Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

Global estimates of marine gross primary production based on machine‐learning upscaling of field observations

4 variables (excluding dimension variables): double GPP_LD_MLD_RF[Lon,Lat,Month]             units: mmol O2 m-2 d-1          fill value: NaN          long_name: Monthly mixed-layer integration of gross primary production trained from the                              dataset determined by the light-dark bottle incubation using Random Forest                              algorithm         coordinates: [Longitude, Latitude Month] double GPP_LD_ZEU_RF[Lon,Lat,Month]                units: mmol O2 m-2 d-1             fill value: NaN             long_name: Monthly euphotic-zone integration of gross primary production trained from                                 the dataset determined by the light-dark bottle incubation using Random                                  Forest algorithm             coordinates: [Longitude, Latitude Month] double GPP_Triple_MLD_RF[Lon,Lat,Month]                units: mmol mmol O2 m-2 d-1             fillvalue: NaN             long_name: Monthly mixed-layer integration of gross primary production trained from                                 the dataset determined by the triple isotopes of dissolved oxygen using                                 Random Forest algorithm             coordinates: [Longitude, Latitude Month]          double GPP_Triple_ZEU_RF[Lon,Lat,Month]                units: mmol O2 m-2 d-1             fill value: NaN             long_name: Monthly euphotic-zone integration of gross primary production trained from                                  the dataset determined by the triple isotopes of dissolved oxygen using                                Random Forest algorithm 3 dimensions:         Lon  Size:181             units: degree_north             long_name: Longitude         Lat  Size:91             units: degree_east             long_name: Latitude         Month  Size:13             units: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Annuual_mean             long_name: Month Author: Yibin Huang & Nicolas Cassar Correspond: nicolas.cassar@duke.edu          Request_for_citation: If you use these data in publications or presentations, please cite: Huang,                                     Y., Nicholson, D., Huang, B., & Cassar, N. (2021). Global estimates of                                      marine gross primary production based on machine‐learning upscaling of                                      field observations. Global Biogeochemical Cycles, 35, e2020GB006718.                                      https://doi.org/10.1029/2020GB006718   Creation date: Dec/6th/2021

opencc-by-4.0May 2024View details →

CEOS LPV DIRECT V2.1: A database of upscaled LAI, FAPAR and Fcover values for satellite biophysical product validation

Ground references of high quality are needed to validate satellite-based products. The DIRECT V2.1 database compiles Leaf Area Index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR) and fraction of vegetation cover (FCover) averaged values over a 3 km x 3 km area. The ground data was upscaled using high spatial resolution imagery following CEOS WGCV LPV (so called CEOS LPV) LAI validation good practices (Fernandes et al., 2014) to properly account for the spatial heterogeneity of the site. Ground measurements performed during several international Cal/Val activities, including VALERI, BigFoot, SAFARI-2000, CCRS, Boston University, were compiled by S. Garrigues (Garrigues et al., 2008) in the DIRECT database, and later ingested in the CEOS LPV OLIVE tool (Weiss et al., 2014) for accuracy assessment. F. Camacho reviewed DIRECT to remove those sites without understory measurements (Camacho et al., 2013) and after that expanded the database adding the ImagineS network of sites (Camacho et al., 2021). DIRECT V2.1 is the last update including 44 new sites from China (Fang et al., 2019; Song et al., 2021) and 2 more sites from ESA FRM4Veg project (Brown et al., 2021). The CEOS LPV DIRECT V2.1 database constitutes a major effort of the international community to provide ground reference for the validation of satellite-based LAI and FAPAR ECVs, with a total of 176 sites around the world (7 main biome types) and 280 LAI values, 128 FAPAR and 122 FCOVER values covering the period from 2000 to 2021.   Data description <ul> <li>LAI, LAIeff, FAPAR and FCOVER upscaled values over 3 km x 3 km.</li> <li>LAI_NoUnderstory, refers to sites where only overstory was measured and thus are not recommended for accuracy assessment of satellite products.</li> </ul> File contents A Header " Sites": General information for each site (coordinates, landcover, method, reference) For each variable: <ul> <li># number of the site (site description in Sites)</li> <li>Lat_cen, latitude centre of 3km x 3km</li> <li>Lon_cen, longitude centre of 3km x 3km</li> <li>Site name, name of the site</li> <li>Date, MM/DD/YYYY</li> <li>Mean, average value over 3km x 3km</li> <li>Uncert, uncertainty over 3 km x 3km (STD)</li> </ul>

opencc-by-4.0Apr 2023View details →

Dataset of acoustic intensity vector measurements around an upscaled ear model

A dataset of acoustic vector (particle velocity vector and scalar sound pressure) measurements of the sound field around an upscaled model of an ear. Data collected in July 2022 at the Aalto Acoustics Lab in Espoo, Finland. See the companion paper at AES for information about the contents of the dataset, measurement methodology, and example scripts. See the companion repository <a href="https://github.com/aaron-geldert/upscaled-ear-model-scripts">github.com/aaron-geldert/upscaled-ear-model-scripts</a> for example MATLAB scripts using the dataset. Correspondence should be directed to <a href="mailto:aarongeldert@gmail.com?subject=RE%20Big%20Ear%20Dataset%20(Zenodo)">Aaron Geldert (aarongeldert@gmail.com)</a>.

opencc-by-4.0Jan 2023View details →

Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning

Data Description: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: ├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage] ├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage] ├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content] ├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content] ├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity] ├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity] ├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content] ├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content] ├── depthWT_30arc_us.tif                                                        [depth to water table] ├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor] ├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor] ├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH] ├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH] ├── pondingFre_30arc_us.tif                                                     [ponding frequency] ├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content] ├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content] ├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content] ├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content] ├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content] └── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content] SOC TIF files: ├──30cm SOC mean.tif                             [30 cm depth soil SOC] ├──100cm SOC mean.tif                           [100 cm depth soil SOC] ├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation] └──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data Data format: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. Geospatial projection:  <pre><code>GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre>

opencc-by-4.0Jun 2022View details →

Upscaling tracer-aided ecohydrological EcH2O-iso model in larger catchments: model setup and model simulations in the Selke catchment, central Germany

This data repository is associated with the scientific article "Upscaling Tracer-aided Ecohydrological Modeling to Larger Catchments: Implications for Process Representation and Heterogeneity in Landscape Organization" by Yang et al. (submitted to Water Resources Research). This dataset includes the model setup information of the EcH2O-iso model in the Selke catchment, central Germany (./model_setup_Selke), and all model simulations and data analyses that are necessary to rebuilt the work (./model_sim_data). Please also refer to https://github.com/XYang-EcoHydroWQ/EcH2O-iso_largescale for corresponding model source code of the EcH2O-iso model, including modifications for larger-scale modeling.

opencc-by-4.0Feb 2019View details →

Dataset for "Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations"

This dataset provides wetland methane (CH4) emissions, their uncertainties and underlying CH4 flux densities north from 45 N using three different wetland maps. The data products are derived using data from several eddy covariance CH4 flux sites, random forest machine learning algorithms and three prescribed wetland maps. The data are at 0.5 by 0.5 deg or 1 by 1 deg resolution, depending on the wetland map used. The dataset covers years 2013 and 2014. CH4 flux densities are provided only for grid cells with > 5 % wetland coverage. The three data products are provided in netCDF format files (.nc). Please see more details in the attributes saved in the netCDF files. RF-DYPTOP.nc Upscaling based on DYPTOP dynamic wetland map. At 1 by 1 deg resolution. RF-GLWD.nc Upscaling using GLWD static wetland map. At 0.5 by 0.5 deg resolution. RF-PEATMAP.nc Upscaling using PEATMAP static wetland map. At 0.5 by 0.5 deg resolution.   This dataset is related to Peltola et al. (2019) manuscript submitted to Earth System Science Data. Please cite this publication if you use this dataset in your work. Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., Kowalska, N., Kutzbach, L., Lienert, S., Lohila, A., Mammarella, I., Nadeau, D. F., Nilsson, M. B., Oechel, W. C., Peichl, M., Pypker, T., Quinton, W., Rinne, J., Sachs, T., Samson, M., Schmid, H. P., Sonnentag, O., Wille, C., Zona, D., and Aalto, T.: Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-28, in review, 2019.

opencc-by-4.0Nov 2019View details →

Upscaling of elastic properties in carbonates: a modeling approach based on a multi-scale geophysical dataset

Dataset for the article "Upscaling of elastic properties in carbonates: a modeling approach based on a multi-scale geophysical dataset" by Bailly C., Fortin J., Adelinet M., Hamon Y. submitted to Journal of Geophysical Research: Solid Earth. Please refer to the ReadMe file for more details.

opencc-by-4.0Apr 2023View details →

Foliar N, P and K global upscaled maps in woody plants

Global foliar N,  P and K maps in woody plants. Further details in: Vallicrosa, H., Sardans, J., Maspons, J., Zuccarini, P., Fernández-Martínez, M., Bauters, M., Goll, D.S., Ciais, P., Obersteiner, M., Janssens, I.A. and Peñuelas, J. (2022), Global maps and factors driving forest foliar elemental composition: the importance of evolutionary history. New Phytol, 233: 169-181. <a href="https://doi.org/10.1111/nph.17771">https://doi.org/10.1111/nph.17771</a>

opencc-by-4.0Dec 2022View details →

Upscaling Tower-Based Net Ecosystem Productivity to global 250m using the Data Augmentation Method by Considering their Spatial Distribution

Terrestrial ecosystems have emerged as critical carbon sinks, holding a crucial role in the carbon cycle. Net ecosystem productivity (NEP) is a highly significant parameter in terrestrial ecosystems, representing the net ecosystem exchange (NEE) between ecosystems and the atmosphere, without considering other carbon fluxes from disturbances. In this NEP product, we harmonized various sets of tower-based NEP from flux sites as target variable, remote sensing product and meteorological data as traning variables. We further optimizied these smaple sets to address the problems in spatial distribution, culminating in a global NEP product spanning the years 2001-2022, achieved through the application of the random forest method. This dataset contains NEP data for global terrestrial ecosystems for the period 2001-2022 in MgC with a temporal resolution of 1 year. The spatial resolution of the product is 250m and the data format is TIFF. For detailed instructions on how to use the dataset, see User Guides.doc!

openapgl-v3Dec 2021View details →

Upscaled and calibrated GONG and MDI magnetograms via Deep Learning

Upscaled GONG and MDI magnetograms between January and April of 2001 via Deep learning. This dataset was obtained using the converter softwared published in this DOI: https://doi.org/10.5281/zenodo.3750372 Currently GONG magnetograms were made only for the Mauna Loa observatory.  Both GONG and MDI are upscaled to multiple integers of their original resolution.

opencc-by-4.0Nov 2021View details →

PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting

Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has great potential in increasing the flexibility and reducing the computational cost of seismic modeling and inversion. We propose a novel implementation of PINN using frequency upscaling and neuron splitting, which allows the neural network model to grow in size as we increase the frequency while leveraging the information from the pre-trained model for lower-frequency wavefields, resulting in fast convergence to high-accuracy wavefield solutions. In this letter, we present the relevant dataset to the paper.

opencc-by-4.0May 2024View details →

Agripreneurship is coming of age. Opportunities for Youth and Women Empowerment in UPSCALE PROJECT.

Unemployment is among the problems that most developing economies are facing. These regions rely  on agriculture for their livelihood and most of the farming systems are ineffective. Nevertheless, the  agricultural sector employs the largest population in both the formal and informal. Self-employment  through innovative ideas that develop products or produce cost-effectively, or a production process  that is eco-friendly to promote biodiversity or value addition through processing, storage, packaging,  or differentiation of the agricultural products is what is termed as agriprenourship.  The aim is to make  the production process affordable, sustainable, profitable, and optimal at the same time maintain the  ecosystem. Agreprenourship is highly evident in production technologies such as the PPT technology  that has been promoted by the UPSCALE project in the region. The project has offered women and  youth opportunities to participate in research, production, value addition, and marketing of the  products produced under the system. Women have been able to produce organic products, control  the striga weed, and fall armyworms, promote soil fertility, prevent soil erosion, produce fooder,  practice dairy farming, and production of desmodium seeds. Through this, household food security has  been attained, increased income, reduced dependency ratio, reduced poverty, creation of  employment, and improved standard of living. Agriprenourship is an important enterprise currently for the  youths and women as it offers a range of opportunities from economic, social, and environmental benefits in  the initiatives such as PPT farming in the UPSCALE project. Therefore, it is recommended that there is a need for  Policy dialogue in both the seed and product value chains to create enabling policies that can upscale the PPT  farming system, subsidize the inputs, and establish effective extension methods, advocacy, and lobbying  government authorities to promote PPT.