, Volume 109, Issue 3, pp 2151–2157 | Cite as

On the use of databases about research performance: comments on Karlovčec and Mladenić (2015) and others using the SICRIS database

  • Romina Rodela


The accuracy of interdisciplinarity measurements depends on how well the data is used for this purpose and whether it can meaningfully inform about work that crosses disciplinary domains. At present, there are no ad hoc databases compiling information only and exclusively about interdisciplinary research, and those interested in assessing it have to reach out to existing databases that have been compiled for other purposes. Karlovčec and Mladenić (Scientometrics 102:433–454, 2015) saw an opportunity in a national database that brings together information meant to be used for assessing the scientific performance of the Slovene academic community, which they used to obtain information that was then applied to measure interdisciplinarity. However, the context and purpose for which databases are produced have certain implications on their use. In their study, the authors overlooked the social and political context within which that specific database was created, is maintained and is used for (evaluation of research performance). This resulted in an incomplete interpretation of the results obtained and description of the current situation. This commentary addresses two aspects that warrant further consideration: one pertains to the limitations of the dataset itself and the measures used to debunk these, while the second pertains to the line of reasoning behind the integration and use of IDR measures in this study.


Interdisciplinarity Measurements of interdisciplinarity SICRIS Scientific collaborations Policy tools Research evaluation 


  1. Čadež, S., Dimovski, V., & Okorn, K. (2013). Raziskovalna produktivnost in ustvarjanje znanja v slovenskih ekonomsko-poslovnih šolah. Eonomic and Business Review, 15(2), 75–96.Google Scholar
  2. Demšar, F., & Boh, T. (2008). Uvajanje načel transparentnost v delo javne uprave: Primer Javne agencije za raziskovalno dejavnost Republike Slovenije. Družboslovne Razprave, XXIV(58), 89–105.Google Scholar
  3. Ferligoj, A., Kronegger, L., Mali, F., Snijders, T. B., & Doreian, P. (2015). Scientific collaboration dynamics in a national scientific system. Scientometrics, 104, 985–1012.CrossRefGoogle Scholar
  4. Good, B., Vermeulen, N., Tiefenthaler, B., & Arnold, E. (2015). Counting quality? The Czech performance-based research funding system. Research Evaluation, 24, 91–105.CrossRefGoogle Scholar
  5. Južnič, P., Pečlin, S., Žaucer, M., Mandelj, T., Pušnik, M., & Demšar, F. (2010). Scientometric indicators: Peer-review, bibliometric methods and conflict of interests. Scientometrics, 85, 429–441.CrossRefGoogle Scholar
  6. Karlovčec, M., & Mladenić, D. (2015). Interdisciplinarity of scientific fields and its evolution based on graph of project collaboration and co-authoring. Scientometrics, 102, 433–454.CrossRefGoogle Scholar
  7. Kojima, T., & Patrick Barron, J. (2016). Potentially dangerous mistakes in publication ethics: Unethical authorship. The Japanese Society of Gastroenterological Surgery, 49, 469–471.CrossRefGoogle Scholar
  8. Kronegger, L., Ferligoj, A., & Doreian, P. (2011). On the dynamics of national scientific systems. Quality and Quantity, 45, 989–1015.CrossRefGoogle Scholar
  9. Kronegger, L., Mali, F., Ferligoj, A., & Doreian, P. (2012). Collaboration structures in Slovenian scientific communities. Scientometrics, 90, 631–647.CrossRefGoogle Scholar
  10. Lužar, B., Levnajić, Z., Povh, J., & Perc, M. (2014). Community structure and the evolution of interdisciplinarity in slovenia’s scientific collaboration network. PLoS One, 9, e94429.CrossRefGoogle Scholar
  11. Mali, F. (2013). Why an unbiased external evaluation system is important for the progress of social sciences: The case of a small social science community. Social Sciences, 2, 284.CrossRefGoogle Scholar
  12. Mataković, H., Pejić Bach, M., & Radočaj Novak, I. (2013). Scientific productivity in transition countries: Trends and obstacles. Interdisciplinary Description of Complex Systems, 11(2), 174–189.CrossRefGoogle Scholar
  13. Wagner, C. S., Roessner, J. D., Bobb, K., Klein, J. T., Boyack, K. W., Keyton, J., et al. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 5, 14–26.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  1. 1.School of Natural Sciences, Technology and Environmental StudiesSödertörn UniversityHuddingeSweden
  2. 2.Laboratory of Geo-Information Science and Remote SensingWageningen UniversityWageningenThe Netherlands

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