Find research datasets worth reusing

opencc-by-4.0Dec 2020View details →

Data: The Role of Urban Trees in Reducing Land Surface Temperatures in European Cities

Data on the LST differences between urban fabric, urban trees and urban green spaces for each city and the LST differences between urban fabric, rural forests and rural pastures (for hot days and JJA (June, July and August) average). In addition, estimates of the evapotranspiration of forests and pastures of each city and albedo estimates of urban fabric and forests are provided. The description of the column names is provided in the readme file.

opencc-by-4.0Aug 2022View details →

Global Hourly, 5-km, All-sky Land Surface Temperature (GHA-LST) from 2011 - now

GHA-LST is a global, hourly, 5-km, all-sky, gap-free, and all-weather land surface temperature (LST) dataset. The manuscript describing this dataset has been accepted by Earth System Science Data (ESSD) (<a href="https://doi.org/10.5194/essd-15-869-2023" target="_new" rel="noopener">https://doi.org/10.5194/essd-15-869-2023</a>). Due to storage limitations on Zenodo, the full dataset is available at <a href="http://glass.umd.edu/allsky_LST/GHA-LST" target="_new" rel="noopener">glass.umd.edu/allsky_LST/GHA-LST</a>. The dataset is updated annually. For further details, please contact Dr. Aolin Jia at <a rel="noopener">aolin@terpmail.umd.edu</a>.

opencc-by-4.0Oct 2023View details →

Ground surface temperature data 2007-2021 at different sites of the PERMATHERMAL monitoring network in Livingston and Deception Islands, SouthShetland Archipelago, Antarctica.

Ground Surface Temperature (GST) corrected data adquired between 2007 and 2021 at different stations of the PERMATHERMAL monitoring network at Livingston and Deception Islands, South Shetland Archipelago, Antarctica. (To be completed)

opencc-by-4.0Sep 2023View details →

Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"

Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble". 1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>). 2. data_txt.tar contains the results used to obtain all the figures given in the article.

edi44/100

Surface and bottom hourly water temperature from the San Francisco Estuary, 2012-2019

Projected temperature increases due to global climate change are likely to have localized impacts on the San Francisco Estuary (SFE). Increased water temperature in the SFE will lead to challenges for managing water resources. Many native species, such as salmon and smelt, rely on cooler water, and will be further stressed by increased water temperature, which may cause them to seek microrefugia. While several state and federal agencies in the SFE collect real-time water temperature data, most of the water temperature collection sites are at a fixed location or floating near the surface of the water column. This dataset includes four real-time water quality stations that provide water temperature data for both the surface and bottom positions in the water column. We obtained surface water temperature data from an integrated hourly water temperature dataset (https://doi.org/10.6073/pasta/7385985f68b02c0deb2a9e425a9f3ad8). This dataset included data downloaded from the California Data Exchange Center (CDEC; https://cdec.water.ca.gov/) and cleaned with a series of quality control (QC) checks (see integrated dataset metadata). We obtained bottom temperature data from the California Department of Water Resources (DWR) internal database Water Quality Portal (WQP). Data were integrated and standardized to hourly water temperature data in degrees Celsius, and the same series of quality control (QC) checks from the surface dataset were applied in a consistent manner to all stations. Bottom temperatures were selected from surface temperatures to provide measures of temperature difference. Datasets included in this package include source hourly surface and bottom data, both obtained from DWR’s WQP, as well as an integrated dataset of cleaned hourly surface and bottom data, with calculated surface-bottom temperature differences. Both datasets are filtered to the timeframe used in an analysis of surface-bottom temperature differences. Additionally, information regarding current

openCC (other)Dec 2021View details →

edi44/100

PIE LTER 15-minute surface water dissolved oxygen, temperature, and salinity of six high marsh ponds, Rowley, MA, during the summer of 2016.

We estimated the oxygen metabolism of six ponds in three regions of the PIE-LTER marshes during summer 2016. The goal was to assess whether pond metaoblism rates varied predictably with pond dimensions (e.g., surface area, volume) or geographic attributes (e.g., elevation, distance from upland, marsh region). Sensors recording dissolved oxygen (DO), temperature, and salinity were deployed at mid-depth and rotated between the six ponds through the June - August study period. Metaoblism rates were calcluated based on a free-water diel oxygen approach.

openCC (other)Jan 2020View details →

opencc-by-4.0Nov 2019View details →

Reconstructed remote sensing land surface temperature data in North America in 2002-2018

In order to more accurately study the change trend of land surface temperature in North America in recent years, we combined remote sensing and meteorological station data and used various restoration models to generate more accurate and more complete remote sensing land surface temperature data. Our data covered the North American continent from 2002 to 2018, with a spatial resolution of 0.05°×0.05°. In order to facilitate the statistics of the data, we set the projection mode of the data as World_Cylindrical_Equal_Area. We collated the data from different time dimensions, including month, season and year.

opencc-by-4.0Jun 2021View details →

GFDL CM2.1 Partially-Coupled Simulations Data for "Understanding Lead Times of Warm-Water-Volumes to ENSO Sea Surface Temperature Anomalies"

GFDL CM2.1 partially-coupled idealized simulations: Two sets of idealized experiments with prescribed EP and CP ENSO SST anomaly patterns. Each set of experiments has a prescribed idealized sinusoidal ENSO oscillation with periodicities of 48, 36, and 24 months, respectively. For the details please refer to our paper; Zhao, S., Jin, F.-F., & Stuecker, M. F. (2021). Understanding Lead Times of Warm Water Volumes to ENSO Sea Surface Temperature Anomalies. Geophysical Research Letters, 48(19), e2021GL094366. <a href="https://doi.org/10.1029/2021GL094366">https://doi.org/10.1029/2021GL094366</a>

opencc-by-4.0Jun 2021View details →

Raw and analysed data for contribution paper "Low temperature plasma deoxidation of copper surfaces"

Abstract: In this study, the application of a DBD plasma for metal deoxidation was shown on differently oxidized copper surfaces in an Ar/H2 atmosphere at 100 hPa and room temperature. Plasma treatments with a discharge voltage of 11 kV and a frequency of 8.8 kHz yielded an almost complete deoxidation of samples with native Cu2O layers and samples with pre-oxidized CuO layers, both within minutes of the Ar/H2 plasma treatment. The chemical state of the samples was characterized via X-ray photoelectron spectroscopy (XPS). The plasma was analysed by optical emission spectroscopy (OES). Additionally, confocal laser scanning microscopy (CLSM) images show that the employed deoxidation method did not change the morphology of the copper surfaces.

opencc-by-4.0Nov 2014View details →

Time projections of Sea Surface Temperature, for RCP 4.5 and RCP 8.5, for decades 2020, 2030, 2040 and 2040

The CMIP5 Sea Surface Temperature models projections correspond to the Coupled atmosphere-ocean general circulation models’ output named ‘tos’ (Temperature Of Surface) with a monthly time-step (12 values per year, from 2006 to 2100), for RCP 4.5 and RCP 8.5. For a given RCP, some models can have different sets of input parameters (called input ensemble), numbered r1i1p1, r1i1p2, etc., corresponding to different settings, resulting is an output for each rXiYpZ input. Variable ‘tos’ is provided by 86 combinations of models and input ensembles (see list in Annex). To compute an ensemble mean with equal weight for each model, the different outputs of a single model are first averaged. The resulting averaged models outputs, 1 average per model, are then regridded to a common grid, defined as a regular grid,  with a spatial resolution of ½ ° in latitude per ½ ° in longitude, from 0° to 360° in longitude, and -85° to 85° in latitude. Then, the regridded averages are averaged all together with the same weight. The averaging operations are grid-cell and time independent, which means that the averaging operator is not applied along the space and time dimensions, only in-between the different models values for the same place and time. The result of the operation is a time series of ocean surface temperature, from 2020 to 2050, at a grid resolution of 0.5°. Because of the difference in the spatial gridding, and difference in the land mass representation, some grid points did not used the same number of models averages to compute the final average: the number of model averages per grid cell is given in the final product, as well as the min-max amplitude between model averages.

opencc-by-4.0Sep 2022View details →

sea-surface temperature proxy data (TEX86 and UK'37) from Ocean Drilling Program Site 1168

These 2 data files contain the TEX86 and UK'37 sea surface temperature proxy data from Ocean Drilling Program Site 1168, covering the Eocene to recent (35–0 Ma). These were updated compared to previous versions, wherein some alkenone data was omitted.

Effects of Tide-Induced Mixing on the Surface Temperature Gradients Between the Equator and Poles During the Middle Miocene Climate Optimum -- Dataset

The files contain the data related to the figures in this paper.-- Fig.1 The topographic roughness of the PI and MMCO before and after reconstruction-- Fig.2 The 300-year time series of the annual mean SAT and SST-- Fig.3 The data of SSH for PI_TF experiment-- Fig.4 The tidal dissipation and mixing for MMCO_TM, and the ocean vertical mixing-- Fig.5 The annual mean SAT and SST for the MMCO_TM and MMCO_noTM-- Fig.6 The global meridional heat transport for the MMCO_TM and MMCO_noTM-- Fig.7 The net sea surface heat flux for the MMCO_TM and MMCO_noTM-- Fig.8 The GMOC and AMOC for the MMCO_TM and MMCO_noTM

SST forcing files and Model Builds for "Inter-basin versus intra-basin sea surface temperature forcing of the Western North Pacific subtropical high's westward extensions"

This repository provides archives of the Community Earth System Model version 2.2.0 (CESM2.2.0) and case directories for the simulations used in the "Inter-basin versus intra-basin sea surface temperature forcing of the Western North Pacific subtropical high's westward extensions" manuscript. The repository includes:<ul><li>The original sea surface temperature forcing files used in each experiment (SST_Forcing Files) </li><li>The F2000CLIMO compset model builds forced for each experiment </li></ul>

Climatological global-mean Sea Surface Temperature (SST) in AWI-CM-1-1-MR simulations for CMIP6, in preindustrial, present-day, +2°C, +3°C, and +4°C climates

Daily climatologies of global-mean sea surface temperature (SST, parameter 'tos') free-running simulations performed using the coupled climate models AWI-CM-1-1-MR. The unstructured grid-ocean component FESOM was conservatively remapped to the ERA5 grid. Data was averaged across the 5 ensemble members and temporally averaged over 10-year long time periods: 1850-1859 for preindustrial climate, 2015-2024 for present-day, 2034-2043 for +2°C climate, 2061-2079 for +3°C climate, and 2091-2100 for +4°C climate. Data is provided in .nc files, one for each climate.

NOAA PSL Soil Moisture and Surface Temperature Probe Data for SPLASH

This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth of: Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches) obtained during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA).  These data were collected around the SPLASH campaign areas near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from between June 1st, 2022 and September 18th, 2023, under support from the NOAA Physical Sciences Laboratory and NOAA Weather Program Office under award NA21OAR4590363.Two file formats are provided: one version is text csv format and the second version is in NetCDF.Volumetric water content calculations: Data were calibrated and adjusted, with a soil-specific sample set, to improve accuracy and compensate for the meter's default "standard" soil type used in the sampling.  VWC data was correlated by measuring the weight of a known volume of soil from a range of saturation values. Samples were measured and weighed, dried at 105 degrees C for 48 hours, then weighed again. Calculations of VWC (VWC = 100*(Mwet - Mdry)/(w*Vtot) )were plotted against TDR readings.  Where: Mwet, Mdry = mass (g) of wet and dry soil respectively Vtot = total soil volume (ml) w = density of water (1g/ml) A regression analysis  to correlate TDR readings to the samples is below and was applied to the dataset.vwc_calculated = vwc_probe * slope + interceptslope = 1.20665, intercept = 0.0837017 m3/m3, slope_std_error = 0.09229, intercept_std_error = 0.0217403 m3/m3Definitions:TDR (Time Domain Reflectometry): A technique for measuring soil moisture content that uses the fact that water has a much higher dielectric permittivity than air, soil minerals, and organic matter. VWC (Volumetric Water Content): The ratio of the volume of water in a given volume of soil to the total soil volume expressed as a decimal or a percentage. The percent of the soil volume that is filled with water. At saturation, the VWC will equal the soil porosity (Saturation is typically around 50%).EC (Electrical Conductivity): A measure of how well the soil solution conducts electricity. The EC is influenced by the amount of salt and water in the soil. The VWC measured by TDR is an average over the length of the waveguide. Soil Characteristics:Soil at both Kettle Ponds (KEP1 and KPA) locations and Avery Picnic (AYP) were lab tested for composition as follows:Sample ID       Depth(in.)       Sand(%)     Silt(%)     Clay(%)     Soil Texture------------------------------------------------------------------------------------------------------  KEP1               2                      43                35            22              LoamAYP                 2                      40                35            25              LoamKPA                 2                      35                42            22              Loam------------------------------------------------------------------------------------------------------

opencc-by-4.0Feb 2019View details →

Operating diagram of hatching module in Zoug jars, this system consists of a 300-litre temperature-controlled isothermal enclosure containing 10 one-litre Zoug jars, each able to accommodate several hundred eggs. An ascending current holds the eggs in suspension and carries the larvae to the surface. Another bottle connected to this device collects the larvae. The water circulating in the jars is independent of that used in the filtration circuit. A cooling unit and UV sterilizer complete the installation. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum

Operating diagram of hatching module in Zoug jars, this system consists of a 300-litre temperature-controlled isothermal enclosure containing 10 one-litre Zoug jars, each able to accommodate several hundred eggs. An ascending current holds the eggs in suspension and carries the larvae to the surface. Another bottle connected to this device collects the larvae. The water circulating in the jars is independent of that used in the filtration circuit. A cooling unit and UV sterilizer complete the installation.

opencc-by-4.0Dec 2018View details →

Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June.

opencc-by-4.0Dec 2018View details →

Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC).

opencc-by-4.0Nov 2024View details →

Global LAke Surface water Temperature (GLAST): Compound thermal extremes in lakes

This database contains daily maximum temperature, daily minimum temperature, and daily mean temperature for 92,245 lakes globally from 1981 to 2020. These daily time series were derived from hourly simulation data. The database also includes annual statistics of six types of thermal extreme events calculated from the daily lake temperature time series: <ul> <li>daytime hot extreme events (hot day–mild night)</li> <li>nighttime hot extreme events (mild day–hot night)</li> <li>compound hot extreme events (hot day–hot night)</li> <li>daytime cold extreme events (cold day–mild night)</li> <li>nighttime cold extreme events (mild day–cold night)</li> <li>compound cold extreme events (cold day–cold night)</li> </ul>   The annual statistics provided for these events include metrics such as frequency, intensity, duration, and total days. Additionally, annual statistics of extreme air temperature events over the lakes are included. Details about the hourly-scale lake temperature simulation methodology can be found in the paper "Global lakes are warming slower than surface air temperature due to accelerated evaporation" (Tong et al., 2023, Nature Water). Definitions and calculation methods for thermal extreme events in lakes and atmosphere are provided in "Day-night compound thermal extremes in lakes" (Tong et al., 2025). For detailed information about the contents of each data file, please refer to the accompanying readme.docx file. For more datasets on global aquatic environments, please visit the official website of the Global Aqua Remote Sensing (GARS) Laboratory, led by Prof. Lian Feng:  <a href="https://garslab.com/?cat=1">https://garslab.com/?cat=1</a>.