Information reconstruction in educational institutions data from the European tertiary education registry
Autor: | Cinzia Daraio, Renato Bruni, Davide Aureli |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Higher education
Computer science European universities lcsh:Computer applications to medicine. Medical informatics 03 medical and health sciences 0302 clinical medicine Data imputation Machine learning Imputation (statistics) Data Imputation Reconstructed Institutional Microdata Machine Learning Higher Education Institutions lcsh:Science (General) Data Article 030304 developmental biology 0303 health sciences Multidisciplinary business.industry Higher education institutions 4. Education Reconstructed institutional microdata Missing data Data science lcsh:R858-859.7 business 030217 neurology & neurosurgery Educational systems lcsh:Q1-390 |
Zdroj: | Data in Brief, Vol 34, Iss, Pp 106611-(2021) Data in Brief |
ISSN: | 2352-3409 |
Popis: | Universities and other organizations providing higher level education are collectively called Higher Education Institutions. Their detail data, for instance number of students, number of graduates, etc., constitute the basis for several important analyses of the educational systems. This work provides data of the European Tertiary Education Register (ETER), which describes the Educational Institutions of Europe. These data have been gathered through the National Statistical Authorities of all the Countries participant in the ETER Project. However, they include many scattered missing values. Therefore, we have developed and applied an imputation methodology (see “Imputation Techniques for the Reconstruction of Missing Interconnected Data from Higher Educational Institutions, Bruni et al. [3]) to replace the missing values with feasible values being as similar as possible to the original values that have been lost and are now unknown. Thus, we also provide the imputed version of the same dataset, which allows more in-depth analyses of the European Higher Education Institutions. Both datasets (before and after imputation) are provided in two versions: with or without bibliometric information for the Institutions, so the user can also consider these additional information if interested. |
Databáze: | OpenAIRE |
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