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Found 4 result(s)

Inspire-HEP

Inspire-High Energy Physics

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CERN, DESY, Fermilab and SLAC have built the next-generation High Energy Physics (HEP) information system, INSPIRE. It combines the successful SPIRES database content, curated at DESY, Fermilab and SLAC, with the Invenio digital library technology developed at CERN. INSPIRE is run by a collaboration of CERN, DESY, Fermilab, IHEP, IN2P3 and SLAC, and interacts closely with HEP publishers, arXiv.org, NASA-ADS, PDG, HEPDATA and other information resources. INSPIRE represents a natural evolution of scholarly communication, built on successful community-based information systems, and provides a vision for information management in other fields of science.

Europeana collections

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Europeana is the trusted source of cultural heritage brought to you by the Europeana Foundation and a large number of European cultural institutions, projects and partners. It’s a real piece of team work. Ideas and inspiration can be found within the millions of items on Europeana. These objects include: Images - paintings, drawings, maps, photos and pictures of museum objects Texts - books, newspapers, letters, diaries and archival papers Sounds - music and spoken word from cylinders, tapes, discs and radio broadcasts Videos - films, newsreels and TV broadcasts All texts are CC BY-SA, images and media licensed individually.

European Soil Data Centre

ESDAC

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The European Soil Data Centre (ESDAC) is the thematic centre for soil related data in Europe. Its ambition is to be the single reference point for and to host all relevant soil data and information at European level. It contains a number of resources that are organized and presented in various ways: datasets, services/applications, maps, documents, events, projects and external links.

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.