Bilgi Üretiminin Nicelleştirilmesi: Türkiye Temel Bilimlerinde Tez Üretimine Yönelik Doğal Dil İşleme (NLP) Tabanlı Analizler ve Tahmin Modelleri

Abstract

Günümüz dünyasında temel bilimler alanındaki araştırmalar büyük ölçüde akademik kurumlarda yürütülmektedir. Lisansüstü tezler bu sürecin temel taşlarından biri olup, bireylerin akademik yolculuğunun ilk adımlarını ve bilimsel bilgiye katkılarını temsil eder. Bu çalışma, 1960-2024 yılları arasında Türkiye’de temel bilimler alanında yazılmış lisansüstü tezlerin kapsamlı bir bibliyometrik analizini sunmaktadır. Araştırmada zaman içinde tez sayısındaki eğilimler, yıllara, tez türlerine ve üniversitelere göre dağılımları incelenmiş; ayrıca kelime bulutu aracılığıyla tekrar eden anahtar kelimeler ve araştırma temaları görselleştirilmiştir. Veriler, Ulusal Tez Merkezi’nden Instant Data Scraper programı kullanılarak toplanmış ve toplamda 68.883 tez analiz edilmiştir. Kullanılan istatistiksel yöntemler arasında betimsel istatistikler, zaman serisi tahmin modelleri ve doğal dil işleme (NLP) teknikleri yer almaktadır. Sonuçlar, yıllar içinde tez üretiminde istikrarlı bir artış olduğunu göstermekte; Gazi Üniversitesi ve Orta Doğu Teknik Üniversitesi’nin (ODTÜ) bu alandaki önemli katkılarını ortaya koymaktadır. Kimya ve biyoloji alanlarında güçlü bir faaliyet gözlenirken, matematik ve kuramsal bilimler nispeten düşük temsil edilmiştir. Tahmin modelleri, tez üretiminde artışın devam edeceğini, ancak bu artışın dışsal etkenlerden etkilenecek şekilde değişkenlik göstereceğini öngörmektedir. Bu bulgular, Türkiye’nin lisansüstü araştırma yapısına dair değerli içgörüler sunmakta ve gelecekteki akademik ve politik gelişmelere yön verebilecek niteliktedir.

Keywords

• Doğal Dil İşleme (NLP)
• Zaman Serisi Tahmini
• Lisansüstü Tezler
• Bibliyometrik Analiz
• Türkiye'de Temel Bilimler

Quantifying Knowledge Production: NLP-Driven Insights and Forecasting Models for Thesis Output in Turkey’s Basic Sciences

Abstract

In the contemporary world, research in the field of basic sciences is predominantly conducted within academic institutions. Graduate theses play a pivotal role in this process, as they not only represent the initial steps on an individual's academic journey but also their contributions to scientific knowledge. This study presents a comprehensive bibliometric analysis of graduate theses written in Türkiye in the basic sciences from 1960 to 2024. The research aims to investigate the trends in the number of theses over time, their distribution by years, types, and universities, as well as visualizing recurring keywords and research themes through a word cloud. By using the Instant Data Scraper program to gather data from the National Thesis Center, a total of 68,883 theses were analyzed. The statistical methods employed include descriptive statistics, time-series forecasting, and natural language processing (NLP) techniques. The results indicate a steady increase in thesis production over the years, with significant contributions from Gazi University and Middle East Technical University (METU). While chemistry and biology show strong activity, mathematics and theoretical sciences are underrepresented. Forecasting models predict continued growth in thesis production, with variations reflecting external influences. These results provide valuable insights into Türkiye’s graduate research landscape and can guide future academic and policy development.

Keywords

• Natural Language Processing (NLP)
• Time Series Forecasting
• Graduate Theses
• Bibliometric Analysis
• Basic Sciences in Türkiye

References

1. Guzikova, L.A. (2018). What Do We Expect From Basic Science? 2018 Dec 31;1283–94.
2. Lopes, L.G.F., Sadler, P.J., Bernardes-Génisson, V., Moura, J.J.G., Chauvin, R., Bernhardt, P.V., et al. (2020). THE FUNDAMENTAL IMPORTANCE of BASIC SCIENCE: EXAMPLES of HIGH-IMPACT DISCOVERIES from AN INTERNATIONAL CHEMISTRY NETWORK. Quim Nova [Internet], 43(8), 1–14. [cited 2025 Feb 13]; Available from: https://www.researchgate.net/publication/342728366_THE_FUNDAMENTAL_IMPORTANCE_of_BASIC_SCIENCE_EXAMPLES_of_HIGH-IMPACT_DISCOVERIES_from_AN_INTERNATIONAL_CHEMISTRY_NETWORK
3. Yanuarti, E.A., & Suprapto, N. (2021). Ten Years of Research on History of Science (Physics): A Bibliometric Analysis. Studies in Philosophy of Science and Education, 2(1), 7–16.
4. Sinatra, R., Deville, P., Szell, M., Wang, D., & Barabási, A.L. (2015). A century of physics. Nat Phys [Internet], 11(10), 791–6. [cited 2025 Jul 18]; Available from: https://www.nature.com/articles/nphys3494
5. Vesterinen, V.M., & Aksela, M. (2009). A novel course of chemistry as a scientific discipline:How do prospective teachers perceive nature of chemistry through visits to research groups? Chemistry Education Research and Practice [Internet], 10(2), 132–41. [cited 2025 Feb 13]; Available from: https://www.researchgate.net/publication/244554906_A_novel_course_of_chemistry_as_a_scientific_disciplineHow_do_prospective_teachers_perceive_nature_of_chemistry_through_visits_to_research_groups
6. Dolino, L.G.O. (2018). Chemistry as a creative science. Found Chem [Internet], 20(1), 3–13. [cited 2025 Feb 13]; Available from: https://www.researchgate.net/publication/314221741_Chemistry_as_a_creative_science
7. Müürsepp, P., Nurysheva, G., Ramazanova, A., & Amirkulova, Z. (2020). Chemistry as the basic science. Foundations of Chemistry 2020 23:1 [Internet], 23(1), 69–83. [cited 2025 Feb 13]; Available from: https://link.springer.com/article/10.1007/s10698-020-09357-5
8. Mahaffy, P.G., Ho, F.M., Haak, J.A., & Brush, E.J. (2019). Can Chemistry Be a Central Science without Systems Thinking. J Chem Educ [Internet], 96(12), 2679–81. [cited 2025 Feb 13]; Available from: https://www.researchgate.net/publication/337871690_Can_Chemistry_Be_a_Central_Science_without_Systems_Thinking

9. Delafield-Butt, J. (2014). Biology. Handbook of Whiteheadian Process Thought [Internet], 157–70. [cited 2025 Feb 13]; Available from: https://www.researchgate.net/publication/262449406_Biology
10. Casanova, J. (2018). Biology, a science of greys. EMBO Rep [Internet], 19(7). [cited 2025 Feb 13]; Available from: https://www.embopress.org
11. Dreyfus, A. (1995). Biological knowledge as a prerequisite for the development of values and attitudes. J Biol Educ [Internet], 29(3), 215–9. [cited 2025 Feb 13]; Available from: https://www.tandfonline.com/doi/abs/10.1080/00219266.1995.9655448
12. Kremer, K., Specht, C., Urhahne, D., & Mayer, J. (2014). The relationship in biology between the nature of science and scientific inquiry. J Biol Educ [Internet], 48(1), 1–8. [cited 2025 Feb 13]; Available from: https://www.researchgate.net/publication/263091845_The_relationship_in_biology_between_the_nature_of_science_and_scientific_inquiry
13. Vega, F.E. (2005). What Makes Biology Unique?: Considerations for the Autonomy of a Scientific Discipline . By Ernst Mayr . Cambridge and New York: Cambridge University Press. $30.00. xiv + 232 p; ill.; index. ISBN: 0‐521‐84114‐3. Q Rev Biol, 80(2), 224–5.
14. Steiner, M. (1989). The Application of Mathematics to Natural Science. J Philos [Internet], 86(9), 449–80. Available from: https://about.jstor.org/terms
15. Atiyah, M. (2010). Mathematics as a basic science*^. Curr Sci [Internet], 99(3), 49–54. [cited 2025 Mar 6]; Available from: [suspicious link removed]
16. Wang, L., Li, M., Yang, T., Wang, L., & Zhou, X. (2021). Mathematics Meets Science in the Brain. Cerebral Cortex [Internet], 32(1), 123–36. [cited 2025 Mar 6]; Available from: https://dx.doi.org/10.1093/cercor/bhab198
17. Diggle, P.J. (2015). Statistics: a Data Science for the 21st Century. J R Stat Soc Ser A Stat Soc [Internet], 178(4), 793–813. [cited 2025 Jul 23]; Available from: https://dx.doi.org/10.1111/rssa.12132
18. Gök, E., Çalık, A., Önal, M.M., Çoşkun, F.B., Doğan, A., & Demir, H. (2022). Yükseköğretim Kurulu Temel Bilimler Toplantısı Raporu. Ankara, Türkiye; 2022 May.
19. Tibayrenc, M. (2002). Basic vs. applied research, field vs. bench work: The never-ending dialogues of the deaf. Infection, Genetics and Evolution [Internet], 1(4), 263–4. [cited 2025 Feb 27]; Available from: https://pubmed.ncbi.nlm.nih.gov/12798004/
20. Bentley, P.J., Gulbrandsen, M., & Kyvik, S. (2015). The relationship between basic and applied research in universities. High Educ (Dordr) [Internet], 70(4), 689–709. [cited 2025 Feb 27]; Available from: https://link.springer.com/article/10.1007/s10734-015-9861-2
21. Agarwal, R., & Ohyama, A. (2013). Industry or academia, basic or applied? Career choices and earnings trajectories of scientists. Manage Sci, 59(4), 950–70.
22. Almeida-Filho, N., & Goldbaum, M. (2003). The value of public health research and the division between basic vs. applied science. Braz J Infect Dis [Internet], 7(1), 82–90. [cited 2025 Feb 27]; Available from: https://pubmed.ncbi.nlm.nih.gov/12807695/
23. Löwe, B. (2002). The Formal Sciences: Their Scope, Their Foundations, and Their Unity. Synthese [Internet], 133(1/2), 5–11. [cited 2025 Feb 27]; Available from: https://about.jstor.org/terms
24. Leboy, P.S., & Madden, J.F. (2012). Limitations on diversity in basic science departments. DNA Cell Biol [Internet], 31(8), 1365–71. [cited 2025 Feb 27]; Available from: https://pubmed.ncbi.nlm.nih.gov/22775445/
25. Yang, W. (2016). Policy: Boost basic research in China. Nature [Internet], 534(7608), 467–9. [cited 2025 Feb 27]; Available from: https://pubmed.ncbi.nlm.nih.gov/27337328/
26. Günay, D., Üniversitesi, M., Fakültesi, M., & Eğitim Köyü, M. (2018). Türkiye’de Lisansüstü Eğitim ve Lisansüstü Eğitime Felsefi Bir Bakış. Üniversite Araştırmaları Dergisi [Internet], 1(2), 71–88. [cited 2025 Feb 27]; Available from: https://dergipark.org.tr/tr/pub/uad/issue/38505/450965
27. Aramış, Z.F., Özturan Ecemi̇ş, Ü., & Faydaoğlu, Ş. (2023). TÜRKİYE’DE MATEMATİK ALAN DİLİ İLE İLGİLİ YAPILAN LİSANSÜSTÜ TEZLERİN İNCELENMESİ. Trakya Eğitim Dergisi [Internet], 13(2), 1382–401. [cited 2025 Feb 27]; Available from: https://dergipark.org.tr/tr/pub/tred/issue/77440/1151888
28. Ağır, H. (2010). Türkiye İle Güney Kore’de Bilim Ve Teknoloji Politikalarının Karşılaştırması. Journal of Knowlede Economy and Knowledge Management [Internet], 5(2). [cited 2025 Feb 27]; Available from: https://dergipark.org.tr/en/pub/beyder/issue/3477/47306
29. Marmara Üniversitesi Sosyal Bilimler Enstitüsü. (2020). LİSANSÜSTÜ TEZ VE PROJE YAZIM KILAVUZU. İstanbul, Türkiye.
30. Liu, C., Hoi, S.C.H., Zhao, P., & Sun, J. (2016). Online ARIMA Algorithms for Time Series Prediction. Proceedings of the AAAI Conference on Artificial Intelligence [Internet], 30(1), 1867–73. [cited 2025 Jul 18]; Available from: https://ojs.aaai.org/index.php/AAAI/article/view/10257
31. Corberán-Vallet, A., Bermúdez, J.D., & Vercher, E. (2011). Forecasting correlated time series with exponential smoothing models. Int J Forecast [Internet], 27(2), 252–65. [cited 2025 Jul 18]; Available from: https://www.sciencedirect.com/science/article/abs/pii/S0169207010001172?via%3Dihub
32. Sivaramakrishnan, S., Rathish, C.R., Premalatha, S., & Niranjana, C. (2023). Introduction to AI Technique and Analysis of Time Series Data Using Facebook Prophet Model. Innovative Engineering with AI Applications [Internet], 171–88. [cited 2025 Jul 18]; Available from: /doi/pdf/10.1002/9781119792161.ch9