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The increasing availability of longitudinal data (repeated numerical observations of the same units at different times) requires the development of flexible techniques to automatically detect errors in such data. Besides standard types of errors, which can be treated with generic error correction techniques, large longitudinal datasets may present specific problems not easily traceable by the generic techniques. In particular, after applying those generic techniques, time series in the data may contain trends, natural fluctuations and possible surviving errors. To study the data evolution, one main issue is distinguishing those elusive errors from the rest, which should be kept as they are and not flattened or altered. This work responds to this need by identifying some types of elusive errors and by proposing a statistical-mathematical approach to capture their complexity that can be applied after the above generic techniques. The proposed approach is based on a system of indicators and works at the formal level by studying the differences between consecutive values of data series and the symmetries and asymmetries of these differences. It operates regardless of the specific meaning of the data and is thus applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing two key variables: "Total academic staff" and "Total number of enrolled students", which are two of the most important variables, often used in empirical analysis as a proxy for size, and are considered by policymakers at the European level. The results are very promising. [ABSTRACT FROM AUTHOR]

Higher education institutions (HEIs), based on learning, innovation, and research, can support the progress of civil society. Many HEIs are implementing sustainability practices and projects to counteract climate change, often involving youth participation. The present study aimed at identifying how sustainable communities may be fostered in a university setting. To that end, a questionnaire was administered to engineering students at the start and end of a course on energy issues, assessing their perceptions of sustainability using multi-criteria decision analysis. The results showed that students placed greater value on sustainability at the end of the course. Additionally, the findings highlight that the implementation of projects aimed at tackling real problems may be useful for disseminating knowledge and sustainable practices. The main implications of this study indicate that sustainable communities in academia lay on six foundational pillars: sustainable education, energy (and resource) independence, subsidies in support of the green economy, initiatives aimed at reducing the carbon footprint, energy community development, and new green professional opportunities. [ABSTRACT FROM AUTHOR]

Purpose: The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities' websites. The information automatically extracted can be potentially updated with a frequency higher than once per year, and be safe from manipulations or misinterpretations. Moreover, this approach allows us flexibility in collecting indicators about the efficiency of universities' websites and their effectiveness in disseminating key contents. These new indicators can complement traditional indicators of scientific research (e.g. number of articles and number of citations) and teaching (e.g. number of students and graduates) by introducing further dimensions to allow new insights for "profiling" the analyzed universities. Design/methodology/approach: Webometrics relies on web mining methods and techniques to perform quantitative analyses of the web. This study implements an advanced application of the webometric approach, exploiting all the three categories of web mining: web content mining; web structure mining; web usage mining. The information to compute our indicators has been extracted from the universities' websites by using web scraping and text mining techniques. The scraped information has been stored in a NoSQL DB according to a semi-structured form to allow for retrieving information efficiently by text mining techniques. This provides increased flexibility in the design of new indicators, opening the door to new types of analyses. Some data have also been collected by means of batch interrogations of search engines (Bing, www.bing.com) or from a leading provider of Web analytics (SimilarWeb, http://www.similarweb.com). The information extracted from the Web has been combined with the University structural information taken from the European Tertiary Education Register (https://eter.joanneum.at/#/home), a database collecting information on Higher Education Institutions (HEIs) at European level. All the above was used to perform a clusterization of 79 Italian universities based on structural and digital indicators. Findings: The main findings of this study concern the evaluation of the potential in digitalization of universities, in particular by presenting techniques for the automatic extraction of information from the web to build indicators of quality and impact of universities' websites. These indicators can complement traditional indicators and can be used to identify groups of universities with common features using clustering techniques working with the above indicators. Research limitations: The results reported in this study refers to Italian universities only, but the approach could be extended to other university systems abroad. Practical implications: The approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites. The approach could be applied to other university systems. Originality/value: This work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping, optical character recognition and nontrivial text mining operations (Bruni & Bianchi, 2020). [ABSTRACT FROM AUTHOR]

Purpose: This paper relates the definition of data quality procedures for knowledge organizations such as Higher Education Institutions. The main purpose is to present the flexible approach developed for monitoring the data quality of the European Tertiary Education Register (ETER) database, illustrating its functioning and highlighting the main challenges that still have to be faced in this domain. Design/methodology/approach: The proposed data quality methodology is based on two kinds of checks, one to assess the consistency of cross-sectional data and the other to evaluate the stability of multiannual data. This methodology has an operational and empirical orientation. This means that the proposed checks do not assume any theoretical distribution for the determination of the threshold parameters that identify potential outliers, inconsistencies, and errors in the data. Findings: We show that the proposed cross-sectional checks and multiannual checks are helpful to identify outliers, extreme observations and to detect ontological inconsistencies not described in the available meta-data. For this reason, they may be a useful complement to integrate the processing of the available information. Research limitations: The coverage of the study is limited to European Higher Education Institutions. The cross-sectional and multiannual checks are not yet completely integrated. Practical implications: The consideration of the quality of the available data and information is important to enhance data quality-aware empirical investigations, highlighting problems, and areas where to invest for improving the coverage and interoperability of data in future data collection initiatives. Originality/value: The data-driven quality checks proposed in this paper may be useful as a reference for building and monitoring the data quality of new databases or of existing databases available for other countries or systems characterized by high heterogeneity and complexity of the units of analysis without relying on pre-specified theoretical distributions. [ABSTRACT FROM AUTHOR]

In this paper we investigate economies of scale and specialization of European universities. The proposed approach builds on the notion that university production is a multi-input multi-output process different than standard production activity. The analyses are based on an interesting database which integrates the main European universities data on inputs and outputs with bibliometric data on publications, impact and collaborations. We pursue a cross-country perspective; we include subject mix and introduce a robust modeling of production trade-offs. Finally we test the statistical significance of scale and specialization and find that they both have a significant impact on the efficiency of the Humboldt model. Nevertheless, confirming previous findings, specialization has not a significant impact on the efficiency of the research model. [ABSTRACT FROM AUTHOR]

This article assesses the productivity change of the French technology transfer offices (TTOs) after the introduction of the July 1999 innovation law and the new public management oriented reform. By using Data Envelopment Analysis (DEA)-based Malmquist productivity index, we find an average increase in the short-term productivity of the French TTOs driven by both positive efficiency and technology change. The bootstrap analysis reveals that these improvements are ascribed to 50% of the TTO systems, while the remaining part does not show significant changes. Moreover, while older TTOs positively contribute to the performance of French TTOs in the short run, young TTOs with hospital seem to contribute negatively to the efficiency of the entire system. [ABSTRACT FROM PUBLISHER]

In the last decades of the twentieth century universities in Europe and other OECD countries have undergone a profound transformation. They have evolved from mainly élite institutions for teaching and research to large (public and private) organisations responsible for mass higher education and the production and distribution of new knowledge. Increasingly, new knowledge is produced by universities not only for its own sake but also for potential economic gains. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of characterization and classification of universities in the European system, by using the recently developed Aquameth dataset. Preliminary cluster analysis based on structural variables identifies systematic differences in size. However, this structural distinction is associated with differences in strategic orientation of universities (towards research or towards teaching, respectively) only in a few countries. In most European countries there are no discernible differences across universities along these dimensions. The paper argues that countries in which universities are more differentiated according to research or teaching dimensions have implemented differentiation policies through a variety of policy instruments. In turn, these countries also are ranked high in international rankings of universities. This suggests a structural linkage between the poor performance of European universities in research-based rankings and the lack of differentiation. [ABSTRACT FROM AUTHOR]

Higher education institutions (HEIs) are crucial to the development of the European Research Area. However, unlike in the USA, the availability of quantitative indicators for individual HEI at the European level is severely limited by several methodological issues, data availability problems and national institutional constraints. The paper discusses strategies for collecting and validating data from different national sources; the limitations of the available data for different categories of indicators and, finally, the influence of the heterogeneity of the national HEIs on comparability at the European level. Based on the experience of two recently completed projects, the paper shows that, despite these problems, it is possible to collect relatively coherent data on European HEIs and to develop a set of consequential indicators. Further, it provides advice on how to exploit them for comparative purposes in a sensible way. It concludes by indicating areas where major improvements are urgently needed, and advocates a European S&T Indicators Platform to maintain and develop these data sets long term. [ABSTRACT FROM AUTHOR]

Educational Institutions data constitute the basis for several important analyses on the educational systems; however they often contain not negligible shares of missing values, for several reasons. We consider in this work the relevant case of the European Tertiary Education Register (ETER), describing the Educational Institutions of Europe. The presence of missing values prevents the full exploitation of this database, since several types of analyses that could be performed are currently impracticable. The imputation of artificial data, reconstructed with the aim of being statistically equivalent to the (unknown) missing data, would allow to overcome these problems. A main complication in the imputation of this type of data is given by the correlations that exist among all the variables. We propose several imputation techniques designed to deal with the different types of missing values appearing in these interconnected data. We use these techniques to impute the database. Moreover, we evaluate the accuracy of the proposed approach by artificially introducing missing data, by imputing them, and by comparing imputed and original values. Results show that the information reconstruction does not introduce statistically significant changes in the data and that the imputed values are close enough to the original values. • Imputation methodology for educational data using information reconstruction and donors. • Designed to satisfactory impute the difficult case of interconnected time series. • Application to an important real case: European Tertiary Education Register (ETER). • Uses a formal and data-driven approach, hence applicable in other contexts. • Analysis of the imputation accuracy by using artificial missing data. [ABSTRACT FROM AUTHOR]