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48 results for “August 2017”
WRF Forecast Data used for Verification of multi-resolution model forecasts of heavy rainfall events of 23rd-26th August 2017 over Nigeria
<p>A deterministic Weather Research and Forecasting model version 4.2 forecast of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) within the period 23<sup>rd</sup>-26<sup>th</sup> August 2017 over Nigeria. The model was setup to perform two nested domain simulations with 18 (parent domain), 6 and 2 km (hereafter WRF18, WRF6 and WRF2) horizontal resolutions. The outer domain covers West Africa and the innermost domain, which runs at convection-permitting scale, focuses on Nigeria. When interpreting the results, it is worthy of note that the data has been regridded to 18 km, which is 3 x the grid scale for WRF6 and 9 x the grid scale for WRF2. This means that there is a fair degree of smoothing that has been applied using a bilinear regridding process to get the models onto a level playing field. Only WRF18 retains its native grid and has not benefited from any additional smoothing.</p> <p>The WRF model setup is similar to the study of Gbode et al. (2019; DOI: https://doi.org/10.1007/s00704-018-2538-x) in terms of the model physics combination used in the model simulations. The parameterization schemes used are the Goddard (GD) WRF model microphysics (MP), the Mellor–Yamada–Janjic (MYJ) planetary boundary layer (PBL) and the Bett-Miller-Janjic (BMJ) cumulus convection (CU) parameterization schemes. This combination was found to reproduce realistic rainfall and temperature relative to gridded observations over West Africa. The GD is a six-class microphysics with graupel and modifications for ice/water saturation. MYJ is a local closure scheme that predicts turbulent kinetic energy and the BMJ CU is a profile adjustment scheme that relaxes both deep and shallow profiles toward a reference profile without explicit updraft, downdraft, or cloud entrainment. However, the CU scheme was turned off in the 2 km domain to explicitly represent convection.</p>
Influences on charitable giving for conservation: Online survey data of 1,331 respondents across the US, August 2017
This dataset records survey data collected from an online panel of 1,331 anonymous, nationwide U.S respondents. Data collection was both initiated and completed in August 2017. Survey data is of two major types. The first type, information about respondents, includes (1) select background and demographic information; (2) a brief version of a social desirability scale, measured to test and control for potential bias related to socially desirable responding; and (3) a scale developed to measure moral inclusivity, conceptualized as the breadth of an individual’s moral community (i.e., to what extent do different types of entities “count,” in a moral sense). The second type of data records information about an experimental message manipulation featured in the survey. The dataset includes one variable indicating which of seven manipulated textual messages each respondent viewed, along with several variables used as metrics of response to the messages, including (1) attitudes toward the message; (2) hypothetical willingness to donate for the cause promoted in the message; (3) perceived moral salience of the message (i.e., the extent to which it was perceived as a matter of moral concern); (4) manipulation checks, to test whether the manipulated elements of the messages were perceived as intended, and (5) a donation set-up, in which individuals were given the option to donate between $0 and $5 for a conservation organization, from an incentive fee provided by the researchers.
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 9 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 9 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_566-DJI_570 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 22 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_659-662 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 17 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 17 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_528, 530, 531-533 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Nuup Kangerlua (Godthåbsfjord), SW Greenland on 11 August 2017
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Nuup Kangerlua (Godthåbsfjord) in southwest Greenland on 11 August 2017. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_330, 332, 334, 335, 336, 337, 338, 339 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
I/Q Recording for 160 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 160 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 80 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 80 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 15 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 15 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 10 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 10 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 20 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 20 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 6 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 6 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
I/Q Recording for 40 Meters During August 21, 2017 Solar Eclipse at W3OA
<p>I/Q Recording for 40 Meters Amateur Band During August 21, 2017 Solar Eclipse<br> Recorded by Richard Williams, W3OA<br> Location was 35 degrees, 34 minutes, 43.55 seconds North; 80 degrees, 56 minutes, 50.80 secondes West<br> Antenna was an 80 meter dipole 50 feet high<br> Receiver was a QS1R</p>
Wideband I/Q Recording at VU2PTT for 160m Ham Radio Band during August 21, 2017 Solar Eclipse in USA
<p>Recorded by Prasad Rajagopal, VU2PTT in Bangalore, India</p> <p>Location: Latitude 12°58'24.8"N, Longitude 77°39'15.7"E</p> <p>Grid Locator: MK82tx</p> <p>Recorded using Skimmer Server & CWSL File</p> <p>Playback: Each time stamped I/Q file is a 15 minute slice and can be played back with HDSDR software</p> <p>Antenna was a full size 1/4 wave vertical on 40m (7 MHz)</p> <p>Receiver was a QS1R SDR capable of simultaneously recording 7 bands @ 192 kHz bandwidth </p>
Costa Rica mosquito community species occurrence and site environmental data, July - August 2017
<p>Land use change is an important driver of both biodiversity loss and zoonotic disease transmission in tropical countryside landscapes. Developing solutions for protecting biodiversity, public health, and livelihoods in working landscapes requires understanding the spatial scales at which habitat characteristics such as land cover shape biodiversity, especially for arthropods that transmit pathogens. A growing body of evidence shows that species richness for many taxa correlates with tree cover at small spatial scales of <100 m, indicating that local tree cover management is a promising conservation tool. To investigate whether mosquito species richness, community composition, and presence of specific disease vector species respond to tree cover—and if so, whether at spatial scales similar to other taxa—we surveyed mosquito communities along a tree cover gradient and across agricultural, residential, and forested land uses in rural southern Costa Rica. We found that tree cover was both positively correlated with mosquito species richness and negatively correlated with the presence of the common invasive dengue vector <em>Aedes albopictus</em>, particularly at small spatial scales of 80 – 200m<em>. </em>Beyond tree cover, land use type predicted community composition and <em>Ae. albopictus </em>presence, but not species richness. The results suggest that preservation and expansion of tree cover at local scales can protect biodiversity for a wide range of taxa and also confer protection against disease vector occurrence.</p>
Solar and meteorological data collected from the Reserve Francois Leguat station (Mauritius) by the ENERGY-Lab at the University of La Reunion between May 2017 and August 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Water quality dataset from stream water in the Khibiny massif, Kola Peninsula (Russia) in August 2017
<p>The dataset includes base chemistry, elemental concentrations, and sulfur isotope (δ34S_SO4) measurements from 11 stream water sampling locations.</p> <p>The measurements were taken in the Belaya and Vuonnemiok stream systems within the Khibiny massif (Kola Peninsula, Russia) during 25-30th of August 2017 and were analyzed at laboratories at Stockholm University, Sweden.</p> <p>The data was gathered to investigate potential pollution spreading in hydrological pathways from active apatite mining within the catchments, and it can be used for comparison with other mining-impacted sites in the Arctic.</p>
Wideband I/Q Recording at VU2PTT for 80m Ham Radio Band during August 21, 2017 Solar Eclipse in USA
<p>Recorded by Prasad Rajagopal, VU2PTT in Bangalore, India</p> <p>Location: Latitude 12°58'24.8"N, Longitude 77°39'15.7"E</p> <p>Grid Locator: MK82tx</p> <p>Recorded using Skimmer Server & CWSL File</p> <p>Playback: Each time stamped I/Q file is a 15 minute slice and can be played back with HDSDR software</p> <p>Antenna was a full size 1/4 wave vertical on 40m (7 MHz)</p> <p>Receiver was a QS1R SDR capable of simultaneously recording 7 bands @ 192 kHz bandwidth </p>
Wideband I/Q Recording at VU2PTT for 40m Ham Radio Band during August 21, 2017 Solar Eclipse in USA - PART 2
<p>Recorded by Prasad Rajagopal, VU2PTT in Bangalore, India</p> <p>Location: Latitude 12°58'24.8"N, Longitude 77°39'15.7"E</p> <p>Grid Locator: MK82tx</p> <p>Recorded using Skimmer Server & CWSL File</p> <p>Playback: Each time stamped I/Q file is a 15 minute slice and can be played back with HDSDR software</p> <p>Antenna was a full size 1/4 wave vertical on 40m (7 MHz)</p> <p>Receiver was a QS1R SDR capable of simultaneously recording 7 bands @ 192 kHz bandwidth </p>
Wideband I/Q Recording at VU2PTT for 40m Ham Radio Band during August 21, 2017 Solar Eclipse in USA - PART 1
<p>Recorded by Prasad Rajagopal, VU2PTT in Bangalore, India</p> <p>Location: Latitude 12°58'24.8"N, Longitude 77°39'15.7"E</p> <p>Grid Locator: MK82tx</p> <p>Recorded using Skimmer Server & CWSL File</p> <p>Playback: Each time stamped I/Q file is a 15 minute slice and can be played back with HDSDR software</p> <p>Antenna was a full size 1/4 wave vertical on 40m (7 MHz)</p> <p>Receiver was a QS1R SDR capable of simultaneously recording 7 bands @ 192 kHz bandwidth </p>
ScienceDex guides
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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)
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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.