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5,155 results for “resolution”
High resolution precipitation event data from a tipping bucket gauge near former Jornada Basin LTER Biodiversity study site, 1996-ongoing
This data package contains high temporal resolution values from a tipping bucket rain gauge during rain events near the former Biodiversity study site at the Jornada Basin LTER in southern New Mexico, USA. Data collection at the Biodiversity site began in 1996 and is now complete. Data collection from the tipping bucket rain gauge near this site commenced in April 1996. The data file included here reports 1-second (1997-2016) or 1-minute (2016-present) frequency precipitation data, in millimeters, from this rain gauge during precipitation events. There are no data records for rain amounts less than 0.1 mm. Data collection from this tipping bucket rain gauge is ongoing and collected on a monthly basis (data package may be updated less frequently).
SBC LTER: Ocean: High-resolution Landsat 8 chlorophyll imagery of the Santa Barbara Channel
This is a timeseries of Landsat 8 images of the Santa Barbara Channel, processed for both chlorophyll and particulate backscattering. These data are at a particularly high spatial resolution (30 m), making them useful for analysis of submesoscale biophysical and biogeochemical interactions in the SBC, and for comparison with high-resolution modeling results. Data are contained in netcdf files, and there are 88 images in total, spanning the deployment of Landsat 8 (2013 - present). MATLAB scripts are available to load the netcdf files, flag the bad pixels, interpolate over the flagged pixels, and plot images, in the matlab folder. In addition, Landsat8_chl_imagery.zip contains jpegs of all of the images. A recommended workflow to users would be looking through all the jpegs to decide what images you want, and then downloading those specific netcdf files and using the matlab scripts and accompanying functions to process them. Our hope is that concurrent study of submesoscale variability in phytoplankton biomass via both submesoscale-resolving model studies and high-resolution satellite imagery may reveal further insights into the importance of submesoscale biophysical variability to regional and global biogeochemical processes.
'Wind theft' from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution
<p>Data and namelists from Pryor S.C., Shepherd T.J., Volker P., Hahmann A.N. and Barthelmie R.J.: ‘Wind theft’ from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution. <em>Journal of Applied Meteorology and Climatology (doi:10.1175/JAMC-D-19-0235.1)</em></p>
Daily MODIS snow cover maps for the European Alps from 2002 onwards at 250m horizontal resolution along with a nearly cloud-free version
<p><strong>NOTE: We discovered some errors in the data for images after February 2019. They will be fixed in version >= 1.1.x, until then, usage of the data after Feb 2019 is not advised. The rest of the data is fine.</strong></p> <p> </p> <p>This is the data to the same-titled Data paper, which can be found at <a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>.</p> <p>Along with auxilary files for the <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">cloudremoval package</a>, and example scripts on how to access chunks of the data.</p> <p>The files contain:</p> <ol> <li><strong>python-cloudremoval-aux-data.tar.gz</strong> : auxilary data (altitude, aspect, ...) to run the cloudremoval module which can be found at <a href="https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal">https://gitlab.inf.unibz.it/earth_observation_public/modis_snow_cloud_removal</a></li> <li><strong>python-example-data-access.html </strong>: Example script how to access parts of the data using python</li> <li><strong>R-example-data-access.html</strong> : Example script how to access parts of the data using R</li> <li><strong>zenodo_01_original.tar.gz</strong> : time series of snow cover maps, developed at the Institute for Earth Observation, Eurac Research, Bolzano, Italy. More information in same-title Data paper (<a href="https://doi.org/10.3390/data5010001">https://doi.org/10.3390/data5010001</a>), and for algorithm at <a href="https://doi.org/10.3390/rs5010110">https://doi.org/10.3390/rs5010110</a>.</li> <li><strong>zenodo_02_cloudremoval.tar.gz</strong> : time series of cloud filtered maps, based on 2. above, using code mentioned in 1. More information in same-titled Data paper.</li> </ol> <p> </p> <p>The maps are GeoTIFF with integer based values:</p> <p>0 = no data; 1 = snow; 2 = land; 3 = cloud; 4&5 = water bodies / nodata</p> <p> </p> <p>Version history:</p> <p>1.0.0 : initial upload<br> 1.0.1 : changes after revision of Data paper<br> 1.0.2 : added example scripts</p> <p> </p> <p> </p>
Homogeneous multifocal excitation for high-throughput super-resolution imaging - Expanded centriole particles
<p>Datasets containing the segmented expanded centriole particles. The prefix Hs is used to denote particles acquired in synchronized RPE-1 human cells. Otherwise particles were collected from expanded isolated centrioles from <em>Chlamydomoanas reinhardtii</em>. Resized datasets have uniform voxel size of 14x14x14 nm3 after expansion (56x56x56 nm3 before expansion). Non-resized datasets have 14x14x30 pixel size (56x56x120 nm3 before expansion). All files should be mirrored horizontally/vertically to account for the chirality inversion due to the imaging process</p> <p>The channels in different datasets are:</p> <ul> <li>Chlamy acetylated sample: <ul> <li>C1: acetylated tubulin-Alexa488</li> <li>C2: aTubulin-Alexa568</li> </ul> </li> <li>Chlamy MonoE sample <ul> <li>C1: aTubulin-Alexa488</li> <li>C2: GT335-Alexa568</li> </ul> </li> <li>Chlamy PolyE sample <ul> <li>C1: PolyE-Alexa488</li> <li>C2: aTubulin-Alexa568</li> </ul> </li> <li>Hs sample: <ul> <li>C1: PolyE-Alexa488</li> <li>C2: acetylated tubulin-Alexa586</li> </ul> </li> </ul>
Burial mounds dataset associated with the paper Geomorphometric methods for tumuli recognition and extraction from high resolution LiDAR DEMs
<p>This dataset consist of the burial (tumuli) mounds delineation and the associated data produced for the article Geomorphometric methods for tumuli recognition and extraction from high resolution LiDAR DEMs, submitted to Sensors. The dataset work in conjunction with the script (http://doi.org/10.5281/zenodo.3628805) to make the work reproductible. The DEM is available only by request to mihai.niculita@uaic.ro.</p>
Model outputs for validation and inference of high‐resolution information (downscaling) of ENETwild abundance model for wild boar, January 2020 update
<p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results.</p> <p>Objectives:</p> <p>- Validation of previously produced hunting yield maps and new ones<br> - Downscaling to 10x10 km grid >>> file "January_2020_HY_nut01_10x10.tif"<br> - Downscaling to 2x2 km grid >>> file "January_2020_HY_nut00_2x2.tif"</p> <p><br> Model settings and predictors: <br> - Assuming cells as municipality in 10x10 km grid downscaling<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling </p> <p>Conclusions guiding future methodological steps:<br> - To update wild boar hunting yield data for some specific regions<br> - To increase hunting yield data resolution<br> - To explore model independent parametrization for each bioregion</p> <p>For further details and methodological approach see the paper:</p> <p>ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A. Cohen, R. Soriguer, J. Vicente (2020) Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar. EFSA supporting publication 2020:EN-1787. 23pp. doi:10.2903/sp.efsa.2020.EN-1787.</p> <p>Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.<br> </p>
The Resolution of Keller's Conjecture - Computation Logs
<p>Logs of the computations performed to settle Keller's conjecture on cube tilings. For every value of s∊{3,4,6} there are files:</p> <ul> <li>s?.cnf encoding the problem for that value of s as explained in the paper, with additional symmetry breaking clauses.</li> <li>s?.dnf which is a tautology of assignments that need to be verified by SAT solvers.</li> </ul> <p>Inside the Keller-logs.zip archive, for every value of s∊{3,4,6} you will find:</p> <ul> <li>A folder sym-s? containing all the verification logs of the symmetry breaking clauses added to the original encoding of the problem for that value of s as a SAT instance in order to obtain s?.cnf.</li> <li>A folder unsat-s? containing one log file for reach assignment in s?.dnf.</li> </ul>
Configuration files for model stations presented in the manuscript "Sensitivity of shelf sea marine ecosystems to temporal resolution meteorological forcing"
<p>This repository contains configuration files for running GOTM-FABM-ERSEM at stations L4 and CCS to produce results presented in the manuscript "Sensitivity of shelf sea marine ecosystems to meteorological forcing" in addition to meteorology files for running the sensitivity analysis presented in the manuscript. Ncfiles containing model results for all scenarios presented in the manuscript are also included within the zip files for both stations</p> <p><br> GOTM code is freely available from: <br> https://github.com/gotm-model/code</p> <p><br> FABM code is freely available from:<br> https://github.com/fabm-model/fabm.git</p> <p><br> ERSEM code is freely available from:</p> <p><a href="https://www.pml.ac.uk/Modelling_at_PML/Access_Code">https://www.pml.ac.uk/Modelling_at_PML/Access_Code</a><br> </p> <p>Instructions for compiling GOTM-FABM-ERSEM can be found in the ERSEM git repository after registering for the code using the link above. </p> <p>Versions/commits for the model code used to create results presented in this manuscript are:</p> <p>GOTM: commit 38e5d5b77adc7b3b5364aed7d7e4921b04b1781f </p> <p>FABM: commit 69da88c87ec59a51d1e2143c1f76111526ed6498 </p> <p>ERSEM: Version 19.04</p> <p> </p> <p> </p>
A multiple model high-resolution head-related impulse response database for aided and unaided ears (SOFA format)
<p>The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation). The data here is stored in SOFA format, with the data identical to the HDF5 files in <a href="https://zenodo.org/record/1226873">doi:10.5281/zenodo.1226873</a>.</p>
Variability of OB stars from TESS southern Sectors 1-13 and high-resolution IACOB and OWN spectroscopy
<p>Typical MESA and GYRE inlists associated with <a href="https://arxiv.org/abs/2005.09658">Burssens et al. 2020</a>. MESA v. 12155, GYRE version v. 5.2.</p> <p><em>Context:</em> Lack of high-precision long-term continuous photometric data for large samples of stars has prevented the large-scale exploration of pulsational variability in the OB star regime. As a result, the candidates for in-depth asteroseismic modelling remained limited to a few tens of dwarfs. The TESS nominal space mission has surveyed the southern sky, including parts of the galactic plane, yielding continuous data of at least 27 d for hundreds of OB stars.<br> <em>Aims:</em> We aim to couple TESS data in the southern sky with ground-based spectroscopy to study the variability in two dimensions, mass and evolution. We focus mainly on the presence of coherent pulsation modes that may or may not be present in the predicted theoretical instability domains and unravel all frequency behaviour in the amplitude spectra of the TESS data.<br> <em>Methods: </em>We compose a sample of 98 OB-type stars observed by TESS in Sectors 1-13 and with available multi-epoch, high-resolution spectroscopy gathered by the IACOB and OWN surveys. We present the short-cadence 2-min light curves of dozens of OB-type stars, that have one or more spectra in the IACOB or OWN database. Based on these light curves and their Lomb-Scargle periodograms we perform variability classification and frequency analysis. We place the stars in the spectroscopic Hertzsprung-Russell diagram to interpret the variability in an evolutionary context.<br> <em>Results:</em> We deduce diverse origins of the mmag-level variability found in all of the 98 OB stars in the TESS data. We find among the sample several new variable stars, including three hybrid pulsators, three eclipsing binaries, high frequency modes in a Be star, and potential heat-driven pulsations in two Oe stars. <br> <em>Conclusions:</em> We identify stars for which future asteroseismic modelling is possible, provided mode identification is achieved. By comparing the position of the variables to theoretical instability strips we discuss the current shortcomings in non-adiabatic pulsation theory, and the distribution of pulsators in the upper Hertzsprung-Russell diagram.</p> <p> </p> <p> </p>
Raw data for "Multi-slice ptychography enables high-resolution in situ measurements in extended chemical reactors"
<p>Raw data used in "Multi-slice ptychography enables high-resolution in situ measurements in extended chemical reactors" by M. Kahnt, L. Grote, D. Brückner, M. Seyrich, F. Wittwer, D. Koziej and C.G. Schroer.</p>
Interactive 3D PDF file for the structures 10 hr juvenile of Oikopleura dioica and supplementary movie S1-20 with high resolution
<p>The larvacean, Oikopleura dioica is a planktonic chordate, which is an emerging model organism with short life cycle of 5 days and belongs to tunicates (urochordates). Organ formation in the trunk proceeds in seven hours form hatching of tailbud larvae at three hours after fertilization (hpf) to completion of organ formation in fully functional juveniles that start feeding at 10 hpf and are just miniature of adult form.</p> <p>The juveniles were fixed at 10.5 hours post fertilization, 30 min after the tail shift (Nishida, H., 2008 Development of the appendicularian Oikopleura dioica: culture, genome, and cell lineages. Dev. Growth Differ. 50, S239–S256.). The data would be used as a basic morphological data of this animal to explore organ structures and cellular composition.</p> <p>This PDF file allows one to view 3D juvenile of O. dioica in any desired directions. One can show or hide the organs and rotate them by mouse. Open this PDF file and activate it as described in Adobe Acrobat User Guide (https://helpx.adobe.com/acrobat/using/displaying-3d-models-pdfs.html). Click Toggle "Model Tree button" with three small rectangulars in the tool bar. Then in the left panel, one can select the organs and nuclei you want to see. Select lighting as "daylight". Select solid or transparency mode.<br> The Juvenile 3D image was reconstructed with Amira software using 1967 serial section images of SBF-SEM (Serial block face scanning electron microscopy) that are available at https://doi.org/10.5061/dryad.d51c5b012.</p> <p>The Zip file also contains supplementary movie S1-20 (avi file) with high resolution and the legends (PDF file).</p> <p>This data is related to the paper, 3D reconstruction of structures of hatched larva and young juvenile of the larvacean Oikopleura dioica using SBF-SEM. (https://www.nature.com/articles/s41598-021-83706-y).</p>
Reproduction packages for the paper "Spectral and Imaging properties of Sgr A∗ from High-Resolution 3 DGRMHD Simulations with Radiative Cooling"
<p>This is a basic reproduction package for the paper"Spectral and Imaging properties of Sgr A∗ from High-Resolution 3D GRMHD Simulations with Radiative Cooling" by Yoon et al. (2020). It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Implementation of a 4Pi-SMS super-resolution microscope - Example data II
<p>4Pi-SMS image of Nup96-SNAP labelled with BG-Alexa 647 in the lower nuclear envelope of a U2OS cell in TDE-based index-matching imaging buffer</p>
DC2 High resolution future hydrological data for Sweden
<p>Hourly river flow and total runoff were computed for the southern part of Sweden using the hourly version of a high resolution hydrological model S-HYPE, which is operationally used by SMHI. The model was calibrated and validated using radar based hourly precipitation and an operationally used hourly reanalysis temperature data. Projection of the impact of climate change was performed by running the model with hourly forcing data from an ensemble of EURO-COREX climate model simulations over 1971 - 2100. Four GCM-RCM combinations were used under two emission scenarios, RCP4.5 and RCP8.5. The results can be used to assess the risk of riverine flooding in areas located along a small to meso-scale river basin. The results can, in particular, be used to assess the risk of flash flooding that can result from heavy precipitation of short duration.</p>
Close range hyperspectral camera dataset with high temporal resolution of strawberry with eco-physiological data of one leaf
<p>This high temporal resolution dataset of a strawberry plant was captured in two experiments, each lasting 100h. On leaf was inserted into a leaf chamber of the LI-6400XT gas exchange system, capturing information on transpiration, photosynthesis and stomatal conductance. Environmental characteristics are also captured at canopy height. These experiments were conducted in a growth chamber at ILVO (Melle, Belgium) and only covered conditions that did not result in stress in the plant. As such, this dataset attempts to capture subtle dynamic variation in the plant.</p>
DeepBedMap: A super-resolution neural network created bed topography of Antarctica
<p>Going beyond BEDMAP2 using a super resolution deep neural network.</p> <p>deepbedmap_v1.1.0.zip: Python code for the DeepBedMap Super-Resolution Generative Adversarial Network.</p> <p>deepbedmap_dem.tif: Digital Elevation Model (250 m spatial resolution) in GeoTiff format, using Antarctic Polar Stereographic Projection (EPSG:3031).</p> <p>srgan_generator_model_weights.npz: The Generator neural network weights/parameters as a NumPy zip file.</p> <p> </p>
ModIs Dust AeroSol (MIDAS): A global fine resolution dust optical depth dataset
<p>Monitoring and describing the spatiotemporal variability of dust aerosols is crucial to understand their multiple effects, related feedbacks and impacts within the Earth system. This study describes the development of the MIDAS (ModIs Dust AeroSol) dataset. MIDAS provides columnar daily dust optical depth (DOD) at 550 nm at global scale and fine spatial resolution (0.1° x 0.1°) over a 15-year period (2003-2017). This new dataset combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6.1, Level 2), along with DOD-to-AOD ratios provided by MERRA-2 reanalysis to derive DOD on the MODIS native grid. The uncertainties of MODIS AOD and MERRA-2 dust fraction with respect to AERONET and LIVAS, respectively, are taken into account for the estimation of the total DOD uncertainty. MERRA-2 dust fractions are in very good agreement with those of LIVAS across the “dust belt”, in the Tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere where dust sources are smaller. MIDAS, MERRA-2 and LIVAS DODs strongly agree when it comes to annual and seasonal spatial patterns, with collocated global DOD averages of 0.033, 0.031 and 0.029, respectively; however, deviations in dust loading are evident and regionally dependent. Overall, MIDAS is well correlated with AERONET-derived DODs (R=0.89), only showing a small positive bias (0.004 or 2.7%). Among the major dust areas of the planet, the highest R values (> 0.9) are found at sites of N. Africa, Middle East and Asia. MIDAS expands, complements and upgrades existing observational capabilities of dust aerosols and it is suitable for dust climatological studies, model evaluation and data assimilation.</p>
EstSoil-EH: A high-resolution eco-hydrological modelling parameters dataset for Estonia (dataset)
<p>For the EstSoil-EH dataset, we synthesized more than 20 extended eco-hydrological variables for Estonia as numerical and categorical values from the original Soil Map of Estonia, the Estonian 5m Lidar DEM, Estonian Topographic Database and EU-HydroSoilGrids layers. The Soil Map of Estonia maps more than 750 000 soil units throughout Estonia at a scale of 1:10 000 and forms the basis for EstSoil-EH. It is the most detailed and information-rich dataset for soils in Estonia, with 75% of mapped units smaller than 4.0 ha, based on Soviet era field mapping. For each soil unit, it describes the soil type (i.e. soil reference group), soil texture, and layer information with a composite text code, which comprises not only of the actual texture class, but also of classifiers for rock content, peat soils, distinct compositional layers and their depths. To use these as eco-hydrological process properties in modelling applications we translated the text codes into numbers. The derived parameters include soil profiles (e.g., layers, depths), texture (clay, silt, sand components), coarse fragments and rock content. In addition, we aggregated and predicted physical variables related to water and carbon (bulk density, hydraulic conductivity, organic carbon content, available water capacity).<br> The developed methodology and dataset will be an important resource for the Baltic region, but possibly also all other regions where detailed field-based soil mapping data is available. Countries like Lithuania and Latvia have similar historical soil records from the Soviet era that could be turned into value-added datasets such as the one we developed for Estonia.</p> <p> </p> <p>We created an extended eco-hydrological dataset for Estonia, the EstSoil-EH, containing derived numerical values for the following data in all of the mapped soil units in the 1:10 000 soil map: soil profiles (e.g., layers, depths), texture (clay, silt, and sand components), rockiness, and physical variables related to water and carbon (bulk density, hydraulic conductivity, organic carbon content). Ultimately, our objective was to develop a reproducible method for deriving numerical values to support modelling and prediction of eco-hydrological processes in Estonia using the popular Soil and Water Assessment Tool.</p> <p>For more information on the development of this dataset look for "EstSoil-EH: a high-resolution eco-hydrological modelling parameters dataset for Estonia", Alexander Kmoch, Arno Kanal†, Alar Astover, Ain Kull, Holger Virro, Aveliina Helm, Meelis Pärtel, Ivika Ostonen and Evelyn Uuemaa, 2021, Earth Syst. Sci. Data, 13, 83–97, <a href="https://doi.org/10.5194/essd-13-83-2021">https://doi.org/10.5194/essd-13-83-2021</a> </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)
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.