, Volume 109, Issue 2, pp 979–996 | Cite as

Mapping and classification of agriculture in Web of Science: other subject categories and research fields may benefit

  • Tomaz BartolEmail author
  • Gordana Budimir
  • Primoz Juznic
  • Karmen Stopar


Fields of science (FOS) can be used for the assessment of publishing patterns and scientific output. To this end, WOS JCR (Web of Science/Journal Citation Reports) subject categories are often mapped to Frascati-related OECD FOS (Organization for Economic Co-operation and Development). Although WOS categories are widely employed, they reflect agriculture (one of six major FOS) less comprehensively. Other fields may benefit from agricultural WOS mapping. The aim was to map all articles produced nationally (Slovenia) by agricultural research groups, over two decades, to their corresponding journals and categories in order to visualize the strength of links between the categories and scatter of articles, based on WOS-linked raw data in COBISS/SciMet portal (Co-operative Online Bibliographic System and Services/Science Metrics) and national CRIS—Slovenian Current Research Information System. Agricultural groups are mapped into four subfields: Forestry and Wood Science, Plant Production, Animal Production, and Veterinary Science. Food science is comprised as either plant- or animal-product-related. On average, 50 % of relevant articles are published outside the scope of journals mapped to WOS agricultural categories. The other half are mapped mostly to OECD Natural-, Medical- and Health Sciences, and Engineering-and-Technology. A few selected journals and principal categories account for an important part of all relevant documents (core). Even many core journals/categories as ascertained with power laws (Bradford’s law) are not mapped to agriculture. Research-evaluation based on these classifications may underestimate multidisciplinary dimensions of agriculture, affecting its position among scientific fields and also subsequent funding if established on such ranking.


Classification Fields of science Research evaluation Power laws Agriculture Research groups 



This research has been supported by the Slovenian Research Agency (ARRS): Research Project V5-1425 (B), and (partially) Research Programme P4-0085 (D).


  1. Abramo, G., D’Angelo, C. A., & Cicero, T. (2012). What is the appropriate length of the publication period over which to assess research performance? Scientometrics, 93(3), 1005–1017. doi: 10.1007/s11192-012-0714-9.CrossRefGoogle Scholar
  2. Acosta, M., Coronado, D., Ferrándiz, E., & León, M. D. (2014). Regional scientific production and specialization in Europe: The role of HERD. European Planning Studies, 22(5), 949–974. doi: 10.1080/09654313.2012.752439.CrossRefGoogle Scholar
  3. Aksnes, D., Olsen, T., & Seglen, P. (2000). Validation of bibliometric indicators in the field of microbiology: A Norwegian case study. Scientometrics, 49(1), 7–22. doi: 10.1023/A:1005653006993.CrossRefGoogle Scholar
  4. Albarrán, P., Ortuño, I., & Ruiz-Castillo, J. (2011). Average-based versus high- and low-impact indicators for the evaluation of scientific distributions. Research Evaluation, 20(4), 325–339. doi: 10.3152/095820211X13164389670310.CrossRefGoogle Scholar
  5. Aleixandre, J. L., Aleixandre-Tudó, J. L., Bolaños-Pizzaro, M., & Aleixandre-Benavent, R. (2013). Mapping the scientific research on wine and health (2001–2011). Journal of Agricultural and Food Chemistry, 61(49), 11871–11880. doi: 10.1021/jf404394e.CrossRefGoogle Scholar
  6. Bartol, T. (2010). Scientometric assessment of publishing patterns and performance indicators in agriculture in the JCEA member countries. Journal of Central European Agriculture, 11(1), 1–9.CrossRefGoogle Scholar
  7. Bartol, T., Budimir, G., Dekleva-Smrekar, D., Pusnik, M., & Juznic, P. (2014). Assessment of research fields in Scopus and Web of Science in the view of national research evaluation in Slovenia. Scientometrics, 98(2), 1491–1504. doi: 10.1007/s11192-013-1148-8.CrossRefGoogle Scholar
  8. Batagelj, V., & Mrvar, A. (2012). Pajek. Programs for large networks analysis.
  9. Bornmann, L., & Marx, W. (2015). Methods for the generation of normalized citation impact scores in bibliometrics: Which method best reflects the judgements of experts? Journal of Informetrics, 9(2), 408–418. doi: 10.1016/j.joi.2015.01.006.CrossRefGoogle Scholar
  10. Borsi, B., & Schubert, A. (2011). Agrifood research in Europe: A global perspective. Scientometrics, 86(1), 133–154. doi: 10.1007/s11192-010-0235-3.CrossRefGoogle Scholar
  11. Bourke, P., & Butler, L. (1998). Institutions and the map of science: Matching university departments and fields of research1. Research Policy, 26(6), 711–718. doi: 10.1016/S0048-7333(97),00046-2.CrossRefGoogle Scholar
  12. Bradford, S. C. (1934). Sources of information on specific subject. Engineering, 137, 85–86.Google Scholar
  13. Chavarro, D., Tang, P., & Rafols, I. (2014). Interdisciplinarity and research on local issues: Evidence from a developing country. Research Evaluation, 23(3), 195–209. doi: 10.1093/reseval/rvu012.CrossRefGoogle Scholar
  14. Cova, T. F. G. C., Jarmelo, S., Formosinho, S. J., de Melo, J. S. S., & Pais, A. A. C. C. (2015). Unsupervised characterization of research institutions with task-force estimation. Journal of Informetrics, 9(1), 59–68. doi: 10.1016/j.joi.2014.11.005.CrossRefGoogle Scholar
  15. Ferligoj, A., Kronegger, L., Mali, F., Snijders, T. A. B., & Doreian, P. (2015). Scientific collaboration dynamics in a national scientific system. Scientometrics, 104(3), 985–1012. doi: 10.1007/s11192-015-1585-7.CrossRefGoogle Scholar
  16. Gautam, P., & Yanagiya, R. (2012). Reflection of cross-disciplinary research at Creative Research Institution (Hokkaido University) in the Web of Science database: Appraisal and visualization using bibliometry. Scientometrics, 93(1), 101–111. doi: 10.1007/s11192-012-0655-3.CrossRefGoogle Scholar
  17. Glänzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357–367. doi: 10.1023/A:1022378804087.CrossRefGoogle Scholar
  18. Glänzel, W., Thijs, B., & Debackere, K. (2014). The application of citation-based performance classes to the disciplinary and multidisciplinary assessment in national comparison and institutional research assessment. Scientometrics, 101(2), 939–952. doi: 10.1007/s11192-014-1247-1.CrossRefGoogle Scholar
  19. Huang, S., Yang, B., Yan, S., & Rousseau, R. (2014). Institution name disambiguation for research assessment. Scientometrics, 99(3), 823–838. doi: 10.1007/s11192-013-1214-2.CrossRefGoogle Scholar
  20. Jarneving, B. (2009). The publication activity of Region Västra Götaland: A bibliometric study of an administrative and political Swedish region during the period 1998–2006. Information Research, 14(2), Paper 397.
  21. Jonkers, K. (2009). Models and orphans; concentration of the plant molecular life science research agenda. Scientometrics, 83(1), 167–179. doi: 10.1007/s11192-009-0024-z.CrossRefGoogle Scholar
  22. Juznic, P., Peclin, S., Zaucer, M., Mandelj, T., Pusnik, M., & Demsar, F. (2010). Scientometric indicators: Peer-review, bibliometric methods and conflict of interests. Scientometrics, 85(2), 429–441.CrossRefGoogle Scholar
  23. Klavans, R., & Boyack, K. W. (2009). Toward a consensus map of science. Journal of the American Society for Information Science and Technology, 60(3), 455–476. doi: 10.1002/asi.20991.CrossRefGoogle Scholar
  24. Kutlaca, D., Babic, D., Zivkovic, L., & Strbac, D. (2014). Analysis of quantitative and qualitative indicators of SEE countries scientific output. Scientometrics, 102(1), 247–265. doi: 10.1007/s11192-014-1290-y.CrossRefGoogle Scholar
  25. Larsen, P., & von Ins, M. (2010). The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. Scientometrics, 84(3), 575–603. doi: 10.1007/s11192-010-0202-z.CrossRefGoogle Scholar
  26. Morillo, F., Bordons, M., & Gómez, I. (2003). Interdisciplinarity in science: A tentative typology of disciplines and research areas. Journal of the American Society for Information Science and Technology, 54(13), 1237–1249. doi: 10.1002/asi.10326.CrossRefGoogle Scholar
  27. OECD. (2007). OECD/OCDE. Revised field of science and technology (FOS) classification in the Frascati manual.
  28. Persson, O. (2010). Bibexcel: A toolbox for bibliometricians. Inforsk, Umea university. Accessed 10 Nov 2015.
  29. Pudovkin, A. I., & Garfield, E. (2002). Algorithmic procedure for finding semantically related journals. Journal of the American Society for Information Science and Technology, 53(13), 1113–1119. doi: 10.1002/asi.10153.CrossRefGoogle Scholar
  30. Rafols, I., Leydesdorff, L., O’Hare, A., Nightingale, P., & Stirling, A. (2012). How journal rankings can suppress interdisciplinary research: A comparison between Innovation studies and business and management. Research Policy, 41(7), 1262–1282. doi: 10.1016/j.respol.2012.03.015.CrossRefGoogle Scholar
  31. Ren, J.-L., Lyu, P.-H., Wu, X.-M., Ma, F.-C., Wang, Z.-Z., & Yang, G. (2013). An informetric profile of water resources management literatures. Water Resources Management, 27(13), 4679–4696. doi: 10.1007/s11269-013-0435-8.CrossRefGoogle Scholar
  32. Rinia, E. J., van Leeuwen, T. N., Bruins, E. E. W., van Vuren, H. G., & van Raan, A. F. J. (2002). Measuring knowledge transfer between fields of science. Scientometrics, 54(3), 347–362. doi: 10.1023/A:1016078331752.CrossRefGoogle Scholar
  33. Ruiz-Castillo, J., & Waltman, L. (2015). Field-normalized citation impact indicators using algorithmically constructed classification systems of science. Journal of Informetrics, 9(1), 102–117. doi: 10.1016/j.joi.2014.11.010.CrossRefGoogle Scholar
  34. Schoeneck, D. J., Porter, A. L., Kostoff, R. N., & Berger, E. M. (2011). Assessment of Brazil’s research literature. Technology Analysis & Strategic Management, 23(6), 601–621. doi: 10.1080/09537325.2011.585029.CrossRefGoogle Scholar
  35. Siegmeier, T., & Möller, D. (2013). Mapping research at the intersection of organic farming and bioenergy: A scientometric review. Renewable and Sustainable Energy Reviews, 25, 197–204. doi: 10.1016/j.rser.2013.04.025.CrossRefGoogle Scholar
  36. Testa, J. (2003). The Thomson ISI journal selection process. Serials Review, 29(3), 210–212. doi: 10.1080/00987913.2003.10764831.CrossRefGoogle Scholar
  37. Thelwall, M., & Fairclough, R. (2015). Geometric journal impact factors correcting for individual highly cited articles. Journal of Informetrics, 9(2), 263–272. doi: 10.1016/j.joi.2015.02.004.CrossRefGoogle Scholar
  38. Toivanen, H. (2014). The shift from theory to innovation: The evolution of Brazilian research frontiers 2005–2011. Technology Analysis & Strategic Management, 26(1), 105–119. doi: 10.1080/09537325.2013.850160.MathSciNetCrossRefGoogle Scholar
  39. Valderrama-Zurián, J.-C., Aguilar-Moya, R., Melero-Fuentes, D., & Aleixandre-Benavent, R. (2015). A systematic analysis of duplicate records in Scopus. Journal of Informetrics, 9(3), 570–576. doi: 10.1016/j.joi.2015.05.002.CrossRefGoogle Scholar
  40. van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. doi: 10.1007/s11192-009-0146-3.CrossRefGoogle Scholar
  41. Vanloqueren, G., & Baret, P. V. (2009). How agricultural research systems shape a technological regime that develops genetic engineering but locks out agroecological innovations. Research Policy, 38(6), 971–983. doi: 10.1016/j.respol.2009.02.008.CrossRefGoogle Scholar
  42. Vilar, P., Juznic, P., Bartol, T., & GreyNet, G. L. N. S. (2012). Information-seeking behaviour of Slovenian researchers: Implications for information services. The Grey Journal, 8(1), 43–53.Google Scholar
  43. Waltman, L., Calero-Medina, C., Kosten, J., Noyons, E. C. M., Tijssen, R. J. W., van Eck, N. J., et al. (2012). The Leiden ranking 2011/2012: Data collection, indicators, and interpretation. Journal of the American Society for Information Science and Technology, 63(12), 2419–2432. doi: 10.1002/asi.22708.CrossRefGoogle Scholar
  44. Yan, E., Ding, Y., Cronin, B., & Leydesdorff, L. (2013). A bird’s-eye view of scientific trading: Dependency relations among fields of science. Journal of Informetrics, 7(2), 249–264. doi: 10.1016/j.joi.2012.11.008.CrossRefGoogle Scholar
  45. Zhang, L., Liu, X., Janssens, F., Liang, L., & Glänzel, W. (2010). Subject clustering analysis based on ISI category classification. Journal of Informetrics, 4(2), 185–193. doi: 10.1016/j.joi.2009.11.005.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  1. 1.Agronomy Department, Biotechnical FacultyUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Institute of Information ScienceMariborSlovenia
  3. 3.Department of Library and Information Science and Book Studies, Faculty of ArtsUniversity of LjubljanaLjubljanaSlovenia

Personalised recommendations