Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,113
datasets available to search
ShareScore release 0.7.1
Dataset results
2,113 results for “Very High Resolution”
Figure 11 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 11. Characteristic conodonts from the Aras Valley section (scale bars equal to 100 µm); all specimens stored in the collection of the Ferdowsi University, Mashhad. (a) Merrillina ultima Kozur, 2004, Pa element, FUM no. AJ204.13, Elikah Formation, Aras Member, lateral view; (b) Stepanovites sp., Sc element, FUM no. AJ205-1, Elikah Formation, Aras Member, lateral view; (c) Hindeodus typicalis (Sweet, 1970), FUM no. AJ200-27, Ali Bashi Formation, Paratirolites Limestone, lateral view; (d) Hindeodus julfensis (Sweet, in Teichert et al., 1973), FUM no. AJ183-8, Ali Bashi Formation, Paratirolites Limestone, lateral view; (e) Hindeodus julfensis (Sweet, in Teichert et al., 1973), FUM no. AJ183-5, Ali Bashi Formation, Paratirolites Limestone, lateral view; (f) Hindeodus bicuspidatus Kozur, 2004, FUM no. AJ200-32, Ali Bashi Formation, Paratirolites Limestone, lateral view; (g) Hindeodus praeparvus Kozur, 1996, FUM no. AJ201-4, Elikah Formation, Aras Member, lateral view; (h) Hindeodus eurypyge Nicoll et al., 2002, FUM no. AJ208-2, Elikah Formation, Aras Member, lateral view; (i) Hindeodus parvus (Kozur and Pjatakova, 1976), FUM no. AJ206-2, Elikah Formation, Aras Member, lateral view; (j) Hindeodus magnus Kozur, 2004, FUM no. AJ211-15, Elikah Formation, Claraia Beds, lateral view; (k) Hindeodus anterodentatus (Dai et al., 1989), FUM no. AJ208-7, Elikah Formation, Aras Member, lateral view; (l) Isarcicella staeschei Dai & Zhang, 1989, FUM no. AJ216-2, Elikah Formation, Claraia Beds, upper view; (m) Isarcicella isarcica (Huckriede, 1958), FUM no. AJ217-13, Elikah Formation, Claraia Beds, upper view.
Figure 3 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 3. Columnar section of the late Permian to Early Triassic succession in the Aras Valley section with colour indications and numbers of microfacies and conodont samples.
Figure 2 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 2. The Permian–Triassic boundary section near the Aras Valley, NW Iran. View towards the north, in the background, beyond the Aras Valley, mountains in Azerbaijan consisting of Triassic rocks.
Figure 12 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 12. Quantity of ostracod specimens per 500 g of rock material and species richness in the Paratirolites Limestone, the Aras Member, and the Claraia Beds of the Aras Valley section and important ostracod species in the lithological units. Scale bar for figured ostracods = 100 µm. Figured ostracods are as follows. (a) Bairdia kemerensis Crasquin-Soleau, 2004. (b) Bairdiacypris ottomanensis CrasquinSoleau, 2004. (c) Liuzhinia sp. 2. (d) Langdaia sp. (e) Cavellina sp. (f) Microcheilinella sp. (g) Cavellina sp. nov. (h) Kempfina qinglaii (Crasquin), 2008. (i) Fabalicypris sp. nov. (j) Carinaknightina sp. nov. (k) Iranokirkbya brandneri Kozur and Mette, 2006. (l) Fabalicypris obunca Belousova, 1965. (m) Fabalicypris blumenstengeli Crasquin, 2008. (n) Orthobairdia sp. nov. (o) Hungaroleberis sp. nov. (p) gen. nov. sp. nov.
Figure 6 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 6. Carbonate microfacies of samples from the upper Julfa Formation and the Paratirolites Limestone of the Aras Valley section. (a) Peloidal–foraminiferal packstone; sample AJ144 (−18.00 m). (b) Peloidal–foraminiferal packstone with algae; sample AJ190 (−2.20 m). (c) Microfacies sample from the topmost 4 cm of the Paratirolites Limestone (sample AJ200; = 0.00 to −0.04 m). Lower part: burrowed bioclastic–intraclastic wackestone with ammonoids, bivalves and ostracods, lithoclasts, and micrite clasts. Upper part: sponge packstone with ammonoids, bellerophontids, and ostracods; uppermost 10 mm with a densely packed sponge meshwork of possible keratose sponges. Scale bar units = 1 mm.
Figure 5 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 5. Carbonate microfacies of samples from the Paratirolites Limestone of the Aras Valley section. (a) Burrowed bioclastic mudstone with ammonoids and micritic intraclasts; sample AJ182 (−3.65 m). (b) Burrowed bioclastic wackestone with ammonoids and echinoderms (E); sample AJ186 (−2.95 m). (c) Burrowed bioclastic–intraclastic mudstone with Fe-encrusted ammonoid; sample AJ188 (−2.70 m). (d) Burrowed bioclastic–intraclastic wackestone with shell debris and echinoderms, micrite clasts, and intense brecciation; sample AJ197 (−0.45 m). Scale bar units = 1 mm.
Figure 9 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 9. Succession of conodont species and zones in the Aras Valley section. (Wu – Wuchiapingian; Ch – Changhsingian; EH – extinction horizon; P – Permian; Tr – Triassic).
Figure 1 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 1. (a) Geographic position of Permian–Triassic boundary sections in the Transcaucasus and in NW Iran (after Arakelyan et al., 1965); important sections are highlighted. (b) Detail map showing the position of the Aras Valley section. (c) Palaeogeographic position of the Julfa area during the PTB time interval (after Stampfli and Borel, 2002).
Figure 8 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 8. Microfacies types and carbonate content of samples from the Aras Valley section. Dashed lines indicate transitional microfacies change or possible continuation of the microfacies type. (Wu – Wuchiapingian; Ch – Changhsingian; EH – extinction horizon; P – Permian; Tr – Triassic).
Figure 4 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 4. Carbonate microfacies of samples from the lower Julfa Formation (a), upper Julfa Formation (b, c), and Zal Member (d, e) of the Aras Valley section. (a) Crinoidal wacke- to packstone with crinoids, brachiopods, rugose coral, and gastropods as well as peloids in a microspar matrix; sample AJ124 (−27.90 m). (b) Crinoidal wackestone with shell debris and crinoids; sample AJ139 (−21.30 m). (c) Wackestone with disarticulated ostracods, brachiopods, and sub-rounded micritic intraclasts; sample AJ157 (−14.00 m). d) Mudstone with ostracod and echinoderm fragments; sample AJ165 (−10.35 m). (e) Burrowed mudstone; sample AJ174 (−5.65 m). Scale bar units = 1 mm.
Figure 4 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 4. Columnar sections of the Paratirolites Limestone in the Aras Valley, Ali Bashi 4 and Ali Bashi 1 sections with their conodont and ammonoid zonation as well as the weight % of CaCO3 (determined by the weight loss–acid digestion method) of the Ali Bashi 1 section.
Figure 1 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 1. (A) Geographical position of Permian–Triassic boundary sections in the Transcaucasus and in NW Iran (after Arakelyan et al., 1965); sections investigated in this study are highlighted. (B) Palaeogeographic position of the Julfa area (after Stampfli and Borel, 2002).
Figure 3. Ali Bashi 4 in High-resolution stratigraphy of the Changhsingian (Late Permian) successions of NW Iran and the Transcaucasus based on lithological features, conodonts and ammonoids
Figure 3. Ali Bashi 4 section and columnar sections of the entire Changhsingian in Ali Bashi 4, Ali Bashi 1 and Ali Bashi M sections with their conodont zonation.
Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset
<h2>Abstract</h2> <p><span>With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.</span></p>
Evaluation of high-resolution WRF simulation in urban areas - Effect of different physics schemes on simulation performance in the Rhine-Main-Neckar area
<p>This dataset contains data sampled from a WRF sensitivity study saved in NetCDF format. The study was run over 4 months of the year 2020. The folders contain the following data:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Datasets</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>wrf_met_sample_full.nc</td> <td>all</td> <td>WRF meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>wrf_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>wrf_met_sample_full_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_met_sample_full_and_quant_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full_and_quant.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_pblh_sample_full.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain</td> </tr> <tr> <td>wrf_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_met_sample_full.nc</td> <td>all</td> <td>ERA5 meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>era5_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain, resampled onto the measured meteorology and with summary statistics</td> </tr> </tbody> </table> <p>Each of these files contains the samples and statistics as NetCDF Variables. These Variables have multiple dimensions, which describe the individual datapoints. For the WRF samples, these dimensions are:</p> <table> <tbody> <tr> <td><strong>Dimension</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Time</td> <td>time since start of simulation</td> </tr> <tr> <td>station_id</td> <td>the ID of the station where the sample was taken (meteo - length 19, PBLH - length 2)</td> </tr> <tr> <td>pbl</td> <td>Planetary Boundary Layer scheme (Bou-Lac / MYJ / YSU)</td> </tr> <tr> <td>lsm</td> <td>Land Surface Model scheme (N / NMP)</td> </tr> <tr> <td>slm</td> <td>Surface Layer Model scheme (MM5 / MO)</td> </tr> <tr> <td>urb</td> <td>Urban Parametrization scheme (SLUCM / BEP)</td> </tr> </tbody> </table> <p>Not all combinations between different simulation schemes exist, so some values in the NetCDF Variables are NaNs.</p>
High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany
<p>Open access publication under review.</p> <p>Visit <a href="https://ee-forestfuels-ger.projects.earthengine.app/view/gedi-fuels"><strong>this Earth Engine app</strong></a> to explore the data interactively.</p> <p> </p> <p>Abstract:</p> <p>Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data.</p> <p><br>We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH),<br>Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomass<br>models. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching.<br>Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution.</p> <p><br>Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validation<br>with estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH)<br>rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of<br>model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs.<br>The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.</p> <p> </p> <p>Data:</p> <p>This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.</p> <p> </p> <p>Disclaimer:<br>Maps in this repository are predicted using canopy fuel estimates from GEDI measurements. These are limited the region between 51.6° North and South. Map predictions exceeding this range should be considered an extrapolation of the model to an unknown biophysical domain. Error maps (6) can aid in utilizing our canopy fuel maps.</p>
Data for: "High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula"
<p>Data accompanying the publication: High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula. For filenames starting with T: Exponential fit parameters time0 and mag0 for InSAR coherence data. they are binary files, where fit = a*exp(-b*x); a = -log(mag0); b = 1/time0. timeerr contains the uncertainty of the time0 parameter, and maghigh/maglow contain the high and low uncertainty for the mag0 parameter, respectively. For for each frame or overlap region (T101, T28, T130, T28_T101, T130_T28), there is a vrt file (T..._20180524.time0.vrt), which is the metadata file applicable to all files of the same frame. Files starting with mags_times: Exponential fit parameters for ASCAT/SMAP/GLDAS data. the same parameters (time0, timeerr, mag0, maghigh, maglow) can be found in these matlab structure files. In addition, the .mat files contain the offset parameter and related uncertainty, as well as lat/lon information. </p>
High-resolution scans of impact spatter patterns
<p>High-resolution scans of spatter patterns used in the following peer-reviewed research article<br> <br> Article Title: Do impact spatters depend on impact velocity, impact energy or impactor shape?<br> DOI : 10.1007/s00348-021-03341-1<br> Journal: Experiments in Fluids<br> Publisher: Springer<br> Authors: R Faflak and D Attinger</p> <p><br> Spatial resolution is 600 pixel per inch (a flatbed scanner A3 Epson Expression 11000XLwas used)<br> Images are oriented so that gravity points downwards<br> The file format is <br> impactor material_impactor shape_height of free fall_impactor mass in gram_<br> For instance the following file <br> PP,Al_F_h30_m644,7(2)_stitch_CLEAN.tiff<br> means<br> impactor material is Polypropylene and Aluminum<br> impactor shape is Flat<br> free fall height is 30 cm<br> impactor mass in gram is 644.7g</p> <p>A summary description of the impact setup and of the impact parameters is in the Adobe PDF document "description of geometry and parameters.pdf"</p>
Supporting dataset for the paper : " Hydro-geomorphic metrics for high resolution fluvial landscape analysis"
<p>This repository contains all the original data supporting the results of Bernard et al., 2021: "Consistent hydro-geomorphic indicators for high resolution topographic analysis".<br> The parameter used to perform hydraulic simulations are also available.<br> </p>
High-resolution spatialization for estimation of precipitation in the Cordillera Blanca, Peru
<p>Supplementary materials for the article "High-resolution spatialization for estimation of precipitation in the Cordillera Blanca, Peru"</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.