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66 results for “Multi-source”
Investigating multi-physical process and deformation mechanism of reservoir landslide using integrated multi-source monitoring
<p>Data to support this study are available.</p>
dataset for Spatial Distribution of Wildlife on University Campuses and Its Correlations with Environmental Factors: A Multi-Source Data Analysis
Open the record for dataset details and reuse information.
FIGURE 2 in Synonymy of two important crop pests of burrower bugs, Cyrtomenus mirabilis and C. bergi (Hemiptera: Cydnidae), based in a multi-source approach
FIGURE 2. Distributional map of (A) C. mirabilis and (B) C. bergi.
Improved 3D Characterization of in-situ Soil Desiccation Cracking by multi-source Data Integration
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Data from: Live, dead, and fossil mollusks in Florida freshwater springs and spring-fed rivers: taphonomic pathways and the formation of multi-sourced, time-averaged death assemblages
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MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V011
The Multi-Source Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1.1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel-2A and 2B Multispectral Instrument (MSI) provides the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy at 30m spatial resolution. These data sets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and land cover, land use, and land cover change mapping. Provided in the MSLSP product are layers for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, and maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low resolution browse image representing maximum EVI is also available for each MSLSP30NA granule.Known Issues* Data are sparse in 2016 and early 2017, as Sentinel-2B was not yet launched, and Sentinel-2A was not fully operational, leading to poorer quality retrievals of phenology in 2016 and 2017. However, poor quality pixels can be masked with Quality Assurance (QA) flags.* Disturbance has not been explicitly accounted for or mapped, which can lead to premature detections of senescence and dormancy when sharp spectral changes occur.* Pixels with more than two growth cycles per year (e.g., alfalfa fields) may not be accurately characterized, especially if they occur in rapid succession.Improvements/Changes from Previous Version* Modest changes were made to the spline fitting algorithm used to estimate the MSLSP30NA product in V011. Only gaps greater than 20 days were filled using observations from the outside of the target year to reduce the computational burden. Sensitivity analyses demonstrated that this change had negligible impact on product results. * The Quality Assurance (QA) fields were updated to reflect the changes in the gap-filling. Version 1 included QA values from 1-7, whereas Version 1.1 includes QA values from 1-6, 9, and 10.* Peak date corresponding to the maximum EVI2 value in a growth cycle (Peak and Peak_2) and number of days with clear observations in calendar year (numObS) layers were added.
NACP Greenhouse Gases Multi-Source Data Compilation, 2000-2009
This data set is a collection of measurements of carbon dioxide (CO2) and non-CO2 greenhouse gases made across North America by nine independent atmospheric monitoring networks from 2000 - 2009. During this North American Carbon Program (NACP) sponsored activity, data were compiled from the following networks: AGAGE, COBRA, CSIRO, INTEX-A, INTEX B, Irvine Latitude Network, NOAA CMDL, SCRIPPS, and Stanley Tyler-UC Irvine. The files presented here are the products of merging multiple original measurement results files for selected sites across North America from each monitoring network. The primary focus of this effort was the compilation of non-CO2 greenhouse gases over North America, but numerous CO2 observations are also included. The data files for each network are accompanied by detailed readme documentation files prepared by the respective network investigators. Project descriptions, objectives, references, sampling and analysis methods, and data file descriptions are included in these READMEs. Table 1 in the documentation displays the monitoring network sites, sample types, analytes, and links to the detailed network README files. Network- and laboratory-specific data citations are included in the README documentation and should be used to acknowledge the use of these data as appropriate. The data files for each monitoring network and each sampling type (continuous or flasks) have been combined into one compressed (*.zip) file along with the detailed README document. There are 17 compressed files that when expanded contain data files which represent one year�s data for that specific campaign and sampling method. The number of annual files that were compiled from a network into this collection varies.
MuSLI Multi-Source Land Surface Phenology Yearly North America 30 m V001
MSLSP V1 data was decommissioned on December 14, 2021. Users are encouraged to use the improved [MSLSP V1.1](https://doi.org/10.5067/Community/MuSLI/MSLSP30NA.011) data product.NASA’s Multi-Source Land Imaging (MuSLI) Land Surface Phenology (LSP) Yearly North America 30 meter (m) Version 1 product (MSLSP) provides a Land Surface Phenology product for North America derived from Harmonized Landsat Sentinel-2 (HLS) data. Data from the combined Landsat 8 Operational Land Imager (OLI) and Sentinel 2A and 2B Multispectral Instrument (MSI) provide the user community with dates of phenophase transitions, including the timing of greenup, maturity, senescence, and dormancy. MSLSP30NA is aligned with the Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system)) at 30 m spatial resolution. These datasets are useful for a wide range of applications, including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, land cover, land use, and land cover change mapping.Provided in the MSLSP product are variables for percent greenness, onset greenness dates, Enhanced Vegetative Index (EVI2) amplitude, maximum EVI2, and data quality information for up to two phenological cycles per year. For areas where the data values are missing due to cloud cover or other reasons, the data gaps are filled with good quality values from the year directly preceding or following the product year. A low-resolution browse image representing maximum EVI is also available for each MSLSP30NA granule.Known Issues* Data are sparse in 2016 and early 2017, as Sentinel-2B was not yet launched, and Sentinel-2A was not fully operational, leading to poorer quality retrievals of phenology in 2016 and 2017. However, poor quality pixels can be masked with Quality Assurance (QA) flags.* Disturbance has not been explicitly accounted for or mapped, which can lead to premature detections of senescence and dormancy when sharp spectral changes occur.* Pixels with more than two growth cycles per year (e.g., alfalfa fields) may not be accurately characterized, especially if they occur in rapid succession.
LMHLD: A Large-scale Multi-source High-resolution Landslide Dataset for Landslide Detection based on Deep Learning
<p>LMHLD collects remote sensing images of five satellite sensors in seven areas of the world: Wenchuan, China (2008); Rio de Janeiro, Brazil (2011); Gorkha, Nepal (2015); Jiuzhaigou, China (2015); Taiwan, China (2018); Hokkaido, Japan (2018); Emilia-Romagna, Italy (2023), a total of 25,365 patches by setting different patch sizes according to different landslide scale.</p> <p>Specifically, patch sizes vary across different areas: 32 for image segmentation in Emilia-Romagna, Italy and Gorkha, Nepal; 64 in Rio de Janeiro, Brazil; 128 in Jiuzhaigou, China and Hokkaido, Japan; and 224 in Wenchuan and Taiwan, China. All the above patches constitute LMHLD, a large-scale multi-source high-resolution heterogeneous landslide dataset.</p> <p><strong><em>LMHLD.rar</em></strong> includes all the Train, Validation, Test data used for the experiments.</p> <p>If you use our data, please cite our work published in <strong><em>IEEE Transactions on Geoscience and Remote Sensing</em></strong>. Or if you have any other questions about LMHLD, please contact us: <strong>guan.ting.liu2000@gmail.com</strong>.</p> <p><strong>Paper:</strong> Liu G, Wang Y, Chen X, et al. LMHLD: A Large-scale Multi-Source High-Resolution Landslide Dataset for Landslide Detection based on Deep Learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025. (<strong>DOI: </strong><a href="https://doi.org/10.1109/TGRS.2025.3619062" target="_blank" rel="noopener">10.1109/TGRS.2025.3619062</a>)</p>
Data from: Simultaneous fMRI and EEG during the multi-source interference task
Open the record for dataset details and reuse information.
FIGURE 1 in Synonymy of two important crop pests of burrower bugs, Cyrtomenus mirabilis and C. bergi (Hemiptera: Cydnidae), based in a multi-source approach
FIGURE 1. Depictions of linear measurements taken and landmarks digitized. (A–B) Measurements taken on the (A) head and (B) body. (C–F) Position of landmarks digitized (C) on the head, (D) pronotum, (E) scutellum, and (F) hemelytron. HL, head length; HW, head width; IO, interocular distance; OW, ocellar width; OE, ocelli-eye distance; PL, pronotal length; PW, pronotal width; SL, scutellar length; SW, scutellar width; AL, abdominal length. Scale bar: 1 mm.
FIGURE 4 in Synonymy of two important crop pests of burrower bugs, Cyrtomenus mirabilis and C. bergi (Hemiptera: Cydnidae), based in a multi-source approach
FIGURE 4. Variation in each morphometric character, corrected for total body length by linear regression, of C. mirabilis and C. bergi and variation in pooled specimens according to latitudinal groupings. Asterisks (*) indicate measurements with significant differences between species. OE/OW, ratio between ocelli-eye distance and ocelli width; HL, head length; HW, head width; IO, interocular distance; OW, ocellar width; OE, ocelli-eye distance; PL, pronotal length; PW, pronotal width; SL, scutellar length; SW, scutellar width.
Source data for "Fast multi-source nanophotonic simulations using augmented partial factorization"
<p>Source data for Fig. 3b-c and Fig. 5a-b</p>
Prediction Model of Treatment Efficacy for Age-related Macular Degeneration Based on Multi-source Imaging Modalities
ClinicalTrials.gov study NCT06583109. IPD Sharing: NO. Countries: 0. Publications: 0.
Enhancing reservoir water level time series in the Mekong river basin by improving area-elevation models and integrating multi-source satellite data
<p>This is a reservoir water surface area and water levels dataset including 32 major reservoirs in Mekong River basin. For all the 32 reservoirs, water levels were inverted by using improved DEM-derived A-E model (combined with actual reservoir parameters limitation), improved DEM-derived A-E model (combined with actual reservoir parameters limitation) or satellite-derived A-E model based on the Landsat-derived surface area. An initial time series was constructed based on the optimal improved A-E model according to their own altimetry data availability. Then all the altimetry water levels (if available) were merged into the initial time series to construct the final time series water levels.Altimetry water level was preferred when the date of two datasets was identical.</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2013-2014)
<div> <p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p> </div>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2017-2018)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2011-2012)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2009-2010)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p>
Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2007-2008)
<p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</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.