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2,113 results for “High resolution”
Berkeley High Resolution (BEHR) OMI NO2 - Gridded pixels, daily profiles
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Berkeley High Resolution (BEHR) OMI NO2 - Native pixels, monthly profiles
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Berkeley High Resolution (BEHR) OMI NO2 - Gridded pixels, monthly profiles
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Berkeley High Resolution (BEHR) OMI NO2 - Native pixels, daily profiles
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Segmented high-resolution transmission electron microscopy images of nanoparticles
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Denitrification losses in response to N fertiliser rates - integrating high temporal resolution N2O, in-situ 15N2O and 15N2 measurements and fertiliser 15N recoveries in intensive sugarcane systems
Denitrification is a key process in the global nitrogen (N) cycle, causing both nitrous oxide (N2O) and dinitrogen (N2) emissions. However, estimates of seasonal denitrification losses (N2O+N2) are scarce, reflecting methodological difficulties in measuring soil-borne N2 emissions against the high atmospheric N2 background and challenges regarding their spatio-temporal upscaling. This study investigated N2O+N2 losses in response to N fertiliser rates (0, 100, 150, 200 and 250 kg N ha-1) on two intensively managed tropical sugarcane farms in Australia, by combining automated N2O monitoring, in-situ N2 and N2O measurements using the 15N gas flux method and fertiliser 15N recoveries at harvest. Dynamic changes in the N2O/(N2O+N2) ratio (< 0.01 to 0.768) were explained by fitting generalised additive mixed models (GAMMs) with soil factors to upscale high temporal-resolution N2O data to daily N2 emissions over the season. Cumulative N2O+N2 losses ranged from 12 to 87 kg N ha-1, increasing non-linearly with increasing N fertiliser rates. Emissions of N2O+N2 accounted for 31–78% of fertiliser 15N losses and were dominated by environmentally benign N2 emissions. The contribution of denitrification to N fertiliser loss decreased with increasing N rates, suggesting increasing significance of other N loss pathways including leaching and runoff at higher N rates. This study delivers a blueprint approach to extrapolate denitrification measurements at both temporal and spatial scales, which can be applied in fertilised agroecosystems. Robust estimates of denitrification losses determined using this method will help to improve cropping system modelling approaches, advancing our understanding of the N cycle across scales.
Seeing the light: high temporal frequency (5-10min resolution) measurements of dissolved oxygen, photosynthetically active radiation, temperature, and depth used to estimate metabolism in restored and unrestored Baltimore streams.
The continually increasing global population residing in urban landscapes impacts numerous ecosystem functions and services provided by urban streams. Urban stream restoration is often employed to offset these impacts and conserve or enhance the various functions and services these streams provide. Despite the assumption that ‘if you build it, [the function] will come’, current understanding of the effects of urban stream restoration on stream ecosystem functions are based on short term studies which may not capture variation in restoration effectiveness over time. We quantified the impact of stream restoration on nutrient and energy dynamics of urban streams by studying 10 urban stream reaches (five restored, five unrestored) in the Baltimore, Maryland, USA, region over a two-year period. We measured gross primary production (GPP) and ecosystem respiration (ER) at the whole-stream scale continuously throughout the study and nitrate (NO3-N) spiraling rates seasonally (spring, summer, autumn) across all reaches. There was no significant restoration effect on NO3-N spiraling across reaches. However, there was a significant canopy cover effect on NO3-N spiraling, and directly comparing paired sets of unrestored-restored reaches showed that restoration does affect NO3-N spiraling after accounting for other environmental variation. Furthermore, there was a change in GPP:ER seasonality, with restored and open-canopied reaches exhibiting higher GPP:ER during summer. The restoration effect, though, appears contingent upon altered canopy cover, which is likely to be a temporary effect of restoration and is a driver of multiple ecosystem services, e.g., habitat, riparian nutrient processing. Our results suggest that decision-making about stream restoration, including evaluations of nutrient benefits, clearly needs to consider spatial and temporal dynamics of canopy cover and tradeoffs among multiple ecosystem services. Here we provide the raw dissolved oxygen, temperature, li
Assembled file of one minute averages for high resolution surface meteorological (Met) and sea water intake (SWI) data from continuous underway measurements from CCE LTER process cruises in the CCE region, 2006 - 2019.
As the research vessel is underway for the duration of a CCE Process Cruise (since 2006, ongoing), 30 parameters are continuously measured regarding the oceanographic surface and atmospheric and navigational environment of the vessel, along the ship's trackline in the CCE region.
High resolution LiDAR Data for Hog Island, VA, 2013
High Resolution LiDAR elevation and nearshore bathymetry data for Hog Island, Northampton County, VA, collected on May 26, 2013 on behalf of the USACE Engineer Research and Development Center using the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. CZMIL integrates a lidar sensor with topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. Hyperspectral imagery is provided as a separate VCRLTER dataset. Four data entities are included here: (1) the LiDAR point cloud (approximate point density of 5-30 points per square meter [denser over structures and dense vegetation]) in standard LiDAR LAS file format; (2) a rasterized digital elevation model (DEM) derived from the point cloud depicting elevation of the water-free first return surface with a cell resolution of 1 meter; (3) a DEM of the same first return surface with a cell resolution of 5 meters; and (4) a DEM depicting the bare earth surface with vegetation and structures removed, at a 1 meter resolution. DEMs are in georegistered TIFF format. ArcGIS and FGDC metadata files in XML format are also included. To obtain vegetation and building heights, subtract the bare earth model from the surface model. Areas of open water with sparse or no bottom returns (either due to water depth or clarity issues) are masked out in the DEM data.
High resolution LiDAR Data for Hog Island, VA, 2011
High Resolution LiDAR elevation and nearshore bathymetry data for Hog Island, Northampton County, VA, collected on October 11, 2011 on behalf of the USACE Engineer Research and Development Center using the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. CZMIL integrates a lidar sensor with topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. RGB air photo imagery is provided as a separate VCRLTER dataset. Two data entities are included here: (1) the LiDAR point cloud (approximate point density of 100 points per square meter [denser over structures and dense vegetation], average point spacing of 0.48 m.) contained in a mosaic of 211 LAS files (standard LiDAR LAS file format); and (2) a polygon INDEX shapefile showing the footprint of each LAS file and containing a summary description of each LAS file in the attribute table (LAS file name, point count, point spacing, and minimum and maximum elevation). Note that the two co-collected 2011 USACE datasets (LiDAR and RGB ) are in different coordinate systems: (A) the horizontal and vertical units of the LiDAR data are in US feet, not meters. (B) the horizontal units of the associated RGB mosaic images are in [standard] meters. Also Note that THESE ARE VERY LARGE DATASETS and download should not be attempted unless you have a fast network connection and plenty of disk space. The LAS file data collection is 7.3 GB compressed (11.7 GB uncompressed). The RGB imagery mosaic data collection is 12.9 GB compressed (30.8 GB uncompressed).
High resolution Air Photo Mosaic for Hog Island, VA, 2011
High resolution (6 cm pixel) RGB true color tiled air photo mosaics for Hog Island, Northampton County, VA, collected on October 11, 2011 on behalf of the USACE Engineer Research and Development Center using the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. CZMIL integrates a lidar sensor with topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. LiDAR data is provided as a separate VCRLTER dataset. Two data entities are included here: (1) a tiled collection of air photo mosaics created from the CZMIL camera imagery (6 cm. resolution; tiles approximately 690 x 715 m. in X and Y, resp.) composed of 71 individual georeferenced TIFF files; and (2) a polygon INDEX shapefile showing the footprint of each TIFF file and containing ID information in the attribute table (column, row, and colrow matching the TIFF naming convention). Note that the two co-collected 2011 USACE datasets (LiDAR and RGB ) are in different coordinate systems: (A) the horizontal and vertical units of the LiDAR data are in US feet, not meters; and (B) the horizontal units of the associated RGB mosaic images are in [standard] meters. Also Note that THESE ARE VERY LARGE DATASETS and download should not be attempted unless you have a fast network connection and plenty of disk space. The LAS file data collection is 7.3 GB compressed (11.7 GB uncompressed). The RGB imagery mosaic data collection is 12.9 GB compressed (30.8 GB uncompressed).
Data set for initializing and forcing of high-resolution local area model implementations in two ATLAS case study areas, Rockall Bank and Condor Seamount
<p>The data set includes all necessary data for set up, initial and boundary conditions of high-resolution local area model implementations using the ROMS-AGRIF model in two case study areas, Rockall Bank and Condor Seamount, conducted as part of the EU ATLAS project. The data include all computational grids, initialization fields (temperature, salinity) and boundary conditions (temperature salinity, currents, sea surface height) for each case study area.</p>
High resolution deformation data from the surface of a Nickel-based superalloy: Coarse precipitates
<p>High resolution digital image correlation (HRDIC) and electron backscattered diffraction (EBSD) data provided that quantifies the deformation on the surface of Nickel-based superalloy with coarse gamma prime precipitates (250 nm diameter) after 2% strain in tension.</p>
High-resolution climate model output for selected extreme precipitation events in Cyprus
<p>This dataset consists of high-resolution model output for selected past and future extreme precipitation events for Cyprus. It was generated in the framework of the BINGO Research Project (http://www.projectbingo.eu/) . BINGO has received funding from the European Union’s Horizon 2020 Research and Innovation programme, under Grant Agreement number 641739. More details about the dataset and the design of the simulations in:</p> <p>G. Zittis, A. Bruggeman, C. Camera, P. Hadjinicolaou, J. Lelieveld,<br> The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean,<br> Atmospheric Research, Volume 191, 2017, Pages 20-33, https://www.sciencedirect.com/science/article/pii/S0169809516307153</p>
Dar Es Salaam Very-High-Resolution Land Cover Map
<p>This is a very-high-resolution land cover map of Dar es Salaam derived from satellite imagery (Pleiades, 0.5m resolution). The majority of the area is classified from a 2016 (July) image while a small part of it from two images collected in January and March 2018, respectively.</p> <p> The pixel values related to the following legend:</p> <p>5=tree<br> 8=shadow<br> 3=artificial ground surface<br> 4=low vegetation<br> 2=water<br> 7=bare ground<br> 1=building<br> 113=high elevated buildings<br> 112=medium elevated buildings<br> 111=low elevated buildings</p> <p>The Out of Bag error of the product is 6,38% with the following class errors:</p> <p>Building = 0.035826</p> <p>Water = 0.049934</p> <p>Artificial Ground Surface = 0.077108</p> <p>Low Vegetation = 0.108709</p> <p>Tall Vegetation = 0.062278</p> <p>Bare Ground = 0.13803</p> <p>Shadow = 0.019872</p> <p>References:</p> <p>[1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” <em>Remote Sensing</em> 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>.</p> <p>[2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>.</p> <p>[3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>.</p> <p>This dataset was produced in the frame of REACT (<a href="http://react.ulb.be/">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
High resolution deformation data from the surface of a Nickel-based superalloy: Fine precipitates
<p>High resolution digital image correlation (HRDIC) and electron backscattered diffraction (EBSD) data provided that quantifies the deformation on the surface of Nickel-based superalloy with fine gamma prime precipitates (70 nm diameter) after 2% strain in tension.</p>
High resolution WRF simulation of Hurricane Sandy (2012) wind during landfall
<p>A 96-hour high-resolution simulation of Hurricane Sandy was conducted using the Advanced Research version 3.3.1 of the Weather Research and Forecasting (WRF) model initialized at 1200 UTC 26 October 2012 (Johnsen et al., 2013) and using a horizontal resolution of 500 m. Domain size was 2,660x2,500 km with 150 vertical layers. The Yonsei University planetary boundary layer (PBL) scheme, the Noah land model, and the MM5 Monin-Obukhov similarity theory surface layer model were used. Cloud physics was modeled using WSM6 6-class microphysics with graupel. Convection parameterization was not used for this high-spatial-resolution simulation. National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses were used for initialization and boundary conditions, with the latter updated every 6 hours.</p> <p>Data herein is a landfall-regime subset (Schiavone et al., 2016) of the full simulation. The spatial domain is 500x500 km containing the 50 lowest layers, with a time domain that spans the 12-hour period beginning at1200 UTC 29 October 2012 and with 3-hour time steps. Format is NetCDF containing detailed metadata. Three-dimensional meteorological field variables include the 3 wind components, potential temperature, water vapor mixing ratio, geopotential, and atmospheric pressure. Two-dimensional variables include horizontal wind components at 10 m AGL, potential temperature and water vapor mixing ratio at 2 m AGL, atmospheric pressure at the surface, terrain height, and the Coriolis sine latitude term, which is required to calculate potential vorticity.</p> <p>These WRF simulation data were used, along with WSR-88D Doppler radar data and surface wind observations, to demonstrate that roll vortices occurred in the atmospheric boundary layer over the landfall region during Hurricane Sandy's landfall period (Schiavone et al., 2020).</p>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (2/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020).</p> <p>data270rdc-my34.tar.xz: for Ls=270-300 (48 Sols)</p> <p>data300rdc-my34.tar.xz: for Ls=300-330 (51 Sols)</p> <p>data330rdc-my34.tar.xz: for Ls=330-360 (56 Sols)</p>
High-resolution wind power generation time series for Germany in the period 2000-2015
<p>High-resolution wind power generation time series for Germany in the period 2000-2015. A paper describing the applied methodology can be found in this repository as well.</p> <p>The final temporal resolution is hourly and the spatial resolution NUTS 3. The data is stored in csv and hdf5 files.</p> <p>More information on the data set, e.g. missing time stamps and versioning, can be found in readme.txt.</p>
High resolution cryoEM structure of huntingtin in complex with HAP40
<p><strong>High resolution cryoEM structure of huntingtin in complex with HAP40</strong></p> <p>This dataset relates the following depositions in the PDB: 6X9O and EMDB: EMD-22106 which are the structure solution of HTT-HAP40 complex at 2.6 angstrom resolution by cryoEM. </p> <p>Files contained within this dataset are detailed in the "Upload_information.xlsx" file. </p> <p> </p>
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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.