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Dataset results
403 results for “satellite data”
Simulation data for the article "On the Effect of the Large Magellanic Cloud on the Orbital Poles of Milky Way Satellite Galaxies"
<p>This archive contains the data shared in the context of the "On the Effect of the Large Magellanic Cloud on the Orbital Poles of Milky Way Satellite Galaxies" article.</p> <p>In particular, it contains the initial conditions and final snapshots of our N-body simulation.</p>
Data from: Synergistic use of UAV surveys, satellite tracking data and mark-recapture to estimate abundance of elusive species
<p>Estimating population abundance is central to many ecological studies and important in conservation planning. Yet the elusive nature of many species makes estimating their abundance challenging. Abundance estimates of sea turtles, marine birds and seals are usually made when breeding adults are ashore, while life-stages spent at sea, including as juveniles, are often poorly sampled. We used a combination of high-resolution satellite tracking (Fastloc-GPS), Unmanned Aerial Vehicle (UAV) surveys and catch-mark-recapture approaches to assess abundance of immature hawksbills (Eretmochelys imbricata) and green turtles (Chelonia mydas) in a tidal lagoon of the Chagos Archipelago (Indian Ocean). We captured, marked, and released 50 turtles (48 hawksbill and 2 green turtles) prior to UAV surveys and used satellite tracking data from 27 immature turtles (25 hawksbill and 2 green turtles) to refine the estimated numbers of marked turtles available for resighting and those likely to have emigrated from the study area. We estimated a total of 339 turtles in the lagoon with a density between 265 turtles km-2 at high water and 499 turtles km-2 at low water. Of these 84% were hawksbills and 16% were green turtles. These hawksbill densities are the highest reported amongst 17 foraging sites recorded around the world, likely reflecting successful long-term protection of turtles in the Chagos Archipelago. </p>
Remaining bands of Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Remaining bands of Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Back scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Remaining bands of Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Remaining bands of Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Title: Forward scattering data of AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</strong></p> <p>Ricardo Dalagnol (ricds@hotmail.com)</p> <p> </p> <p><strong>This dataset is associated with the dataset found in the Zenodo repository below and a paper under review. Feel free to use this dataset, but please cite the repository below (while the paper is under review).</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
Data for "Cloud thinning in the mixed-phase regime as geoengineering concept" : Satellite data, ICON-LES simulations, and ECHAM-HAM simulations
<p><br> This repository contains:</p> <p>- Satellite product combination: Used to evaluate the cloud radiative effect of mixed-phase regime clouds.</p> <p>- ICON-LES simulations: Mixed-phase stratocumulus deck during M-PACE simulated with artificial droplet freezing.<br> - ref: reference.<br> - 0P1Nd:1% per hour<br> - 0P01Nd:0.1% per hour<br> - 0P001Nd:0.01% per hour</p> <p>- ECHAM-HAM 2-year simulations: Different scenarios with enhanced droplet freezing and with seeding concentrations of dust ice-nucleating particles.<br> - ori: reference<br> - ABS_1e4: 1e1 per Liter<br> - ABS_1e8: 1e5 per Liter<br> - CDNCx1e_7: 1e-4% per hour<br> - CDNCx1e_0 : 1e3% per hour</p> <p>- ECHAM-HAM 25-year simulations of Mixed-phase regime Cloud Thinning (MCT) including a mixed-layer ocean.<br> - ori: reference<br> - CDNCx1e_3: SEED simulation</p>
Mid-Atlantic Satellite Derived Shorelines, Transects, Trends, Reference Shorelines, Timeseries Data
<p>Geographic scope: Delmarva Peninsula, New Jersey Shore, Long Island (ocean side)</p> <p>Temporal range: 1984-2022 (variable timespacing)</p> <p>Satellites: L5, L7, L8, S2</p> <p>The shorelines and timeseries data were extracted with code and models available at <a href="https://github.com/mlundine/Shoreline_Extraction_GAN">https://github.com/mlundine/Shoreline_Extraction_GAN</a>.</p> <p>Each region's folder contains the extracted shorelines, the transects (200 m longshore spacing), the transects scaled to the linear trends, the reference shoreline, the csvs for each transect's cross-shore position timeseries (Region_#.csv) and the linear fit (Site_#_linear_trend.csv). The cross-shore positions here have not been corrected to tides or wave data. Feel free to experiment with corrections and/or timeseries analysis tools.</p> <p> </p> <p> </p>
NOAA Coastwatch Satellite Course (Set up an Application Model of Digital Satellite Data Simulation by Video Graphic Technology of Oceanic data Remotely Sensed of algerian coast)
<p>The goal of the course is to familiarize NOAA/university researchers, Sea Grant professionals and agency/org. partners with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research/outreach using their choice of software (NOAA ,2023)</p>
Water level of Qinghai Lake based on multi-source satellite altimetry data
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Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods - data and weights
<p>Release for "Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods", submitted to "Atmospheric Chemistry and Physics<br><br>- Train, validation and test dataset for Lippendorf, Boxberg, and Turow CNN applications.<br>- Test dataset for OCO3 SAM application.<br>- Weights and architecture of the trained CNN.</p>
Satellite and Celestial Data for Machine Learning (SCD-ML)
<p>These datasets contains information about Kosmos 2514 satellite, and can be used for machine learning. The data comes from the <a href="https://www.juntadeandalucia.es/institutodeestadisticaycartografia/" target="_blank" rel="noopener">Institute of Statistics and Cartography of Andalusia (IECA)</a>. Then there is a detailed explanation:</p> <ul> <li><em><strong>satellite_data:</strong></em> This dataset includes ephemerides (precise satellite position and velocity data) about the satellite</li> <li><em><strong>sgdp4_celestial_data</strong></em>: This dataset contains ephemerides for the Kosmos 2514 satellite, positions of various celestial bodies in the solar system, and SGDP4 predictions for the satellite's position.</li> <li><em><strong>sequential_data_smj</strong></em>: This dataset includes sequences of 10 positions for the Sun, Moon, and Jupiter.</li> <li><em><strong>sequential_data_svmmj</strong></em>: Similar to the previous dataset, this one contains sequences of 10 positions for the Sun, Venus, Moon, Mars, and Jupiter. Each sequence also spans from the initial position to the SGDP4 predicted position of the satellite.</li> </ul> <p>The last two datasets, <em><strong>sequential_data_smj </strong></em>and <em><strong>sequential_data_svmmj</strong></em>, only provide sequences that are linked to the corresponding rows in <em><strong>sgdp4_celestial_data</strong></em>. They detail 10 positions of celestial bodies over the period between the initial position and the SGDP4 predicted position of the satellite.</p>
The initial assessment of ionospheric radio occultation data of MSS-1 satellite and its applications in scintillation exploration
<p>This dataset contains the GNSS data in the first three months of MSS-1 operation.</p> <p>The <em>.pdf files are level-2 radio occultation electron density files; </em>.csv are level-2 scintillation files.</p>
Data for "Using Simulated Radiances to Understand the Limitations of Satellite Retrieved Volcanic Ash Data and the Implications for Volcanic Ash Cloud Forecasting"
<p>This location contains all of the data used in the analysis for the paper "Using Simulated Radiances to Understand the Limitations of Satellite Retrieved Volcanic Ash Data and the Implications for Volcanic Ash Cloud Forecasting" which is currently in prep.</p> <p>All of the retrieved satellite data can be seen in the retrieved_satellite_data.zip folder. the data is organised by the input ash cloud properties being simulated and the hdf files contain all of the retrieved variables where ash has been successfully detected.</p> <p>All of the output dispersion model data is available in NAME_output_data.zip. </p> <p>All of the input source data used in the dispersion model simulations (including the data from REFIR) is available in NAME_source_data_REFIR.zip.</p>
Raw in situ observational data sets for manuscript entitled "Effect of Typhoon Kalmaegi (2014) in northern South China Sea explored using Muti-platform satellite and Buoy observations data"
<p> Raw in situ observational data sets for manuscript entitled "Effect of Typhoon Kalmaegi (2014) in northern South China Sea explored using Muti-platform satellite and Buoy observations data" , which has been submitted to Progress in Oceanography for reviewing. Only data sets collected by field observations were uploaded. Other data sets can be download from the relevant open access websites (see the descriptions in the manuscript).</p>
Volcanic ash source inversion data for paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals"
<p>This dataset consists of volcanic ash source inversion data for the paper "A near-real-time method for estimating volcanic ash emissions using satellite retrievals" by Rachel E. Pelley, David J. Thomson, Helen N. Webster, Michael C. Cooke, Alistair J. Manning, Claire S. Witham and Matthew C. Hort, Atmosphere, 2021, 12, 1573, https://doi.org/10.3390/atmos12121573. Satellite retrievals, dispersion model simulations and inversion calculations are included for the eruptions of Eyjafjallajokull in 2010 and Grimsvotn in 2011.</p>
Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning"
<p>Elevation (radar freeboard and sea-level anomaly) fields from CryoSat-2, Sentinel-3A, and Sentinel-3B, over the period December 1st 2018 - April 30th 2019. These data were processed for the Arctic domain using the European Space Agency's Grid Processing on Demand (GPOD) service. Processing follows the steps outlined in Lawrence et al., 2021 (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2019.10.011" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.asr.2019.10.011</a>). These data are provided at along-track, 5 km and 50 km resolution, where gridded data follow the EASE grid definition (<a href="https://doi.org/10.3390/ijgi1010032">https://doi.org/10.3390/ijgi1010032</a>).</p> <p>These data were used to develop the open-source Python programming library GPSat (https://github.com/CPOMUCL/GPSat), which uses local Gaussian Process models to perform scalable interpolation of non-stationary satellite altimetry data. The 'Source_data.xlsx' file contains the data corresponding to figures in the published Nature Communications article 'Scalable interpolation of satellite altimetry data with probabilistic machine learning'.</p>
CSES (ZH-1) Satellite magnetic field detection data with whistlers
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Data from: Global, satellite-driven estimates of heterotrophic respiration
While heterotrophic respiration (Rh) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least observed carbon fluxes, particularly outside the mid-latitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single data-driven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a top-down fashion at monthly temporal resolution and 4x5o spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System- Flux (CMS-Flux) with an optimally-scaled gross primary productivity dataset based on satellite-observed solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially-varying carbon use efficiency retrieved in a model-data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6 ± 19.3 Pg C/yr. Sensitivity and uncertainty analyses showed that the top-down Rh are more sensitive to the choice of input GPP and NEP datasets than to the assumption of a static CUE value, with the possible exception of the wet tropics. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, with new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes, and are 30% greater overall (43.6 Pg C/yr vs. 33.4 Pg C/yr). The uncertainty ranges of both methods are comparable, except poleward of 45 degrees North, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.
Data from: Satellite tracking reveals novel migratory patterns and the importance of seamounts for endangered South Pacific humpback whales
The humpback whale population of New Caledonia appears to display a novel migratory pattern characterized by multiple directions, long migratory paths and frequent pauses over seamounts and other shallow geographical features. Using satellite-monitored radio tags, we tracked 34 whales for between 5 and 110 days, travelling between 270 and 8540 km on their southward migration from a breeding ground in southern New Caledonia. Mean migration speed was 3.53±2.22 km h−1, while movements within the breeding ground averaged 2.01±1.63 km h−1. The tag data demonstrate that seamounts play an important role as offshore habitats for this species. Whales displayed an intensive use of oceanic seamounts both in the breeding season and on migration. Seamounts probably serve multiple and important roles as breeding locations, resting areas, navigational landmarks or even supplemental feeding grounds for this species, which can be viewed as a transient component of the seamount communities. Satellite telemetry suggests that seamounts represent an overlooked cryptic habitat for the species. The frequent use by humpback whales of such remote locations has important implications for conservation and management.
ScienceDex guides
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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.