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Found 12 result(s)
Atmosphere to Electrons (A2e) is a new, multi-year, multi-stakeholder U.S. Department of Energy (DOE) research and development initiative tasked with improving wind plant performance and mitigating risk and uncertainty to achieve substantial reduction in the cost of wind energy production. The A2e strategic vision will enable a new generation of wind plant technology, in which smart wind plants are designed to achieve optimized performance stemming from more complete knowledge of the inflow wind resource and complex flow through the wind plant.
The SuiteSparse Matrix Collection is a large and actively growing set of sparse matrices that arise in real applications. The Collection is widely used by the numerical linear algebra community for the development and performance evaluation of sparse matrix algorithms. It allows for robust and repeatable experiments. Its matrices cover a wide spectrum of domains, include those arising from problems with underlying 2D or 3D geometry (as structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations) and those that typically do not have such geometry (optimization, circuit simulation, economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, power networks, and other networks and graphs.
CSDMS is a virtual home for a vibrant and growing community of about 1,000 international modeling experts and students who study the dynamic interactions of lithosphere, hydrosphere, cryosphere, and atmosphere at Earth’s surface. Participating in cross-disciplinary groups, members develop integrated software modules that predict the movement of water, sediment, and nutrients across landscapes and into the ocean. We share an open library of models, software, and access to high-performance computing. We also share knowledge that helps create higher-resolution simulations, often involving higher complexity algorithms. Together, we support the discovery, use, and conservation of natural resources; mitigation of natural hazards; geotechnical support of commercial and infrastructure development; environmental stewardship; and terrestrial surveillance for global security.
Using a combination of remote sensing data and ground observations as inputs, CHC scientists have developed rainfall estimation techniques and other resources to support drought monitoring and predict crop performance in parts of the world vulnerable to crop failure. Policymakers within governments and non-governmental organizations rely on CHC decision-support products to make critical resource allocation decisions. The CHC's scientific focus is "geospatial hydroclimatology," with an emphasis on the early detection and forecasting of hydroclimatic hazards related to food-security droughts and floods. Basic research seeks an improved understanding of the climatic processes that govern drought and flood hazards in FEWS NET countries (https://fews.net/). The CHC develops better techniques, algorithms, and modeling applications in order to use remote sensing and other geospatial data for hazards early warning.
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EarthByte is an internationally leading eGeoscience collaboration between several Australian Universities, international centres of excellence and industry partners. One of the fundamental aims of the EarthByte Group is geodata synthesis through space and time, assimilating the wealth of disparate geological and geophysical data into a four-dimensional Earth model including tectonics, geodynamics and surface processes. The EarthByte Group is pursuing open innovation via collaborative software development, high performance and distributed computing, “big data” analysis and by making open access digital data collections available to the community.
HydroShare is a system operated by The Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) that enables users to share and publish data and models in a variety of flexible formats, and to make this information available in a citable, shareable and discoverable manner. HydroShare includes a repository for data and models, and tools (web apps) that can act on content in HydroShare providing users with a gateway to high performance computing and computing in the cloud. With HydroShare you can: share data and models with colleagues; manage access to shared content; share, access, visualize, and manipulate a broad set of hydrologic data types and models; publish data and models and obtain a citable digital object identifier (DOI); aggregate resources into collections; discover and access data and models published by others; use the web services application programming interface (API) to programmatically access resources; and use integrated web applications to visualize, analyze and run models with data in HydroShare.
The GRSF, the Global Record of Stocks and Fisheries, integrates data from three authoritative sources: FIRMS (Fisheries and Resources Monitoring System), RAM (RAM Legacy Stock Assessment Database) and FishSource (Program of the Sustainable Fisheries Partnership). The GRSF content publicly disseminated through this catalogue is distributed as a beta version to test the logic to generate unique identifiers for stocks and fisheries. The access to and review of collated stock and fishery data is restricted to selected users. This beta release can contain errors and we welcome feedback on content and software performance, as well as the overall usability. Beta users are advised that information on this site is provided on an "as is" and "as available" basis. The accuracy, completeness or authenticity of the information on the GRSF catalogue is not guaranteed. It is reserved the right to alter, limit or discontinue any part of this service at its discretion. Under no circumstances shall the GRSF be liable for any loss, damage, liability or expense suffered that is claimed to result from the use of information posted on this site, including without limitation, any fault, error, omission, interruption or delay. The GRSF is an active database, updates and additions will continue after the beta release. For further information, or for using the GRSF unique identifiers as a beta tester please contact FIRMS-Secretariat@fao.org.
!!! We will terminate ASTER Products Distribution Service in March 2016 although we have been providing ASTER Products since November 20, 2000. !!! ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) is the high efficiency optical imager which covers a wide spectral region from the visible to the thermal infra-red by 14 spectral bands. ASTER acquires data which can be used in various fields in earth science. ASTER was launched from Vandenberg Air Force Base in California, USA in 1999 aboard the Terra, which is the first satellite of the EOS Project. The purpose of ASTER project is to make contributions to extend the understanding of local and regional phenomena on the Earth surface and its atmosphere. The followings are ASTER related information, which includes ASTER instrument, ASTER Ground Data System, ASTER Science Activities, ASTER Data Distribution and so on. ASTER Search provides services to search and order ASTER data products on the website.
In keeping with the open data policies of the U.S. Agency for International Development (USAID) and Bill & Melinda Gates Foundation, the Cereal Systems Initiative for South Asia (CSISA) has launched the CSISA Data Repository to ensure public accessibility to key data sets, including crop cut data- directly observed, crop yield estimates, on-station and on-farm research trial data and socioeconomic surveys. CSISA is a science-driven and impact-oriented regional initiative for increasing the productivity of cereal-based cropping systems in Bangladesh, India and Nepal, thus improving food security and farmers’ livelihoods. CSISA generates data that is of value and interest to a diverse audience of researchers, policymakers and the public. CSISA’s data repository is hosted on Dataverse, an open source web application developed at Harvard University to share, preserve, cite, explore and analyze research data. CSISA’s repository contains rich datasets, including on-station trial data from 2009–17 about crop and resource management practices for sustainable future cereal-based cropping systems. Collection of this data occurred during the long-term, on-station research trials conducted at the Indian Council of Agricultural Research – Research Complex for the Eastern Region in Bihar, India. The data include information on agronomic management for the sustainable intensification of cropping systems, mechanization, diversification, futuristic approaches to sustainable intensification, long-term effects of conservation agriculture practices on soil health and the pest spectrum. Additional trial data in the repository includes nutrient omission plot technique trials from Bihar, eastern Uttar Pradesh and Odisha, India, covering 2012–15, which help determine the indigenous nutrient supplying ability of the soil. This data helps develop precision nutrient management approaches that would be most effective in different types of soils. CSISA’s most popular dataset thus far includes crop cut data on maize in Odisha, India and rice in Nepal. Crop cut datasets provide ground-truthed yield estimates, as well as valuable information on relevant agronomic and socioeconomic practices affecting production practices and yield. A variety of research data on wheat systems are also available from Bangladesh and India. Additional crop cut data will also be coming online soon. Cropping system-related data and socioeconomic data are in the repository, some of which are cross-listed with a Dataverse run by the International Food Policy Research Institute. The socioeconomic datasets contain baseline information that is crucial for technology targeting, as well as to assess the adoption and performance of CSISA-supported technologies under smallholder farmers’ constrained conditions, representing the ultimate litmus test of their potential for change at scale. Other highly interesting datasets include farm composition and productive trajectory information, based on a 20-year panel dataset, and numerous wheat crop cut and maize nutrient omission trial data from across Bangladesh.