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708 results for “Global dataset”
Thermal Sensation Classification Dataset based on ASHRAE Global Thermal Comfort Database II
<p><span>The dataset is based on ASHRAE Global Thermal Comfort Database II (version 2.01), which includes sets of objective indoor and outdoor climatic observations with accompanying “<em>right-here-right-now</em>” subjective evaluations by the building occupants. Data rows lacking information for any of the selected variables from the original dataset were excluded. Consequently, the dataset contains 10,618 data rows. Description of individual variables in the dataset are as follows:</span></p> <p><span><span>·<span> </span></span></span><span>Tair: Air temperature measured in the occupied zone (°C) ranging from 13.4 to 45.3 with 10,618 data points.</span></p> <p><span><span>·<span> </span></span></span><span>Vair: Air speed in the occupied zone (m/s) ranging from 0 to 4.71.</span></p> <p><span><span>·<span> </span></span></span><span>Relative Humidity (RH): Relative humidity in the occupied zone (%) ranging from 14.5 to 88.8.</span></p> <p><span><span>·<span> </span></span></span><span>SET: Standard Effective Temperature in Celsius (°C) ranging from 10.93 to 38.94.</span></p> <p><span><span>·<span> </span></span></span><span>CLO: Intrinsic clothing ensemble insulation of the occupant (clo) ranging from 0.23 to 2.87.</span></p> <p><span><span>·<span> </span></span></span><span>MET: Average metabolic rate of the occupant (met) ranging from 0.7 to 3.5.</span></p> <p><span><span>·<span> </span></span></span><span>Age: Age of the occupants ranging from 16 to 95 years.</span></p> <p><span><span>·<span> </span></span></span><span>Sex: Sex of the occupants, categorized as Male or Female.</span></p> <p><span><span>·<span> </span></span></span><span>Tout: Outdoor monthly average temperature during the field study (°C) ranging from 5.3 to 38.1.</span></p> <p><span><span>·<span> </span></span></span><span>Season: Season during which the study was conducted, categorized as Spring, Summer, Autumn, or Winter.</span></p> <p><span><span>·<span> </span></span></span><span>Building Operation Mode: Can be Air Conditioned (air, radiant, etc., with no operable windows), Naturally Ventilated (no mechanical cooling, with operable windows), or Mixed Mode (mechanical cooling and operable windows, with concurrent, changeover, or zoned control).</span></p> <p><span><span>·<span> </span></span></span><span>Building type: Type of building where the study was conducted, categorized as Classroom, Office, or Senior Center.</span></p> <p><span><span>·<span> </span></span></span><span>Thermal Sensation Vote (TSV): ASHRAE Thermal Sensation Vote of the occupant, ranging from -3 (cold) to +3 (hot).</span></p>
Cycles global simulation dataset
Open the record for dataset details and reuse information.
A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000 to 2022 (V1.0)
<p>Our released data mainly contains four types of data:</p> <p>(1) Half-hourly or hourly gap-filled LE data: The data are well gap-filled LE data using the novel bias-corrected RF algorithm. In the filenames, “HH” or “HR” indicate half-hourly or hourly scale data, respectively. The time information in the data files includes a pair of timestamps consistent with those in FLUXNET2015. The data are recorded at local time. The start time is “2000-02-18, 00:00:00”, and the end time is the same as the observation time at each site. For the quality control flags (QC), a value of 0 indicates observed data, while 1 indicates gap-filled data.</p> <p>(2) Prolonged daily LE data: This dataset provides the prolonged daily LE data using the novel bias-corrected RF algorithm. The seamless data covers the period from February 18, 2000, to December 31, 2022. For the prolonged part, the quality flag is set to 2. The rest data is consistent with the aggregated daily LE data.</p> <p>(3) Aggregated daily, monthly and yearly LE data: The hourly dataset is aggregated from the gap-filled half-hourly data to a daily scale. The start time is “2000-02-18”, and the end time is the same as the observation time at each site. Data quality control flags are also provided, with the values representing the percentage of hourly observations for each day. The monthly and yearly LE data are aggregated from the prolonged daily LE data. Quality control flags represent the proportion of days with more than 90% of hourly observations in a given month or a given year. No distinction is made between prolonged data and data with complete missing observations within a day. The start time for the monthly data is March 2000, and that for the yearly data is 2001.</p> <p>All files are formatted as csv files. NDVI and debiased reference variables from ERA5-Land are also provided.</p> <p>For more details of our data, please refer to a companion research article submitted to ESSD. <span>Li, W., Yao, Z., Qu, Y., Yang, H., Song, Y., Song, L., Wu, L., and Cui, Y.: A benchmark dataset for global evapotranspiration estimation based on FLUXNET2015 from 2000-2022, Earth Syst. Sci. Data, under review, 2024.</span> </p>
Dataset: Effects of tree presence on forage yield and nutritive value in agroforestry livestock systems: a global systematic review
<p>Simplified information retrieved from the materials and methods of 131 selected articles.</p>
GCL_FCS30: a global coastline dataset with 30-m resolution and a fine classification system from 2010 to 2020
<p><span>A G</span>lobal <span>C</span>oast<span>L</span>ine <span>D</span>ataset (GCL_FCS30) with a detailed classification system, including categories for (0) artificial, (1) biogenic, (2) sandy, (3) muddy, (4) rocky, and (5) estuary coastlines for 2010, 2015, and 2020. The coastline extraction employed a combined algorithm incorporating the Modified Normalized Difference Water Index (MNDWI), an adaptive threshold segmentation method based on the Maximum Between-Class Variance <span>Method </span>(OTSU), and the Canny edge detector. The coastline classification was performed using a hybrid transect classifier that integrates a random forest algorithm with globally stable training samples derived from multi-source geophysical data.</p>
High-resolution Global Dataset of BaP Based on Downscaling (0.1° × 0.1°)
<p>This dataset contains the annual- and monthly-averaged BaP concentrations in the atmosphere based on downscaling by using Relative emission with a resolution of 0.1° × 0.1°.</p>
Dataset and R code for "Relationship between wind speed and plant hydraulics at the global scale"
<p><strong><span>Data collection</span></strong><strong><span> </span></strong></p> <p><span> Plant hydraulic traits and height data were obtained from three sources: (1) field measurements of plant hydraulics for 210 forest species in China; (2) the TRY Plant Traits Database (https://www.try-db.org/TryWeb/Home.php; Kattge et al., 2020); and (3) published literature. For the latter we conducted searches on Web of Science, Google Scholar, and China National Knowledge Infrastructure (http://www.cnki.net) using keywords such as “hydraulic traits,” “xylem hydraulic conductivity,” “xylem vulnerability,” “water potential at 50% loss of hydraulic conductivity,” “xylem embolism resistance,” and “plant water conductivity.” A substantial portion of data in our study were obtained from published literature (Choat et al., 2012; Gleason et al., 2016) and the Xylem Functional Traits Database (XFT; </span><span><a href="https://xylemfunctionaltraits.org/"><span>https://xylemfunctionaltraits.org</span></a></span><span>).</span></p> <p><span>To minimize ontogenetic and methodological variation, we only included data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources; and (e) data values > 3 SD (standard deviation) were removed to reduce the effect of outliers (Carmona et al., 2021); (f) <span>height data were reported at the same site where plant hydraulic traits were measured.</span> </span></p> <p><span>Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2; Fick & Hijmans, 2017; Table 1) if the original data were not available. The following variables measured at ~1 km<sup>2</sup> scale were extracted from WorldClim: mean annual wind speed (<span>μ</span>), mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, wind seasonality (<span>μS; </span>coefficient of variation across monthly measurements × 100), precipitation of driest month, and minimum temperature of coldest month. The VPD data were extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html; Abatzoglou et al., 2018). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data; Zomer et al., 2008). Moisture index (MI), which is the ratio of precipitation to PET. </span></p> <p><strong><span>Data analysis</span></strong></p> <p><span>Trait and environment data were log<sub>10</sub>-transformed to achieve approximate normality, except for <em>P</em><sub>50</sub> and temperature data. We first calculated correlations among all climatic variables and for subsequent analyses retained only those variables with correlation coefficients lower than |0.7| (Dormann et al., 2013). We then ran independent multiple linear models for each trait of interest using the retained climatic variables. Model selection based on a corrected Akaike information criterion and using the R package glmulti (Calcagno & de Mazancourt, 2010), identified the best linear model for each trait. The R package ‘visreg’ (Breheny & Burchett, 2017) was used to visualize the partial relationships between wind speed and hydraulic traits. Two-dimensional contour plots were then used to explore and visualise how plant hydraulic traits varied simultaneously with wind speed and moisture index.</span></p> <p><span>To quantify the strength of wind effects on plant hydraulics, models with wind parameters μ and μS included were compared to those without these wind parameters. </span></p> <p><span>To test for differences in the relationship between hydraulic traits and wind speed among species grouped into different climatic regions (i.e., dry <em>vs</em>. wet sites, and tropical <em>vs</em>. temperate regions), we used standardized major axis (SMA) analyses using the R package ‘smatr’ (Warton et al., 2012).</span><span> </span><span>A grouping factor was added in each SMA to test whether species groups share a common slope, with <em>p</em> > 0.05 indicating species groups share a common slope. </span></p> <p><span>Variance partitioning analysis was performed using the ‘rdacca. hp’ R package to quantify the degree to which the effect of wind speed was independent from other climatic variables (Lai et al., 2022). The individual contribution of each predictor was estimated in this analysis. This analysis also helped to illustrate the significant values of climatic variables on plant hydraulics. </span></p> <p><span>A Random Forest </span><span>machine-learning algorithm (implemented using the R package ‘randomForest’) was utilized to further assess the relative importance of environmental variables for each plant hydraulic trait (Breiman, 2001). To avoid multicollinearity, this analysis only included variables with correlation coefficients lower than |0.7|. A higher value of the mean decrease in accuracy (%IncMSE) indicates the increased importance of a variable (e.g., a %IncMSE value of 50 indicates that the overall mean square error would increase by 50% if that variable were to be excluded from the analysis). This provides a measure of a variable's importance in estimating the value of the target variable across the trees in the forest. </span></p> <p> </p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 3
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: grazing land with (almost) no trees (GL-notrees); cropland used for production of other fibres (CL-OFIB); fallow cropland areas (CL-FALL)</p>
Data from: A daily global mesoscale ocean eddy dataset from satellite altimetry
Mesoscale ocean eddies are ubiquitous coherent rotating structures of water with radial scales on the order of 100 kilometers. Eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. We present a global daily mesoscale ocean eddy dataset that contains ~45 million mesoscale features and 3.3 million eddy trajectories that persist at least two days as identified in the AVISO dataset over a period of 1993–2014. This dataset, along with the open-source eddy identification software, extract eddies with any parameters (minimum size, lifetime, etc.), to study global eddy properties and dynamics, and to empirically estimate the impact eddies have on mass or heat transport. Furthermore, our open-source software may be used to identify mesoscale features in model simulations and compare them to observed features. Finally, this dataset can be used to study the interaction between mesoscale ocean eddies and other components of the Earth System.
GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform
<p>A novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform.</p>
Dataset to train, validate and reconstruct POC over the global ocean for 2009-2013 based on PlankTOM12
<p>The distribution of in situ UVP5 measurements over the period 2009-2013 was used to create synthetic data by sampling a global biogeochemical ocean model PlankTOM12 at UVP5 time and location.</p> <p>The synthetic data set is used to train, validate and test Machine Learning methods to reconstruct particulate organic carbon concentration. </p> <p>These data are part of publication at the GMD. </p>
GlobalHighNO₂: Global Daily Seamless 1 km Ground-Level NO₂ Dataset over Land (2018–Present)
<p>GlobalHighNO<sub>2</sub> is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived gapless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level NO<sub>2</sub> dataset over land <strong>from 2018 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.92 and a root-mean-square error (RMSE) of 4.76 µg m<sup>-3</sup> on a daily basis.</p> <p><strong>More GHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Dataset for "Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme"
<p>Datasets for "Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme". The global simulation outputs (climatologies and daily anomalies), calculated RMM indexes, and the results of the budget analysis are included.</p>
A dataset of global sand flux (1950-2021)
<p>Simply, we use all effective friction velocities derived from the instantaneous estimates of 10 m u-component and v-component hourly wind speeds of the ERA5-Land reanalysis product from 1950 to 2021 to infer sand flux based on the physics formulas of blown sand, and according to the existing studies (Gunn et al., 2021; Chanteloube et al., 2022; Gunn et al., 2022a; Gunn et al., 2022b), further improve the conceptual framework of sand flux, and simply define the flux potential (FP), resultant flux potential (RFP) and azimuthal flux potential (FP_N, FP_NNE, FP_NE, FP_NEE, FP_E, FP_EES, FP_ES, FP_ESS, FP_S, FP_SSW, FP_WS, FP_WWS, FP_W, FP_WWN, FP_NW and FP_NNW). Specifically, FP is the sum of FPs moving to all azimuths; RFP is the resultant flux potential, represents net sand transport potential under the different wind directions; RFD is the resultant flux direction, represents net trend of sand flux; FDV is flux directional variability, defined as the ratio of RFP/FP, represents that the flux moves to the same direction (approximate to 1) or many directions (approximate to 0); FP_azimuth represents the azimuthal flux potential, the sum of azimuthal flux potentials is flux potential (FP); RFP_N or RFP_E represent that FPs to all azimuths are projected to the due-north and due-east directions in order to solve the RFP, RFD and the final FDV. All fluxes are the bulk-volume flux, the units are m<sup>2</sup> yr<sup>-1</sup>.</p> <p>Specifically, FP is the sum of FPs moving to all azimuths; RFP is the resultant flux potential, represents net sand transport potential under the different wind directions; RFD is the resultant flux direction, represents net trend of sand flux; FDV is flux directional variability, defined as the ratio of RFP/FP, represents that the flux moves to the same direction (approximate to 1) or many directions (approximate to 0); FP_azimuth represents the azimuthal flux potential, the sum of azimuthal flux potentials is flux potential (FP); RFP_N or RFP_E represent that FPs to all azimuths are projected to the due-north and due-east directions in order to solve the RFP, RFD and the final FDV.</p> <p> </p> <p>References:</p> <p>1 Gunn, A., Wanker, M., Lancaster, N., Edmonds, D. A., Ewing, R. C. & Jerolmack, D. J. 2021. Circadian rhythm of dune-field activity. Geophysical Research Letters, 48, e2020GL090924.</p> <p>2 Chanteloube, C., Barrier, L., Derakhshani, R., Gadal, C., Braucher, R., Payet, V., Léanni, L. & Narteau, C. 2022. Source-to-sink aeolian fluxes from arid landscape dynamics in the Lut Desert. Geophysical Research Letters, 49, e2021GL097342.</p> <p>3 Gunn, A., Casasanta, G., Di Liberto, L., Falcini, F., Lancaster, N. & Jerolmack, D. J. 2022a. What sets aeolian dune height? Nature Communications, 13, 2401.</p> <p>4 Gunn, A., East, A. & Jerolmack, D. J. 2022b. 21st-century stagnation in unvegetated sand-sea activity. Nature Communications, 13, 3670.</p> <p> </p>
All datasets used for "How Preferential Trade Agreements Affect the Global Income Distribution: A Study of the Global Winners and Losers"
<p>These are all datasets derived from sources in the public domain. Please find below references and links to sources. Refer to websites for conditions of usage.</p> <p>Center for Systemic Peace (2018), ‘Polity5 annual time-series, 1946-2018’. Data retrieved from: https: //www.systemicpeace.org/inscrdata.html [Last accessed: January 4th 2023].</p> <p>Dür, A., Baccini, L. & Elsig, M. (2014), ‘The design of international trade agreements: Introducing a new database’, The Review of International Organizations 9(3), 353–375.</p> <p>United Nations (2020), ‘Human Development Index (HDI)’. Data retrieved from: https://hdr.undp.org/ en/indicators/137506 [Last accessed: January 4th 2023].</p> <p>WID (2022), ‘World Inequality Database’. Data retrieved from: https://wid.world [Last accessed: January 4th 2023].</p> <p>World Bank (2022), ‘World Bank country and lending groups’. Data retrieved from: https://datahelpdesk.worldbank.org/knowledgebase/articles/ 906519-world-bank-country-and-lending-groups [Last accessed: January 4th 2023].</p> <p> </p>
InSAR-derived horizontal velocities in a global reference frame - final output dataset and software codes
<p>The output dataset as described in the article in title, extracted from COMET LiCSAR dataset in March 2021.</p> <p>We also provide a snapshot of the python3 codes used to generate the output dataset.</p> <p>Contents and description of the dataset:</p> <p>uaz_values.csv<br> ==============<br> Contains u_az values and other relevant data in columns:<br> frame - ID of related frame (same as in frame_values.csv)<br> esd_master - reference acquisition epoch<br> epoch - date of acquisition epoch<br> daz_total_wrt_orbits - original extracted azimuth shift w.r.t. orbits<br> daz_cc_wrt_orbits - original extracted azimuth shift w.r.t. orbits from intensity cross-correlation (prior to spectral diversity)<br> drg_wrt_orbits - original extracted range shift w.r.t. orbits<br> orbits_precision - precision of applied orbits (P..precise, R..restituted)<br> version - orbits version<br> daz_iono_grad_mm - u_az from ionosphere propagation<br> tecs_A - estimated TECs at centre of hyphotetical burst A<br> tecs_A - estimated TECs at centre of hyphotetical burst B<br> daz_mm_notide - u_az after correction of solid-earth tides<br> daz_mm_notide_noiono_grad - u_az after correction of solid-earth tides and ionospheric gradient propagation<br> is_outlier_* - flag of outlier datapoint, as identified through Huber loss function (related to velocity estimates in frame_values.csv)</p> <p>frame_values.csv<br> ================<br> Contains along-track velocity estimates and other relevant data in columns:<br> frame - ID of related frame<br> master - reference acquisition epoch<br> center_lon - longitude coordinate of the frame centre<br> center_lat - latitude coordinate of the frame centre<br> heading - satellite heading angle (from the geograpic north)<br> azimuth_resolution - extracted azimuth pixel spacing (in metres)<br> range_resolution - extracted range pixel spacing (in metres)<br> avg_incidence_angle - average incidence angle of the frame<br> centre_range_m - approximate slant distance between the satellite and centre of the frame<br> centre_time - acquisition time (UTC) of centre of the frame at the reference epoch (appliable to other epochs)<br> s1AorB - flag of S-1A or B of the reference epoch<br> slope_plates_vel_azi_itrf2014 - along-track velocity estimated from ITRF2014 plate motion model<br> slope_daz_mm_mmyear - estimated along-track velocity from the original u_az values (in mm/year)<br> slope_daz_mm_notide_mmyear - estimated along-track velocity from u_az values after correction on solid-earth tides<br> slope_daz_mm_notide_noiono_grad_mmyear - estimated along-track velocity from u_az values after correction on solid-earth tides and ionosphere<br> intercept_* - corresponding intercept (in mm)<br> *_RMSE_selection - RMSE of outlier-free u_az data samples<br> *_count_selection - count of outlier-free u_az data samples used to estimate corresponding velocity<br> *_RMSE_mmy_full - RMSE from all u_az data samples applying corresponding velocity and intercept (in mm/year)</p> <p><br> decomposed_grid.csv<br> ===================<br> Contains decomposed velocities (in 250x250 km spacing grid) and other relevant data in columns:<br> count - count of frames used for the decomposition<br> opass - orbital pass codes of the input frames (D..descending, A..ascending)<br> centroid_lon - longitude coordinate of the grid cell centre<br> centroid_lat - latitude coordinate of the grid cell centre<br> VEL_N_noTI - northward velocity component from data corrected for solid-earth tides and ionosphere<br> VEL_E_noTI - eastward velocity component from data corrected for solid-earth tides and ionosphere<br> VEL_N_noT - northward velocity component from data corrected for solid-earth tides<br> VEL_E_noT - eastward velocity component from data corrected for solid-earth tides<br> ITRF_N - northward velocity component from averaged ITRF2014 plate motion model<br> ITRF_E - eastward velocity component from averaged ITRF2014 plate motion model<br> *RMSE_* - RMSE of corresponding data</p>
Annually resolved propagation of CFCs and SF6 in the global ocean over eight decades - dataset
<p>This archive contains the reconstructed anthropogenic transient tracer concentrations (CFC-11, CFC-12, and SF6) at annual resolution 1940-2021 and their associated uncertainties. The details of the method and data used are described in: Cimoli, L., Gebbie, G., Purkey, S. G., & Smethie, W. M. (2023). Annually resolved propagation of CFCs and SF6 in the global ocean over eight decades. Journal of Geophysical Research: Oceans, 128, e2022JC019337. https://doi.org/10.1029/2022JC019337.</p> <p>Each netcdf file returns the result for a neutral density surface (see file's name). Each file contains:<br> - latitude, longitude coordinates and time array of the reconstruction<br> - the reconstructed tracer concentration (e.g. cfc11_rec)<br> - the errorbars associated with the reconstruction; note that they are asymmetric and so both positive a negative errorbars are reported (e.g. cfc11_rec_posErr and cfc11_rec_negErr)<br> - the anthropogenic transient tracer observations on that density surface (e.g cfc11_obs)</p> <p>If you find this dataset useful, please cite the associated publication. If you want to collaborate on further analysis using this dataset or for any questions, please reach out to laura.cimoli@damtp.cam.ac.uk</p>
Global high-resolution sea surface hemispherical broadband emissivity dataset (1980-2020)
<p>This is the global sea surface hemispherical broadband emissivity (BBE, 8-13.5 micrometer) dataset (0.1°) derived by a lookup table-based method developed by Cheng et al., (2017). The accuracy of the estimated hemispherical BBE was 0.003 given a wind speed of zero. The foam effect was also incorporated into the BBE dataset.</p> <p>This dataset spans from 1980 to 2020.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 1980.01-2020.12</li> <li>Spectral Range: 8~13.5μm</li> <li>Spatial Resolution: 0.1°</li> <li>Temporal Resolution: Daily (instantaneous at 12:30)</li> <li>Projection: GCS_WGS_1984</li> <li>Data Format: HDF</li> </ul> <p><strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong></p> <ol> <li> <p>Cheng, J., et al. (2017). A Lookup Table-Based Method for Estimating Sea Surface Hemispherical Broadband Emissivity Values (8–13.5 μm). <em>Remote Sensing, 9</em></p> </li> <li> <p>Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. IEEE Geoscience and Remote Sensing Letters, 10, 401-405</p> <p>If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).</p> </li> </ol>
Three datasets of global monthly gross primary productivity (GPP) during 2003-2018 derived from SIF, NIRv and LAI and their best-matching environmental factors
<p>As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous remote sensing vegetation indices (VIs) have participated in the estimation of global GPP. However, the relative performance of various VIs in estimating GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood.</p> <p>We used the Random Forest (RF) algorithm to identify the factors with the most powerful explanation of GPP and to explore the importance of these predictors. We trained six RF models to select features, i.e., two types of models (Plant Functional Type [PFT]-specific and universal) for each vegetation index (SIF, NIRv, and LAI). Each model comprised 100 decision trees, was sampled without replacement, and was trained using 70% of the data. Model performance was evaluated using out-of-bag (OOB) R-squared (R<sup>2</sup>) and root mean square error (RMSE) values. The predictor with the lowest importance score in the iteration was removed and the whole procedure was then repeated until only the vegetation index, CO<sub>2</sub>, and PFTs were left. The predictors used to estimate GPP were identified based on the performance curve of OOB R<sup>2</sup> and RMSE. The determination of the model is based on the principle that further reductions in the number of predictors would considerably reduce model performance, while increasing the number of predictors would not significantly improve model performance.</p> <p>Here we provide a set of high-spatial resolution (1/12°) global gridded products of monthly GPP for 2003-2018 generated for each vegetation index based on a generic model with an optimal configuration, i.e., an optimal combination of VI and other relevant variables using the RF algorithm. R<sup>2</sup> of three optimal VI-based GPP estimation models ranges from 0.84 to 0.85, and RMSE ranges from 1.51g C·m<sup>−2</sup>·d<sup>−1</sup> to 1.54g C·m<sup>−2</sup>·d<sup>−1</sup>. More information about the datasets can be found in Zhao and Zhu (2022) <strong><em>Remote Sensing</em></strong>.</p> <p><em>Zhao W, Zhu Z. Exploring the Best-Matching Plant Traits and Environmental Factors for Vegetation Indices in Estimates of Global Gross Primary Productivity[J]. Remote Sensing, 2022, 14(24): 6316.</em></p>
Datasets for "Reconstructing Global High Quality 3–day Surface Soil Moisture from ESA CCI and SMAP product from 2015 to 2021 using Conditional Variational Auto-Encoder"
<p>These are supporting datasets for the paper titled, "<strong>Reconstructing Global High Quality 3–day Surface Soil Moisture from ESA CCI and SMAP product during 2015–2021 using Conditional Variational Auto-Encoder</strong>"</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.