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672 results for “August”
July and August 2012, 10 minute interval, creek water temperature, salinity, oxygen, tidal stage and net ecosystem metabolism in Sweeney and West Creek off Rowley River, MA.
July and August 2012, 10 minute interval, measurements of creek water temperature, salinity, oxygen, as well as tidal stage data and net ecosystem metabolism in Sweeney Creek, Right branch (Ipswich, MA) and West Creek Left branch (IRowley, MA) tidal creeks.
Marsh vegetation burial experiment biomass data, August 2014
End of year biomass harvested from marsh vegetation burial experiment on Hog Island. Experiment was set up at two sites - one in the high marsh and one in the low marsh. Within each site, a 6x6 array of mesocosms was established with 6 replicates of 6 different burial treatments (0, 5, 10, 15, 30, and 60 centimeters). After one growing season, the biomass was clipped from the top of each mesocosm, the mesocosm and sand layer was removed, and the vegetation within the sand layer was clipped at the marsh surface. This biomass was then returned to the lab, washed, dried to a constant weight, and weighed.
CABLE model results submitted to TRENDYv6 in August 2017
<p>These data are the results from the CABLE terrestrial biosphere model (TBM) submitted to the TRENDYv6 experiment, using the common protocols and forcing data described in documents/GlobalCarbonBudget_protocol_2017.pdf. The TRENDYv6 ensemble of TBMs, including CABLE, encapsulate mechanistic understanding of the terrestrial carbon cycle and are used to inform the global carbon budget. The results include four simulations, S0 to S3 described in the protocol as follows:</p> <p>Models can have dynamic vegetation but all will use observed time evolving cropland and grazing (=pasture+rangeland) distribution for S3. The models will be forced over the 1860-2016 period with changing CO2, climate and land use according to the following simulations.<br> S0: No forcing change (needed to diagnose any issues / drift)<br> S1: CO2 (time-invariant “pre-industrial” land use mask)<br> S2: CO2 and climate (time-invariant “pre-industrial” land use mask)<br> S3: CO2, climate and land use (Using updated annual LULCC maps to 2016)</p> <p>For S3, the harvest components are given in a separate directory.</p>
Finland COVID-19 R-value estimate March - August
<p>This dataset contains the time-dependent effective reproduction number R<sub>t</sub> of the COVID-19 epidemic in Finland for the period Feb 27th 2020 - August 14th 2020. The reproduction number was estimated using the daily new coronavirus cases in <a href="https://thl.fi/en/web/thlfi-en/statistics/statistical-databases/open-data/confirmed-corona-cases-in-finland-covid-19-">the infectious diseases registry of Finland</a> with an unscented RTS smoother and the SEIR epidemic model.</p> <p>The SEIR model was defined as<br> <span class="math-tex">\(S(t+1) = S(t) - \frac{R_t(t)}{T_i} I(t) \frac{S(t)}{N} + \omega_{S}\\ E(t+1) = E(t) + \frac{R_t(t)}{T_i} I(t) \frac{S(t)}{N} - \frac{E(t)}{T_e} + \omega_{E}\\ I(t+1) = I(t) + \frac{E(t)}{T_e}-\frac{I(t)}{T_i} + \omega_{I} \\ R_t(t+1) = R_t(t) + \omega_{R_t}\)</span></p> <p>with S, E, and I being the susceptible, exposed, and infective populations, R<sub>t</sub> the time-dependent effective reproduction number, and ωs are normally distributed random numbers with zero mean and small (1e-8) variance except for ω<sub>Rt</sub> whose variance was set to 0.00025. The exposed state duration was set to T<sub>e</sub>=3 days and infective state duration T<sub>i</sub>=5 days.</p> <p>The measurement model (from the SEIR state to observed daily new cases) was defined as<br> <span class="math-tex">\(P_\text{new cases}(k) \propto \mathcal{N}\left[ \alpha \frac{R_t(t-5 days)}{T_i}I(t-5\text{days}) \frac{S(t-5\text{days})}{N}, \sigma_\text{new cases}^2(t) \right]\)</span>, i.e., via a time-delayed gaussian process whose variance was computed by the observed daily new cases with a 7-day rolling mean. Here the detection rate α is set to 10%, but has no significant affect on the computed R-values.</p> <p> </p> <p>The data is a CSV-format file with the columns</p> <ul> <li>date = ISO 8601 date string</li> <li>Rt = maximum a posteriori estimate of R<sub>t</sub></li> <li>Rt_lower50 = lower limit of the 50% credible interval for R<sub>t</sub></li> <li>Rt_upper50 = upper limit of the 50% credible interval for R<sub>t</sub></li> <li>Rt_lower90 = lower limit of the 90% credible interval for R<sub>t</sub></li> <li>Rt_upper90 = upper limit of the 90% credible interval for R<sub>t</sub></li> </ul>
Georeferenced drone thermal video and related data, recorded on 6 and 28 August 2019 in Siikaneva peatland
<p>Data files in Matlab .mat format.</p> <p>1) EC_and_timeaxes.mat: Eddy Covariance data covering the drone measurement periods, time axes for drone 1Hz sequences and EC data;</p> <p>2) T_fluctuation_corr.mat: Four georeferenced sequences of surface temperature fluctuation T'(x,y,z) derived from the four measured thermal videos;</p> <p>3) T_georef_orig.mat: Four georeferenced sequences of surface temperature T(x,y,z) corresponding to the four measured thermal videos;</p> <p>4) UTMgrid_anemometer_RGBpic.mat: UTM grid for the T'(x,y,z) and T(x,y,z) sequences, pixel coordinates of anemometer location, georeferenced UAV RGB image (with the same UTM grid as the thermal images).</p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 16:56 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 20 August 2018. The UAV survey commenced at 16:56 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. Reference data from images DJI_814-817 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 Dickson Fjord (NE Greenland) on 21 August 2018 at 21:31 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 21 August 2018. The UAV survey commenced at 21:31 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. Reference data from images DJI_767-773 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>
Data from: "De novo transcriptome assembly of the mountain fly Drosophila nigrosparsa using short RNA-seq reads" in Genomic Resources Notes Accepted 1 August 2014-30 September 2014
Drosophila (Drosophila) nigrosparsa is a habitat specialist restricted to the European montane/alpine zone (Bächli 2008). Mountain biodiversity is considered highly vulnerable to ongoing climate warming (IPCC 2013), and organisms at high altitudes have only limited possibility to shift to cooler habitats at elevations above (Pertoldi & Bach 2007). For such species, rapid evolution may offer a solution for long-term survival. We are establishing D. nigrosparsa as a model system to test the extent and tempo of adaptive evolution under thermal stress in the laboratory. In this study, we used Illumina high-throughput sequencing to assemble the species' transcriptome using the pooled mRNA from 22 developmental and physiological stages.
Data from: "Polar bear (Ursus maritimus) transcriptome assembly and SNP discovery" in Genomic Resources Notes accepted 1 August 2013-30 September 2013
Polar bears (Ursus maritimus) in the Western Hudson Bay subpopulation have been declining in size and body condition for decades, as climate change causes earlier sea ice breakup, reduced hunting time on the ice, and an increasingly long fasting season. As Western Hudson Bay females have decreased in size, rates of litter production and average litter size have also decreased, while cub mortality and average time to independence have increased. Although these changes have potential evolutionary consequences, little is yet known about the adaptive genetic variation in body size or fat accumulation that would have to underlie any such change. In this study, we used high-throughput Illumina sequencing to develop SNPs from pooled blood and fat transcriptomes, using samples from five adult female polar bears and five (unrelated) dependent cubs. In total, we generated 371,258 transcripts of which 36,755 were deemed to be "full length" (i.e., covered more than 90% of their best BLAST hit), and we identified 63,020 SNPs. Since this study was conducted, we have used a subset of these SNPs to develop an Illumina BeadArray for quantitative genetics research in Western Hudson Bay.
Data from: "Transcriptome resources for the oriental rat flea and primary plague vector, Xenopsylla cheopis" in Genomic Resources Notes Accepted 1 August 2015 to 31 September 2015
This article documents the public availability of raw transcriptome sequence data, 45,254 assembled unigenes as well as their functional annotations of a plague vector Xenopsylla cheopis.
Jagdsäule (Marienberg) - Friedrich August III.
https://de.wikipedia.org/wiki/Jagds%C3%A4ule_(Marienberg) Source: Objaverse 1.0 / Sketchfab
YKM 2020 August
YKM 2020 August Source: Objaverse 1.0 / Sketchfab
SENTINEL-2 SATELLITE IMAGE (2015, AUGUST 7) FOR CHANGE DETECTION ON "MURGIA ALTA" - TIME T2 DATA
<p><strong>Time T2 data:</strong> Sentinel-2 image, 10 bands at 20 meters spatial resolution; 2015, August 7; subset of the "Murgia Alta" protected area; projected in WGS84/UTM33; coregistered on the time T1 data.</p>
Ebro Shelf circulation (March-August 2014)
<p>The files publicated here are the ROMS model output used to analyze the circulation at the North Ebro Shelf under NW wind-jet conditions in the article: <em>Ràfols, L., Grifoll, M., Jordà, G., Espino, M., Sairouní, A. and Bravo, M. Shelf circulation induced by an orographic wind jet. Journal of Geophysical Research - Oceans. Manuscript under review.</em></p> <p>The model was run for 6 months (from 1 March until 1 Setember 2014; the numbers of the files correspond to each simulated month) and was forced with atmospheric data from the WRF model implemented at the Catalan Service of Metheorology (http://meteo.cat/). The initial and boundary conditions were generated using data from the IBI-MFC model (http://marine.copernicus.eu).</p>
Ionospheric Electron Densities, Neutral Temperature, Winds and Post-processed Output from TIEGCM Simulations in Support of Publication "Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill"
This dataset is associated with the publication "Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill". In particular, the NCAR- Community model: high-resolution thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) has been used to investigate the response of ionospheric F2-region electron density (Ne) at Millstone Hill (42.610N, 71.480W, maximum obscuration: 63%) to the Great American Solar Eclipse on 21 August 2017. Two sets of model runs were done, one with the eclipse and the other without. Model outputs of winds, temperatures and electron densities, as well as diagnostic variables of key chemical and physical processes that determined the ionosphere responses to the eclipse, were analyzed and used in producing a paper: Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill.
2017 August Eclipse GITM simulation
<p>This data present GITM simulation results using different solar masks during the 2017 August Eclipse. Detailed descriptions of the masks used for the simulation can be found in Mrak et al. (2021) DOI: 10.1002/essoar.10509183.1.</p>
S Figure 1. Maximum Photosynthetically Active Radiation (PAR) during the experiments in the HTP platform, for A) batch 1 (1 July – 5 August 2021; 87 – 122 DAS) and B) batch 2 (7 August – 6 September 2021; 92 – 123 DAS)
<p>The figure is presented in a figure (S Figure 1.<strong> </strong>Maximum Photosynthetically Active Radiation (PAR) during the experiments in the HTP platform, for A) batch 1 (1 July – 5 August 2021; 87 – 122 DAS) and B) batch 2 (7 August – 6 September 2021; 92 – 123 DAS) as supplement of a manuscript in MDPI <i>Agronomy | </i>Special Issue<i> "Novel Insights into Abiotic Stress Tolerance of Crops" </i>journal. </p>
Rates of Vertical Land Motion (VLM) on Tutuila Island, American Samoa (December 3, 2015 to August 16, 2022)
<p>[Version 4] Latest version, updated to fix errors that were found in the vlm.mat data file.</p><p>VLM rates across Tutuila Island from December 5, 2015 to August 16, 2022, derived using a redundant multi-primary variant of the PS-InSAR technique. Dataset associated with the published version of the publication "Mapping Vertical Land Motion in Challenging Terrain: Six-Year Trends on Tutuila Island, American Samoa, With PS-InSAR, GPS, Tide Gauge, and Satellite Altimetry Data" by S. A. Huang, J. M. Sauber, and R. Ray. Available at <a href="https://doi.org/10.1029/2022GL101363">https://doi.org/10.1029/2022GL101363</a>.</p><p>ampl.mat: Amplitude backscatter averaged from all scenes, multilooked for speckle reduction and resampled to the dataset size. Normalized to 1.</p><p>latvals.mat: Latitude vector associated with the dataset.</p><p>lonvals.mat: Longitude vector associated with the dataset.</p><p>plot_ps_data.m: Matlab script to visualize the data included in all the *.mat files. Requires Computer Vision Toolbox.</p><p>vlm.mat: Computed VLM rates across Tutuila Island [mm/yr]. All non-PS have an "NaN" value.</p><p>vlm_se.mat: Standard error of VLM across Tutuila Island [mm/yr]. All non-PS have an "NaN" value.</p>
Interpretation Gates of hell Auguste Rodin
Plaster made interpretation of the gates of hell by August Rodin Source: Objaverse 1.0 / Sketchfab
August von Gneisenau - Back
-- coming soon -- Source: Objaverse 1.0 / Sketchfab
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International Brain Laboratory public data
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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.