A performance indicator for academic communities based on external publication profiles
- 336 Downloads
Studying research productivity is a challenging task that is important for understanding how science evolves and crucial for agencies (and governments). In this context, we propose an approach for quantifying the scientific performance of a community (group of researchers) based on the similarity between its publication profile and a reference community’s publication profile. Unlike most approaches that consider citation analysis, which requires access to the content of a publication, we only need the researchers’ publication records. We investigate the similarity between communities and adopt a new metric named Volume Intensity. Our goal is to use Volume Intensity for measuring the internationality degree of a community. Our experimental results , using Computer Science graduate programs and including both real and random scenarios, show we can use publication profile as a performance indicator.
KeywordsBibliometry Data similarity Analysis
This work was funded by the authors’ individual grants from CNPq and FAPEMIG.
- Brandão, M. A., Moro, M. M., & Almeida, J. M. (2014). Experimental evaluation of academic collaboration recommendation using factorial design. Journal of Information and Data Management, 5(1), 52–63.Google Scholar
- Garfield, E. (1999). Journal impact factor: A brief review. Canadian Medical Association Journal, 161(8), 979–980.Google Scholar
- Gonçalves, G. D., Figueiredo, F., Almeida, J. M., & Gonçalves, M. A. (2014). Characterizing scholar popularity. A case study in the computer science research community. In: JCDL, London, pp. 57–66.Google Scholar
- Lima, H., Silva, T. H. P., Moro, M. M., Santos, R. L. T, Jr., & Meira, W., Laender AHF,. (2013). Aggregating productivity indices for ranking researchers across multiple areas. JCDL (pp. 97–106). USA: Indianapolis.Google Scholar
- Lopes, G. R., Moro, M. M., da Silva, R., Barbosa, E. M., & de Oliveira, J. P. M. (2011). Ranking strategy for graduate programs evaluation. In: ICITA, Sydney, Australia, pp 59–64.Google Scholar
- Menezes, G. V., Ziviani, N., & Laender, A. H., Almeida, V. (2009). A geographical analysis of knowledge production in computer science. In: WWW, Madrid, Spain, pp 1041–1050.Google Scholar
- Ortega, J. L., López-Romero, E., & Fernández, I. (2011). Multivariate approach to classify research institutes according to their outputs: The case of the csic’s institutes. Journal of Informetrics, 5(3), 323–332.Google Scholar
- Ribas, S., Ribeiro-Neto, B., de Souza e Silva, E., Ueda, A. H., & Ziviani, N. (2015). Using reference groups to assess academic productivity in computer science. In: WWW Companion, pp 603–608.Google Scholar
- Silva T. H. P., Moro, M. M., Silva, A. P. C., Meira, W. Jr., & Laender, A. H. F. (2014) Community-based endogamy as an influence indicator. In: JCDL, London, UK, pp 67–76.Google Scholar
- Silva, T. H. P,, Moro, M. M., & Silva, A. P. C. (2015a). Authorship contribution dynamics on publication venues in computer science: An aggregated quality analysis. In: SAC, Salamanca, Spain, pp 1142–1147.Google Scholar
- Silva, T. H. P., Moro, M. M., & Silva, A. P. C. (2015b) Tc-index: A new research productivity index based on evolving communities. In: TPDL, Poznań, Poland, pp 209–221.Google Scholar