Abstract
In this paper, an overview is given of the project data instances available in the literature to carry out academic research in the field of integrated project management and control. This research field aims at integrating static planning methods and risk analyses with dynamic project control methodologies using the state-of-the-art knowledge from literature and the best practices from the professional project management discipline. Various subtopics of this challenging discipline have been investigated from different angles, each time using project data available in literature, obtained from project data generators or based on a sample of empirical case studies. This paper gives an overall overview of the wide variety of project data that are available and are used in various research publications. It will be shown how the combination of artificial data and empirical data leads to improved knowledge on and deeper insights into the structure and characteristics of projects useful for academic research and professional use. While the artificial data can be best used to test novel ideas under a strict design in a controlled academic environment, empirical data can serve as the necessary validation step to translate the academic research results into practical ideas, aiming at narrowing the bridge between the theoretical knowledge and practical relevance. A summary of the available project data discussed in this paper can be downloaded from http://www.projectmanagement.ugent.be/research/data.

Abstract
This paper presents a new solution approach to solve the resource-constrained project scheduling problem in the presence of three types of logical constraints. Apart from the traditional AND constraints with minimal time-lags, these precedences are extended to OR constraints and bidirectional (BI) relations. These logical constraints extend the set of relations between pairs of activities and make the RCPSP definition somewhat different from the traditional RCPSP research topics in literature. It is known that the RCPSP with AND constraints, and hence its extension to OR and BI constraints, is NP-hard. The new algorithm consists of a set of network transformation rules that removes the OR and BI logical constraints to transform them into AND constraints and hereby extends the set of activities to maintain the original logic. A satisfiability (SAT) solver is used to guarantee the original precedence logic and is embedded in a metaheuristic search to resource feasible schedules that respect both the limited renewable resource availability as well as the precedence logic. Computational results on two well-known datasets from literature show that the algorithm can compete with the multi-mode algorithms from literature when no logical constraints are taken into account. When the logical constraints are taken into account, the algorithm can report major reductions in the project makespan for most of the instances within a reasonable time.

Abstract
In Project TEMPUS “Enhancement of Russian Creative Education: new Master Programme in Digital Arts in line with EU standards” (2014-2016) the Russian students had the opportunity to study in EU Universities for one semester. The Universidade Aberta, in Portugal, didn’t have a master degree in Digital Arts so a pilot programme had to be created: a new postgraduation in Digital Art Practice. This new curriculum, using blearning (based on online and face to face activities) with transdisciplinary methods, aims a practice oriented training on digital art. It started with a deep understanding of Lisbon, the relationship between people, cultural and artistic spaces and their environments. This knowledge inspired the students to produce and to create an artistic artefact presented in exhibition to an audience. With this postgraduation new possibilities started for reflection about global challenges for education in the millennium.

Abstract
Software Defined Networks (SDNs) provides a separation between the control plane and the forwarding plane of networks. The software implementation of the control plane and the built in data collection mechanisms of the OpenFlow protocol promise to be excellent tools to implement Machine Learning (ML) network control applications. A first step in that direction is to understand the type of data that can be collected in SDNs and how information can be learned from that data. In this work we describe a simple architecture deployed in an enterprise network that gathers traffic data using the OpenFlow protocol. We present the data-sets that can be obtained and show how several ML techniques can be applied to it for traffic classification. The results indicate that high accuracy classification can be obtained with the data-sets using supervised learning.

Abstract
Satellite altimetry allows the study of sea-level long-term variability on a global and spatially uniform basis. Here quantile regression is applied to derive robust median regression trends of mean sea level as well as trends in extreme quantiles from radar altimetry time series. In contrast with ordinary least squares regression, which only provides an estimate on the rate of change of the mean of data distribution, quantile regression allows the estimation of trends at different quantiles of the data distribution, yielding a more complete picture of long-term variability. Trends derived from basin-wide averaged regional mean sea level time series are robust and similar for all quantiles, indicating that all parts of the data distribution are changing at the same rate. In contrast, trends are not robust and diverge across quantiles in the case of local time series. Trends are under- (over-)estimated in the western (eastern) equatorial Pacific. Furthermore, trends in the lowermost quantile (0.05) are larger than the median trend in the western Pacific, while trends in the uppermost quantile (0.95) are lower than the median trend in the eastern Pacific. These differences in trends in extreme mean sea level quantiles are explained by the exceptional effect of the strong 1997-1998 El Nino-Southern Oscillation (ENSO) event.

Abstract
The classification ofmultivariate time series in terms of their corresponding temporal dependence patterns is a common problem in geosciences, particularly for large datasets resulting from environmental monitoring networks. Here a wavelet-based clustering approach is applied to sea level and atmospheric pressure time series at tide gauge locations in the Baltic Sea. The resulting dendrogram discriminates three spatially-coherent groups of stations separating the southernmost tide gauges, reflecting mainly high-frequency variability driven by zonal wind, from the middle-basin stations and the northernmost stations dominated by lower-frequency variability and the response to atmospheric pressure.