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Found 4 result(s)

Forschungsdatenzentrum Internationale Umfrageprogramme

FDZ Internationale Umfrageprogramme

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The Research Data Center (RDC) “International Survey Programs“ provides researchers with data, services, and consultation on a number of important international study series which are under intensive curation by GESIS. They all cover numerous countries and, quite often, substantial time spans. The RDC provides optimal data preparation and access to a wide scope of data and topics for comparative analysis.

Multimodal Learning Corpus Exchange

MULCE

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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.

ECHO – Cultural Heritage Online

Open Access Infrastructure for a Future Web of Culture and Science

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The ECHO initiative aims to create an infrastructure to bring cultural heritage on the Internet, and builds up a network of institutions, research projects and other users which provide content and technology for the common infrastructure, with the aim to enrich the "agora" and to create a future Web of Culture and Science.

OpenML

Open Machine Learning

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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.