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6,782
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6,782 results for “project”
NASA Water Vapor Project MEaSUREs (NVAP-M) CLIMATE Layered Precipitable Water
NVAP_CLIMATE_Layered-Precipitable-Water data set is designed to provide the most stable water vapor data set over time for use in climate applications. NASA Water Vapor Project MEaSUREs (NVAP-M) Climate only includes data from stable instruments that have undergone intercalibration efforts to ensure consistency between data from the same instrument flying on multiple satellite platforms. The new NVAP data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets.
NASA Water Vapor Project MEaSUREs (NVAP-M) WEATHER Layered Precipitable Water
NVAP_WEATHER_Layered-Precipitable-Water data set is designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Land GPS sites were added beginning in 1997. The new NASA Water Vapor Project (NVAP) data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets.
NASA Water Vapor Project MEaSUREs (NVAP-M) NVAP WEATHER Total Precipitable Water
NVAP_WEATHER_Total-Precipitable-Water data set is designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. The new NASA Water Vapor Project (NVAP) data sets are produced under the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program and is named NVAP-M. It supersedes the previous NVAP data set. NVAP-M continues the legacy of providing high-quality, model-independent global estimates of total column and layered water vapor. The use of improved, intercalibrated data sets and algorithms that were not available for the heritage NVAP data set results in an improved and extended water vapor data set that is stable enough for climate research and of a resolution appropriate for studies on smaller spatial and temporal scales. The true value of NVAP-M will be seen in outcomes from applied and research users of the data set in various fields. Some initial NVAP-M findings are presented in Vonder Haar et al. (2012). In addition to the time-dependent artifacts present in the previous NVAP data set, a wealth of new data has become available since the last NVAP processing in 2003. These include an additional SSM/I instrument, additional NOAA satellites, the NASA Earth Observing System (EOS)-Aqua Satellite, which carries the Atmospheric Infrared Sounder (AIRS), as well as water vapor information from Global Positioning System (GPS) satellites. This extension and reprocessing effort increases the temporal coverage from 14 to 22 (1988-2009) years, making the data set more useful and consistent for investigation of the long-term trends which are hypothesized to occur as Earth warms. In addition to the long-standing daily, 1-degree gridded Total Precipitable Water (TPW) and layered Precipitable Water (PW) products, NVAP-M includes additional products geared towards different scientific needs. Three separate processing streams produced products directed towards specific research goals. These are NVAP-M Climate, designed to provide the most stable water vapor data set over time for use in climate applications, and NVAP-M Weather, designed to provide higher spatial and temporal resolution products for use in studies on shorter time scales as well as weather case studies. Additionally, an ocean-only (NVAP-M Ocean) version includes only data from the SSM/I and is intended to mirror other available SSM/I-only water vapor data sets.
List of licenses discovered in the study of Potential Code Borrowing and License Violations in Java Projects on GitHub
<p>This is the list of licenses discovered in the study of Potential Code Borrowing and License Violations in Java Projects on GitHub. The licenses are ranged by the amount of files that they cover, there are a total of 95 different licenses. Where possible, the names are presented as identifiers at https://spdx.org/licenses/. "GitHub" stands for no license in the file or the project.</p>
Increasing of Renewables Hosting Capacity in Czech Republic in Terms of European Project InterFlex
<p>dataset for scientific paper</p>
MERLIN H2020 Project: Building the right IP for your business (podcast)
<p>Interview with Mrs. Esther Ford and Mr. Simon Portman of Marks & Clerk about IP management and protection. July 2019.</p>
MERLIN H2020 Project: Interview with Polish participant (podcast)
<p>Marcin Śmiglak, CEO Innosil, Head of Materials Synthesis Group at Poznan Science and Technology Park. June 2019.</p>
MERLIN H2020 Project: The Perils of Early Stage Commercialisation (Podcast)
<p>Interview with Dr. Uday Phadke, serial entrepreneur and associate professor at Møller Institute. October 2019.</p>
MERLIN H2020 Project: Video of Faces of MERLIN: Dr Francisco J. Escribano
<p>Video of Faces of MERLIN: Dr Francisco J. Escribano, 2019</p>
MERLIN H2020 Project: Video of Faces of MERLIN, Maria Józefowicz
<p>Project: Video of Faces of MERLIN, Maria Józefowicz, 2019</p>
MERLIN H2020 Project: Video of the MERLIN Webinar SME Growth (I): grant opportunities
<p>Video of the MERLIN Webinar SME Growth (I): grant opportunities</p>
MERLIN H2020 Project: Video of Faces of MERLIN; Dr. Marcin Śmiglak (INNOSIL)
<p>Video of Faces of MERLIN: Dr. Marcin Śmiglak (INNOSIL) , 2019</p>
MERLIN H2020 Project: Video of the MERLIN workshop, 8th June 2018 - CIVITTA
<p>Video of the MERLIN workshop, 8th June 2018 - CIVITTA</p>
MERLIN H2020 Project: Video of Faces of MERLIN; Dr. Teona Mirea and Mario de Miguel
<p>Video of Faces of MERLIN; Dr. Teona Mirea and Mario de Miguel, 2019</p>
MERLIN H2020 Project: Video of Faces of MERLIN, Mr Andrew Dunn (Crossbridge Scientific)
<p>Video of Faces of MERLIN, Mr Andrew Dunn (Crossbridge Scientific), 2019</p>
MERLIN H2020 Project: Video of Faces of MERLIN, Dr. Juan B. Tomás (Cubicfort)
<p>Video of Faces of MERLIN: Dr. Juan B. Tomás (Cubicfort), 2019</p>
MERLIN H2020 Project: Video of Webinar 'Finance for Growth (II)' - David Gill - St Johns Innovation Centre
<p>Video of Webinar 'Finance for Growth (II)' - David Gill - St Johns Innovation Centre, 2019</p>
MERLIN H2020 Project: Video of the Marshmallow challenge time lapse at SAAEI 2018 Conference
<p>Video of the Marshmallow challenge time lapse at SAAEI Conference, July 2018</p>
MERLIN H2020 Project: Cómo lanzar una spin-off en España y no morir en el intento - Ask-Me-Anything session
<p>Cómo lanzar una spin-off en España y no morir en el intento - Ask-Me-Anything, 2019</p>
MERLIN H2020 Project: Video of Faces of MERLIN; Dr Loretta de Chiara
<p>Video of Faces of MERLIN; Dr Loretta de Chiara, 2019</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.