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Found 7 result(s)
The SURF Data Repository is a user-friendly web-based data publication platform that allows researchers to store, annotate and publish research datasets of any size to ensure long-term preservation and availability of their data. The service allows any dataset to be stored, independent of volume, number of files and structure. A published dataset is enriched with complex metadata, unique identifiers are added and the data is preserved for an agreed-upon period of time. The service is domain-agnostic and supports multiple communities with different policy and metadata requirements.
The KNMI Data Centre (KDC) provides access to weather, climate and seismological datasets of KNMI. For each dataset descriptive information is available (metadata), including a point of contact. The KNMI Data Centre (KDC) provides access to KNMI data on weather, climate and seismology. You will find KNMI data on various topics such as: the most recent 10 minutes of observations, historical data, data on meteorological stations, modeling, earthquake data and satellite products.
The University of Amsterdam (UvA) and the Amsterdam University of Applied Sciences (AUAS/HvA) cooperate to connect academic research with the insights and experiences from professional practice, and together the UvA and AUAS offer students a wide range of education pathways.
EASY is the online archiving system of Data Archiving and Networked Services (DANS). EASY offers you access to thousands of datasets in the humanities, the social sciences and other disciplines. EASY can also be used for the online depositing of research data.
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.