DCSI -- An improved measure of cluster separability based on separation and connectedness

Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be quantified by separability measures. A review of the existing literature shows that neither classification-based complexity measures nor cluster validity indices (CVIs) adequately incorporate the central aspects of separability for density-based clustering: between-class separation and within-class connectedness. A newly developed measure (density cluster separability index, DCSI) aims to quantify these two characteristics and can also be used as a CVI. Extensive experiments on synthetic data indicate that DCSI correlates strongly with the performance of DBSCAN measured via the adjusted rand index (ARI) but lacks robustness when it comes to multi-class data sets with overlapping classes that are ill-suited for density-based hard clustering. Detailed evaluation on frequently used real-world data sets shows that DCSI can correctly identify touching or overlapping classes that do not form meaningful clusters

Diversity in action: Exchange of perspectives and reflections on taxonomies of individual differences

Throughout the last 2500 years, the classification of individual differences in healthy people and their extreme expressions in mental disorders has remained one of the most difficult challenges in science that affects our ability to explore individuals' functioning, underlying psychobiological processes and pathways of development. To facilitate analyses of the principles required for studying individual differences, this theme issue brought together prominent scholars from diverse backgrounds of which many bring unique combinations of cross-disciplinary experiences and perspectives that help establish connections and promote exchange across disciplines. This final paper presents brief commentaries of some of our authors and further scholars exchanging perspectives and reflecting on the contributions of this theme issue

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This deliverable contains all contributed papers that were presented at the “First WAVIL

The Copernicus Atmosphere Monitoring Service emissions dataset of anthropogenic and natural emissions

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The Copernicus Atmosphere Monitoring Service global and regional emissions (April 2019 version)

In order to drive atmospheric models performing forecasts and analyses of air quality and atmospheric composition, an accurate quantification of surface emissions from anthropogenic and natural sources is required. As part of the European Copernicus Atmosphere Service (CAMS), diverse emission datasets have been developed. Global and regional European anthropogenic emissions for several sectors for a large number of atmospheric compounds have been developed. In addition, detailed emissions from ships based on ship identification systems have been developed. Different datasets providing natural emissions are being processed, such as the emissions of biogenic volatile organic compounds from vegetation, nitrogen compounds emissions from soils, emissions from the oceans and emissions from volcanoes. Methodologies for evaluating the emissions and their consistency at different scales are being generated. Temporal profiles at different scales are also being developed.All the emissions developed in CAMS are available from the ECCAD (Emissions of atmospheric Compounds and Compilation of Ancillary Data (eccad.aeris-data.fr) database

Surface emissions for global and regional air quality forecasting

International audienc