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Climate models, weather data, oceanography, hydrology, atmospheric science, environmental monitoring
28,225 datasets
Global land area daily aerosol optical depth (AOD) and PM2.5 concentration data from 2000 to 2021 at 1-km resolution. The dataset was created by Kaixu Bai at East China Normal University using an attention-reinforced tensor construction framework integrating satellite, ground monitor, and model data.
A Long-term Gap-free High-resolution Air Pollutants concentration dataset provides global daily 1-km resolution aerosol optical depth and near-surface PM2.5 concentration grids from 2000 to 2021. The dataset was created by Kaixu Bai of East China Normal University using an analytic framework integrating satellite, ground monitor, and model data. It is archived in NetCDF format with Python, MATLAB, R, and IDL codes provided for reading and visualization.
21-year-long (2000–2020) gap-free annual mean Aerosol Optical Depth (AOD), PM2.5, and PM10 concentration data with a 1-km resolution covering the land area of China. The LGHAP.v1 dataset was generated by Kaixu Bai of East China Normal University from daily gap-free AOD data using a machine learning regression model that integrated data from multiple sensors and platforms. Data is provided in NetCDF format with Python, Matlab, R, and IDL codes for reading and visualization.
22-year-long gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration data with daily 1-km resolution covering global land areas from 2000 to 2021. The dataset, LGHAP v2, was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework that integrates multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. A scene-aware ensemble learning graph attention network (SCAGAT) was developed to predict PM2.5 concentrations, particularly for regions with limited in situ measurements.
22-year-long gap-free aerosol optical depth and near-surface PM2.5 concentration data with daily 1-km resolution covering global land areas. Kaixu Bai from East China Normal University produced this dataset by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. An ensemble learning graph attention network was developed to improve PM2.5 predictions, especially for regions with limited ground measurements.
22-year-long gap-free aerosol optical depth and near-surface PM2.5 concentration data with daily 1-km resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai at East China Normal University, integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an attention-reinforced tensor construction framework.
22-year daily gap-free aerosol optical depth (AOD) and PM2.5 concentration grids at 1-km resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating satellite, ground monitor, and model data. Data is provided in NetCDF format with Python, MATLAB, R, and IDL codes for reading and visualization.
A 22-year-long global land dataset provides daily, 1-km resolution aerosol optical depth (AOD) and near-surface PM2.5 concentration grids from 2000 to 2021. Kaixu Bai from East China Normal University created it by integrating multimodal satellite, ground monitor, and numerical model data using an attention-reinforced tensor construction framework. The data is archived in NetCDF format with yearly submissions and includes Python, MATLAB, R, and IDL codes for reading and visualization.
22-year-long gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids from 2000 to 2021. The dataset provides daily 1-km resolution data covering global land areas, created by Kaixu Bai of East China Normal University. It was generated by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an attention-reinforced tensor construction framework.
LGHAP v2 is a 22-year-long gap-free dataset of aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework that integrates multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. Data are archived in NetCDF format with Python, MATLAB, R, and IDL codes provided for reading and visualization.
22-year-long daily gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids at 1-km resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework that integrates multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. Data is archived in NetCDF format with Python, MATLAB, R, and IDL codes provided for reading and visualization.
22-year-long gap-free daily PM2.5 concentration grids at 1-km resolution covering global land areas from 2000 to 2021. Kaixu Bai from East China Normal University created this dataset using an improved big earth data analytic framework integrating satellite, ground monitor, and numerical model data. The dataset is archived in NetCDF format with provided Python, MATLAB, R, and IDL codes for reading and visualization.
22-year-long daily gap-free grids of aerosol optical depth (AOD) and near-surface PM2.5 concentrations at 1-km resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating multimodal AODs and air quality measurements from satellites, ground monitors, and models. Data is archived in NetCDF format with Python, MATLAB, R, and IDL codes provided for reading and visualization.
Supplementary data and code from a 2021 Nature Climate Change study by Kewei Lyu et al. The repository contains constrained projections for upper-2000m and full-depth ocean heat content and thermosteric sea level rise for the period 2081-2100 under the SSP5-8.5 scenario. The data is used to generate figures for the paper and includes comparisons with IPCC AR6 projections.
A 1979 six-week geological cruise collected 120 successful sampling stations across the Exmouth and Wallaby Plateaus, with samples from depths of 100 to 5200 meters. The expedition successfully sampled pre-Quaternary strata, revealing Early Jurassic shelf carbonates, Middle Jurassic coal measures, and volcanic sequences beneath major unconformities. Thirty-one single-channel seismic profiles were run to guide the sampling of Cretaceous shelf sediments and Cainozoic pelagic carbonates.
Approximately 40% of Australia's groundwater is stored in fractured rock aquifers, which supply water for irrigation, town supplies, and domestic use. This dataset provides descriptive attributes for these aquifers, covering themes like geology, hydrogeology, groundwater management, and land use, with a focus on the Lachlan Orogen in New South Wales. It supports analysis of groundwater resources in a region containing significant mineral deposits like orogenic gold and porphyry copper-gold.
1964-2015 nitrogen and phosphorus water chemistry data for assessing atmospheric nutrient deposition effects on Western U.S. mountain lakes. The database contains 148,336 chemistry results from 51,048 samples across 3,602 lakes, compiled from public databases, government agencies, literature, and researchers. Jason Williams and S.G. Labou constructed this spatially-extensive database, which is transparent and reproducible with provided R code.
13 activity sectors are attributed for PM2.5 and ozone exposure metrics in the UNECE region. The data includes three different ECLIPSE v6b emission scenarios (CLE BASE, MFR-BASE, SDS-MFR) used in a submitted journal publication. Claudio A. Belis of the European Commission authored the associated research paper.
32.5 t/ha of standing biomass characterizes the Acacia woodland monitored by this flux station 170 km north of Alice Springs. The dataset contains processed, gap-filled flux tower measurements of heat, water vapor, and carbon exchange, partitioned into Gross Primary Productivity and Ecosystem Respiration. Data were processed using PyFluxPro (v3.4.17) and include supplementary radiation, precipitation, and soil measurements from above and below the canopy.
English river basin districts and the Severn RBD contain groundwater bodies assessed for risk of failing Water Framework Directive objectives or deteriorating from current status. The Environment Agency produced this risk assessment data to support cycle 2 river basin management plans. This dataset was previously covered by identifiers AFA400 and AFA401.