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1,255 results for “High-resolution”
High-resolution soil moisture data (1km)
<p>High-resolution soil moisture data based on ESA CCI surface soil moisture data in southwestern Europe (Iberia Peninsula).</p> <p>Refs:</p> <p>He, K., Zhao, W., Brocca, L., and Quintana-Seguí, P.: SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation, Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023, 2023.</p> <p> </p>
High-resolution tropical rain-forest canopy climate data
<p><span>Canopy habitats challenge researchers with their intrinsically difficult access. The current scarcity of climatic data from forest canopies limits our understanding of the conditions and environmental variability of these diverse and dynamic habitats. We present 307 days of climate records collected between 2019 and 2020 in the tropical rainforest canopy of the Yasuní National Park, Ecuador. We monitored climate with a 10-minute temporal resolution in the middle crowns of eight canopy trees. The distance between canopy climate stations ranged from 700 m to 10 km. Apart from air temperature, relative humidity, leaf wetness, and photosynthetically active radiation (PAR), measured in each canopy climate station, global radiation, rainfall, and wind speed were measured in different subsets of them. We processed the eight data series to omit erroneous records resulting from sensor failures or lack of the solar-based power supply. In addition to the eight original data series, we present three derived data series, two aggregating canopy climate for valleys or for ridges (from four stations each), and one overall average (from the eight stations). This last derived data series contains 306 days, while the shortest of the original data series covers 22 days and the longest 296 days. In addition to the data, two open-source tools, developed in RStudio, are presented that facilitate data visualization (a dashboard) and data exploration (a filtering app) of the original and aggregated records.</span></p>
CR2MET: A high-resolution precipitation and temperature dataset for the period 1960-2021 in continental Chile.
<p>The Center for Climate and Resilience Research Meteorological dataset (CR2MET) includes two spatially-distributed products of daily precipitation and maximum/minimum near surface temperatures. The dataset covers the domain of continental Chile over a regular 0.05 degree latitude-longitude grid, and spans the period 1960-2021. Both a products are built on statistical models of the corresponding variables, calibrated against quality-controlled observational records. The CR2MET models are nurtured with a combination of data that includes different variables from ECMWF reanalysis ERA5, topographic parameters and land-surface temperature estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor.</p>
Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation
<p>Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for single-cell based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dementional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as spatial reference, integrates small FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images and demonstrated the advantage of the dual-modality imaging strategy.</p>
High-resolution figures of Hassenbach et al. 2023
<p>High-resolution figures of Hassenbach et al. 2023 "An expanded view on the morphological diversity of long-nosed antlion larvae further supports a decline of silky lacewings in the past 100 million years" in Insects (MDPI).</p>
High-resolution figures of Amaral et al. 2023
<p>High-resolution figures of Amaral et al. 2023 "Expanding the fossil record of soldier fly larvae – an important component of the Cretaceous amber forest" in Diversity (MDPI).</p>
Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China
<p>Here presented the forcasted results of Potential Morbidity Risk Index (PMRI) for the personal patients with allerigc rhinitis and the public health administrations, and these results are supplied to the published paper of "Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China".</p>
Data from: high-resolution bioclimatic surfaces for southern Peru: an approach to climate reality for biological conservation
<p>Climatic and bioclimatic surfaces were elaborated for southern Peru (Arequipa, Moquegua and Tacna). For the interpolations, meteorological information from in-situ stations, as well as orographic and geographic covariates were used. Statistical evaluations gave good results, showing some differences with other models also performed for the area. These data will contribute to a better understanding of the ecoclimatic requirements of the species in terms of ENMs and SDMs. </p>
High-resolution (5 m) surface water persistence map for 2021 in East-Africa
<p>Raster surface water image highlighting the percentage of time in 2021 that there was water at a certain pixel in East Africa. </p> <p>Script which classifies individual countries: <a href="https://code.earthengine.google.com/3f508773522979dfa62a75bda7750b5f?noload=true">https://code.earthengine.google.com/3f508773522979dfa62a75bda7750b5f?noload=true</a></p> <p>Script which combines the individual maps and filters the end-result: <a href="https://code.earthengine.google.com/ed8ee1bbade0b51f3759565b30da37ab?noload=true">https://code.earthengine.google.com/ed8ee1bbade0b51f3759565b30da37ab?noload=true</a></p> <p> </p>
High-Resolution Water Surface Slopes from Multi-Mission Satellite Altimetry
<p><strong>1. Summary</strong>:</p> <p>This dataset contains water surface slopes (WSS) every kilometer along 11 Polish rivers derived from cross-calibrated multi-mission satellite altimetry (<em>Schwatke et al. 2023a</em> (in review). ). The approach to derive WSS is based on a weighted least-squares approach, which is described in detail in <em>Schwatke et al. 2023b</em> (in review).</p> <p><strong>2. Data Formats</strong>:</p> <p>This dataset is provided in netCDF and shapefile formats. Each netCDF file contains the data of a single river and parameters such as river chainage, WSS, WSS error, location, and nearest centerline information from the SWORD database (v1.1, <em>Altenau et al., 2021</em>). The shapefile consists of five files (.cpg, .dbf, .prj, .shp, .shx) containing the data of the 11 Polish rivers. The attributes are identical to the netCDF, but the river name has been added.</p> <p><strong>3. Attribute Description</strong>:</p> <p>The attributes of netCDFs and shapefiles are described in the following list:</p> <ul> <li> <p><strong>river_chainage</strong>: The <em>river chainage</em> describes the distance from the river mouth to the location of each bin along the river (units: km)</p> </li> <li> <p><strong>wss</strong>: Water surface slopes (WSS) at each bin along the river. WSS are set to NaN/NULL for unprocessed lakes/reservoirs or short river segments (units: mm/km).</p> </li> <li> <p><strong>wss_error</strong>: Errors of WSS at each bin along the river. WSS errors are set to NaN/NULL for unprocessed lakes/reservoirs or short river segments (units: mm/km).</p> </li> <li> <p><strong>longitude</strong>: Longitude of the 1 km bins along the river (units: degree).</p> </li> <li> <p><strong>latitude</strong>: Latitude of the 1 km bins along the river (units: degree).</p> </li> <li> <p><strong>centerline_id</strong>: Nearest <em>centerline id </em>extracted from the SWORD database (v1.1, <em>Altenau et al., 2021</em>).</p> </li> <li> <p><strong>node_id</strong>: <em>Node id</em> from the SWORD database (v1.1, <em>Altenau et al., 2021</em>) for the corresponding <em>centerline id</em>.</p> </li> <li> <p><strong>reach_id</strong>: <em>Reach id</em> from the SWORD database (v1.1,<em> Altenau et al., 2021</em>) for the corresponding <em>centerline id</em>.</p> </li> <li> <p><strong>river_name</strong>: The name of the river is only available in the Shapefile.</p> </li> </ul> <p><strong>4. References</strong>:</p> <p><em>Schwatke C., Dettmering D., Passaro M., Hart-Davis M., Scherer D., Müller F. L., Bosch W., Seitz F.: </em><strong>OpenADB: DGFI-TUM`s Open Altimeter Database</strong>. Geoscience Data Journal, 2023a (in Review)</p> <p><em>Schwatke C., Halicki M., Scherer D</em>.: <strong>Generation of high-resolution water surface slopes from multi-mission satellite altimetry</strong>. Water Resources Research, 2023b (in Review)</p> <p><em>Altenau E.H., Pavelsky T.M., Durand M.T., Yang X., Frasson R.P.d.M., Bendezu L.</em>: <strong>SWOT River Database (SWORD) (Version v1)</strong> [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4917236">https://doi.org/10.5281/zenodo.4917236</a>, 2021</p>
All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal: high-resolution figures
<p>High resolution versions of Figure 2 and Figure S3 for the corrigendum of the paper "All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal" by Warszawski et al. (2021) published in Environmental Research Letters. </p> <p>Fig. 2: Spider plots for each of the 22 scenarios in the filtered ensemble (the corresponding model and scenario is printed above each plot), in order of increasing coverage, <em>V<sub>i</sub> </em>. Note that the AIM/CGE2.1 TERL_15D_LowCarbonTransportPolicy scenario has coverage of V<sub>i</sub>=1, despite E<sub>2050</sub> lying below themedium upper bound due to how the two energy-sector levers are combined to calculate the coverage (see Supplementary material). Each lever has been normalised to the high upper bound (the bold black inner circle on each plot; the absolute value of the upper bound is printed below the lever label). The centre of each spider plot corresponds to the minimum value across the entire ensemble of 50 scenarios for each lever. The medium upper bounds are shown as a dashed polygon. The absolute value of the lever for the given scenario is also printed on the plot. The top row contains the two scenarios singled out in figure <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abfeec#erlabfeecf1">1</a>(c), which exceed the SR1.5 remaining carbon budget for staying below 1.5 °C with a 50% likelihood; these two scenarios also have the lowest coverage of all scenarios in the filtered ensemble. For a similar plot of the complete ensemble of 1.5 °C scenarios with no or low overshoot (50 scenarios), see the supplement.</p> <p>Fig. S3: Same as Fig. 2 in main text but for all 50 scenarios. Those scenarios shaded grey are categorised as ‘Below 1.5C’ in the SR1.5. All other scenarios fall into the ‘1.5C low overshoot’ category.</p>
COLA-hires: High-resolution (0.5x0.625) regional carbon fluxes inferred from in-situ and OCO-2 data
<p>This dataset contains high-resolution CO<sub>2</sub> inversion estimate in North America, East Asia, and Europe at 0.5x0.625 resolution from 2015 to 2018 using the Carbon in Ocean-Land-Atmosphere (COLA) system. The in-situ observations obtained from NOAA obspack and the land-nadir/land-glint retrevials from OCO-2 are assimilated.</p>
Accompanying Data for the Manuscript "There's more to life than O2: Simulating the detectability of a range of molecules for ground-based high-resolution spectroscopy of transiting terrestrial exoplanets"
<p>This file contains results for all cases considered in the manuscript titled "There's more to life than O2: Simulating the detectability of a range of molecules for ground-based high-resolution spectroscopy of transiting terrestrial exoplanets"</p>
CubaPrec1: A 48 years long term gridded daily precipitation dataset at very high-resolution for Cuba.
<p>CubaPrec1 is a new high-resolution gridded dataset for daily precipitation across Cuba from 1961-2008. The dataset was built using the information from the data series of 630 stations from the network operated by the National Institute of Water Resources. The original station data series were quality controlled using a spatial coherence process of the data, and the missing values were estimated on each day and location independently. Using the filled data series, a grid of 3 × 3 km spatial resolution was constructed by estimating daily precipitation and their corresponding uncertainties at each grid box. This new product represents a precise spatiotemporal distribution of precipitation in Cuba and provides a useful baseline for future studies in hydrology, climatology, and meteorology.</p>
CBRA: The first multi-annual (2016-2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with Super-resolution Segmentation from Sentinel-2 imagery
<p>Large-scale and up-to-date maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, existing large-scale BRA datasets, such as those from Microsoft and Google, do not include China, hence there are no full-coverage maps of BRA in China. To this end, we produce the multi-annual China building rooftop area dataset (CBRA) with 2.5 m resolution from 2016-2021 Sentinel-2 images. The CBRA is the first full-coverage and multi-annual BRA data in China. The CBRA achieves good performance with the F1 score of 62.55% (+10.61% compared with the previous BRA data in China) based on 250,000 testing samples in urban areas, and the recall of 78.94% based on 30,000 testing samples in rural areas. </p> <p>The CBRA is organized as GeoTIFF (.tif) raster file format with a single band and GCS_WGS_1984 coordinate system. The pixel values are 0 and 255, with 0 representing the background and 255 representing the building rooftop area. Furthermore, to facilitate the use of the data, the CBRA is split into 215 tiles of spatial grid, named “CBRA_year_E/W**N/S**.tif”, where “year” is the sampling year, the “E/W**N/S**” is the latitude and longitude coordinates found in the upper left corner of the tile data.</p> <p> </p> <p>Version 2.0: In version 1.0, there were empty raster images (because they didn't contain buildings). In version 2.0, these raster images were removed.</p>
A high-resolution global land daily drought index dataset during 1979–2022
<p>A global daily drought index dataset named as daily evapotranspiration deficit index (DEDI) is constructed using daily actual evapotranspiration and potential evapotranspiration data provided by European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The DEDI dataset has a spatial grid resolution of 0.25°×0.25° and covers global land areas for the period 1979 to 2022. The DEDI dataset can be a good index for assessing the dry and wet severity in terms of spatial patterns and temporal evolutions when compared to other available daily drought indices. Moreover, the DEDI dataset is also demonstrated to have advantages in detecting ecological or agricultural droughts. The DEDI dataset also appears reasonable and promising in facilitating drought monitoring and early warning from a daily perspective.</p><p>This dataset accompanies the following publication: Zhang, X., Duan, J., Cherubini, F. et al. A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022. Sci Data 10, 824 (2023). https://doi.org/10.1038/s41597-023-02756-1</p>
High-resolution throughfall measurement design, Hainich, Germany, project AquaDiva
<p>This dataset contains the sampling design for throughfall data used for the analysis published in Metzger et al. (2017) and Fischer et al. (2023). It gives spatially distributed throughfall measurement points and their forest structural properties. The measurement points are grouped into randomly distributed “kernel” points and “transect” points which are not part of the random design.</p> <p>The field site and sampling design are described in Metzger et al. (2017). The throughfall data is given in an associated published dataset (Metzger and Hildebrandt, 2023).</p>
ValEqt: A high-resolution Earthquake and Repeating earthquakes catalog of the 2017 Valparaiso sequence
<p><strong>The ValEqt earthquake catalog</strong></p> <p>Description:</p> <p>Catalog of earthquakes detected near the 2017 Mw=6.9 Valparaiso (Chile) earthquake from 01/01/2016 to 01/01/2021. We also include a catalog of ValEqt's repeating earthquakes.</p> <p>Methods used to build this dataset are extensively described in this paper : <em>Upcomming paper Doi</em></p> <p>Files:</p> <ol> <li><em>ValEqt.txt </em>: Earthquake catalog</li> <li><em>Repeater.txt</em> : Repeating earthquake catalog</li> </ol> <p> </p>
Full-coverage high-resolution (Daily, 1-km) PM2.5 dataset in China (2000-present)
<p>We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2022 in China using a random forest-based hindcast modeling method. <strong>Our modeling method focused on improving pre-2013 PM2.5 estimates because for those years no available PM2.5 measurements can be directly used for constructing the model and evaluating the model performance. </strong>In our proposed method, observed predictor information before 2013 was incoporated into the modeling for the first time. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The daily average data during 2000-2022 are released here and free for non-commercial use. <em><strong>If you want use our dataset, please cite the following publication. </strong></em></p> <p>The estimates in 2021-2022 are separately predicted using the same modeling method developed in the publication below and samples in the corresponding predictive year (sample-based 10-fold cross validation R2 [RMSE] values are 0.91 [8.84 ug/m3] for 2021 and 0.93 [7.42 ug/m3] for 2022, respectively. </p> <p> </p> <p><strong>-He, Q., Ye, T., Wang, W., Luo, M., Song, Y., & Zhang, M. (2023). Spatiotemporally continuous estimates of daily 1-km PM2. 5 concentrations and their long-term exposure in China from 2000 to 2020. <em>Journal of Environmental Management</em>, <em>342</em>, 118145.[<a href="https://doi.org/10.1016/j.jenvman.2023.118145">url</a>]</strong></p> <p><strong>-He, Q., Wang, W., Song, Y., Zhang, M., & Huang, B. (2023). Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020. <em>Atmospheric Research</em>, <em>281</em>, 106481.[<a href="https://doi.org/10.1016/j.atmosres.2022.106481">url</a>]</strong></p> <p>Full-coverage daily estimates spanning the years 2015 to Jun 2021 are archived here. These records, organized by month, are available for download in CSV format. For Jul-Dec 2021, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>.</p> <p>If you want more data (e.g.daily estimates before 2015), have any question, or further collaborate with us, please contact us via qqhe@whut.edu.cn.</p> <p>If you want to use <strong>monthly</strong> estimates from 2000 to 2022, please go to <a href="https://zenodo.org/record/8084388">10.5281/zenodo.8084388</a>.</p> <p><strong>We also estimate other atmospheric data:</strong></p> <p>For full-coverage, 1-km, AOD data in China, please go to <a href="https://dataverse.harvard.edu/dataverse/atmospheric_data_by_WHUT">harvard dataverse</a>. This dataset was imputed based on MODIS MAIAC 1-km AOD retrievals.</p> <p> </p> <p> </p>
Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets - training datasets
<p>Sample datasets for the <strong>Case Studies</strong> section of the <em> Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets </em>web book (<a href="https://isprs-gis-sdg.readthedocs.io">https://isprs-gis-sdg.readthedocs.io</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.