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2,113 results for “Very High Resolution”
High-resolution figures of Braig et al. 2023
<p>High-resolution figures of Braig et al. 2023 "The diversity of larvae with multi-toothed stylets from about 100 million years ago illuminates the early diversification of antlion-like lacewings" in Diversity (MDPI)</p>
Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data
<p>This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023).</p> <p>First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized.</p> <ul> <li><strong>Cl Concentrations - Wang et al. (2021).zip:</strong> Original Cl concentrations field from Wang et al. (2021). </li> <li><strong>CH4 Fluxes - Saunois et al. (2020).zip: </strong>Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).</li> </ul> <p>Second, we store the processed input data generated for the purpose of our study.</p> <ul> <li><strong>CH4 Fluxes - LMDz9696.zip:</strong> Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>d13C Signatures - LMDz9696.zip:</strong> δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>dD Signatures - LMDz9696.zip:</strong> δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>OH O1D Concentrations - LMDz9696-INCA.zip:</strong> OH and O1D monthly concentrations simulated with LMDz-INCA.</li> <li><strong>Masks regions.zip</strong>: Masks for the regions used for the input data and the analysis.</li> </ul> <p> </p>
Sea ice and wave breaking patterns during the Terra Nova Bay Polynya event on September 19, 2019, based on very high resolution satellite imagery
<p>Results of the WorldView-2 (WV2) panchromatic image analysis, described in Herman and Bradtke 2023 "Fetch-limited, strongly forced wind waves in waters with frazil and grease ice — spectral modelling and satellite observations in an Antarctic coastal polynya". The image, which cover part of the polynya in Terra Nova Bay, was taken on September 19, 2019 at 21:22 UTC. The spatial resolution of source data is 0.5 m.</p> <p>The zip file contains:</p> <ol> <li> “AOI” folder with the analyzed area outline in SHAPEFILE format</li> <li>“whitecaps” folder with breaking wave signatures (polygons) in SHAPEFILE format</li> <li>Sea ice (1) - water (2) mask in GeoTIFF format (0 means NoData)</li> </ol> <p>Data are provided in WGS 1984 / UTM Zone 58S projection (EPSG:32758)</p>
Data from: Solanum pennellii (LA5240) backcross inbred lines (BILs) for high resolution mapping in tomato
<p>Wild species are an invaluable source of new traits for crop improvement. Over the years the tomato community bred cultivated lines that carry introgressions from different species of the tomato tribe to facilitate trait discovery and mapping. The next phase in such projects is to find the genes that drive the identified phenotypes. This can be achieved by genotyping a few thousand individuals resulting in fine-mapping that can potentially identify the causative gene. To couple trait discovery and fine mapping we are presenting large, recombination-rich, Backcross Inbred Line (BIL) populations involving an unexplored accession of the wild, green-fruited species Solanum pennellii (LA5240; the Lost Accession) with two modern tomato inbreds: LEA, determinate, and TOP indeterminate. The LEA and TOP BILs are in BC2F6-8 generation and include 1,400 and 500 lines respectively. The BILs were genotyped with ~5,000 SPET markers, showing that in the euchromatic regions there was one recombinant every 17-18 Kb while in the heterochromatin a recombinant every 600-700 Kb (TOP and LEA respectively). To gain perspective on the topography of recombination we compared five independent members of the self-pruning gene family with their respective neighboring genes; based on PCR markers, in all cases we found recombinants. Further mapping analysis of two known morphological mutations that segregated in the BILs (Self-pruning and Hair), showed that the maximal delimited intervals were 73 Kb and 210 Kb respectively and included the known causative genes. The LOST_BILs provide a solid framework to study traits derived from a tolerant wild tomato.</p>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151</p>
High resolution land cover 2017 Ile-de-France
<p><strong>High resoultion land cover for the Ile-de-France, 2017.</strong></p> <p>The rasters are mostly based on vector files, which were rasterised, merged and resampled to 5m. The encoded values represent the different thematic classes in the dataset. </p> <p>For ease of processing and computation, all datasets were tiled and are provided as a single continuous raster. This map shows the extents of the datasets “Hauteur vegetation” and “cadastre vert”, which were only available for a limited region within the Ile- de- France region. </p> <p></p> <p>The datasets used in the production of this map were MosPlus 2017-81, Cadastre Verte, Copernicus Small and Woody features, Copernicus Street tree layer, Hauteur vegetation and the densibati dataset. </p> <p> </p> <p>Class Codec </p> <table> <tbody> <tr> <td> <p><strong>Class</strong><strong> </strong></p> </td> <td> <p><strong>NumCodec</strong><strong> </strong></p> <p><strong>8bit compatible</strong><strong> </strong></p> </td> </tr> <tr> <td> <p>Building<strong> </strong></p> </td> <td> <p>10 </p> </td> </tr> <tr> <td> <p>Built parcel<strong> </strong></p> </td> <td> <p>19 </p> </td> </tr> <tr> <td> <p>Mineral surface<strong> </strong></p> </td> <td> <p>21 </p> </td> </tr> <tr> <td> <p>Bare soil<strong> </strong></p> </td> <td> <p>22 </p> </td> </tr> <tr> <td> <p>Grass<strong> </strong></p> </td> <td> <p>31 </p> </td> </tr> <tr> <td> <p>Shrub<strong> </strong></p> </td> <td> <p>40 </p> </td> </tr> <tr> <td> <p>Shrub round<strong> </strong></p> </td> <td> <p>41 </p> </td> </tr> <tr> <td> <p>Shrub linear<strong> </strong></p> </td> <td> <p>42 </p> </td> </tr> <tr> <td> <p>Tree<strong> </strong></p> </td> <td> <p>50 </p> </td> </tr> <tr> <td> <p>Water<strong> </strong></p> </td> <td> <p>60 </p> </td> </tr> <tr> <td> <p>Lake<strong> </strong></p> </td> <td> <p>61 </p> </td> </tr> <tr> <td> <p>River<strong> </strong></p> </td> <td> <p>62 </p> </td> </tr> <tr> <td> <p>Agriculture<strong> </strong></p> </td> <td> <p>80/81</p> </td> </tr> <tr> <td> <p>NonAOI/ unclassified<strong> </strong></p> </td> <td> <p>99 /0 </p> </td> </tr> </tbody> </table> <p>Data sources </p> <p>European Union's Copernicus Land Monitoring Service information (2018), Small Woody Features 2018,<a href="https://doi.org/10.2909/7fd9d32e-8c2f-42b2-b959-c8e12b843821" target="_blank" rel="noopener">https://doi.org/10.2909/7fd9d32e-8c2f-42b2-b959-c8e12b843821</a> </p> <p>European Union's Copernicus Land Monitoring Service information (2018), Urban Atlas Street Tree Layer 2018, <a href="https://doi.org/10.2909/205691b3-7ae9-41dd-abf1-1fbf60d72c8c" target="_blank" rel="noopener">https://doi.org/10.2909/205691b3-7ae9-41dd-abf1-1fbf60d72c8c</a> </p> <p>Atelier Parisien d'Urbanisme 2017 HAUTEUR VEGETATION 2015 </p> <p><a href="https://opendata.apur.org/datasets/Apur::hauteur-vegetation-2015/about" target="_blank" rel="noopener">https://opendata.apur.org/datasets/Apur::hauteur-vegetation-2015/about</a> </p> <p>Département des Hauts-de-Seine (2012) Cadastre vert - Masses vertes. https://opendata.hauts-de-seine.fr/explore/dataset/cadastre-vert-masses-vertes/information/?disjunctive.commune&basemap=mapbox.streets-satellite&location=11,48.83991,2.24091 </p> <p>L'Institut Paris Region (2017) Mode d'occupation du sol (MOS) 2017 a 81 postes. <a href="https://www.institutparisregion.fr/vente-de-donnees/" target="_blank" rel="noopener">https://www.institutparisregion.fr/vente-de-donnees/</a> </p> <p>L'Institut Paris Region (2018) Densibati 2018. <a href="https://www.institutparisregion.fr/vente-de-donnees/" target="_blank" rel="noopener">https://www.institutparisregion.fr/vente-de-donnees/</a> </p>
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>
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>
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>
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>
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.
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.
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.