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Found 9 result(s)
The Health and Medical Care Archive (HMCA) is the data archive of the Robert Wood Johnson Foundation (RWJF), the largest philanthropy devoted exclusively to health and health care in the United States. Operated by the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan, HMCA preserves and disseminates data collected by selected research projects funded by the Foundation and facilitates secondary analyses of the data. Our goal is to increase understanding of health and health care in the United States through secondary analysis of RWJF-supported data collections
The central mission of the NACJD is to facilitate and encourage research in the criminal justice field by sharing data resources. Specific goals include providing computer-readable data for the quantitative study of crime and the criminal justice system through the development of a central data archive, supplying technical assistance in the selection of data collections and computer hardware and software for data analysis, and training in quantitative methods of social science research to facilitate secondary analysis of criminal justice data
Cell phones have become an important platform for the understanding of social dynamics and influence, because of their pervasiveness, sensing capabilities, and computational power. Many applications have emerged in recent years in mobile health, mobile banking, location based services, media democracy, and social movements. With these new capabilities, we can potentially be able to identify exact points and times of infection for diseases, determine who most influences us to gain weight or become healthier, know exactly how information flows among employees and productivity emerges in our work spaces, and understand how rumors spread. In an attempt to address these challenges, we release several mobile data sets here in "Reality Commons" that contain the dynamics of several communities of about 100 people each. We invite researchers to propose and submit their own applications of the data to demonstrate the scientific and business values of these data sets, suggest how to meaningfully extend these experiments to larger populations, and develop the math that fits agent-based models or systems dynamics models to larger populations. These data sets were collected with tools developed in the MIT Human Dynamics Lab and are now available as open source projects or at cost.
CPES provides access to information that relates to mental disorders among the general population. Its primary goal is to collect data about the prevalence of mental disorders and their treatments in adult populations in the United States. It also allows for research related to cultural and ethnic influences on mental health. CPES combines the data collected in three different nationally representative surveys (National Comorbidity Survey Replication, National Survey of American Life, National Latino and Asian American Study).
ICPSR maintains a data archive of more than 250,000 files of research in the social and behavioral sciences. It hosts 21 specialized collections of data in education, aging, criminal justice, substance abuse, terrorism, and other fields. ICPSR advances and expands social and behavioral research, acting as a global leader in data stewardship and providing rich data resources and responsive educational opportunities for present and future generations.
Mulce (MUltimodal contextualized Learner Corpus Exchange) is a research project supported by the National Research Agency (ANR programme: "Corpus and Tools in the Humanities", ANR-06-CORP-006). A teaching corpus (LETEC - Learning and Teaching Corpora) combines a systematic and structured data set, particularly of interactional data, and traces left by a training course experimentation, conducted partially or completely online and completed by additional technical, human, pedagogical and scientific information to enable the data to be analysed in context.
OpenML is an open ecosystem for machine learning. By organizing all resources and results online, research becomes more efficient, useful and fun. OpenML is a platform to share detailed experimental results with the community at large and organize them for future reuse. Moreover, it will be directly integrated in today’s most popular data mining tools (for now: R, KNIME, RapidMiner and WEKA). Such an easy and free exchange of experiments has tremendous potential to speed up machine learning research, to engender larger, more detailed studies and to offer accurate advice to practitioners. Finally, it will also be a valuable resource for education in machine learning and data mining.