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Dataset results
5 results for “2001-2010”
A 1 km daily soil moisture dataset over the Qinghai-Tibet Plateau (2001-2010)
<p>Soil moisture is the key variable of water and energy cycle, but the long-term, high-resolution soil moisture data with high accuracy is still relatively lacking on the Qinghai-Tibet Plateau. Therefore, we provide the 1 km seamless daily soil moisture data over the Qinghai-Tibet Plateau during 2001-2010 (named as BTCH). Firstly, several predictors including the vegetation index (NDVI and EVI), land surface temperature (LST), evapotranspiration, precipitation, topography (DEM, aspect, slope, TWI), soil properties and three soil moisture related indices (SWCI SIWSI VSDI) were utilized. Five machine/deep learning methods including the artificial neural network (ANN), convolutional neural network (CNN), residual neural networks (ResNet), the long short-term memory network (LSTM) and XGBoost were trained for each year taking the ESA CCI soil moisture data as target. Then the Bayesian three-cornered hat method was adopted for model integration and the final dataset was generated. Evaluaion against four in-situ measurement networks shows that the BTCH dataset has relativey high accuracy both as station and network scales with mean unbiased RMSE values of 0.048 m3/m3 and 0.034 m3/m3. The dataset can be used for various regional hydrological, meteorological, ecological analysis and modeling.</p>
The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 1 (2001-2010)
<p>Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.</p>
Annual Ice Velocity of the Greenland Ice Sheet (2001-2010)
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Michigan Department of Environmental Quality, Michigan's Lake Water Quality Assessment Program (MI-LWQA), 2001-2010
The sampling methodology and program was designed to replicate the methods used historically by MI-DEQ to sample inland lakes in the 1970-1980’s. Each year, lakes were selected randomly from 7-10 major watersheds in MI. Watersheds were monitored in a 5-yr cycle in conjunctions with current MI-DEQ water quality activities. Lakes > 25 acres were chosen that had public boat access.
CMS: Fire Weather Indices for Interior Alaska, 2001-2010
This dataset provides daily fire weather indices for interior Alaska during the active fire seasons from 2001 to 2010. Data are gridded at 60-m resolution. The active fire season is defined as May 24-September 18 (days of the year 144-261) in this dataset. Fire weather is the use of meteorological parameters such as relative humidity, wind speed and direction, cloud cover, mixing heights, and soil moisture to determine whether conditions are favorable for fire growth and smoke dispersion. The six indices provided in this dataset are defined and produced following the methodology of the Canadian Forest Fire Weather Index System: Fine Fuel Moisture Code, Duff Moisture Code, Drought Code, Initial Spread Index, Buildup Index, Fire Weather Index. The dataset was developed following point source data interpolation from weather station observations.
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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.