Site Lookup:
Government Agency Domains Select domain(s) that contain a specific agency name(s) in the URL
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Title: |
BD2K Home Page | Data Science at NIH
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Archival URL:
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http://eot.us.archive.org/eot/20170119113452/http://datascience.nih.gov/bd2k |
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Live URL: |
http://datascience.nih.gov/bd2k |
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Coverage: |
November 23, 2016 - January 19, 2017 |
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Description: |
Official website of Data science at NIH, harnessing Big Data to advance research in biomedical sciences coordinated by the
NIH Scientific Data Council and the NIH Office of the Associate Director for Data Science (ADDS).
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Title: |
Data Science at Scale
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Archival URL:
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http://eot.us.archive.org/eot/20161118062009/http://datascience.lanl.gov/index.html |
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Live URL: |
http://datascience.lanl.gov/index.html |
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Coverage: |
November 18, 2016 - November 18, 2016 |
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Description: |
Los Alamos National Lab Data Science at Scale Team's Home Page. Extremely large datasets and extremely high-rate data streams
are becoming increasingly common due to the operation of Moore's Law as applied to sensors, embedded computing, and traditional
high-performance computing. Interactive analysis of these datasets is widely recognized as a new frontier at the interface
of information science, mathematics, computer science, and computer engineering. Text searching on the web is an obvious example
of a large dataset analysis problem; however, scientific and national security applications require far more sophisticated
interactions with data than text searches. These applications represent the 'data to knowledge' challenge posed by extreme-scale
datasets in, for example, astrophysics, biology, climate modeling, cyber security, earth sciences, energy security, materials
science, nuclear and particle physics, smart networks, and situational awareness. In order to contribute effectively to LANL's
overall national security mission, we need a strong capability in Data Science at Scale. This capability rests on robust and
integrated efforts in data management and infrastructure, visualization and analysis, high-performance computational statistics,
machine learning, uncertainty quantification, and information exploitation. The Data Science at Scale capability provides
tools capable of making quantifiably accurate predictions for complex problems with the efficient use and collection of data
and computing resources.
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Title: |
Data Science at NIH |
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Archival URL:
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http://eot.us.archive.org/eot/20161123024832/http://datascience.nih.gov/ |
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Live URL: |
http://datascience.nih.gov/ |
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Coverage: |
November 23, 2016 - November 23, 2016 |
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Description: |
Official website of Data science at NIH, harnessing Big Data to advance research in biomedical sciences coordinated by the
NIH Scientific Data Council and the NIH Office of the Associate Director for Data Science (ADDS).
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Title: |
Data Science at Scale
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Archival URL:
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http://eot.us.archive.org/eot/20161118000126/http://datascience.lanl.gov/ |
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Live URL: |
http://datascience.lanl.gov/ |
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Coverage: |
November 18, 2016 - November 18, 2016 |
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Description: |
Los Alamos National Lab Data Science at Scale Team's Home Page. Extremely large datasets and extremely high-rate data streams
are becoming increasingly common due to the operation of Moore's Law as applied to sensors, embedded computing, and traditional
high-performance computing. Interactive analysis of these datasets is widely recognized as a new frontier at the interface
of information science, mathematics, computer science, and computer engineering. Text searching on the web is an obvious example
of a large dataset analysis problem; however, scientific and national security applications require far more sophisticated
interactions with data than text searches. These applications represent the 'data to knowledge' challenge posed by extreme-scale
datasets in, for example, astrophysics, biology, climate modeling, cyber security, earth sciences, energy security, materials
science, nuclear and particle physics, smart networks, and situational awareness. In order to contribute effectively to LANL's
overall national security mission, we need a strong capability in Data Science at Scale. This capability rests on robust and
integrated efforts in data management and infrastructure, visualization and analysis, high-performance computational statistics,
machine learning, uncertainty quantification, and information exploitation. The Data Science at Scale capability provides
tools capable of making quantifiably accurate predictions for complex problems with the efficient use and collection of data
and computing resources.
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