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Varieties of Democracy (V-Dem) is a new approach to conceptualizing and measuring democracy. We provide a multidimensional and disaggregated dataset that reflects the complexity of the concept of democracy as a system of rule that goes beyond the simple presence of elections. The V-Dem project distinguishes between five high-level principles of democracy: electoral, liberal, participatory, deliberative, and egalitarian, and collects data to measure these principles.
The Comparative Welfare Entitlements Dataset (CWED) contains information about the structure and generosity of social insurance benefits in 33 countries around the world. The data contained here are an updated and extended version of CWED 1, which has been available since 2004. This web site allows you to download customized portions of the CWED 2 data, browse the Working Paper Series or access documentary material. For recent updates to this dataset, please see https://www.cwep.us/
The American National Election Studies (ANES) conducts national surveys and pilot studies and provides large, multifaceted datasets. Time Series Studies are conducted during years of national elections, with pre-election and post-election surveys conducted in presidential election years and post-election surveys conducted during congressional election years. Pilot Studies are normally conducted in years when there is no national election and are designed to test new, or to refine existing, instrumentation and study designs. Other Major Data Collections includes panel studies and other special studies.
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This data archive of experiments studying the dynamics of pedestrians is build up by the Institute for Advanced Simulation 7: Civil Safety Research of Forschungszentrum Jülich. The landing page provides our own data of experiments. Data of research colleagues are listed within the data archive at https://ped.fz-juelich.de/extda For most of the experiments, the video recordings, as well as the resulting trajectories of single pedestrians, are available. The experiments were performed under laboratory conditions to focus on the influence of a single variable. You are very welcome to use our data for further research, as long as you name the source of the data. If you have further questions feel free to contact Maik Boltes.
The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. It is used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times.
The Project on Human Development in Chicago Neighborhoods (PHDCN) is a large-scale, interdisciplinary study of how families, schools, and neighborhoods affect child and adolescent development. It was designed to advance the understanding of the developmental pathways of both positive and negative human social behaviors. In particular, the project examined the causes and pathways of juvenile delinquency, adult crime, substance abuse, and violence. At the same time, the project also provided a detailed look at the environments in which these social behaviors take place by collecting substantial amounts of data about urban Chicago, including its people, institutions, and resources. Nearly all PHDCN data require an individual application with supporting materials to obtain the data. Applications are handled by the the National Archive of Criminal Justice Data (NACJD). Further instructions will appear on the study home page (linked from search results), where relevant.