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Climate models, weather data, oceanography, hydrology, atmospheric science, environmental monitoring
27,061 datasets
ACCESS-CCM chemistry-climate model output includes five distinct simulation runs, such as REF-C1 and SEN-C2-fODS. The AU_AADC organization processed the raw PP format files into NetCDF-4 files and IDL Savesets. The dataset was last updated on January 1, 2100.
Parks Canada monitors wetland hydrology at four basin marsh ponds in PEI National Park using in-situ water level loggers. The dataset provides five annual hydrological parameters, including mean daily water level and the Richards-Baker Index, calculated for each site based on a May 1 to April 30 water year.
NOAA's Unified Forecast System Land Data Assimilation System provides offline simulations using the Noah-MP land surface model. The system uses the Joint Effort for Data assimilation Integration (JEDI) software and requires near-surface atmospheric forcing data as input. Sample forcing and restart data are included in the data bucket.
NOAA's NClimGrid provides four climate variables—maximum, minimum, and average temperature, and precipitation—on a 5x5 kilometer grid for the Continental United States. Monthly data is available from 1895 to the present, while a derivative daily product (EpiNOAA) provides county-scale data from 1951 onward. The data is derived from the GHCN-D dataset and is updated with preliminary and final versions on defined schedules.
NOAA's Climate Data Records provide long-term, vetted information on terrestrial changes. The records are created by merging surface, atmosphere, and space-based sensor data across decades, applying modern analysis to historical and current satellite measurements. These records are maintained by NOAA's National Centers for Environmental Information (NCEI) and vetted using standards established by the National Research Council.
NOAA's authoritative climate records merge surface, atmosphere, and space-based data across decades to assess environmental change. These records consist of calibrated sensor data, such as radiances and brightness temperatures, that have been improved and quality-controlled over time. The datasets are vetted using standards established by the National Research Council to ensure scientific soundness and traceability.
100 Korean rice cultivars from three maturity groups were assessed for phenotypic plasticity of reproductive traits like panicle length and seed number. Measurements were taken in a controlled greenhouse environment and in contrasting field conditions during a dry year (2018) and a wet year (2020). The dataset was created by Joon Ki Hong and last updated on March 19, 2026.
A dataset of 100 Korean rice cultivars from three maturity groups, assessing phenotypic plasticity of key reproductive traits. Measurements were taken from a controlled greenhouse environment and two contrasting field environments from a dry year (2018) and a wet year (2020). The dataset was created by Joon Ki Hong and last updated on 2026-03-19.
Joon Ki Hong's dataset, published on figshare in March 2026, assesses phenotypic plasticity in 100 Korean rice cultivars across three maturity groups. It combines measurements from a controlled greenhouse environment with field data from a dry year (2018) and a wet year (2020). The analysis focuses on key reproductive traits like panicle length and seed number to understand environmental influences.
100 Korean rice cultivars from three maturity groups were assessed for phenotypic plasticity in key reproductive traits. Measurements combine data from a precision-controlled greenhouse and contrasting field environments from a dry year (2018) and a wet year (2020). The dataset, created by Joon Ki Hong and last updated in March 2026, uses Z-score standardization and kernel density estimation to analyze environmental influences on traits like panicle length and seed number.
Data Sheet 1 by Joon Ki Hong, last updated March 2026, contains a dataset evaluating phenotypic plasticity in 100 Korean rice cultivars across three maturity groups. Measurements of reproductive traits like panicle length and seed number were taken from a controlled greenhouse and contrasting field environments in a dry year (2018) and a wet year (2020). The analysis uses Z-score standardization and kernel density estimation to reveal environmental influences on trait expression.
A dataset from 2018, 2020, and 2023 combines measurements from a controlled greenhouse and two contrasting field environments to assess phenotypic plasticity in 100 Korean rice cultivars. The data includes key reproductive traits like panicle length and total seed number across three maturity groups, analyzed using Z-score standardization and kernel density estimation. It was created by Joon Ki Hong and last updated in March 2026.
A 2026 dataset from figshare by Joon Ki Hong assesses phenotypic plasticity in 100 Korean rice cultivars across three maturity groups. Measurements combine data from a controlled greenhouse environment and contrasting field conditions from a dry year (2018) and a wet year (2020). The analysis focuses on key reproductive traits like panicle length and seed number to understand environmental influences on yield potential.
A dataset combining measurements from a controlled greenhouse and two contrasting field environments (2018 and 2020) for 100 Korean rice cultivars. The data, created by Joon Ki Hong and last updated in March 2026, assesses plasticity in traits like panicle length and seed number across three maturity groups under varying climatic conditions.
100 Korean rice cultivars from three maturity groups were assessed for phenotypic plasticity of key reproductive traits. Measurements were taken from a controlled greenhouse environment and two contrasting field environments in a dry year (2018) and a wet year (2020). The dataset, created by Joon Ki Hong and last updated in March 2026, uses Z-score standardization and kernel density estimation to analyze trait distributions and environmental shifts.
100 Korean rice cultivars from three maturity groups were assessed for phenotypic plasticity of key reproductive traits. Data combines measurements from a precision-controlled greenhouse environment with field trials from a dry year (2018) and a wet year (2020). The dataset, created by Joon Ki Hong and last updated in March 2026, uses Z-score standardization and kernel density estimation to analyze trait distributions and environmental shifts.
Joon Ki Hong's dataset assesses phenotypic plasticity in 100 Korean rice cultivars across three maturity groups. It combines measurements from a controlled greenhouse environment with field data from a dry year (2018) and a wet year (2020). The analysis focuses on key reproductive traits like panicle length and total seed number to understand environmental influences.
Tong Wang's dataset provides panel data for 334 counties across 13 major grain-producing provinces in China from 1998 to 2022. It likely contains variables for climate metrics, grain productivity, and financial institution distribution. The data was last updated on March 19, 2026.
Cefas Historic Secchi Depth Measurements comprise 470 observations collected to study the effects of the light climate on pelagic fish. The data was gathered in discrete years: 1931, 1937, 1946-1950, and 1968. The dataset is provided by the Government Digital Service via the eu_open_data platform.
470 Secchi depth measurements collected to study the effects of the light climate on pelagic fish. The data were gathered in specific years: 1931, 1937, 1946-1950, and 1968. The dataset originates from the Government Digital Service via the eu_open_data platform.