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5,155 results for “resolution”
Long time series (2001-2015) high-resolution crop yield and water productivity dataset of China
<p>A long-term data series, at 1-km resolution, of crop yield (kg/ha) and crop water productivity (kg/m3) for maize and wheat across China, based on the MOD16 ET product, multiple remotely sensed crop physiological and environmental indicators, and crop phenological information, using a random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. These high-resolution crop yield and CWP datasets generated in this study revealed spatiotemporal patterns of agricultural production in China and may be applied to many scenarios, including understanding effects of climate change on agricultural production capacity in China under increasing demand for food security to optimize agricultural production strategies.</p>
Automatic monitoring of neural activity with single-cell resolution in behaving Hydra
<p>The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian <em>Hydra vulgaris</em>. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic <em>Hydra </em>and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in <em>Hydra</em>, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.</p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
GSHHG: Global Self-consistent Hierarchical High-resolution Geography
<p><a href="http://www.soest.hawaii.edu/pwessel/gshhg/"><strong>Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG)</strong></a> is a high-resolution geography data set, amalgamated from two databases: World Vector Shorelines (WVS) and CIA World Data Bank II (WDBII). The former is the basis for shorelines while the latter is the basis for lakes, although there are instances where differences in coastline representations necessitated adding WDBII islands to GSHHG. The WDBII source also provides political borders and rivers. GSHHG data have undergone extensive processing and should be free of internal inconsistencies such as erratic points and crossing segments. The shorelines are constructed entirely from hierarchically arranged closed polygons.<br><br>GSHHG combines the older GSHHS shoreline database with WDBII rivers and borders, available in either ESRI shapefile format or in a native binary format. Geography data are in five resolutions: crude(c), low(l), intermediate(i), high(h), and full(f). Shorelines are organized into four levels: boundary between land and ocean (L1), boundary between lake and land (L2), boundary between island-in-lake and lake (L3), and boundary between pond-in-island and island (L4). Datasets are in WGS84 geographic (simple latitudes and longitudes; decimal degrees).</p> <p>GSHHG is released under the <a title="external link to GNU license" href="http://www.gnu.org/licenses/lgpl.html">GNU Lesser General Public license</a>, and is developed and maintained by Dr. Paul Wessel, SOEST, University of Hawai'i, and Dr. Walter H. F. Smith, NOAA Laboratory for Satellite Altimetry. <strong>Please notify Dr. Paul Wessel and Dr. Walter H.F. Smith if any changes are made to the GSHHG data set for commercial use.</strong></p> <p><strong>Processing and assembly of the GSHHG data:<br></strong>Wessel, P., and W. H. F. Smith (1996), A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101(B4), 8741–8743, <a href="https://doi.org/10.1029/96JB00104">doi:10.1029/96JB00104.</a></p>
1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence.<br>The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka.</p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation,<br>n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.</p>
Mapping sugarcane globally at 10 m resolution using GEDI and Sentinel-2
<p><strong>Dataset Abstract:</strong><br>Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90% of global production. Maps were generated for the 2019-2022 period by combining data from the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that sugar is typically the only tall crop growing for a substantial fraction of time during the study period. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and recall of our maps. Agreement with field data at the pixel level exceeded 80% in most countries, and sub-national sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks, or to under-reporting of sugarcane area by governments. <br>The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields.</p> <p><strong>USAGE: Users must mask the provided sugarcane map with the most appropriate crop mask from the ones provided. If none of the provided crop masks are suitable, users can use an external crop mask instead.</strong></p> <p>Validation results for the sugarcane maps are detailed in Section 4.3 of the paper. For Indonesia and Guatemala, no field-level data or raster datasets were available for validation of our sugarcane maps.</p> <p><br><strong>Dataset:</strong> <br>5 bands<br>b1: Number of tall months<br>b2: Sugarcane Map: 0 = non-sugarcane, 1 = sugarcane<br>b3: ESA crop mask: 0 = non-cropland, 1 = cropland<br>b4: ESRI crop mask: 0 = non-cropland, 1 = cropland<br>b5: GLAD crop mask: 0 = non-cropland, 1 = cropland</p> <p> </p> <p>The dataset can be accessed on Google Earth Engine (GEE) at <br><strong><a href="https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD">https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD</a><br></strong><br>Example GEE script for visualizing and masking the sugarcane maps by country available at:<br><strong><a href="https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2Flobell-lab%2Fgedi_sugarcane%2Fmaps%2FimgColl_10m_ESAESRIGLAD">https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2fl Bell-lab%2Fgedi_sugarcane%2 Maps%2FimgColl_10m_ESAESRIGLAD</a></strong></p>
Interim model output of estimated bare peat/erosion density in Aberdeenshire and Angus at 1m resolution.
<p>This dataset contains the estimated unit density of peat erosion accross Aberdeenshire and Angus at 1m resolution. These outputs are derived from high resolution aerial imagery using the PeatNet pipeline for predicting the location and extent of peatland degradation features. This output was created to estimate the particulate organic carbon (POC) emissions from eroding peat based on multiple scenarios.</p>
SeaFlow data v1: High-resolution abundance, size and biomass of small phytoplankton measured by flow-cytometry
<p>SeaFlow is an underway flow cytometer designed to continuously monitor the optical properties of the smallest phytoplankton from a ship's flow-through seawater system. It collects high-resolution data, generating the equivalent of 1 sample every 3 minutes or every 1 km (for a ship moving at 10 knots).</p> <p>The dataset provides measurements of cell abundance, cell size (equivalent spherical diameter) and carbon biomass for small phytoplankton populations: the cyanobacteria Prochlorococcus, Synechococcus, Crocosphaera, and small eukaryotic phytoplankton (<5 μm ESD). Data processing followed the methods outlined in <a href="https://doi.org/10.1038/s41597-019-0292-2">Ribalet et al. (2019)</a>. For more information, visit the <a href="https://seaflow.netlify.app/">SeaFlow website</a>.</p> <p><strong>New in version 1.6 </strong>The updated dataset includes flow cytometric measurements from 89 cruises, spanning nearly 14,000 hours of observations across 130,000 km of the surface oceans.</p>
Estimated Annual Agricultural Pesticide Use for USA48 2000 to 2019 (gridded maps at 250-m resolution)
<p>Estimated low and high pescticide uses based on census data for USA48, provided for the cropland mask at 250-m spatial resolution. The original <a href="https://water.usgs.gov/nawqa/pnsp/usage/maps/">USGS maps</a> are only available as PNGs, so we have prepared here a code to rasterize the values and produce GeoTIFFs. For cropland mask we use the <a href="https://www.usgs.gov/centers/eros/science/national-land-cover-database">National Land Cover Database (NLCD)</a>. All processing is fully documented in: <a href="https://github.com/Envirometrix/pesticide-use-USA48">https://github.com/Envirometrix/pesticide-use-USA48</a>. All maps are projected in the <a href="https://epsg.io/5070-1252">EPSG:5070</a>. Description of the layers:</p> <ul> <li><code>croplands_20**_250m.tif</code> = cropland mask (0–100%) estimated based on NLCD.</li> <li><code>GLYPHOSATE_EPEST.HIGH.KG.KM2_20**_250m.tif</code> = estimated pesticide use per county (high estimate) expressed in kg/km-square.</li> <li><code>diff.GLYPHOSATE_EPEST.HIGH.KG.KM2_250m.tif</code> = annual difference in pesticide use from 2000 to 2019;</li> </ul> <p><strong>Disclaimer</strong>: this code is under construction and USGS makes is clearly available that there are some limitations to this data:</p> <ul> <li>These estimates are made by using projected county crop acres from the previous Census of Agriculture and are expected to be revised upon availability of updated crop acreages in the following Census of Agriculture.</li> <li>The files do not include pesticide use estimates for California. Data for California are obtained from the online Department of Pesticide Regulation-Pesticide Use Reporting (DPR-PUR) database and are typically not available at the time the preliminary pesticide use estimates are generated for the rest of the U.S.</li> </ul> <p>Pease have in mind these limitations when using these maps for further modeling.</p>
A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics
<p>This dataset includes measured photovoltaic (PV) power generation data and on-site weather data collected from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using Brick schema was developed, which simplifies the data comprehension and the development of smart analytics applications. The detailed Brick model is stored in the .ttl file format, which can be accessed for retrieving metadata through the use of SPARQL queries.This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems.</p>
The Adult Adansonia digitata L. (baobab tree) distribution map derived from very high resolution satelite imagery for 2010s across the Sahel at 1km resolution
<p>The baobab tree (<em>Adansonia digitata</em> <em>L.</em>) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3 million baobab trees in the Sahel, a dryland region of 1.5 million km<sup>2</sup>. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. The map's overall underestimate bias is 0.27. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to 1 × 1 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m). The baobab tree count map is also available for these three different size classes. </p>
FIGURE 2 in Comparative performance of a multi-locus barcoding approach to enhance taxonomic resolution of New Zealand mosquitoes (Diptera: Culicidae)
FIGURE 2 The statistical parsimony network showing the genetic relationships between Culex asteliae (n = 3), Culex pervigilans (n = 15) and Culex rotoruae (n = 3) based on their (a) COI sequences and (b) ITS2 sequences derived from the current study. Each COI/ITS2 singleton sequence is represented by one circle with size proportional to their frequency. The colours refer to the mosquito species to which each individual belongs. Small white circles connecting coloured circles indicate 'missing' or hypothetical singletons. The line linked two circles indicates one basepair difference on the sequence.
FIGURE 1 in Comparative performance of a multi-locus barcoding approach to enhance taxonomic resolution of New Zealand mosquitoes (Diptera: Culicidae)
FIGURE 1 The phylogenetic relationships of New Zealand endemic and exotic mosquito species based on (a) COI and (b) ITS2 sequences using maximum likelihood method. Anopheles annulipes was outgroup taxon for both trees. Only bootstrap support values greater than 50% is present at branches on the tree. The sequences collected for the current study and derived from New Zealand endemic, introduced or recently eradicated species are highlighted in bold. The specimens collected outside of New Zealand were highlighted by underlines. The complete phylogenetic trees of New Zealand endemic and exotic mosquito species based on COI and ITS2 sequences are available in Figure S3.
Plate interface geometry complexity and persistent heterogenous coupling revealed by a high-resolution earthquake focal mechanism catalog in Mentawai, Sumatra
<p>This website contains all the outputs from the study entitled “Plate interface geometry complexity and persistent heterogenous coupling revealed by a high-resolution earthquake focal mechanism catalog in Mentawai, Sumatra”. The contents include the seismic stations used in this study, obtained focal mechanism solutions, corresponding waveform fits, relocation results, and depth-phase modeling results. Each figure (started with ${ID}) is corresponding to the Earthquake ID as shown in Table S1.txt.</p>
High-resolution simulations of Mediterranean windstorm Adrian with the Meso-NH atmospheric model
<p>The dataset provides numerical simulations of Mediterranean windstorm Adrian of 29 October 2018 at two horizontal resolutions and using different representations of surface turbulent fluxes at the air-sea interface. The simulations are run with the Meso-NH non-hydrostatic mesoscale atmospheric model of the French research community, version 5.4, freely available under CeCILL-C license agreement: <a href="http://mesonh.aero.obs-mip.fr/" target="_blank" rel="noopener">http://mesonh.aero.obs-mip.fr/</a></p> <p>The data is formatted in Network Common Data Form (NetCDF) using the CF Metadata Conventions and standard Meso-NH names for physical variables. The data files are named as following: <strong>EXP.N.CONTENT.nc</strong></p> <ul> <li><strong>EXP</strong> describes the numerical experiment (name of the parameterization of surface turbulent fluxes or their absence) </li> <li><strong>N</strong> the horizontal resolution (1=1000m, mesoscale simulation; 2=200m, large-eddy simulation) </li> <li><strong>CONTENT</strong> the type of data (3D zoom over the windstorm center or vertical profiles in the same area at 1530 UTC, or 2D surface fields every 6 min from 12 to 18 UTC)</li> </ul>
Figure 17 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 17. Selected representatives of nautiloids from the Aras Valley section. (a) Domatoceras parallelum (Abich, 1878), specimen MB.C.29346 from the lower Julfa Formation. (b) Pleuronautilus sp., specimen MB.C.29347 from the lower Julfa Formation. (c) Tainoceras (?) sp., specimen MB.C.29348 from the upper Julfa Formation. (d) Pleuronautilus sp., specimen MB.C.29349 from the Zal Member. (e) Liroceras sp., specimen MB.C.29350 from the lower Julfa Formation. (f) Permoceras abichi (Kruglov, 1928), specimen MB.C.29351 from the lower Julfa Formation. (g) Liroceras sp., specimen MB.C.29352 from the lower Julfa Formation.
Figure 18 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 18. Succession carbon isotopes (δ13 C) in the Aras Valley section and correlation with the conodont stratigraphy. Abbreviated carb conodont zones: (1) Clarkina bachmanni, (2) Clarkina abadehensis, (3) Clarkina hauschkei, (4) Merrillina ultima–Stepanovites mostleri.
Figure 15 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 15. Selected representatives of ammonoids from the Aras Valley section. (a) Prototoceras discoidale Ruzhencev, 1963, specimen MB.C.29343 from the lower Julfa Formation. (b) Vedioceras fusiforme Korn & Ghaderi, 2019, holotype MB.C.29132 from the upper Julfa Formation. (c) Iranites transcaucasius (Shevyrev, 1965), specimen MB.C.29148 from the Zal Member. (d) Pseudotoceras sp., specimen MB.C.29344 from the lower Julfa Formation. (e) Dzhulfoceras sp., specimen MB.C.29345 from the upper Julfa Formation. (f) Dzhulfites nodosus Shevyrev, 1965, specimen MB.C.29182 from the Zal Member at −9.50 m. (g) Araxoceltites cristatus Korn, Ghaderi and Ghanizadeh Tabrizi, 2019, holotype MB.C.22706 from the Zal Member. (h) Phisonites triangulus Shevyrev, 1965, specimen MB.C.22703 from the Zal Member at −12.90 m.
Figure 13 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 13. Succession of ostracod species in the Paratirolites Limestone, Aras Member, and Claraia Beds of the Aras Valley section .
Figure 7 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 7. Carbonate microfacies of samples from the Aras Member (a, b) and the Claraia Beds (c–e) of the Aras Valley section. (a) Burrowed mudstone with calcite fan structures; sample AJ202 (+1.65 m). (b) Burrowed mudstone with calcite fans; sample AJ203 (+2.00 m). (c) Gastropod mudstone and wackestone with microgastropods and sponge remains of possible keratose sponges; sample AJ204 (+2.35 m). (d) Laminated mudstone with irregularly shaped sparry calcite crystals; sample AJ210 (+3.80 m). (e) Laminated mudstone with subrounded sparry calcite crystals; sample AJ216 (+4.95 m). Scale bar units = 1 mm.
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.