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Found 6 result(s)
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The GEOROC data repository hosts research data within the scope of the GEOROC database: geochemical compositions of rocks, glasses, minerals and inclusions from all geological settings on Earth. The repository is curated by the Digital Geochemical Data Infrastructure (DIGIS) project at Göttingen University.
-----<<<<< The repository is no longer available. This record is out-dated. The Matter lab provides the archived database version of 2012 and 2013 at https://www.matter.toronto.edu/basic-content-page/data-download. Data linked from the World Community Grid - The Clean Energy Project see at https://www.worldcommunitygrid.org/research/cep1/overview.do and on fighshare https://figshare.com/articles/dataset/moldata_csv/9640427 >>>>>----- The Clean Energy Project Database (CEPDB) is a massive reference database for organic semiconductors with a particular emphasis on photovoltaic applications. It was created to store and provide access to data from computational as well as experimental studies, on both known and virtual compounds. It is a free and open resource designed to support researchers in the field of organic electronics in their scientific pursuits. The CEPDB was established as part of the Harvard Clean Energy Project (CEP), a virtual high-throughput screening initiative to identify promising new candidates for the next generation of carbon-based solar cell materials.
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In the framework of the Collaborative Research Centre/Transregio 32 ‘Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling, and Data Assimilation’ (CRC/TR32, www.tr32.de), funded by the German Research Foundation from 2007 to 2018, a RDM system was self-designed and implemented. The so-called CRC/TR32 project database (TR32DB, www.tr32db.de) is operating online since early 2008. The TR32DB handles all data including metadata, which are created by the involved project participants from several institutions (e.g. Universities of Cologne, Bonn, Aachen, and the Research Centre Jülich) and research fields (e.g. soil and plant sciences, hydrology, geography, geophysics, meteorology, remote sensing). The data is resulting from several field measurement campaigns, meteorological monitoring, remote sensing, laboratory studies and modelling approaches. Furthermore, outcomes of the scientists such as publications, conference contributions, PhD reports and corresponding images are collected in the TR32DB.
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The public MorpheusML model repository collects, curates, documents and tests computational models for multi-scale and multicellular biological systems. Model must be encoded in the model description language MorpheusML. Subsections of the repository distinguish published models from contributed non-published and example models. New models are simulated in Morpheus or Artistoo independently from the authors and results are compared to published results. Successful reproduction is documented on the model's webpage. Models in this repository are included into the CI and test pipelines for each release of the model simulator Morpheus to check and guarantee reproducibility of results across future simulator updates. The model’s webpage provides a History-link to all past model versions and edits that are automatically tracked via Git. Each model is registered with a unique and persistent ID of the format M..... The model description page (incl. the biological context and key results of that model), the model’s XML file, the associated paper, and all further files (often simulation result videos) connected with that model can be retrieved via a persistent URL of the format https://identifiers.org/morpheus/M..... - for technical details on the citable ModelID please see https://registry.identifiers.org/registry/morpheus - for the model definition standard MorpheusML please see https://doi.org/10.25504/FAIRsharing.78b6a6 - for the model simulator Morpheus please see https://morpheus.gitlab.io - for the model simulator Artistoo please see https://artistoo.net/converter.html
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.