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Found 4 result(s)

MultiDark Database

Multimessenger Approach for Dark Matter Detection

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<<<!!!<<< This MultiDark application is now integrated into CosmoSim (https://www.cosmosim.org/ , all data and much more is available there. The old MultiDark server is no longer available. >>>!!!>>> The MultiDark database provides results from cosmological simulations performed within the MultiDark project. This database can be queried by entering SQL statements directly into the Query Form. The access to that form and thus access to the public & private databases is password protected.

IAGOS Data Centre

In-service Aircraft for a Global Observing System

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IAGOS aims to provide long-term, regular and spatially resolved in situ observations of the atmospheric composition. The observation systems are deployed on a fleet of 10 to 15 commercial aircraft measuring atmospheric chemistry concentrations and meteorological fields. The IAGOS Data Centre manages and gives access to all the data produced within the project.

biodiversity.aq

ANTABIF dataportal

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Antarctic marine and terrestrial biodiversity data is widely scattered, patchy and often not readily accessible. In many cases the data is in danger of being irretrievably lost. Biodiversity.aq establishes and supports a distributed system of interoperable databases, giving easy access through a single internet portal to a set of resources relevant to research, conservation and management pertaining to Antarctic biodiversity. biodiversity.aq provides access to both marine and terrestrial Antarctic biodiversity data.

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.