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748 results for “surface temperature”
The infrared instrument for sea surface temperature (iriss): An innovative and simplified design for measuring ocean surface skin temperature
Open the record for dataset details and reuse information.
Data from: Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity
Open the record for dataset details and reuse information.
GLASS Land Surface Temperature product (1981-2000): Orbital Drift Corrected LST
<ul> <li>GLASS Land Surface Temperature product (1981-2000): the ODC LST product is an orbital drift corrected (ODC) version of the instantaneous GLASS LST product. To compensate the effect of orbital drift on LST, an improved ODC method was used to normalize the instantaneous GLASS LSTs to 14:30 solar time.</li> <li>The dataset is organized by year and a sample data is provided in simple.zip</li> <li>Further details can be found in the readme.pdf.</li> </ul>
GLASS Land Surface Temperature product (1981-2000): monthly averaged LST
<ul> <li>The LST product contains monthly averages of GLASS ODC LST.</li> <li>The dataset is organized by year and a sample data is provided in simple.zip</li> <li>Further details can be found in the readme.pdf.</li> </ul>
Changes in dive patterns of leatherback turtles with sea surface temperature and potential foraging habitats
<p>Marine mesotherms are able to occupy broader thermal niches than ectotherms; however, this means they must exhibit greater tolerance to diverse environmental conditions across the ocean. Knowledge remains limited about how differences in environmental conditions within occupied habitats affect the bioenergetics of mesotherms and associated ecological traits. Here, we report that leatherback turtles (<i>Dermochelys coriacea</i>) migrating across the North Pacific changed their dive behavior regionally, possibly in response to changes in sea surface temperature and prey abundance. Our results demonstrate that dives became deeper when the surface water was warmer, presumably because leatherbacks dive to deep cold waters to avoid overheating. Moreover, the patterns of presumed foraging dives indicate that leatherbacks engage in behavioral thermoregulation in warmer foraging regions, which perhaps limit the time available for foraging activity. In contrast, mesothermy allows leatherbacks to spend more time foraging in cool-temperate regions. However, it might not produce greater reproductive output due to additional migration cost to these areas, which are more distant from their nesting beaches. Our results highlight that mesothermy might not provide a direct fitness advantage to all individual leatherback turtles; rather, it affords a species-level fitness advantage by allowing a greater diversity of habitats to be utilized.</p>
Summer land surface temperature from MODIS Aqua and Terra satellites for Houston in 2014 and Phoenix in 2003 at 1km resolution
<p>Satellite remote-sensing is used to collect important atmospheric and geophysical data at various spatial resolutions, providing insight into spatiotemporal surface and climate variability globally. These observations are often plagued with missing spatial and temporal information of Earth's surface due to (1) cloud cover at the time of a satellite passing and (2) infrequent passing of polar-orbiting satellites. While many methods are available to model missing data in space and time, in the case of land surface temperature (LST) from thermal infrared remote sensing, these approaches generally ignore the temporal pattern called the 'diurnal cycle' which physically constrains temperatures to peak in the early afternoon and reach a minimum at sunrise. In order to infill an LST dataset, we parameterize the diurnal cycle into a functional form with unknown spatiotemporal parameters. Using multiresolution spatial basis functions, we estimate these parameters from sparse satellite observations to reconstruct an LST field with continuous spatial and temporal distributions. These estimations may then be used to better inform scientists of spatiotemporal thermal patterns over relatively complex domains. The methodology is demonstrated using data collected by MODIS on NASA's Aqua and Terra satellites over both Houston, TX and Phoenix, AZ USA.</p>
Out-of-phase Decadal Change in Drought over Northeast China between Early Spring and Late Summer around 2000 and Its Linkage to the Atlantic Sea Surface Temperature
<p>This file is for the upload of CN05.1 data for 2020JD034048R.</p>
Data from: Modeling seasonal surface temperature variations in secondary tropical dry forests
Secondary tropical dry forests (TDFs) provide important ecosystem services such as carbon sequestration, biodiversity conservation, and nutrient cycle regulation. However, their biogeophysical processes at the canopy-atmosphere interface remain unknown, limiting our understanding of how this endangered ecosystem influences, and responds to the ongoing global warming. To facilitate future development of conservation policies, this study characterized the seasonal land surface temperature (LST) behavior of three successional stages (early, intermediate, and late) of a TDF, at the Santa Rosa National Park (SRNP), Costa Rica. A total of 38 Landsat-8 Thermal Infrared Sensor (TIRS) data and the Surface Reflectance (SR) product were utilized to model LST time series from July 2013 to July 2016 using a radiative transfer equation (RTE) algorithm. We further related the LST time series to seven vegetation indices which reflect different properties of TDFs, and soil moisture data obtained from a Wireless Sensor Network (WSN). Results showed that the LST in the dry season was 15–20 K higher than in the wet season at SRNP. We found that the early successional stages were about 6–8 K warmer than the intermediate successional stages and were 9–10 K warmer than the late successional stages in the middle of the dry season; meanwhile, a minimum LST difference (0–1 K) was observed at the end of the wet season. Leaf phenology and canopy architecture explained most LST variations in both dry and wet seasons. However, our analysis revealed that it is precipitation that ultimately determines the LST variations through both biogeochemical (leaf phenology) and biogeophysical processes (evapotranspiration) of the plants. Results of this study could help physiological modeling studies in secondary TDFs.
Data from: The relationship between geographic range extent, sea surface temperature and adult traits in coastal temperate fishes
Aim: We use publicly available data to assess the influence of ocean basin, various biological traits and sea surface temperature on biogeographic range extent for temperate, continental shelf fish species spanning 141 families. Location: Coastal waters of the temperate Northern Hemisphere. Taxon: Teleost Fishes (Infraclass Teleostei). Methods: We assess the relationship between species range extent and depth range, maximum body length, schooling behaviour and use of multiple habitats for 1,251 species of northern, temperate, continental shelf fishes in different basins (Atlantic vs. Pacific) and margins (east vs. west) using linear mixed‐effect models with family and genus as nested random effects. We further assess the relationship between species range endpoint distribution and latitudinal temperature gradient using generalized linear models. Results: We found strong positive relationships between the number of species northern range endpoints and the steepness of the latitudinal sea surface temperature gradient on the western margins of the Atlantic and Pacific Oceans, but no relationship on the eastern margins of these ocean basins. The strongest predictors of range extent in our global model are ocean basin/margin and depth range. Maximum body length, schooling behaviour and use of multiple habitats are also significant predictors of range extent in the global model. The factors influencing range extent differ by basin and margin. Main conclusions: There are broad differences in patterns of species range extent and distribution of species ranges among basins/margins. These differences appear to be driven in part by variation in latitudinal water temperature gradient between basin margins. Our data suggest that sharp latitudinal temperature gradients may pose a barrier to dispersal and range expansion along the western margins of the Atlantic and Pacific Oceans, but not necessarily on the eastern margins. Our work also suggests that several post‐settlement traits may be associated with range extent either globally or in some temperate basins.
Alkenone carbon isotopic fractionation and sea surface temperature trends from 30 to 16 Ma. Sites IODP 1406, ODP 1168 and ODP 925
Open the record for dataset details and reuse information.
Equatorial waves for: A prediction attempt using equatorial waves for tropical sea surface temperature anomaly by Atlantic zonal mode
<p>The dataset is the wave-induced geopotential output from linear ocean models and potential energy flux by a group-velocity-based wave energy flux scheme in the period (1992–2016), which is involved in building a lightweight model, as well as showing a simple instance of utilizing the wave energy transfer for the prediction of Atlantic Niño/Niñas.</p>
Continuous snow temperature profiles from the Snow Ice Mass Balance Apparatus (SIMBA) (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), November 2022-June 2023
<p>Raw (Level 1) measurements from the Snow Ice Mass Balance Apparatus (SIMBA) deployed at the Avery Picnic site (~ 38°58.345' N, 106°59.811' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from November 2021 through June 2023. The SIMBA, originally designed for observing the mass balance of sea ice, is comprised of a thermistor chain with 2 cm spacing (Jackson et al., 2013). This system was configured for terrestrial snowpack by the manufacturer, SAMS Enterprise, to the specifications for SPLASH. The chain was installed suspended from a tripod and fixed to a rigid plastic bar near in time to the onset of snowpack in November 2022. The lowest 10 cm of the chain were buried within the soil. The top of the chain reached approximately 180 cm above the soil surface and snow was permitted to accumulate around the chain throughout the winter of 2022-2023. In the files, negative values of the "height" vector are below the soil surface and positive levels are above, which may be either snow or air depending on the snow depth. The system also uses a low-power heating cycle to measure thermistor's temperature response time for aiding in determining material interfaces: see Jackson et al. (2013) for details. </p><p>There are several cautions to be aware of when using these data. The data has been ingested into daily netCDF and metadata (in attributes) have been provided but no quality control has been carried out on this raw version of the data set. From 1 November through 22 December 2022, the sensor obtained profiles every 10 min after which corruption of the configuration file reverted the profiles to every 6 hours (0, 6, 12, and 18 UTC). After 1 January a problem in the firmware caused the system to lose connection to the time-synching GPS network and therefore the clock drifted from January through June 2023 (the maximum potential time stamping error is likely < 81 sec). Finally, from 23 March through 4 April 2023, the depth of the snow at the location of the sensor was deeper than 180 cm and thus measurements in the upper part of the snowpack were not observed then.</p><p>Jackson, K., J. Wilkinson, T. Maksym, D. Meldrum, J. Beckers, C. Haas, and D. Mackenzie (2013) A novel and low-cost sea ice mass balance buoy. Journal of Atmosphere and Oceanic Technology, 30(11), 2676-2688, https://doi.org/10.1175/JTECH-D-13-00058.1.</p>
All-weather 1km land surface temperature at global scale from 2000-2020 from MODIS data
<p>All-weatherLand Surface Temperature product (2000-2020): LSTs from Moderate Resolution Imaging Spectroradiometer(MODIS)/Terra have been produced. The LST data were generated by integrating multiple data from MODIS, reanalysis, and ground in situ measurements using meachine learning method. </p> <ul> <li>The dataset is organized by year.</li> <li>The data is stored in tif format.</li> </ul>
Databases generated for Manuscript titled "Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures"
<p>Databases generated for manuscript "<strong>Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures</strong>"</p> <p>There are two zip files containing generated databases:</p> <p>The zip file titled "database.zip" contains generated database for calculated fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtb.csv (e.g. 000N000Etb.csv for 0°N, 0°E). Each .csv file contains four separate columns and variable rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to Thermal Emission Spectrometer (TES) measured temperature. "Tau" corresponds to calculated Dust visible opacities using the methodology presented in this work. The rows in each file vary based on the temporal observations from TES at each location. </p> <p>The zip file titled "fitdatabase.zip" contains generated database for fitted fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtbf.csv (e.g. 000N000Etbf.csv for 0°N, 0°E). Each .csv file contains six separate columns and three hundred and sixty rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to measured temperature. The fitting algorithm interpolates points between values in the calculated flux database and applies a rolling mean fit with a window spanning ten degrees in solar longitude centered at each calculated flux point. "FLAG" indicates the amount of points of calculated flux points that exist within the ten degree window centered at each flux point to demonstrate to the user how much data had to be fitted. "From Ls" shows the leftmost edge of the rolling mean fit window. "To Ls" shows the rightmost edge of the rolling mean fit window. The rows in each file correspond to one degree of solar longitude the fitting algorithm was designed to cover each solar longitude bin. </p> <p> </p>
Consistent ground surface temperature records for the CMA stations over China for 1956–2022 via numerical simulation
<p>The ground surface represents the land-atmosphere interface and plays a crucial role in exchanging energy, matter, and biochemical fluxes. The ground surface temperature (T<sub>s</sub>) is hence widely investigated as an indicator to understand the thermal state of soil in a warming world. However, regular and continuous T<sub>s</sub> measurements are rare worldwide, and the early T<sub>s</sub> records were derived from snow surface measurements and are not comparable with the measurements of the modern automatic systems. In this dataset, we reconstructed the T<sub>s</sub> records of the China Meteorological Administration (CMA) for 1956–2022 by numerical simulation.</p>
ELITE land surface temperature: FY-4A/AGRI hourly 4km seamless LST (2023.1-2023.5)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth's radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE hourly seamless 4 km LST dataset covering the FY-4A/AGRI nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W). First, an improved temperature and emissivity separation algorithm was used to obtain the clear-sky LST. Then, under the framework of the SEB theory, a unique way was proposed to solve the temperature difference between the cloudy-sky LST and hypothetical clear-sky LST caused by cloud radiative effects. The in situ validation results show that the bias (RMSE) of the AGRI hourly seamless LST is 0.02 K (2.84 K). The temporal resolution and spatial resolution of this dataset are 1 hour and 4 km, respectively.</p> <p>This is the ELITE FY-4A/AGRI seamless LST product in 2023. Please <a href="../records/10595576"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2022.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: AGRI nominal fixed disc (80.6°N-80.6°S, 24.1°E-174.7°W)</li> <li>Temporal Coverage: 2023.1-2023.5</li> <li>Spatial Resolution: 4 km (subsatellite point)</li> <li>Temporal Resolution: one hour</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Liu, W., Cheng, J. & Wang, Q. (2023). Estimating Hourly All-Weather Land Surface Temperature From FY-4A/AGRI Imagery Using the Surface Energy Balance Theory. <em>IEEE Transactions on Geoscience and Remote Sensing, 61</em>,<em> 5001518</em></li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).</p>
ELITE land surface temperature: hourly seamless 0.02° LST over East Asia (2022.7-2022.12)
<p>The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (<a href="https://elite.bnu.edu.cn/">https://elite.bnu.edu.cn</a>).</p> <p>This dataset is the ELITE hourly seamless 0.02 ° LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ validation results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively.</p> <p>This is the seamless LST dataset in 2022.7-2022.12. Please <a href="../records/8260240"><strong><em>click here</em></strong></a> to download the ELITE LST product in 2021.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: East Asia (0-60°N, 80°E-140°E)</li> <li>Temporal Coverage: 2022.7-2022.12</li> <li>Spatial Resolution: 0.02 °</li> <li>Temporal Resolution: one hour</li> <li>Data Format: Geotiff</li> <li>Scale: 0.01</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li>Dong, S., Cheng, J., Shi, J., Shi, C., Sun, S., & Liu, W. (2022). A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data. Remote Sensing, 14, 5170</li> <li>Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7105-7124.</li> </ol> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p>
Quantifying Human Contributions to Near-Surface Temperature Inversions: Insights from COVID-19 Natural Experiments
<p>These are the processed data and code used to generate our analysis in the article: </p> <ul> <li>Zhang, Z., Wang, J., & Ge, Y. (2024). Quantifying Human Contributions to Near-Surface Temperature Inversions: Insights From COVID-19 Natural Experiments. Geophysical Research Letters, 51(6), e2023GL107964. https://doi.org/10.1029/2023GL107964</li> </ul> <p>If you have any question about our data or code used in the analysis, please contact us at zhangzy.20b@igsnrr.ac.cn</p>
Sub-Mesoscale Ocean Dynamics Experiment: Surface currents and Sea Surface Temperature
<p>Airborne observations of surface currents from Doppler Scatterometer and sea surface temperature from infrared camera.</p> <p><span>This data set is derived from the S-MODE project <span> </span><span><a title="https://urldefense.us/v3/__https:/github.com/podaac/2022-SMODE-Open-Data-Workshop__;!!PvBDto6Hs4WbVuu7!KGw6WDLV5oVZZv5MSJfZzLeZUUOOanKvBtRTARLrqCGRTLJl1FNP0nby2uQNCVA7GcoulOIL0eeT0ifzTu9iHii22cisZMe8$" href="https://urldefense.us/v3/__https:/github.com/podaac/2022-SMODE-Open-Data-Workshop__;!!PvBDto6Hs4WbVuu7!KGw6WDLV5oVZZv5MSJfZzLeZUUOOanKvBtRTARLrqCGRTLJl1FNP0nby2uQNCVA7GcoulOIL0eeT0ifzTu9iHii22cisZMe8$">https://github.com/podaac/2022-SMODE-Open-Data-Workshop</a></span>.<br></span></p> <p> </p> <p>Description of the data:</p> <p>DopplerScatt surface current spatial resolution of 200 m</p> <p>MOSES SST nominal spatial resolution of 10 m, but interpolated to DopplerScatt grid at 200 m resolution.</p> <p>Description of the name: SMODE_DScatt_and_MOSES_20221023_120546_ver_L2E_Smoothing_1km_Submesoscale_Frontogenesis.nc</p> <p>Date = 20221023_120546 (YYYY/mm/dd HH:MM:SS)</p> <p>Level of smoothing = 1 kilometer</p> <p>Submesoscale_Frontogenesis = The variables included in the file are necessary to compute frontogenesis</p> <p> </p> <p>Variables:</p> <p>SST_moses = sea surface temperature</p> <p>U_hp = high-pass east-west velocity component</p> <p>V_hp = high-pass north-south velocity component</p> <p>Tx = SST gradients in east-west direction</p> <p>Ty = SST gradients in north-south direction</p> <p>U_hp_(rot,div) = high-pass east-west velocity component rotational and divergent component</p> <p>V_hp_(rot,div) = high-pass north-south velocity component rotational and divergent component</p> <p>Fs_rot = Frontogenetic tendency due to the rotational component of the velocity field</p> <p>Fs_div = Frontogenetic tendency due to the divergent component of the velocity field</p> <p>Fs_tot = Frontogenetic tendency</p> <p>Strain_rot = Strain deformation due to the rotational component of the velocity field smoothed at 1 km</p> <p>Strain_div = Strain deformation due to the divergent component of the velocity field smoothed at 1 km</p> <p>rv_lp = relative vorticity smoothed at 1 km</p> <p>dv_lp = divergence smoothed at 1 km</p> <p>Strain_lp = strain deformation smoothed at 1 km</p> <p>Strain_normal = normal strain deformation smoothed at 1 km</p> <p>Strain_shear = shear component of the strain deformation smoothed at 1 km</p> <p> </p> <p> </p>
Clear-sky profile database for the development of Land Surface Temperature algorithms
<p>This dataset includes clear sky atmospheric profiles from the European Centre for Medium Range Forecast (ECMWF) version-5 reanalysis (ERA5), specially selected to support the development of algorithms of Land Surface Temperature (LST) retrieval from Earth observation (EO) data. The profiles were re-sampled from an ERA5 dataset covering the 2009-2019 period, with a 1x1 degree spatial resolution, hourly sampling and using the full vertical resolution (137 model levels). The re-sampling technique is based on a dissimilarity criterion applied to profiles of temperature and specific humidity, in order to obtain regular distributions of atmospheric variables of relevance for LST retrieval in the Thermal Infrared (TIR) spectral range. The database is limited to clear-sky conditions over land, being therefore suitable for the development of satellite land products relying on optical and thermal infrared imagery in general, despite targeting especially LST.</p> <p><strong>Dataset description:</strong></p> <p>The dataset is divided in multiple netCDF4 files based on the range of skin temperature (Tskin; Kelvin) and the range of total column water vapour (TCWV; mm). Each file includes the following variables:</p> <ul> <li>Time</li> <li>Longitude</li> <li>Latitude</li> <li>2-m temperature (t2m)</li> <li>Surface pressure (sp)</li> <li>Total cloud cover (tcc)</li> <li>Total column water vapour (tcwv)</li> <li>Skin temperature (skt)</li> <li>Surface emissivity (emis)</li> <li>Land cover classification (lcc)</li> <li>Temperature profile (t)</li> <li>Specific humidity profile (q)</li> <li>Ozone profile (o3)</li> <li>Pressure profile (p)</li> </ul> <p>All profiles are provided on model levels. For each profile, 6 values of skin temperature and 25 values of emissivity are provided (see publication for details). Emissivity values correspond to the wavelengths of ~11 and ~12 µm.</p> <p><strong>Credit:</strong></p> <p>To use this data please cite this dataset and the respective journal publication:</p> <p>Ermida, S.L.; Trigo, I.F. (2022) A Comprehensive Clear-Sky Database for the Development of Land Surface Temperature Algorithms. <em>Remote Sens.</em>, <em>14</em>, 2329. <a href="https://doi.org/10.3390/rs14102329">https://doi.org/10.3390/rs14102329</a> </p> <p> </p> <p><strong>Access: </strong></p> <p>Currently, Zenodo does not provide a simple way to download datasets with a large number of files. We recomend trying the <a href="https://zenodo.org/record/3676567#.YnJAxtPMJhE">Zenodo_get</a> to simplify the download.</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.