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Found 3 result(s)

NOAA National Centers for Environmental Information - formerly: National Geophysical Data Center

formerly: NGDC

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<<<!!!<<< The demand for high-value environmental data and information has dramatically increased in recent years. To improve our ability to meet that demand, NOAA’s former three data centers—the National Climatic Data Center, the National Geophysical Data Center, and the National Oceanographic Data Center, which includes the National Coastal Data Development Center—have merged into the National Centers for Environmental Information (NCEI). >>>!!!>>> The NOAA National Centers for Environmental Information (formerly the National Geophysical Data Center) provide scientific stewardship, products and services for sea floor and lakebed data, including geophysics (gravity, magnetics, seismic reflection, bathymetry, water column sonar), and data derived from sediment and rock samples. NCEI compiles coastal and global digital elevation models, high-resolution models for tsunami inundation studies, provides stewardship for NOS data supporting charts and navigation, and is the US national long-term archive for MGG data

AmeriFlux

Measuring carbon, water and energy flux across the Americas

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AmeriFlux is a network of PI-managed sites measuring ecosystem CO2, water, and energy fluxes in North, Central and South America. It was established to connect research on field sites representing major climate and ecological biomes, including tundra, grasslands, savanna, crops, and conifer, deciduous, and tropical forests. As a grassroots, investigator-driven network, the AmeriFlux community has tailored instrumentation to suit each unique ecosystem. This “coalition of the willing” is diverse in its interests, use of technologies and collaborative approaches. As a result, the AmeriFlux Network continually pioneers new ground.

UC Irvine Machine Learning Repository

UCI Machine Learning Repository

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The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. It is used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times.