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Bibliometric analysis of quantitative genetics research in animal science in the last decade

Yıl 2023, Cilt: 28 Sayı: 2, 363 - 378, 17.08.2023

Adile Tatlıyer Tunaz

https://doi.org/10.37908/mkutbd.1216763

Öz

This study aimed to analyse the articles published with Clarivate Analytics’ Web of Science (WoS) in quantitative genetics in animal science with the bibliometric method, which can be used in all disciplines. The research data consists of a total of 1281 studies published between 2012-2021, title-based from WoS. A bibliometric approach was applied to the data with a comprehensive overview of thematic focus, citation analysis, country productivity, country collaboration, conceptual structure, historically direct citation network using the "bibliometrix" function in R software. Studies were categorized using K-means clustering and multiple concordance analysis (MCA). Clusters were created on the thematic map with KeyWord Plus. The results were as follows: the Journal of Dairy science was the most active journal. The most cited countries and hence the most productive countries were Brazil and the USA. The most preferred keyword in publications was “selection”. Two separate clusters were formed in the conceptual structure map, generally on "milk production" and "genetic parameters". With the KeyWord Plus analysis, the most preferred keyword in the publications was "selection". Researchers can gain a general sense of what's going on in the field based on the findings, and also the findings may even motivate researchers to collaborate in the field. It is thought that this study can present useful contributions to researchers by clearly presenting trend research hotspots and the future direction of the field with a comprehensive overview.

Anahtar Kelimeler

Bibliometrics Co-citation R package Quantitative Genetic

Teşekkür

The author thanks Prof. Dr. Çiğdem Takma (Ege University) for her significant contributions to this study.

Kaynakça

  • Abubakar, H.O., Etuk, A.S., Arilesere, J.I., & Abiodun, O.J.B. (2021). Bibliometric analysis of research productivity of academic staff in college of animal science and livestock production, Federal University of Agriculture, Abeokuta, Ogun State. Nigeria. Library Philosophy and Practice, 1-20.
  • Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics, 11 (4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  • Aria, M., Alterisio, A., Scandurra, A., Pinelli, C., & D’Aniello, B. (2021). The scholar’s best friend: Research trends in dog cognitive and behavioral studies. Animal Cognition, 24 (3), 541-553.
  • Beaver, D., & Rosen, R. (1979). Studies in scientific collaboration. Part III. Professionalization and the natural history of modern scientific co-authorship. Scientometrics, 1 (3), 231-245. https://doi.org/10.1007/bf02016308
  • Bjelland, D.W., Weigel, K.A., Vukasinovic, N., & Nkrumah, J.D. (2013). Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding. Journal of Dairy Science, 96 (7), 4697-4706. https://doi.org/10.3168/jds.2012-6435
  • Blondel, V.D., Guillaume, J.L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
  • Cahlik, T. (2000). Comparison of the maps of science. Scientometrics, 49 (3), 373-387. https://doi.org/10.1023/a:1010581421990
  • Callon, M., Courtial, J.P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22 (1), 155-205. https://doi.org/10.1007/bf02019280
  • Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57 (3), 359-377. https://doi.org/10.1002/asi.20317
  • Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5 (1), 146-166. https://doi.org/10.1016/j.joi.2010.10.002
  • Cobo, M.J., López‐Herrera, A.G., Herrera‐Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63 (8), 1609-1630. https://doi.org/10.1002/asi.22688
  • Cobo, M.J., Martínez, M.Á., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at knowledge-based systems: a bibliometric analysis. Knowledge-based Systems, 80, 3-13. https://doi.org/10.1016/j.knosys.2014.12.035
  • Crane, D. (1973). Invisible colleges: Diffusion of knowledge in scientific communities. Chicago: University of Chicago Press. Phys. Today. 6 (1), 72.
  • Derviş, H. (2019). Bibliometric analysis using Bibliometrix an R Package. Journal of Scientometric Research, 8 (3), 156-160. https://doi.org/10.5530/jscires.8.3.32
  • Ding, Y., Chowdhury, G.G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing & Management, 37 (6), 817-842. https://doi.org/10.1016/S0306-4573(00)00051-0
  • Egghe, L. (2006). Theory and practice of the g-index. Scientometrics, 69 (1), 131-152. https://doi.org/10.1007/s11192-006-0144-7
  • Elango, B., & Rajendran, P. (2012). Authorship trends and collaboration pattern in the marine sciences literature: a scientometric study. International Journal of Information Dissemination and Technology, 2 (3), 166-169.
  • Falagas, M.E., Pitsouni, E.I., Malietzis, G.A., & Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. The FASEB Journal, 22 (2), 338-342. https://doi.org/10.1096/fj.07-9492LSF
  • Falconer, D.S. (1996). Introduction to Quantitative Genetics. Pearson Education India.
  • Franceschet, M. (2010). A comparison of bibliometric indicators for computer science scholars and journals on Web of Science and Google Scholar. Scientometrics, 83 (1), 243-258. https://doi.org/10.1007/s11192-009-0021-2
  • Gagolewski, M. (2011). Bibliometric impact assessment with R and the CITAN package. Journal of Informetrics, 5 (4), 678-692. https://doi.org/10.1016/j.joi.2011.06.006
  • Garfield, E. (2004). Historiographic mapping of knowledge domains literature. Journal of Information Science, 30 (2), 119-145. https://doi.org/10.1177/0165551504042802
  • Glänzel, W., & Schubert, A. (2004). Analysing scientific networks through co-authorship. In Handbook of Quantitative Science and Technology Research (pp. 257-276). Springer, Dordrecht. https://doi.org/10.1007/1-4020-2755-9_12
  • Greenacre, M., & Blasius, J. (Eds.). (2006). Multiple correspondence analysis and related methods (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781420011319
  • Guler, A.T., Waaijer, C.J., & Palmblad, M. (2016). Scientific workflows for bibliometrics. Scientometrics, 107 (2), 385-398. https://doi.org/10.1007/s11192-016-1885-6
  • Hill, W.G. (2010). Understanding and using quantitative genetic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 365 (1537), 73-85. https://doi.org/10.1098/rstb.2009.0203
  • Hirsch, J.E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102 (46), 16569-16572. https://doi.org/10.1073/pnas.0507655102
  • Ismail, S., Nason, E., Marjanovic, S., & Grant, J. (2012). Bibliometrics as a tool for supporting prospective R&D decision-making in the health sciences: strengths, weaknesses and options for future development. Rand Health Quarterly, 1 (4). PMID: 28083218; PMCID: PMC4945260.
  • Kessler, M.M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14 (1), 10-25. https://doi.org/10.1002/asi.5090140103
  • Khanna, V.K. (2014). Bibliometric scoring of an individual’s research output in science and engineering. Annals of Library and Information Studies, 61, 121-131.
  • Koseoglu, M.A. (2016). Mapping the institutional collaboration network of strategic management research: 1980-2014. Scientometrics, 109 (1), 203-226. https://doi.org/10.1007/s11192-016-1894-5
  • Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: a comparative analysis. Physical Review E, 80 (5), 056117. https://doi.org/10.1007/s11192-016-1894-5
  • Leung, X.Y., Sun, J., & Bai, B. (2017). Bibliometrics of social media research: A co-citation and co-word analysis. International Journal of Hospitality Management, 66, 35-45. https://doi.org/10.1016/j.ijhm.2017.06.012
  • Lundberg, J. (2006). Bibliometrics as a research assessment tool: Impact beyond the impact factor (Order No. 28427053). Available from ProQuest Dissertations & Theses Global. (2564468545). Retrieved from https://www.proquest.com/dissertations-theses/bibliometrics-as-research-assessment-tool-impact/docview/2564468545/se-2
  • Luukkonen, T., Persson, O., & Sivertsen, G. (1992). Understanding patterns of international scientific collaboration. Science, Technology, & Human Values, 17 (1), 101-126. https://doi.org/10.1177/016224399201700106
  • Manuelian, C.L., Penasa, M., da Costa, L., Burbi, S., Righi, F., & De Marchi, M. (2020). Organic livestock production: A bibliometric review. Animals, 10 (4), 618. https://doi.org/10.3390/ani10040618
  • Meuwissen, T., Hayes, B., & Goddard, M. (2016). Genomic selection: A paradigm shift in animal breeding. Animal Frontiers, 6 (1), 6-14. https://doi.org/10.2527/af.2016-0002
  • Persson, O., Danell, R., & Schneider, J.W. (2009). How to use Bibexcel for various types of bibliometric analysis. Celebrating scholarly communication studies: A Festschrift for Olle Persson at his 60th Birthday, 5, 9-24.
  • RStudioe3rc Team (2020). RStudio: integrated development for R. Rstudio Team, PBC, Boston, MA. http://www. rstudio.com
  • Sci2 Team (2009) Science of Science (Sci2) Tool. Indiana University and SciTech Strategies, 379. https://sci2.cns.iu.edu
  • Silió, L., Rodríguez, M.C., Fernández, A., Barragán, C., Benítez, R., Óvilo, C., & Fernández, A.I. (2013). Measuring inbreeding and inbreeding depression on pig growth from pedigree or SNP‐derived metrics. Journal of Animal Breeding and Genetics, 130 (5), 349-360. https://doi.org/10.1111/jbg.12031
  • Sonesson, A.K., Woolliams, J.A., & Meuwissen, T.H. (2012). Genomic selection requires genomic control of inbreeding. Genetics Selection Evolution, 44 (1), 1-10. https://doi.org/10.1186/1297-9686-44-27
  • Spurlock, D.M., Dekkers, J.C.M., Fernando, R., Koltes, D.A., & Wolc, A. (2012). Genetic parameters for energy balance, feed efficiency, and related traits in Holstein cattle. Journal of Dairy science, 95 (9), 5393-5402. https://doi.org/10.3168/jds.2012-5407
  • Tijssen, R.J., & Van Raan, A.F. (1994). Mapping changes in science and technology: Bibliometric co-occurrence analysis of the R&D literature. Evaluation Review, 18 (1), 98-115. https://doi.org/10.1177/0193841X9401800110
  • Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84 (2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
  • Van Eck, N.J., & Waltman, L. (2014). CitNetExplorer: A new software tool for analyzing and visualizing citation networks. Journal of Informetrics, 8 (4), 802-823. https://doi.org/10.1016/j.joi.2014.07.006
  • Xiao, Z., Qin, Y., Xu, Z., Antucheviciene, J., & Zavadskas, E.K. (2022). The journal buildings: A bibliometric analysis (2011-2021). Buildings, 12 (1), 37. https://doi.org/10.3390/buildings12010037
  • Yardibi, F., Firat, M.Z., & Teke, E.C. (2021). Trend topics in animal science: a bibliometric analysis using CiteSpace. Turkish Journal of Veterinary & Animal Sciences, 45 (5), 833-840. https://doi.org/10.3906/vet-2001-103

Son on yılda hayvan bilimlerinde kantitatif genetik araştırmalarının bibliyometrik analizi

Yıl 2023, Cilt: 28 Sayı: 2, 363 - 378, 17.08.2023

Adile Tatlıyer Tunaz

https://doi.org/10.37908/mkutbd.1216763

Öz

Bu çalışma, Clarivate Analytics’in Web of Science (WoS) ile hayvan biliminde kantitatif genetik alanında yayımlanan makaleleri, tüm disiplinlerde kullanımı giderek artan bibliyometrik yöntemle analiz etmeyi amaçlamaktadır. Araştırma verileri, WoS'tan başlık bazında, 2012-2021 yılları arasında yayınlanmış toplam 1281 çalışmadan oluşmaktadır. Verilere, R yazılımındaki "bibliometrix" işlevi kullanılarak tematik odak, alıntı analizi, ülke üretkenliği, ülke işbirliği, kavramsal yapı, tarihsel olarak doğrudan alıntı ağının kapsamlı bir genel bakışıyla bibliyometrik bir yaklaşım uygulanmıştır. K-means kümeleme ile çoklu uyum analizi (MCA) kantitatif genetikte yapılan çalışmalar kategorileştirilmiştir. KeyWord Plus ile tematik harita üzerinde kümeler oluşturulmuştur. Sonuçlar şu şekildedir: “Journal of Dairy Science” en aktif dergi olmuştur. En çok alıntı yapılan ülkeler ve dolayısıyla en üretken ülkeler Brezilya ve ABD’dir. MCA ile kavramsal yapı haritasında iki ayrı küme oluşmuş olup, genel olarak “süt üretimi” ve “genetik parametreler” üzerinedir. KeyWord Plus analizi ile yayınlarda en çok tercih edilen anahtar kelime “seleksiyon” olmuştur. Araştırmacılar bulgulara dayanarak alanda neler olup bittiğine dair genel bir fikir edinebilir ve hatta bulgular araştırmacıları söz konusu alanda iş birliği yapmaya bile motive ettirebilir. Bu çalışmanın, trend araştırma noktalarını ve alanın gelecekteki yönünü kapsamlı bir genel bakış ile net bir şekilde sunarak araştırmacılara yararlı katkılar sağlanması amaçlanmıştır.

Anahtar Kelimeler

Bibliyometri Ortak alıntı R paketi Kantitatif genetik

Kaynakça

  • Abubakar, H.O., Etuk, A.S., Arilesere, J.I., & Abiodun, O.J.B. (2021). Bibliometric analysis of research productivity of academic staff in college of animal science and livestock production, Federal University of Agriculture, Abeokuta, Ogun State. Nigeria. Library Philosophy and Practice, 1-20.
  • Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics, 11 (4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  • Aria, M., Alterisio, A., Scandurra, A., Pinelli, C., & D’Aniello, B. (2021). The scholar’s best friend: Research trends in dog cognitive and behavioral studies. Animal Cognition, 24 (3), 541-553.
  • Beaver, D., & Rosen, R. (1979). Studies in scientific collaboration. Part III. Professionalization and the natural history of modern scientific co-authorship. Scientometrics, 1 (3), 231-245. https://doi.org/10.1007/bf02016308
  • Bjelland, D.W., Weigel, K.A., Vukasinovic, N., & Nkrumah, J.D. (2013). Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding. Journal of Dairy Science, 96 (7), 4697-4706. https://doi.org/10.3168/jds.2012-6435
  • Blondel, V.D., Guillaume, J.L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 10, P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
  • Cahlik, T. (2000). Comparison of the maps of science. Scientometrics, 49 (3), 373-387. https://doi.org/10.1023/a:1010581421990
  • Callon, M., Courtial, J.P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22 (1), 155-205. https://doi.org/10.1007/bf02019280
  • Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57 (3), 359-377. https://doi.org/10.1002/asi.20317
  • Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5 (1), 146-166. https://doi.org/10.1016/j.joi.2010.10.002
  • Cobo, M.J., López‐Herrera, A.G., Herrera‐Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63 (8), 1609-1630. https://doi.org/10.1002/asi.22688
  • Cobo, M.J., Martínez, M.Á., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at knowledge-based systems: a bibliometric analysis. Knowledge-based Systems, 80, 3-13. https://doi.org/10.1016/j.knosys.2014.12.035
  • Crane, D. (1973). Invisible colleges: Diffusion of knowledge in scientific communities. Chicago: University of Chicago Press. Phys. Today. 6 (1), 72.
  • Derviş, H. (2019). Bibliometric analysis using Bibliometrix an R Package. Journal of Scientometric Research, 8 (3), 156-160. https://doi.org/10.5530/jscires.8.3.32
  • Ding, Y., Chowdhury, G.G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing & Management, 37 (6), 817-842. https://doi.org/10.1016/S0306-4573(00)00051-0
  • Egghe, L. (2006). Theory and practice of the g-index. Scientometrics, 69 (1), 131-152. https://doi.org/10.1007/s11192-006-0144-7
  • Elango, B., & Rajendran, P. (2012). Authorship trends and collaboration pattern in the marine sciences literature: a scientometric study. International Journal of Information Dissemination and Technology, 2 (3), 166-169.
  • Falagas, M.E., Pitsouni, E.I., Malietzis, G.A., & Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. The FASEB Journal, 22 (2), 338-342. https://doi.org/10.1096/fj.07-9492LSF
  • Falconer, D.S. (1996). Introduction to Quantitative Genetics. Pearson Education India.
  • Franceschet, M. (2010). A comparison of bibliometric indicators for computer science scholars and journals on Web of Science and Google Scholar. Scientometrics, 83 (1), 243-258. https://doi.org/10.1007/s11192-009-0021-2
  • Gagolewski, M. (2011). Bibliometric impact assessment with R and the CITAN package. Journal of Informetrics, 5 (4), 678-692. https://doi.org/10.1016/j.joi.2011.06.006
  • Garfield, E. (2004). Historiographic mapping of knowledge domains literature. Journal of Information Science, 30 (2), 119-145. https://doi.org/10.1177/0165551504042802
  • Glänzel, W., & Schubert, A. (2004). Analysing scientific networks through co-authorship. In Handbook of Quantitative Science and Technology Research (pp. 257-276). Springer, Dordrecht. https://doi.org/10.1007/1-4020-2755-9_12
  • Greenacre, M., & Blasius, J. (Eds.). (2006). Multiple correspondence analysis and related methods (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781420011319
  • Guler, A.T., Waaijer, C.J., & Palmblad, M. (2016). Scientific workflows for bibliometrics. Scientometrics, 107 (2), 385-398. https://doi.org/10.1007/s11192-016-1885-6
  • Hill, W.G. (2010). Understanding and using quantitative genetic variation. Philosophical Transactions of the Royal Society B: Biological Sciences, 365 (1537), 73-85. https://doi.org/10.1098/rstb.2009.0203
  • Hirsch, J.E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102 (46), 16569-16572. https://doi.org/10.1073/pnas.0507655102
  • Ismail, S., Nason, E., Marjanovic, S., & Grant, J. (2012). Bibliometrics as a tool for supporting prospective R&D decision-making in the health sciences: strengths, weaknesses and options for future development. Rand Health Quarterly, 1 (4). PMID: 28083218; PMCID: PMC4945260.
  • Kessler, M.M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14 (1), 10-25. https://doi.org/10.1002/asi.5090140103
  • Khanna, V.K. (2014). Bibliometric scoring of an individual’s research output in science and engineering. Annals of Library and Information Studies, 61, 121-131.
  • Koseoglu, M.A. (2016). Mapping the institutional collaboration network of strategic management research: 1980-2014. Scientometrics, 109 (1), 203-226. https://doi.org/10.1007/s11192-016-1894-5
  • Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: a comparative analysis. Physical Review E, 80 (5), 056117. https://doi.org/10.1007/s11192-016-1894-5
  • Leung, X.Y., Sun, J., & Bai, B. (2017). Bibliometrics of social media research: A co-citation and co-word analysis. International Journal of Hospitality Management, 66, 35-45. https://doi.org/10.1016/j.ijhm.2017.06.012
  • Lundberg, J. (2006). Bibliometrics as a research assessment tool: Impact beyond the impact factor (Order No. 28427053). Available from ProQuest Dissertations & Theses Global. (2564468545). Retrieved from https://www.proquest.com/dissertations-theses/bibliometrics-as-research-assessment-tool-impact/docview/2564468545/se-2
  • Luukkonen, T., Persson, O., & Sivertsen, G. (1992). Understanding patterns of international scientific collaboration. Science, Technology, & Human Values, 17 (1), 101-126. https://doi.org/10.1177/016224399201700106
  • Manuelian, C.L., Penasa, M., da Costa, L., Burbi, S., Righi, F., & De Marchi, M. (2020). Organic livestock production: A bibliometric review. Animals, 10 (4), 618. https://doi.org/10.3390/ani10040618
  • Meuwissen, T., Hayes, B., & Goddard, M. (2016). Genomic selection: A paradigm shift in animal breeding. Animal Frontiers, 6 (1), 6-14. https://doi.org/10.2527/af.2016-0002
  • Persson, O., Danell, R., & Schneider, J.W. (2009). How to use Bibexcel for various types of bibliometric analysis. Celebrating scholarly communication studies: A Festschrift for Olle Persson at his 60th Birthday, 5, 9-24.
  • RStudioe3rc Team (2020). RStudio: integrated development for R. Rstudio Team, PBC, Boston, MA. http://www. rstudio.com
  • Sci2 Team (2009) Science of Science (Sci2) Tool. Indiana University and SciTech Strategies, 379. https://sci2.cns.iu.edu
  • Silió, L., Rodríguez, M.C., Fernández, A., Barragán, C., Benítez, R., Óvilo, C., & Fernández, A.I. (2013). Measuring inbreeding and inbreeding depression on pig growth from pedigree or SNP‐derived metrics. Journal of Animal Breeding and Genetics, 130 (5), 349-360. https://doi.org/10.1111/jbg.12031
  • Sonesson, A.K., Woolliams, J.A., & Meuwissen, T.H. (2012). Genomic selection requires genomic control of inbreeding. Genetics Selection Evolution, 44 (1), 1-10. https://doi.org/10.1186/1297-9686-44-27
  • Spurlock, D.M., Dekkers, J.C.M., Fernando, R., Koltes, D.A., & Wolc, A. (2012). Genetic parameters for energy balance, feed efficiency, and related traits in Holstein cattle. Journal of Dairy science, 95 (9), 5393-5402. https://doi.org/10.3168/jds.2012-5407
  • Tijssen, R.J., & Van Raan, A.F. (1994). Mapping changes in science and technology: Bibliometric co-occurrence analysis of the R&D literature. Evaluation Review, 18 (1), 98-115. https://doi.org/10.1177/0193841X9401800110
  • Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84 (2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
  • Van Eck, N.J., & Waltman, L. (2014). CitNetExplorer: A new software tool for analyzing and visualizing citation networks. Journal of Informetrics, 8 (4), 802-823. https://doi.org/10.1016/j.joi.2014.07.006
  • Xiao, Z., Qin, Y., Xu, Z., Antucheviciene, J., & Zavadskas, E.K. (2022). The journal buildings: A bibliometric analysis (2011-2021). Buildings, 12 (1), 37. https://doi.org/10.3390/buildings12010037
  • Yardibi, F., Firat, M.Z., & Teke, E.C. (2021). Trend topics in animal science: a bibliometric analysis using CiteSpace. Turkish Journal of Veterinary & Animal Sciences, 45 (5), 833-840. https://doi.org/10.3906/vet-2001-103
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ziraat, Veterinerlik ve Gıda Bilimleri
Bölüm Araştırma Makalesi
Yazarlar

Adile Tatlıyer Tunaz 0000-0002-4239-7072

Erken Görünüm Tarihi 30 Temmuz 2023
Yayımlanma Tarihi 17 Ağustos 2023
Gönderilme Tarihi 9 Aralık 2022
Kabul Tarihi 15 Mart 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 28 Sayı: 2

Kaynak Göster

APA Tatlıyer Tunaz, A. (2023). Bibliometric analysis of quantitative genetics research in animal science in the last decade. Mustafa Kemal Üniversitesi Tarım Bilimleri Dergisi, 28(2), 363-378. https://doi.org/10.37908/mkutbd.1216763
 Kapak Resmi İndir

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