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.
5,155
datasets available to search
ShareScore release 0.9.0
Dataset results
5,155 results for “resolution”
Figure 10 in Aras Valley (northwest Iran): high-resolution stratigraphy of a continuous central Tethyan Permian-Triassic boundary section
Figure 10. 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) Clarkina leveni Kozur et al., 1975, FUM no. AJ122-1, lower Julfa Formation, upper view; (b) Clarkina guangyuanensis Dai and Zhang in (Li et al., 1989), FUM no. AJ131-7, upper Julfa Formation, upper view; (c) Clarkina liangshanensis (Wang, 1978), FUM no. AJ179-8, upper Julfa Formation, upper view; (d) Clarkina transcaucasica (Gullo and Kozur, 1992), FUM no. AJ151-5, upper Julfa Formation, upper view; (e) Clarkina orientalis (Barskov and Koroleva, 1970), FUM no. AJ157-9, upper Julfa Formation, upper view; (f) Clarkina changxingensis (Wang and Wang in Zhao et al., 1981b), FUM no. AJ173-1, Ali Bashi Formation, Zal Member, upper view; (g) Clarkina subcarinata (Sweet in Teichert et al., 1973), FUM no. AJ165-7, Ali Bashi Formation, Zal Member, upper view; (h) Clarkina deflecta (Wang and Wang, 1981a), FUM no. AJ177-14, Ali Bashi Formation, Paratirolites Limestone, upper view; (i) Clarkina bachmanni Kozur, 2004, FUM no. AJ185-23, Ali Bashi Formation, Paratirolites Limestone, upper view; (j) Clarkina nodosa Kozur, 2004, FUM no. AJ190-7, Ali Bashi Formation, Paratirolites Limestone, upper view; (k) Clarkina yini Mei, 1998b, FUM no. AJ192-5, Ali Bashi Formation, Paratirolites Limestone, upper view; (l) Clarkina tulongensis (Tian, 1982), FUM no. AJ198-4, Ali Bashi Formation, Paratirolites Limestone, upper view; (m) Clarkina abadehensis abadehensis Ghaderi, 2014, FUM no. AJ198-13, Ali Bashi Formation, Paratirolites Limestone, upper view; (n) Clarkina abadehensis iranica Ghaderi, 2014, FUM no. AJ198-9, Ali Bashi Formation, Paratirolites Limestone, upper view; (o) Clarkina hauschkei Kozur, 2004, FUM no. AJ200-77, Ali Bashi Formation, Paratirolites Limestone, upper view; (p) Clarkina taylorae (Orchard et al., 1994), FUM no. AJ198-21, Ali Bashi Formation, Paratirolites Limestone, upper view; (q) Clarkina cf. chengyuanensis, FUM no. AJI195-23, Ali Bashi Formation, Paratirolites Limestone, upper view.
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>
Bacteriophage Lambda Structure at Atomic Resolution
<p>Bacteriophage Lambda structure at atomic resolution. This structure has been constructed in UCSF Chimera software putting together all the structures that compose bacteriophage lambda using cryoEM reconstructions and pdb structures. By Dr. Victor Padilla-Sanchez, PhD from Washington Metropolitan University. Email: drvictorpadilla@aol.com Website: https://www.drvictorpadillasanchez.com</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>
Global Pasture Watch - Grassland sampling design derived by Feature Space Coverage Sampling (FSCS) at 1-km spatial resolution
<p>Sampling design used in the production of the <strong>global maps of grassland dynamics 2000–2022 at 30 m spatial resolution</strong> in the scope of the Global Pasture Wath initiative. The sampling desing was based in Feature Space Coverage Sampling and resulted in 10,000 sample tiles (1x1 km) distributed across the World, which were visual interpreted in Very-High Resolution imagery thorugh the QGIS plugin <a href="https://plugins.qgis.org/plugins/qgis-fgi-plugin/">QGIS Fast Grid Inspection</a>.</p> <p>FSCS steps include:</p> <ul> <li>Short vegetation mask that includes all pixels mapped as mosaic, shrubland, grassland, and sparse vegetation in at least one year from 1993 to 2021 according to <a href="https://www.esa-landcover-cci.org/">ESA/CCI global land cover</a> (<code>gpw_short.veg.mask_esacci.lc_p_1km_s_19920101_20201231_go_epsg.3857_v1.tif</code>),</li> <li>87 input raster layers (including vegetation indices, terrain, land temperature, climate and water variable),</li> <li>Principal Components Analysis (PCA) using all input layers,</li> <li>Selection of the 10 first components (explaining 75% of variance),</li> <li> K-Means with 10,000 clusters (targeted number of samples - <br><code>gpw_grassland_fscs.kmeans.cluster_c_1km_20000101_20221231_go_epsg.3857_v1.tif</code>)</li> <li>Calculation of euclidean distance (in the principal component space) of all 1-km pixels to the centre of each cluster,</li> <li>Selection of the pixel with the shortest distance for each cluster,</li> <li>Conversion of the selected pixels into sample tiles ()</li> </ul> <p>The file <code>gpw_grassland_fscs_tile.samples_1km_20000101_20221231_go_epsg.3857_v1.gpkg</code> provides the sample tiles and include the follow collumns:</p> <ul> <li><strong>X</strong>: Latitude in Web Mercator projection (EPSG:3857),</li> <li><strong>Y</strong>: Longitude in Web Mercator projection (EPSG:3857),</li> <li><strong>cluster_id</strong>: K-Means output ranging from 0—9999,</li> <li><strong>cluster_distance</strong>: Distance from the selected sample to the centre of the cluster,</li> <li><strong>cluster_size</strong>: Number o 1-km pixels inside the K-Means cluster, estimated using Web Mercator projection (<a href="https://epsg.io/3857">EPSG:3857</a>)</li> <li><strong>cluster_size_equal_area</strong>: Number o 1-km pixels inside the K-Means cluster, estimated using Goode Homolosine Land projection (<a href="https://epsg.io/54052">ESRI:54052</a>)</li> <li><strong>cluster_size_corr</strong>: Correction factor to adjust the area distortion due to Web Mercator projection, estimated by the difference in normalized propotional values of cluster_size and cluster_size_equal_area.</li> <li><strong>rf_n_pred</strong>: Number of pixels predicted by a RF model trained to estimate probability to select the pixel closer to the centre of the KMeans cluster. The RF models were trained individually per each cluster using the 10 first components derived by PCA (<code>gpw_comps_fscs.pca_m_1km_20000101_20221231_go_epsg.3857_v1.tar.gz</code>).</li> <li><strong>rf_samp_prob</strong>: Sampling probability based on RF model (<em>rf_n_pred / cluster_size</em>)</li> <li><strong>rf_samp_wei</strong>: Sampling weight estimated in Web Mercator projection.</li> <li><strong>rf_samp_wei_coor</strong>: Corrected sampling weight estimated in Goode Homolosine Land projection.</li> </ul> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="https://zenodo.org/records/13890400">2000-2002</a> <a href="https://zenodo.org/records/13890402">2003-2005</a> <a href="https://zenodo.org/records/13890404">2006-2008</a> <a href="https://zenodo.org/records/13890408">2009-2011</a> <a href="https://zenodo.org/records/13890410">2012-2014</a> <a href="https://zenodo.org/records/13890412">2015-2017</a> <a href="https://zenodo.org/records/13890414">2018-2020</a> <a href="https://zenodo.org/records/13890416">2021-2022</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2022 (All URLs)</a></li> <li><strong>Grassland reference samples based on VHR imagery (2000–2022):</strong><br><a href="https://doi.org/10.5281/zenodo.11281157">GeoPackage files</a></li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <h3>Support</h3> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></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>
Global hydrology and water quality data from 1980-2019, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<p>Global ~10km (5 arcmin) output data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual and monthly temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Discharge (m3 s-1)</li> <li>Channel storage (m3) </li> <li>Water temperature (K)</li> <li>Total dissolved solids (TDS) load (g s-1)</li> <li>Biological oxygen demand (BOD) load (g s-1)</li> <li>Fecal coliform (FC) load (million cfu s-1)</li> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Note. a minimum discharge threshold of 0.1 m3 s-1 was used when computing salinity (TDS), organic (BOD) and pathogen (FC) concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Thus, if the the average discharge for the month was below 0.1 m3 s-1, concentrations are not calculated (assigned as NA).</p> <p>In-stream water quality aggregated to 0.5 degree (i.e. 30 arcmin) spatial resolution (daily, monthly and annual) can be found at: <a href="https://zenodo.org/records/14675270">https://zenodo.org/records/14675270</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.