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SCEC's mission includes gathering data on earthquakes, both in Southern California and other locales; integrate the information into a comprehensive understanding of earthquake phenomena; and communicate useful knowledge for reducing earthquake risk to society at large. The SCEC community consists of more than 600 scientists from 16 core institutions and 47 additional participating institutions. SCEC is funded by the National Science Foundation and the U.S. Geological Survey.
WorldData.AI comes with a built-in workspace – the next-generation hyper-computing platform powered by a library of 3.3 billion curated external trends. WorldData.AI allows you to save your models in its “My Models Trained” section. You can make your models public and share them on social media with interesting images, model features, summary statistics, and feature comparisons. Empower others to leverage your models. For example, if you have discovered a previously unknown impact of interest rates on new-housing demand, you may want to share it through “My Models Trained.” Upload your data and combine it with external trends to build, train, and deploy predictive models with one click! WorldData.AI inspects your raw data, applies feature processors, chooses the best set of algorithms, trains and tunes multiple models, and then ranks model performance.
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<<<!!!<<< The database is no longer available from 1st July 2018 >>>!!!>>> CRYSTMET was previously included in the NCDS as part of CrystalWorks. Unfortunately we are no longer able to license the CRYSTMET database for access through the NCDS. Therefore the database will no longer be accessible from 1st July 2018. >>>> CRYSTMET contains chemical, crystallographic and bibliographic data together with associated comments regarding experimental details for each study. It is a database of critically evaluated crystallographic data for metals, including alloys, intermetallics and minerals.Using these data, a number of associated files are derived, a major one being a parallel file of calculated powder patterns. These derived data are included within the CRYSTMET product.
The Wolfram Data Repository is a public resource that hosts an expanding collection of computable datasets, curated and structured to be suitable for immediate use in computation, visualization, analysis and more. Building on the Wolfram Data Framework and the Wolfram Language, the Wolfram Data Repository provides a uniform system for storing data and making it immediately computable and useful. With datasets of many types and from many sources, the Wolfram Data Repository is built to be a global resource for public data and data-backed publication.
The Arabidopsis Information Resource (TAIR) maintains a database of genetic and molecular biology data for the model higher plant Arabidopsis thaliana . Data available from TAIR includes the complete genome sequence along with gene structure, gene product information, metabolism, gene expression, DNA and seed stocks, genome maps, genetic and physical markers, publications, and information about the Arabidopsis research community. Gene product function data is updated every two weeks from the latest published research literature and community data submissions. Gene structures are updated 1-2 times per year using computational and manual methods as well as community submissions of new and updated genes. TAIR also provides extensive linkouts from our data pages to other Arabidopsis resources.
Knoema is a knowledge platform. The basic idea is to connect data with analytical and presentation tools. As a result, we end with one uniformed platform for users to access, present and share data-driven content. Within Knoema, we capture most aspects of a typical data use cycle: accessing data from multiple sources, bringing relevant indicators into a common space, visualizing figures, applying analytical functions, creating a set of dashboards, and presenting the outcome.
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KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies