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TÜBİTAK Projelerindeki Güçlü Araştırma İşbirliğinin Sosyal Ağ Analizi ile Dinamiklerinin Değerlendirilmesi

Yıl 2022, Cilt: 13 Sayı: 35, 810 - 828, 22.08.2022

Sevim Unutulmaz

https://doi.org/10.21076/vizyoner.1009511

Öz

İşbirliği ağları, araştırmacıların bilimsel işbirliğini gösteren düğümlerden ve çok sayıda bağlantıdan oluşmaktadır. Bu ağları sosyal ağ analizi yöntemi ile incelemek mümkündür. Bu makalenin amacı, Türkiye’deki üniversiteler tarafından yürütülen TÜBİTAK 1001 projeleri kapsamında ortak işbirliğine dayalı oluşturulan üniversiteler arası işbirliği ağlarının görselleştirmesi ve önemli pozisyonda yer alan üniversitelerin tespit edilmesidir. Bu bağlamda TÜBİTAK 1001 proje işbirliklerinin mevcut durumuna ışık tutmak amaçlanmıştır. Çalışmanın örneklemi 2012-2020 yılları arasında yürütülen 2323 adet TÜBİTAK 1001 projesinden oluşmaktadır. Ağın genel yapısı ise toplam 193 üniversite (düğüm) ve 2805 ortak işbirliğini (bağlantı) kapsamaktadır. Her düğüm TÜBİTAK 1001 proje işbirliği ağındaki bir üniversiteyi temsil ederken toplam araştırmacı sayısı 8.205 kişiden oluşmaktadır. Bilimsel işbirliği UCINET 6.732 ve NetDraw 2.168 yazılımı kullanılarak sosyal ağ analizi yöntemi ile analiz edilmiştir. Bu kapsamda öncelikle TÜBİTAK 1001 projeleri ve işbirlikleri hakkında genel bilgilere yer verilmiştir. Ardından en çok işbirliği yapılan üniversiteler ve en güçlü üniversite işbirlikleri tespit edilmiştir. Daha sonrasında ise TÜBİTAK 1001 proje işbirliğini incelemek için merkezilik analizi gibi çeşitli sosyal ağ analizi (SNA) yöntemleri kullanılmıştır. İşbirliklerin sosyal ağlardaki durumunu yansıtan merkezilik hesaplamaları, ağ analizinde en önemli ölçütlerden biridir. Uygulanan analizler sonucunda üniversitelerin ağdaki performans ve rollerini değerlendirmeye yönelik faydalı bilgiler elde edilmiştir.

Anahtar Kelimeler

Kurumlar Arası İşbirliği Sosyal Ağ Analizi Merkezilik TÜBİTAK 1001 Projeleri

Kaynakça

  • Abbasi, A. ve Altmann, J. (2011). On the correlation between research performance and social network analysis measures applied to research collaboration networks. 011 44th Hawaii International Conference on System Sciences (s. 1-10). IEEE. https://doi.org/10.1109/HICSS.2011.325
  • Abraham, A., Hassanien, A. E. ve Snášel, V. (Eds.). (2009). Computational social network analysis: Trends, tools and research advances. Springer Science & Business Media.
  • Barnett, G. A., Park, H. W., Jiang, K., Tang, C. ve Aguillo, I. F. (2014). A multi-level network analysis of web-citations among the world’s universities. Scientometrics, 99(1), 5–26. https://doi.org/10.1007/s11192-013-1070-0
  • Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of mathematical sociology, 2(1), 113-120. https://doi.org/10.1080/0022250X.1972.9989806
  • Borgatti, S. P. (1995). Centrality and AIDS. Connections, 18(1), 112-114. https://doi.org/10.1016/j.socnet.2004.11.008
  • Chen, Y. Y., LI, X. Y. ve BU, L. L. (2018). Research on scientific collaboration behavior based on centrality and cohesive subgroup analysis. DEStech Transactions on Computer Science and Engineering, (icmsa). https://doi.org/10.12783/dtcse/icmsa2018/23267
  • Demirgil, H. (2018). Süleyman Demirel Üniversitesi yayınlarında bilimsel yoğunlaşma alanları ve bibliyometrik ağ analizi. Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi, 13(2), 36-53. https://doi.org/10.29233/sdufeffd.375482
  • Everett, M. G. ve Borgatti, S. P. (2013). The dual-projection approach for two-mode networks. Social networks, 35(2), 204-210. https://doi.org/10.1016/j.socnet.2012.05.004
  • Ferligoj, A., Kronegger, L., Mali, F., Snijders, T. A. ve Doreian, P. (2015). Scientifc collaboration dynamics in a national scientifc system. Scientometrics, 104(3), 985-1012. https://doi.org/10.1007/s11192-015-1585-7
  • Frenken, K., Van Oort, F. ve Verburg, T. (2007). Related variety, unrelated variety and regional economic growth. Regional Studies, 41, 685–697. https://doi.org/10.1080/00343400601120296
  • Grassi, R., Stefani, S. ve Torriero, A. (2007). Some new results on the eigenvector centrality. Mathematical Sociology, 31(3), 237-248. https://doi.org/10.1080/00222500701373251
  • Hanneman, R. A. ve Riddle, M. (2005). Introduction to social network methods.
  • Hara, N., Solomon, P., Kim, S. L. ve Sonnenwald, D. H. (2003). An emerging view of scientific collaboration: Scientists' perspectives on collaboration and factors that impact collaboration. Journal of the American Society for Information science and Technology, 54(10), 952-965. https://doi.org/10.1002/asi.10291
  • Iglič, H., Doreian, P., Kronegger, L. ve Ferligoj, A. (2017). With whom do researchers collaborate and why?. Scientometrics, 112(1), 153-174. https://doi.org/10.1007/s11192-017-2386-y
  • Isfandyari-Moghaddam, A., Saberi, M. K., Tahmasebi-Limoni, S., Mohammadian, S. ve Naderbeigi, F. (2021). Global scientific collaboration: A social network analysis and data mining of the co-authorship networks. Journal of Information Science, 1(16), 1-16. https://doi.org/10.1177/01655515211040655
  • Katz, J. ve Martin, B. (1997). ‘What is research collaboration?’, Research Policy, 26(1), 1–18. https://doi.org/10.1016/S0048-7333(96)00917-1
  • Lee, J. J. ve Haupt, J. P. (2021). Scientific collaboration on COVID-19 amidst geopolitical tensions between the US and China. The Journal of Higher Education, 92(2), 303-329. https://doi.org/10.1080/00221546.2020.1827924
  • Matveeva, N. ve Ferligoj, A. (2020). Scientific collaboration in Russian universities before and after the excellence initiative Project 5-100. Scientometrics, 124(3), 2383-2407. https://doi.org/10.1007/s11192-020-03602-6
  • Meng, J. ve Guo, J. (2015). Analysis of researcher co-authorship network. 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops (s. 283-286). https://doi.org/10.1109/WAINA.2015.63
  • Mohammadian, S. ve Vaziri, E. (2017). Analysis and visualization of scientific collaboration of Iran universities of medical sciences using social network analysis metrics based on Web of Science database. Tehran University of Medical Sciences, 11(1), 43-56.
  • Niu, F. ve Qiu, J. (2014). Network structure, distribution and the growth of Chinese international research collaboration. Scientometrics, 98(2), 1221-1233. https://doi.org/10.1007/s11192-013-1170-x
  • Özman, M. (2017). Strategic management of innovation networks. Cambridge University Press.
  • Persson, O., Melin, G., Danell, R. ve Kaloudis, A. (1997). Research collaboration at Nordic universities. Scientometrics, 39(2), 209-223. https://doi.org/10.1007/bf02457449
  • Prell, C. (2012). Social network analysis: History theory and methodology. Los Angeles etc.
  • Sala, F. G., Osca-Lluch, J. ve Peñaranda-Ortega, M. (2021). Evolution of scientific collaboration within Spanish Psychology between 1970 and 1989. Actas Luso-Españolas de Neurología, Psiquiatría y Ciencias Afines, 2(8), 7. https://doi.org/10.6018/analesps.474391
  • Schlattmann, S. (2017). Capturing the collaboration intensity of research institutions using social network analysis. Procedia Computer Science, 106, 25-31. https://doi.org/ 10.1016/j.procs.2017.03.005
  • Wu, Y. ve Duan, Z. (2015). Social network analysis of international scientific collaboration on psychiatry research. International Journal of Mental Health Systems, 9(1), 1-10 https://doi.org/10.1186/1752-4458-9-2
  • Wu, Y. ve Jin, X. (2016). Analysis of scientific collaboration in Chinese psychiatry research. BMC Psychiatry, 16(1), 1-9. https://doi.org/10.1186/s12888-016-0870-1
  • Xue, W., Li, H., Ali, R. ve Rehman, R. U. (2020). Knowledge mapping of corporate financial performance research: A visual analysis using cite space and ucinet. Sustainability, 12(9), 3554. https://doi.org/10.3390/su12093554
  • Ye, Q., Song, H. ve Li, T. (2012). Cross-institutional collaboration networks in tourism and hospitality research. Tourism Management Perspectives, 2, 55-64. https://doi.org/10.1016/j.tmp.2012.03.002

Evaluation of the Dynamics of Strong Research Collaboration in TUBITAK Projects by Social Network Analysis

Yıl 2022, Cilt: 13 Sayı: 35, 810 - 828, 22.08.2022

Sevim Unutulmaz

https://doi.org/10.21076/vizyoner.1009511

Öz

Collaboration networks consist of nodes and a large number of links that show the scientific collaboration of researchers. It is possible to examine these networks with the social network analysis method. The aim of the studyis to visualize the cooperation networks among universities based on joint cooperation within the scope of TUBITAK 1001 projects carried out by universities in Turkey and to identify universities that have an important position. In this context, it is aimed to shed light on the current status of TUBITAK 1001 project collaborations. The sample of the study consists of 2,323 TUBITAK 1001 projects carried out in the 2012-2020 period. The general structure of the network includes a total of 193 universities (nodes) and 2,805 joint collaborations (links). While each node represents a university in the TUBITAK 1001 project cooperation network, the total number of researchers is 8,205. Scientific collaboration is analyzed by social network analysis method using UCINET 6.732 and NetDraw 2.168 software. In this context, various social network analysis (SNA) methods such as centrality analysis are used to examine TUBITAK 1001 project collaboration. As a result of the analyzes applied, useful information is obtained for evaluating the performance and role of universities in the network.

Anahtar Kelimeler

Cross-Institutional Collaboration Social Network Analysis Centrality TÜBİTAK 1001 Project

Kaynakça

  • Abbasi, A. ve Altmann, J. (2011). On the correlation between research performance and social network analysis measures applied to research collaboration networks. 011 44th Hawaii International Conference on System Sciences (s. 1-10). IEEE. https://doi.org/10.1109/HICSS.2011.325
  • Abraham, A., Hassanien, A. E. ve Snášel, V. (Eds.). (2009). Computational social network analysis: Trends, tools and research advances. Springer Science & Business Media.
  • Barnett, G. A., Park, H. W., Jiang, K., Tang, C. ve Aguillo, I. F. (2014). A multi-level network analysis of web-citations among the world’s universities. Scientometrics, 99(1), 5–26. https://doi.org/10.1007/s11192-013-1070-0
  • Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of mathematical sociology, 2(1), 113-120. https://doi.org/10.1080/0022250X.1972.9989806
  • Borgatti, S. P. (1995). Centrality and AIDS. Connections, 18(1), 112-114. https://doi.org/10.1016/j.socnet.2004.11.008
  • Chen, Y. Y., LI, X. Y. ve BU, L. L. (2018). Research on scientific collaboration behavior based on centrality and cohesive subgroup analysis. DEStech Transactions on Computer Science and Engineering, (icmsa). https://doi.org/10.12783/dtcse/icmsa2018/23267
  • Demirgil, H. (2018). Süleyman Demirel Üniversitesi yayınlarında bilimsel yoğunlaşma alanları ve bibliyometrik ağ analizi. Süleyman Demirel Üniversitesi Fen Edebiyat Fakültesi Fen Dergisi, 13(2), 36-53. https://doi.org/10.29233/sdufeffd.375482
  • Everett, M. G. ve Borgatti, S. P. (2013). The dual-projection approach for two-mode networks. Social networks, 35(2), 204-210. https://doi.org/10.1016/j.socnet.2012.05.004
  • Ferligoj, A., Kronegger, L., Mali, F., Snijders, T. A. ve Doreian, P. (2015). Scientifc collaboration dynamics in a national scientifc system. Scientometrics, 104(3), 985-1012. https://doi.org/10.1007/s11192-015-1585-7
  • Frenken, K., Van Oort, F. ve Verburg, T. (2007). Related variety, unrelated variety and regional economic growth. Regional Studies, 41, 685–697. https://doi.org/10.1080/00343400601120296
  • Grassi, R., Stefani, S. ve Torriero, A. (2007). Some new results on the eigenvector centrality. Mathematical Sociology, 31(3), 237-248. https://doi.org/10.1080/00222500701373251
  • Hanneman, R. A. ve Riddle, M. (2005). Introduction to social network methods.
  • Hara, N., Solomon, P., Kim, S. L. ve Sonnenwald, D. H. (2003). An emerging view of scientific collaboration: Scientists' perspectives on collaboration and factors that impact collaboration. Journal of the American Society for Information science and Technology, 54(10), 952-965. https://doi.org/10.1002/asi.10291
  • Iglič, H., Doreian, P., Kronegger, L. ve Ferligoj, A. (2017). With whom do researchers collaborate and why?. Scientometrics, 112(1), 153-174. https://doi.org/10.1007/s11192-017-2386-y
  • Isfandyari-Moghaddam, A., Saberi, M. K., Tahmasebi-Limoni, S., Mohammadian, S. ve Naderbeigi, F. (2021). Global scientific collaboration: A social network analysis and data mining of the co-authorship networks. Journal of Information Science, 1(16), 1-16. https://doi.org/10.1177/01655515211040655
  • Katz, J. ve Martin, B. (1997). ‘What is research collaboration?’, Research Policy, 26(1), 1–18. https://doi.org/10.1016/S0048-7333(96)00917-1
  • Lee, J. J. ve Haupt, J. P. (2021). Scientific collaboration on COVID-19 amidst geopolitical tensions between the US and China. The Journal of Higher Education, 92(2), 303-329. https://doi.org/10.1080/00221546.2020.1827924
  • Matveeva, N. ve Ferligoj, A. (2020). Scientific collaboration in Russian universities before and after the excellence initiative Project 5-100. Scientometrics, 124(3), 2383-2407. https://doi.org/10.1007/s11192-020-03602-6
  • Meng, J. ve Guo, J. (2015). Analysis of researcher co-authorship network. 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops (s. 283-286). https://doi.org/10.1109/WAINA.2015.63
  • Mohammadian, S. ve Vaziri, E. (2017). Analysis and visualization of scientific collaboration of Iran universities of medical sciences using social network analysis metrics based on Web of Science database. Tehran University of Medical Sciences, 11(1), 43-56.
  • Niu, F. ve Qiu, J. (2014). Network structure, distribution and the growth of Chinese international research collaboration. Scientometrics, 98(2), 1221-1233. https://doi.org/10.1007/s11192-013-1170-x
  • Özman, M. (2017). Strategic management of innovation networks. Cambridge University Press.
  • Persson, O., Melin, G., Danell, R. ve Kaloudis, A. (1997). Research collaboration at Nordic universities. Scientometrics, 39(2), 209-223. https://doi.org/10.1007/bf02457449
  • Prell, C. (2012). Social network analysis: History theory and methodology. Los Angeles etc.
  • Sala, F. G., Osca-Lluch, J. ve Peñaranda-Ortega, M. (2021). Evolution of scientific collaboration within Spanish Psychology between 1970 and 1989. Actas Luso-Españolas de Neurología, Psiquiatría y Ciencias Afines, 2(8), 7. https://doi.org/10.6018/analesps.474391
  • Schlattmann, S. (2017). Capturing the collaboration intensity of research institutions using social network analysis. Procedia Computer Science, 106, 25-31. https://doi.org/ 10.1016/j.procs.2017.03.005
  • Wu, Y. ve Duan, Z. (2015). Social network analysis of international scientific collaboration on psychiatry research. International Journal of Mental Health Systems, 9(1), 1-10 https://doi.org/10.1186/1752-4458-9-2
  • Wu, Y. ve Jin, X. (2016). Analysis of scientific collaboration in Chinese psychiatry research. BMC Psychiatry, 16(1), 1-9. https://doi.org/10.1186/s12888-016-0870-1
  • Xue, W., Li, H., Ali, R. ve Rehman, R. U. (2020). Knowledge mapping of corporate financial performance research: A visual analysis using cite space and ucinet. Sustainability, 12(9), 3554. https://doi.org/10.3390/su12093554
  • Ye, Q., Song, H. ve Li, T. (2012). Cross-institutional collaboration networks in tourism and hospitality research. Tourism Management Perspectives, 2, 55-64. https://doi.org/10.1016/j.tmp.2012.03.002
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ekonomi
Bölüm Araştırma Makaleleri
Yazarlar

Sevim Unutulmaz 0000-0002-2286-9458

Erken Görünüm Tarihi 22 Ağustos 2022
Yayımlanma Tarihi 22 Ağustos 2022
Gönderilme Tarihi 14 Ekim 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 13 Sayı: 35

Kaynak Göster

APA Unutulmaz, S. (2022). TÜBİTAK Projelerindeki Güçlü Araştırma İşbirliğinin Sosyal Ağ Analizi ile Dinamiklerinin Değerlendirilmesi. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 13(35), 810-828. https://doi.org/10.21076/vizyoner.1009511
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