QUAIL MA;SANDERS M;SHANMUGAM D;SOHAL A;WASMUTH JD;BRUNK B;GRIGG JC;PARKINSON J;ROOS AJ;BERRIMAN M;PAIN A;WASTLING JM,(2012),NA
## 4 PLOEGER S;STUMPFF F;PENNER J;GAEBEL G;MARTENS Z;GUENZEL D;ASCHENBACH JR,(2012),NA
## 5 ALARCON EI;UDEKWU K;SKOG M;PACIONI NL;STAMPLECOSKIE KG;GONZALEZ-BEJAR N;WICKHAM A;RICHTER-DAHLFORS M;SCAIANO JC,(2012),NA
## 6 ZEBELI Q;ASCHENBACH JR;TAFAJ M;BOGUHN BN;DROCHNER W,(2012),NA
## 7 BAKER LA;WATT IN;RUNSWICK MJ;WALKER JE;RUBINSTEIN JL,(2012),NA
## 8 VON KEYSERLINGK MAG;BARRIENTOS A;ITO K;GALO DM,(2012),NA
## 9 SCHWARZ EM;KORHONEN PK;CAMPBELL ND;JEX AR;JABBAR A;HALL A;HOWE AC;PELL J;HOFMANN PR;ZHU X;GREGORY TR;LOUKAS A;WILLIAMS BA;ANTOSHECHKIN I;BROWN PW;GASSER RB,(2013),NA
## 10 LI S;KHAFIPOUR E;KRAUSE DO;KROEKER JC;GOZHO GN;PLAIZIER JC,(2012),NA
## TC TCperYear
## 1 176 29.3
## 2 95 19.0
## 3 87 14.5
## 4 79 13.2
## 5 74 12.3
## 6 73 12.2
## 7 72 12.0
## 8 71 11.8
## 9 67 13.4
## 10 65 10.8
##
##
## Most Productive Countries
##
## Country Articles Freq
## 1 CANADA 1239 0.73706
## 2 USA 136 0.08090
## 3 CHINA 27 0.01606
## 4 BRAZIL 22 0.01309
## 5 IRAN 18 0.01071
## 6 AUSTRALIA 17 0.01011
## 7 GERMANY 16 0.00952
## 8 ENGLAND 15 0.00892
## 9 FRANCE 13 0.00773
## 10 BELGIUM 12 0.00714
##
##
## Total Citations per Country
##
## Country Total Citations Average Article Citations
## 1 CANADA 8768 7.077
## 2 USA 1119 8.228
## 3 GERMANY 248 15.500
## 4 AUSTRALIA 195 11.471
## 5 ENGLAND 176 11.733
## 6 IRELAND 131 11.909
## 7 NETHERLANDS 111 12.333
## 8 FRANCE 108 8.308
## 9 AUSTRIA 106 11.778
## 10 BRAZIL 101 4.591
##
##
## Most Relevant Sources
##
## Sources
## 1 JOURNAL OF DAIRY SCIENCE
## 2 JOURNAL OF ANIMAL SCIENCE
## 3 PLOS ONE
## 4 PREVENTIVE VETERINARY MEDICINE
## 5 CANADIAN VETERINARY JOURNAL-REVUE VETERINAIRE CANADIENNE
## 6 THERIOGENOLOGY
## 7 CANADIAN JOURNAL OF ANIMAL SCIENCE
## 8 CANADIAN JOURNAL OF VETERINARY RESEARCH-REVUE CANADIENNE DE RECHERCHEVETERINAIRE
## 9 VETERINARY MICROBIOLOGY
## 10 VETERINARY CLINICS OF NORTH AMERICA-FOOD ANIMAL PRACTICE
## Articles
## 1 328
## 2 67
## 3 66
## 4 53
## 5 46
## 6 45
## 7 38
## 8 24
## 9 23
## 10 20
##
##
## Most Relevant Keywords
##
## Author Keywords (DE) Articles Keywords-Plus (ID) Articles
## 1 CATTLE 100 CATTLE 353
## 2 DAIRY COW 94 PREVALENCE 137
## 3 ANIMAL WELFARE 54 DAIRY-COWS 121
## 4 MASTITIS 44 COWS 104
## 5 BEEF CATTLE 41 ESCHERICHIA-COLI 92
## 6 DAIRY CATTLE 40 RISK-FACTORS 90
## 7 BOVINE 28 INFECTION 88
## 8 DAIRY COWS 27 CALVES 82
## 9 PARATUBERCULOSIS 26 BEEF-CATTLE 77
## 10 ESCHERICHIA COLI 25 PERFORMANCE 74Thanks to stringr package, I could fix the address information of the authors to get the following graphs.
Figure 1: Evolution of publications in cattle health research.
Figure 2: The 10 most productive research groups.
Note that 2017 is not over yet, so explaining the drop in the number of publications in the first figure.
The most productive group is the University of Guelph, closely followed by Agriculture and Agri-Food Canada.
Measure of collaboration
Dominance factor (DF) can be computed, according to the formula of Kumar & Kumar, 2008. This factor gives an indication of the collaboration in the field, as a ratio of the number of multi-authored articles in which a scholar appears as first author to the total number of multi-authored articles. A value of less than 0.5 reflects a good sign for collaboration; high value shows more dominance of author as first author.
DF <- dominance(results, k = 10)
DF## Dominance Factor Multi Authored First Authored
## BUCZINSKI,S 0.38461538 26 10
## STANFORD,K 0.32142857 28 9
## DEVRIES,TJ 0.12903226 31 4
## VON,KEYSERLINGKMAG 0.08823529 34 3
## WEARY,DM 0.04878049 41 2
## DUFFIELD,TF 0.03448276 29 1
## LESLIE,KE 0.03333333 30 1
## BARKEMA,HW 0.03174603 63 2
## DE,BUCKJ 0.03125000 32 1
## MCALLISTER,TA 0.01515152 66 1
## Rank by Articles Rank by DF
## BUCZINSKI,S 10 1
## STANFORD,K 9 2
## DEVRIES,TJ 6 3
## VON,KEYSERLINGKMAG 4 4
## WEARY,DM 3 5
## DUFFIELD,TF 8 6
## LESLIE,KE 7 7
## BARKEMA,HW 2 8
## DE,BUCKJ 5 9
## MCALLISTER,TA 1 10All authors in the field look like they’re quite collaborative. Buczinski (Université de Montréal) and Stanford (Alberta Agriculture & Forestry) dominate their research team because they appear as first authors in a large proportion of their papers (10 out of 26 for Buczinski and 9 out of 28 for Stanford).
Bilbiometric network matrices
This is where it’s getting interesting: analysing the networks based on the attributes of the bibliometric data.
Authors’ collaboration
Figure 3: Authors’ collaboration.
Different communities appear, around 2 PIs: Barkema and McAllister (remember from above, these two are very productive and have a low DF). Barkema is centered across 2 communities (in green and yellow). Interestingly, the blue community is mainly from eastern Canada (Université de Montréal, University of Prince Edward Island), while the green one is more western (University of Guelph, University of Calgary, University of British Columbia). In both case, authors are working on a variety of topics: clinical diseases (i.e. Fecteau, Francoz), epidemiology (i.e. Dohoo, Pearl), production (LeBlanc), animal sciences (Pellerin, Vasseur), animal welfare (De Vries, Von Keyserlingk), etc. But in most cases, it looks like they’re doing field work. While around McAllister, it looks like it’s more lab work. This would need digging deeper, but it’s a rough first approximation.
Organisations’ collaboration
Figure 4: Organisations’ collaboration.
Collaboration is mainly realised between Canadian institutions, with only one from Europe (University of Ghent, Belgium), and 6 from the US (Kansas State University, Colorado State University, University of Florida, University of California in Davis, Iowa State University, and University of Minnesota).
Author keyword co-occurrences
Figure 5: Author keyword co-occurrences.
According to keywords provided by the authors, we find the usual research themes in cattle health: mastitis, welfare, lameness, Johne’s, BRD.
Co-word analysis: conceptual structure of the field
Here we’re mapping the conceptual structure of the field using the word co-occurrences in the bibliographic collection, by performing a MCA and K-means clustering to identify clusters of documents which express common concepts.
Figure 6: Conceptual structure of the field.
Four fields emerge, one specific to paratuberculosis, one for contagious diseases, one for production diseases and performances, and one probably for genetics.
Final word
This was a rough tentative of exploring Canadian research on cattle health.
Eh! in Canada, you’re either in the gang of Barkema, or McAllister… 😉