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672 results for “August”
Precipitation from the Taylor Slough, just outside Everglades National Park (FCE), South Florida from August 2000 to December 2006
Rain level is recorded at least every thirty minutes at TS/Ph4 and TS/Ph5 (data since 2000). Rain level data are collected using ISCO rain level gages attached to ISCO autosamplers, which also act as dataloggers. We condense these data into daily cumulative rainfall, although hourly rain level data are also available.
Groundwater and surface water phosphorus concentrations, Everglades National Park (FCE), South Florida for June, July, August and November 2003
Seawater intrusion into a coastal aquifer mixes with the discharging fresh water to form a zone of mixed composition. This mixing zone is considered to be geochemical important in a carbonate aquifer as an area of enhanced carbonate mineral dissolution and,or precipitation. Ion exchange reactions are also common within the mixing zone as the dominant cation in seawater, sodium, replaces other ions adsorbed to the aquifer matrix. Phosphorus has a strong affinity for adsorption to calcium carbonate minerals. Both the dissolution of calcium carbonate minerals as well as ion exchange reactions have the potential to release phosphorus from the aquifer matrix to the groundwater. Discharge of this phosphorus-laden groundwater, as induced by the fresh/saline water interface, may then be an additional source of phosphorus to the overlying coastal environments. Both surface water and groundwater were collected across the seawater intrusion zone of the coastal Everglades in south Florida during the summer of 2003. Hydrogen sulfide was released from the groundwater wells during sampling, indicating the groundwater was most likely anoxic. In order to reduce the potential exposure of the groundwater to oxygen during sampling, groundwater samples were collected in the following manner. Groundwater wells were first purged of at least three well volumes prior to sampling. Water samples were then collected using a submersible pump with a 16-gauge needle fitted at the end of the discharge hose. The needle was pushed through a rubber stopper covering an acid-washed vacutainer that was first flushed with nitrogen gas to remove and oxygen, then evacuated with a vacuum pump to establish a negative pressure within the vacutainer. Water samples were stored on ice and transported to the laboratory. Samples were processed for total phosphorus using colorimetery following dry-oxidation/acid hydroloysis methods within 1 to 5 days following sample collection. Salinity of the groundwater and surf
Water Temperature measured at Shark River, Everglades National Park (FCE) from October 2007 to August 2008
American alligators (Alligator mississippiensis) are top predators in the Florida Coastal Everglades (FCE), but their movement patterns and role in the ecosystem are largely unknown. Proposed restoration and management efforts in the Everglades are likely to influence both alligator movements and the spatiotemporal patterns of their effects on ecosystem and community dynamics, making studies of the factors influencing alligator movements important at this time. In October 2007, we initiated a study of alligators in the Shark River Slough of Everglades National Park using two tracking techniques that have yet to be used on crocodilians: GPS tracking and passive acoustic telemetry. GPS units attached to two alligators have provided 120 positions over a two month period while acoustic transmitters attached to 12 alligators have been detected 34,000 times on an array of 8 monitoring stations within the river system. GPS positions are more accurate than acoustic monitoring and can provide data over the entire range an alligator might move, but acoustic transmitters provide more temporally detailed information on movements within a monitoring array and over a longer duration (potentially years rather than months). Our preliminary results show that alligators may remain in localized areas for weeks at a time, but make long-distance moves from the mouth of the river system to the marsh-mangrove ecotone and likely into the marsh. In general, most alligators moved upstream as freshwater inputs decreased, but some individuals remained near the river mouth even when salinities were high. Ultimately, by characterizing alligator movements in relation to spatiotemporal variation in environmental factors we will be able to predict how alligator distributions and populations will respond to planned increases in freshwater flow. Furthermore, movement data combined with dietary and stable isotopic studies may shed light on the possible role of alligators in the redistribution of nutri
Birds observed in newly hayed and adjacent reference salt marsh sites in Rowley, MA, August through September 2000 and August 2001, PIE LTER.
Birds observed in newly hayed marsh and adjacent reference marsh sites near Stackyard Rd. and Patmos Rd, Rowley, Massachusetts. August through September 2000 and August 2001.
Marsh water table height, logging data from the railroad Spartina marsh site on the Parker River for May - August 2001
Measurements of water table height in the Parker River marsh located downstream of the railroad bridge. Measurements were taken every 4 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the railroad site, MAR-PR-Wtable-RR, for May - August 2001.
Marsh water table height, logging data from the railroad Spartina marsh site on the Parker River for May - August 2002
Measurements of water table height in the Parker River marsh located downstream of the railroad bridge. Measurements were taken every 4 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the railroad site, MAR-PR-Wtable-RR, for May - August 2002.
Marsh water table height, logging data from the Typha site on the upper Parker River for June-August 2001
Measurements of water table height in the upper Parker River Typha sp. marsh. Measurements were taken every 4 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the Typha site, MAR-PR-Wtable-T, for June - August 2001.
Marsh water table height, logging data from the Typha site on the upper Parker River for April-August 2002
Measurements of water table height in the upper Parker River Typha sp. marsh. Measurements were taken every 4 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the Typha site, MAR-PR-Wtable-T, for April - August 2002.
High-frequency observations of overwash water and bed elevations from five MeOw (Measuring Overwash) stations: Smith Island, VA, August-November 2019
Water and bed surface elevation measurements from five MeOw (Measuring Overwash) stations installed on Smith Island, VA, from 21 August 2019 to 22 November 2019. The five MeOw stations consist of one ultrasonic distance sensor inside a stilling well to measure water levels, and a second attached to an arm extending outwards from the well to measure the elevation of the sediment surface (when dry) or the water surface (when inundated). Note: Station MeOw2 was damaged and produced no data, and so is not included in this dataset.
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 the Vaigat Strait (NW Greenland) on 3 August 2019
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 3 August 2019. The UAV survey commenced at 15:06 UTC. 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. 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 the Vaigat Strait (NW Greenland) on 6 August 2019
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in the Vaigat Strat in northwest Greenland on 6 August 2019. The UAV survey commenced at 11:10 UTC. 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. 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> <p> </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>
Cumulative PLOS ALM Report - August 2015
Article-Level Metrics (ALM) measure the reach and online engagement of scholarly works. This PLOS ALM report contains the cumulative stats collected for all works through August 10, 2015. Data are generated by the Lagotto open source software. Go to the Lagotto forum for questions or comments.
Figure 5a. from: Three new species of Ametadoria Townsend (Diptera: Tachinidae) from Area de Conservación Guanacaste, Costa Rica - Biodiversity Data Journal 3: e5039 (10 August 2015) https://doi.org/10.3897/BDJ.3.e5039
Figure 5a. - Male terminalia, dorsal viewFigure 5a.Ametadoriakarolramosae (paratype, DHJPAR0040114)Figure 5b.Ametadorialeticiamartinezae (paratype, DHJPAR0018978)Figure 5c.Ametadoriamauriciogurdiani (paratype, DHJPAR0049512) <br> Ametadoriakarolramosae (paratype, DHJPAR0040114)
Figure 4c. from: Three new species of Ametadoria Townsend (Diptera: Tachinidae) from Area de Conservación Guanacaste, Costa Rica - Biodiversity Data Journal 3: e5039 (10 August 2015) https://doi.org/10.3897/BDJ.3.e5039
Figure 4c. - Ametadoriamauriciogurdianisp. nov. a-c: holotype male; d-f: paratype female (DHJPAR0030011)Figure 4a.Habitus, dorsalFigure 4b.Habitus, lateralFigure 4c.Head, frontalFigure 4d.Habitus, dorsalFigure 4e.Habitus, lateralFigure 4f.Head, frontal <br> Head, frontal
Figure 6a. from: Three new species of Ametadoria Townsend (Diptera: Tachinidae) from Area de Conservación Guanacaste, Costa Rica - Biodiversity Data Journal 3: e5039 (10 August 2015) https://doi.org/10.3897/BDJ.3.e5039
Figure 6a. - Male terminalia, lateral viewAbbreviations: acroph = acrophallus; bac scl = bacilliform sclerite; cerc = cercus; distph = distiphallus; epand= epandrium; hypd = hypandrium; pgt = postgonite; phapod = phalloapodeme; pregt = pregonite; sur = surstylus.Figure 6a.Ametadoriakarolramosae (paratype, DHJPAR0040114)Figure 6b.Ametadorialeticiamartinezae (paratype, DHJPAR0018978)Figure 6c.Ametadoriamauriciogurdiani (paratype, DHJPAR0049512) <br> Ametadoriakarolramosae (paratype, DHJPAR0040114)
Figure 3f. from: Three new species of Ametadoria Townsend (Diptera: Tachinidae) from Area de Conservación Guanacaste, Costa Rica - Biodiversity Data Journal 3: e5039 (10 August 2015) https://doi.org/10.3897/BDJ.3.e5039
Figure 3f. - Ametadorialeticiamartinezaesp. nov.; a-c: holotype male; d-f: paratype female (DHJPAR0018974)Figure 3a.Habitus, dorsalFigure 3b.Habitus, lateralFigure 3c.Head, frontalFigure 3d.Habitus, dorsalFigure 3e.Habitus, lateralFigure 3f.Head, frontal <br> Head, frontal
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)
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.