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1,255 results for “High-resolution”
High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"
<p>High quality figures of "An Unstructured Mesh Generation Tool for Efficient High-Resolution Representation of Spatial Heterogeneity in Land Surface Models"</p>
High-resolution mapping of soil carbon stocks in the western Amazon
<h2>Dear Researchers and Interested Parties,</h2> <p> It is with great enthusiasm that we share our page on Zenodo, where we provide <strong>detailed maps</strong> (30 m resolution) of <strong>soil carbon stocks in Rondônia, Brazil</strong>. These maps were generated using machine learning techniques, using the Random Forest model implemented in the caret package. This initiative aims to provide a deeper and more accurate understanding of the spatial distribution of carbon in the soil, contributing significantly to environmental studies and climate change mitigation strategies in the region.</p> <h2>Available resources:</h2> <h3>High Resolution Maps:</h3> <p>We provide detailed maps of the estimates and uncertainties of soil carbon stocks at different depths (0-5; 5-15; 15-30; 30-60 and 60-100 cm). The maps include mean values (Mg ha<sup>-1</sup>), quantiles (Mg ha<sup>-1</sup>) and coefficients of variation (%), all in "tif" format, with a spatial resolution of 30 m and SAD 1969 Lambert South America projection system (<a href="https://epsg.io/102015">EPSG :102015</a>).</p> <p>The entire process was conducted in open source (R Language). The codes and database used <strong>can be found in the <a href="https://github.com/moquedace/ro_soil_carbon_stock" target="_blank" rel="noopener">GitHub repository</a></strong>, and more information about the methodology is available in the following publication:</p> <p>Moquedace, C. M., Baldi, C. G. O., Siqueira, R. G., Cardoso I. M., Souza, E. F. M., Fontes, R. L. F., Francelino, M. R., Gomes, L. C., Fernandes-Filho, E. I. High-resolution mapping of soil carbon stocks in the Western Amazon. <em>Geoderma Regional</em>, v. 36, p. e00773, 2024. DOI: <a href="https://doi.org/10.1016/j.geodrs.2024.e00773">10.1016/j.geodrs.2024.e00773</a></p> <h2>Availability objectives:</h2> <h3>Promote scientific collaborations:</h3> <p>We encourage researchers, scientists, and organizations to explore and use this data to enrich their own research and projects related to soil carbon and climate change.</p> <h3>Enhance environmental understanding:</h3> <p>By providing open access to these maps, we aim to contribute to a deeper understanding of environmental processes in Rondônia, Brazil and, by extension, enable the implementation of sustainable strategies.</p> <h3>Stimulating innovation:</h3> <p>We believe that sharing this data will stimulate innovation in modeling and spatial analysis methods, driving advances in the prediction of soil carbon stocks, especially in the Amazon.</p> <h2>Thank you in advance for your interest and collaboration. Together, we can advance knowledge and the search for sustainable solutions to important environmental challenges.</h2> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
High-resolution environmental and host-related factors impacting questing Ixodes scapularis at their northern range edge
<p>The geographic range of tick populations has expanded in Canada due to climate warming and the associated poleward range shifts of their vertebrate hosts. Abiotic factors, such as temperature, precipitation, and snow, are known to directly affect tick abundance. Yet, biotic factors, such as the abundance and diversity of mammal hosts, may also alter tick abundance and consequent tick-borne disease risk. Here, we incorporated host surveillance data with high-resolution environmental data to evaluate the combined impact of abiotic and biotic factors on questing <em>Ixodes scapularis </em>abundance in Ontario and Quebec, Canada. High-resolution abiotic factors were derived from remote sensing satellites and meteorological towers, while biotic factors related to mammal hosts were derived from active surveillance data that we collected in the field. Generalized additive models were used to determine the relative importance of abiotic and biotic factors on questing <em>I. scapularis</em> abundance. Combinations of abiotic and biotic factors were identified as important drivers of abundances of questing <em>I. scapularis</em>. Positive and negative linear relationships were found for questing <em>I. scapularis </em>abundance with precipitation and accumulated snow, but no effect was found for the relative abundance of white-footed mice. Positive relationships were also identified between questing <em>I. scapularis </em>abundance with monthly mean precipitation and mammal species richness. Therefore, future studies that assess <em>I. scapularis </em>should incorporate host surveillance data with high-resolution environmental factors to determine the key drivers impacting the<em> </em>abundance and geographic spread of tick populations and tick-borne pathogens.</p>
Supplemental data files for: A potential mode of the lithospheric growth and delamination of orogenic belts: Evidence from high-resolution magnetotelluric data
<p>Data files for a 2-D electrical resistivity model in the Greater Xing'an Range, including the observed MT data, the data fitting, the preferred resistivity model, and the raw data in impedance format.</p>
Roll-to-roll, high-resolution 3D printing of shape-specific particles
<p>Particle fabrication has attracted recent attention due to its diverse applications in bioengineering, drug and vaccine delivery, microfluidics, granular systems, self-assembly, microelectronics, and abrasives. Herein we introduce a scalable, high-resolution, 3D printing technique for the fabrication of shape-specific particles based on roll-to-roll continuous liquid interface production (r2rCLIP). We demonstrate r2rCLIP using single-digit, micron-resolution optics in combination with a continuous roll of film (in lieu of a static platform), enabling the rapidly permutable fabrication and harvesting of shape-specific particles from a variety of materials and complex geometries, including geometries not possible to achieve with advanced mould-based techniques. We demonstrate r2rCLIP production of mouldable and non-mouldable shapes with voxel sizes as small as 2.0 × 2.0 μm<sup>2</sup> in the print plane and 1.1 ± 0.3 μm unsupported thickness, at speeds of up to 1,000,000 particles per day. Such microscopic particles with permutable, intricate designs enable direct integration within biomedical, analytical and advanced materials applications.</p>
Dataset of 'Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations'
<p>Dataset of the article 'Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations', recently published in Open Research Europe (DOI 10.12688/openreseurope.17388.1). The code which implements the physics-informed neural network can be found at https://github.com/AlvaroMS90/PINNs-for-high-resolution-weather-reconstruction-from-sparse-weather-stations.</p> <p>Funded by the European Union under action HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships, call HORIZON-MSCA-2021-PF-01 (project number 101059984 with acronym PERSEVERE). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <pre> </pre>
High-Resolution Beach Profile Time Series: Morecambe Bay, 2007-2022
<p><strong>Description:</strong> This dataset provides a comprehensive collection of beach profile measurements for Morecambe Bay, covering a 16-year period from 2007 to 2022. The data was collected and shared by Sefton Council (Sefton MBC), UK, as part of the coastal monitoring programme. Captured biannually during spring and autumn, the data offers valuable insights into the bay's coastal morphology and dynamics.</p> <p><strong>Data Description:</strong></p> <ul> <li><strong>Beach Transects:</strong> Detailed profiles are provided for various locations along the Morecambe Bay coastline. Station numbers are used to identify each measurement point on a dedicated map (included within the data).</li> <li><strong>Temporal Coverage:</strong> The dataset encompasses measurements conducted biannually, capturing seasonal variations in beach profiles.</li> </ul>
Presentation in HEARING: High-resolution structural and functional EAR imaging 2023: Three-dimensional vibration of the human tympanic membrane using a scanning laser doppler vibrometer
<p>This is for Bastian Baselt's poster presentation in HEARING: High-resolution structural and functional EAR imaging, Ascona, Switzerland in 2023, and the related data.</p>
Figure 16 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 16. Succession of ammonoid genera in the Aras Valley section.
Figure 14 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 14. Mass occurrence of ostracod specimens in sample AV171 (+1.71 m). Scale bar units = 1 mm.
Figure 2 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 2. The Permian–Triassic boundary sections in the Ali Bashi Mountains, NW Iran.
An improved long-term high-resolution surface pCO2 data product for the Indian Ocean using machine learning
<p>This dataset contains two improved surface pCO2 products, along with surface pCO2 from the INCOIS-BIO-ROMS model (pCO2_model) and other input variables. It is a long-term, high-resolution dataset developed for the Indian Ocean region (30°E - 120°E, 30°S - 30°N), covering the period from 1980 to 2019. The dataset features a monthly temporal resolution and a spatial resolution of 1/12 degree.</p> <p> The file includes INCOIS-BIO-ROMS model outputs (sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), nitrate (NO3), dissolved inorganic carbon (DIC), and chlorophyll-a (CHL)). These variables are used as inputs for machine learning models to improve the pCO2_model. The machine learning model predicts the surface pCO2 deviants (pCO2_obs - pCO2_model). The file also provides spatiotemporally varying uncertainties associated with the predicted pCO2 deviants.</p> <p><strong>**Users are advised to download Version v2 of the data product, as Version v1 has been deprecated and is no longer recommended for use.</strong></p>
Open-SEA-Rice-10: Open Access High-Resolution Maps of Rice Harvested Area and Cropping Intensity in Southeast Asia
<h2>Cite this article</h2> <p>Ginting, F.I., Rudiyanto, R., Fatchurrachman, F., Mohd Shah, R., Che Soh, N., Goh Eng Giap, S., Fiantis, D., Setiawan, B.I., Schiller, S., Davitt, A., Minasny, B. High-resolution maps of rice cropping intensity across Southeast Asia. Scientific Data [12, 1408] (2025). <a href="https://www.nature.com/articles/s41597-025-05722-1">https://doi.org/10.1038/s41597-025-05722-1</a></p> <p> </p> <h2>Data Description</h2> <p>The datasets are high-resolution mapping of rice cropping intensity across Southeast Asia using the integration of Sentinel-1 and Sentinel-2 data</p> <ul> <li>The data file is in “.tif" format</li> <li>Spatial extent: Southeast Asia</li> <li>Pixel size: 10 m</li> <li>Projection information: EPSG: 4326</li> <li>CropType: Paddy rice.</li> <li>Year: Values from 2021</li> <li>Raster class:<br>-1 is single rice cropping area; <br>-2 is double rice cropping area and<br>-3 is triple rice cropping area</li> <li>The data also can be viewed on the GEE App (<a href="https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10">https://ee-rudiyanto.projects.earthengine.app/view/open-sea-rice-10</a>) and the Climate TRACE platform (<a href="https://climatetrace.org/">https://climatetrace.org/</a>) </li> <li>Correspondence to: Rudiyanto (rudiyanto@umt.edu.my) and Budiman Minasny (budiman.minasny@sydney.edu.au)</li> </ul> <p> </p> <p> </p>
Surface surface wind speed and its different grades over China during 1961-2020 based on a high-resolution observation dataset CN05.1
<p>The daily 10-m wind speed observation dataset CN05.1, which covers the period from 1961 to 2020, with horizontal resolution of 0.25° × 0.25° (latitude × longitude). This dataset was developed by Jia Wu from National Climate Center, China Meteorological Administration.</p> <p>References: Jia Wu, Xue-Jie Gao. A grided daily observation dataset over China region and comparison with the other datasets. <em>Chinese J. Geophys. </em>(in Chinese), 2013, 56(4): 1102-1111, doi: 10.6038/cjg20130406.</p>
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
High-resolution and full coverage AOD downscaling based on the bagging model over the arid and semi-arid areas, NW China
<p>High-resolution and full coverage 250 m monthly AOD product over the arid and semi-arid areas, NW China. the scale factor is 1000.</p>
Data for Klaes et al. High-resolution stalagmite stratigraphy supports the Late Holocene tephrochronology of southernmost Patagonia. Comms. Earth Environ. (2022)..
<p>This data set comprises the LA-ICP-MS, ICP-MS, EPMA and NanoSIMS measurements as well as SEM imagery presented in the research paper 'High-resolution stalagmite stratigraphy supports the Late Holocene tephrochronology of southernmost Patagonia' by Klaes et al. The Th-U data of the revised chronology of stalagmite MA1 with the calculated correction values according to Budsky et al. (2016) are also included. In addition, stable isotope data from Schimpf et al. (2011) with the revised version of the age-depth model are given. For detailed information on the analyses (e.g., instruments used), the user is kindly referred to the methods section of the article. </p>
Dataset supporting the article "Megapixel camera arrays for high-resolution animal tracking in multiwell plates"
<p>IB and LF contributed equally.</p> <p> </p> <p>This deposition contains the supporting dataset for the article:</p> <p><strong>Megapixel camera arrays for high-resolution animal tracking in multiwell plates</strong></p> <p>Ida Barlow, Luigi Feriani, Eleni Minga, Adam McDermott-Rouse, Thomas J O'Brien, Ziwei Liu, Maximilian Hofbauer, John R Stowers, Erik C Andersen, Siyu S Ding, André EX Brown</p> <p> </p> <p> </p> <p><strong>Acknowledgements</strong>:</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 714853) and was supported by the Medical Research Council through grant MC-A658-5TY30. This work was supported by a Research Grant from HFSP (Ref.-No: RGP0001/2019). AMR was supported by a BBSRC CASE studentship part-funded by Syngenta.</p>
Evaluation data for "Global, high-resolution, reduced-complexity air quality modeling for PM2.5 using InMAP (Intervention Model for Air Pollution)"
<p>This zip file contains data for performing Global InMAP model runs and evaluations. To the extent that any of the data is covered by third party licenses, it is the responsibility of the user to follow the terms of those licenses. A description of the contents of this directory is below:</p> <p>measurements.csv<br> Vetted global dataset of ground-level annual-average measurements of total PM2.5 and species (pNO3, pSO4, pNH4) compiled from monitoring networks, used for model performance evaluation. Data sources are: World Health Organization (Global), European Environment Agency (Europe), National Air Pollution Surveillance Program (Canada), Environmental Protection Agency (United States of America), Central Pollution Control Board (India), Australian Government State of the Environment (Australia), and Acid Deposition Monitoring Network In East Asia (EANET) (East Asia).</p> <p>population directory<br> Population count data is from the Gridded Population of The World (v4.10) projected to year 2020. The data is in 15x15 arcminute grids, except for in grid cells where the population is above 80,000, where the population data is 30x30 arcseconds.</p> <p>GlobalInMAPData_v1.ncf<br> Regular-grid Global InMAP input data for the year 2005 for use as the "InMAPData" variable in the InMAP configuration file. It was created from GEOS-Chem v.11-01 simulation outputs with the 'inmap preproc' command.</p> <p>global_inmap_004x003_v1.1.0.gob<br> Global InMAP variable grid resolution input data for coords for year 2016 for use as the "VariableGridData" variable in the InMAP configuration file. It was created with the 'inmap grid' command using GlobalInMAPData_v1.ncf and population.shp.</p> <p>2016_emissions directory<br> Total PM2.5 and precursor emissions to arrive at total PM2.5 concentrations from Global InMAP. Units for polygonized emissions inputs (shapefiles) are short (US) tons/yr, and units for gridded emissions inputs (NetCDF files) are kg/yr.</p> <p>global_emission_changes directory<br> nh3.nc, nox.nc, and sox.nc are gridded emissions for changes in inorganic precursors for comparing Global InMAP and GEOS-Chem. Units are kg/yr. NH4-gc.nc, NIT-gc.nc, and SO4-gc.nc are results for changes in concentrations arising from these changes in emissions for 3 months, 1 month, and 2 months.</p> <p>usa_emission_changes directory<br> Emissions for comparing Global InMAP and US InMAP (described in Tessum et al., 2017).<br> Emissions are derived using the United States National Emissions Inventory (NEI) 2014v.1, processed exactly as in Thakrar et al., 2020.<br> Emissions are coal-powered electricity generation (NEI Source Classification Code: 10100212) and gasoline passenger vehicles (NEI Source Classification Code: 2201210080).<br> Units are ug/s.</p> <p>Tessum, C.W.; Hill, J.D.; Marshall, J.D. InMAP: A model for air pollution interventions. PloS One 2017, 12 (4) e0176131.<br> Thakrar, S.K.; Balasubramanian, S.; Adams, P.J.; Azevedo, I.M.; Muller, N.Z.; Pandis, S.N.; Polasky, S.; Pope III, C.A.; Robinson, A.L.; Apte, J.S.; Tessum, C.W.; Marshall, J.D.; Hill; J.D. Reducing mortality from air pollution in the United States by targeting specific emission sources. Environmental Science & Technology Letters 2020, 7(9), pp.639-645.<br> Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H41V5BX1.</p>
Supplementary dataset for "High-resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake using 3D Finite Element Modeling."
<p>Supplementary dataset for "High-resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake using 3D Finite Element Modeling." </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.