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1,255 results for “High-resolution”
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>
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>
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>
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.
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).
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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.