Following the post by Arthur Charpentier (Freakonometrics), I wondered what would be the outcome considering my current engagement (veterinary medicine, epidemiology, bovine mastitis). Briefly, Arthur Charpentier’s post looked at clusters of journals publishing the same kind of papers.
So I looked at 25 journals (Journal of Dairy Science, Canadian Journal of Veterinary Medicine, Preventive Veterinary Medicine, New Zealand Veterinary Journal, Veterinary Research, Acta veterinaria Scandinavica, Journal of Dairy Research, The Veterinary Record, The Veterinary Journal, Journal of Veterinary Medicine-Series A, Journal of Veterinary Medicine-Series B, Journal of Veterinary Science, Journal of the American Veterinary Medical Association, American Journal of Veterinary Research, Journal of Veterinary Internal Medicine, Canadian Journal of Veterinary Research, Journal of Veterinary Diagnostic Investigation, Veterinary Microbiology, Veterinary Research Communications, BMC Veterinary Research, Australian Veterinary Journal, Theriogenology, Zoonoses and Public Health, Research in Veterinary Science, Tijdschrift voor Diergeneeskunde). But instead of looking at word frequency in the title of the papers, I counted the frequency of the following MeSH terms for the last 20 years in each journal: public health, diagnoses, surveys, dairy product, milk, lactation, cohort studies, case-control study, infections, prevalence, statistical regression, risk factor, bovine mastitis, statistical model, longitudinal study, survival analyses, udder, incidence, zoonoses, pathogen transmission, seroprevalence, cross-sectional studies, communicable disease, Bayesian analysis, evidence-based medicine, epidemiology, lactation disorder, preventive medicine. I then ran a principal component analysis on the 20 most frequent.
library(FactoMineR) estim_ncp(pubmed, ncp.min = 0, ncp.max = NULL, scale = TRUE, method = "Smooth") pca -> PCA(pubmed, scale.unit = TRUE, ncp = 13, graph = FALSE) plot.PCA(pca, axes = c(1, 2), choix = "ind", cex = 0.75, new.plot = FALSE)
The first 2 dimensions explained roughly 80% of the variation. We have Journal of Dairy Science on one corner, Vet Record and JAVMA on a second one, Preventive Veterinary Medicine in-between and Journal of Dairy Research on a third corner. All other journals are making a pack all together. Are they all the same?
Two distinct groups here: the second dimension is made of statistics and lactation/bovine mastitis/dairies, and the first dimension groups the other terms.
That’s interesting! The first dimension is related to journal from Veterinary Science, while the second dimension is related to journals from Agriculture, Dairy, Animal Science and Food Science Technology (= Journal of Dairy Science and Journal of Dairy Research). And the impact factor is in the direction of these journals…
Preventive Veterinary Medicine, Vet Record and JAVMA are now more clearly separated, with the first one oriented more towards statistical models, risk factors, Vet Record for transmission, prevalence, incidence of disease and JAVMA for epidemiologic studies (cohort, case-control).
Regarding clusters of journals, we have:
distance -> dist(pubmed) cah -> hclust(distance) plot(cah)
Using the sna library:
library(sna) gplot(pubmed, gmode = "twomode", displaylabels = TRUE, edge.lwd =0, edge.col = "light blue", label.cex =0.7)
where we have on the upper right corner the 2 Animal Science journal, Journal of Dairy Science and Journal of Dairy Research, with their MeSH terms related to dairy, udder and stats. Facing them we have the terms related to epidemiologic study design, zoonoses, transmission of disease, and in-between we have the terms for public health, diagnoses, prevalence, surveys and risk factors. In this center, we have our JAVMA, Vet Record and Preventive Veterinary Medicine, while the other Veterinary Science journals are spread evenly above and below this left-right diagonal. So not enough diversity in Vet Science journals?