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Found 13 result(s)

Canadian Astronomy Data Centre

Centre canadien de données astronomiques

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The Canadian Astronomy Data Centre (CADC) was established in 1986 by the National Research Council of Canada (NRC), through a grant provided by the Canadian Space Agency (CSA). Over the past 30 years the CADC has evolved from an archiving centre---hosting data from Hubble Space Telescope, Canada-France-Hawaii Telescope, the Gemini observatories, and the James Clerk Maxwell Telescope---into a Science Platform for data-intensive astronomy. The CADC, in partnership with Shared Services Canada, Compute Canada, CANARIE and the university community (funded through the Canadian Foundation for Innovation), offers cloud computing, user-managed storage, group management, and data publication services, in addition to its ongoing mission to provide permanent storage for major data collections. Located at NRC Herzberg Astronomy and Astrophysics Research Centre in Victoria, BC, the CADC staff consists of professional astronomers, software developers, and operations staff who work with the community to develop and deliver leading-edge services to advance Canadian research. The CADC plays a leading role in international efforts to improve the scientific/technical landscape that supports data intensive science. This includes leadership roles in the International Virtual Observatory Alliance and participation in organizations like the Research Data Alliance, CODATA, and the World Data Systems. CADC also contributes significantly to future Canadian projects like the Square Kilometre Array and TMT. In 2019, the Canadian Astronomy Data Centre (CADC) delivered over 2 Petabytes of data (over 200 million individual files) to thousands of astronomers in Canada and in over 80 other countries. The cloud processing system completed over 6 million jobs (over 1100 core years) in 2019.

Environmental Information Data Centre

EIDC

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The Environmental Information Data Centre (EIDC) is part of the Natural Environment Research Council's (NERC) Environmental Data Service and is hosted by the UK Centre for Ecology & Hydrology (UKCEH). We manage nationally-important datasets concerned with the terrestrial and freshwater sciences.

Southern California Earthquake Data Center

SCEDC

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The Southern California Earthquake Data Center (SCEDC) operates at the Seismological Laboratory at Caltech and is the primary archive of seismological data for southern California. The 1932-to-present Caltech/USGS catalog maintained by the SCEDC is the most complete archive of seismic data for any region in the United States. Our mission is to maintain an easily accessible, well-organized, high-quality, searchable archive for research in seismology and earthquake engineering.

Kaggle

Your home for data science

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Kaggle is a platform for predictive modelling and analytics competitions in which statisticians and data miners compete to produce the best models for predicting and describing the datasets uploaded by companies and users. This crowdsourcing approach relies on the fact that there are countless strategies that can be applied to any predictive modelling task and it is impossible to know beforehand which technique or analyst will be most effective.

Biological and Chemical Oceanography Data Management Office

BCO-DMO

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The Biological and Chemical Oceanography Data Management Office (BCO-DMO) is a publicly accessible earth science data repository created to curate, publicly serve (publish), and archive digital data and information from biological, chemical and biogeochemical research conducted in coastal, marine, great lakes and laboratory environments. The BCO-DMO repository works closely with investigators funded through the NSF OCE Division’s Biological and Chemical Sections and the Division of Polar Programs Antarctic Organisms & Ecosystems. The office provides services that span the full data life cycle, from data management planning support and DOI creation, to archive with appropriate national facilities.

Harvard Dataverse

The Dataverse Project

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The Harvard Dataverse is open to all scientific data from all disciplines worldwide. It includes the world's largest collection of social science research data. It is hosting data for projects, archives, researchers, journals, organizations, and institutions.

TheDataWeb

DataFerrett

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<<<!!!<<< This repository is no longer available. >>>!!!>>> Be advised that DataFerrett was decommissioned June 30, 2020; see https://www.census.gov/data/data-tools/dataferrett.html

KNB Data Repository

Knowledge Network for Biocomplexity

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The KNB Data Repository is an international repository intended to facilitate ecological, environmental and earth science research in the broadest senses. For scientists, the KNB Data Repository is an efficient way to share, discover, access and interpret complex ecological, environmental, earth science, and sociological data and the software used to create and manage those data. Due to rich contextual information provided with data in the KNB, scientists are able to integrate and analyze data with less effort. The data originate from a highly-distributed set of field stations, laboratories, research sites, and individual researchers. The KNB supports rich, detailed metadata to promote data discovery as well as automated and manual integration of data into new projects. The KNB supports a rich set of modern repository services, including the ability to assign Digital Object Identifiers (DOIs) so data sets can be confidently referenced in any publication, the ability to track the versions of datasets as they evolve through time, and metadata to establish the provenance relationships between source and derived data.

Registry of Open Data on AWS

Registry of Open Data on Amazon Web Services

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The Registry of Open Data on AWS provides a centralized repository of public data sets that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge to their users. Anyone can access these data sets from their Amazon Elastic Compute Cloud (Amazon EC2) instances and start computing on the data within minutes. Users can also leverage the entire AWS ecosystem and easily collaborate with other AWS users.

Knoema

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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.

Remote Sensing Systems

RSS

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Remote Sensing Systems is a world leader in processing and analyzing microwave data from satellite microwave sensors. We specialize in algorithm development, instrument calibration, ocean product development, and product validation. We have worked with more than 30 satellite microwave radiometer, sounder, and scatterometer instruments over the past 40 years. Currently, we operationally produce satellite retrievals for SSMIS, AMSR2, WindSat, and ASCAT. The geophysical retrievals obtained from these sensors are made available in near-real-time (NRT) to the global scientific community and general public via FTP and this web site.

GitHub

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GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over three million people use GitHub to build amazing things together. With the collaborative features of GitHub.com, our desktop and mobile apps, and GitHub Enterprise, it has never been easier for individuals and teams to write better code, faster. Originally founded by Tom Preston-Werner, Chris Wanstrath, and PJ Hyett to simplify sharing code, GitHub has grown into the largest code host in the world.

Virgo Millenium Database

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When published in 2005, the Millennium Run was the largest ever simulation of the formation of structure within the ΛCDM cosmology. It uses 10(10) particles to follow the dark matter distribution in a cubic region 500h(−1)Mpc on a side, and has a spatial resolution of 5h−1kpc. Application of simplified modelling techniques to the stored output of this calculation allows the formation and evolution of the ~10(7) galaxies more luminous than the Small Magellanic Cloud to be simulated for a variety of assumptions about the detailed physics involved. As part of the activities of the German Astrophysical Virtual Observatory we have created relational databases to store the detailed assembly histories both of all the haloes and subhaloes resolved by the simulation, and of all the galaxies that form within these structures for two independent models of the galaxy formation physics. We have implemented a Structured Query Language (SQL) server on these databases. This allows easy access to many properties of the galaxies and halos, as well as to the spatial and temporal relations between them. Information is output in table format compatible with standard Virtual Observatory tools. With this announcement (from 1/8/2006) we are making these structures fully accessible to all users. Interested scientists can learn SQL and test queries on a small, openly accessible version of the Millennium Run (with volume 1/512 that of the full simulation). They can then request accounts to run similar queries on the databases for the full simulations. In 2008 and 2012 the simulations were repeated.