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5,155 results for “resolution”
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
Seasonal climate data (growing season, spring, fall/winter, and previous growing season) at an annual resolution for eight sites from the Cooperative Alaska Forest Inventory (CAFI) from 1945 - 2013.
This dataset contains site-level measurements from 1945 - 2013 of seasonal (growing season, spring, fall/winter, and previous growing season) climate moisture index (CMI), average temperature, and total precipitation at an annual resolution for eight sites from the Cooperative Alaska Forest Inventory (CAFI) from 1945 - 2013.
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).
Transcranial detection of amyloid-beta at single plaque resolution in vivo with large-field multifocal illumination fluorescence microscopy
<p>The abnormal deposition of beta-amyloid proteins in the brain is one of the major histopathological hallmarks of Alzheimer’s disease. Currently available intravital microscopy techniques can visualize plaques with high resolution, but are limited to a small field-of-view and with depth limitation. Here, we report the transcranial detection of amyloid-beta deposits at the whole brain scale with 20 mm resolution in APP/PS1 and arcAb mouse models of Alzheimer’s disease amyloidosis using a novel large-field multifocal illumination (LMI) fluorescence microscopy technique. High sensitive and specific detection of amyloid-beta deposits at single plaque level in APP/PS1 and arcAb mice was facilitated using luminescent conjugated oligothiophene HS-169. Immunohistochemical staining with HS-169, anti-Ab antibody 6E10, conformation antibodies OC (fibrillar) on brain tissue sections further showed that HS-169 resolved compact parenchymal and vessel-associated amyloid deposits. In conclusion, we demonstrate a new <em>in vivo </em>imaging platform for detection of amyloid-beta deposits at single plaque resolution in murine models of Alzheimer’s disease amyloidosis.</p>
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>
AgMIP's GGCMI Phase II: Crop model Emulators at 0.5 degree global resolution
<p>GGCMI Phase II crop model polynomial emulator parameters. See https://www.geosci-model-dev.net/special_issue1062.html for more details. A0: no growing season adaptation. A1: with growing season adaptation.</p>
Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution
<p>Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution based on a compilation of data sets (Biome6000k, Geo-Wiki, LandPKS, mangroves soil database, and from various literature sources; total of about 65,000 training points). We used a comparable thematic legend used to produce the Dynamic Land Cover 100m: Version 2. Copernicus Global Land Operations product (Buchhorn et al. 2019), which is based on the UN FAO Land Cover Classification System (LCCS), so that users can compare actual (https://lcviewer.vito.be/) vs potential (this data set) land cover. Two classes not available in the LCCS were added: "subtropical/tropical mangrove vegetation" and "sub-polar or polar barren-lichen-moss, grassland". The map was created using relief and climate variables representing conditions the climate for the last 20+ years and predicted at 250 m globally using an Ensemble Machine Learning approach as implemented in the mlr package for R. Processing steps are described in detail <a href="https://github.com/Envirometrix/PNVmaps"><strong>here</strong></a>. Maps with "_sd_" contain estimated model errors per class. Antarctica is not included.</p> <p>Produced for the needs of the <a href="https://naturemap.earth/"><strong>NatureMap</strong></a> which is project run by the <strong>International Institute for Applied Systems Analysis</strong> (IIASA), the <strong>International Institute for Sustainability</strong> (IIS), the <strong>UN Environment Programme World Conservation Monitoring Centre</strong> (UNEP-WCMC), and the <strong>UN Sustainable Development Solutions Network</strong> (SDSN). NatureMap is funded by Norway’s International Climate Initiative (NICFI).</p> <p>Maps will also be made available via: <a href="https://OpenLandMap.org">OpenLandMap.org</a>. These are initial predictions for testing purposes only. A publication explaining all processing steps is pending.</p> <p>If you discover a bug, artifact or inconsistency in the predictions, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://github.com/Envirometrix/PNVmaps/issues">https://github.com/Envirometrix/PNVmaps/issues</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>pnv = theme: potential natural vegetation,</li> <li>potential.landcover = variable: potential land cover type (e.g. "open forest, evergreen needleleaf"),</li> <li>probav.lc100 = classification model: ProbaV-based land cover mapping legend (LCCS),</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: period 2000-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
In Situ Investigation of Fluid-Limestone Interactions at Ångstrom Resolution
<p>Video demonstrating the adsorption of water to a micropore in an Edwards limestone sample. Adsorption-induced strain is observed as changes to the size and shape of the pore during water injection. Video was obtained from a Titan G2 Environmental Transmission Electron Microscope. </p> <p>The vacuum gauges labeled PP/0 and BC/0 readout the pressure of the atmosphere immediately surrounding the sample.</p> <p> </p>
In Situ Investigation of Fluid-Quartz Interactions at Ångstrom Resolution
<p>Video demonstrating the adsorption of water to the walls of micropores in a Berea sandstone sample. Adsorption-induced strain is observed as changes to the sizes and shapes of the pores during water injection. Video was obtained from a Titan G2 Environmental Transmission Electron Microscope. </p> <p>The vacuum gauges labeled PP/0 and BC/0 readout the pressure of the atmosphere immediately surrounding the sample.</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>
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.