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17 results for “grid point”
WSC - Gridded sample points at Wibu field site including yield, soil texture, water table depth, and estimated soil water retention parameters
A variety of data from gridded sampling points at the Wibu field site. The gridded sampling scheme is described in the Point Locations dataset. This dataset includes 2012 and 2013 absolute and normalized yield, soil textural characteristics (organic content, porosity, bulk density, particle size metrics, % sand/silt/clay), a variety of water table depth metrics (mean, percentiles, sum exceedance values, moving averages), and soil water retention parameters estimated using the Rosetta pedotransfer function. It was collected as part of a study of the impacts of water table depth, soil texture, and growing season weather conditions on corn production at the Wibu field site, described in Zipper et al. (in review). The Wibu field site is a commercial agricultural field, which grew corn in the 2012, 2013, and 2014 growing seasons. See Zipper and Loheide (2014) Ag. For. Met. for more information about the field site.
H2020 Platone German Demonstrator - Baseline Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are computed values for the active power exchange at the medium (MV)/low voltage grid connecting feeder (active power). The data are provided as 15-minutes mean values in kilowatt. The computed indicate the power exchange that would have been measured, in case no use case would have been applied in the field (control of batteries).</p> <p><strong>Data Description:</strong></p> <ul> <li>p_tei_c_mean = arithmetic mean of p_tei computed in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_c_min = the minimum value (1-minute mean) computed within the period of p_tei_mean (15-minutes)</li> <li>p_tei_c_max = the maximum value (1-minute mean) computed within the period of p_tei_mean period (15-minutes)</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The field test setup of the demonstrator consists of a MV/LV substation, 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh capacity. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
Elevation at grid points on the Luquillo Forest Dynamics Plot (LFDP)
This file contains data that describe the physical and environmental attributes of the LFDP. The attributes include elevation, topography type, and percentage slope. All data are given for the 20 m by 20 m quadrat scale. Information on soils are taken form an interpolation of the soil map produced by the Natural Resources Conservation Service, US Department of Agriculture (Soil Survey 1995). Other information from the elevation of each of the corner posts defining the quadrats. The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and "cleaned". The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these data, and ensure data accuracy, LFDP Principal Investigators request that researchers intending to use this data comply with the requests below. Through complying with these requests we can ensure that the data are interpreted correctly, analyses are not repeated unnecessarily, beneficial collaboration between users is promoted and the Principle Investigators investment in this project is protected. Submit to the LFDP PIs a short (1 page) description of how you intend to use the data; · Invite LFDP PIs to be co-authors on any publication that uses the data in a substantial way (some PIs may decline and other LFDP scientists may need to be included); If the LFDP PIs are not co-authors, send the PIs a draft of any paper using LFDP data, so that the PIs may comment upon it; In the methods section of any publication using LFDP data, describe that data as coming from the "Luquillo Forest Dynamics Plot, part of the Luquillo Experimental Forest Long-Term Ecological Research Program"; Acknowledge in any pu
Saddle snowfence grid points, Niwot Ridge LTER, Colorado
Point coverage of Saddle snow fence point grid. 1:500 scale. The Saddle snow fence is a 60-meter long, 2.6-meter high fence erected annually in the fall for manipulating winter snowpack and snow drift along a topoedaphic gradient. The fence was first installed in fall of 1993 and is taken down every summer to avoid influencing summer ambient wind. The fence is oriented along a north-south axis, as prevailing winds on Niwot Ridge come from the west, and delineates shrub and moist meadow communities on the windward (west) side and dry meadow communities on the leeward (east) side. The snowfence grid exists within a 60m x 125m area that contains the snowfence. Of the 146 points in the snowfence grid dataset, 84 are located on the leeward (east) side of the snowfence, 56 are located on the windward (west) side of the snowfence, and 6 are control locations outside of but near the snowfence experiment area. See Fig. 4 in Walker et al. (1999) for graphical depiction of the Saddle snow fence grid, snow fence, and vegetation communities within. Snow depth is measured annually at the snow fence grid points, and the grid has been used for locating experimental plots and instrumentation (e.g. see Walker et al. 1993b, Brooks et al. 1995, Walker et al. 1999). This dataset is part of the Saddle grid geographic information system (GIS). Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993a).
Saddle grid stake points, Niwot Ridge LTER, Colorado
Point coverage with the GPS data points for each of the 88 Saddle grid points. 1:500 scale. This dataset is part of the Saddle grid geographic information system (GIS). Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
Dataset used in the publication entitled "Multi-Point Method using Effective Demodulation and Decomposition Techniques allowing Identification of Disturbing Loads in Power Grids"
<p>Dataset obtained from experimental research carried out in the prepared laboratory setup and information on the performed numerical simulation studies. Based on the dataset, the proposed new method of identification of sources of voltage fluctuation has been validated in the publication: Kuwałek P., Wiczyński G., Multi-Point Method using Effective Demodulation and Decomposition Techniques allowing Identification of Disturbing Loads in Power Grids. The description of the prepared laboratory setup is presented in this publication. The research results are part of the work under the project entitled "Voltage fluctuation diagnostic focused on identification and localization disturbing loads in power grids" funded by the National Science Centre, Poland - 2021/41/N/ST7/00397.</p>
MeshRIR: Dataset of Room Impulse Responses on Meshed Grid Points
<p>MeshRIR is a dataset of acoustic room impulse responses (RIRs) at finely meshed grid points. Two subdatasets are currently available: one consists of IRs in a 3D cuboidal region from a single source, and the other consists of IRs in a 2D square region from an array of 32 sources. This dataset is suitable for evaluating sound field analysis and synthesis methods. </p> <p>See the link below for the details:</p> <p><a href="https://sh01k.github.io/MeshRIR/">https://sh01k.github.io/MeshRIR/</a></p>
Simulated Neutral Landscape Models and Scaling Results for Testing of Multi-Dimensional Grid-Point Scaling Algorithm
<p>The data package contains (1) simulations of neutral landscape models of categorical data for benchmark testing of scaling algorithms, and (2) scaling results for testing consistency and sensitivity of the newly developed Multi-Dimensional Grid-Point (MDGP) scaling algorithm.</p> <p>Neutral landscapes were generated using the "nlmpy" python module. The MDGP scaling algorithm and the test framework were implemented in R (https://github.com/gannd/landscapeScaling). </p>
CEDS Gridded SO2 Emissions v_2021_4_21 with Point Sources 0.5 Degrees
<p>Preliminary release of gridded SO2 emissions from 2000-2019 based on the 2021_04_21 CEDS release with direct inclusion of point sources as time series. This release contains global grids at 0.5 degree resolution.</p>
CEDS Gridded SO2 Emissions v_2021_4_21 with Point Sources 0.1 Degrees
<p>Preliminary release of gridded SO2 emissions from 2000-2019 based on the 2021_04_21 CEDS release with direct inclusion of point sources as time series. This release contains global grids at 0.1 degree resolution.</p>
Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019)
<h4><strong>Publication</strong></h4> <p>Please cite this publication if you use the dataset:</p> <p>Sarrazin, F. J., Attinger, A., Kumar, R., Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019), submitted to Earth System Science Data.</p> <p>Please also refer to the above publication for methodological details.</p> <h4><strong>License</strong></h4> <p>The "Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019)" is freely available under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0, https://creativecommons.org/licenses/by-nc-sa/4.0), in compliance with the terms of use of the Food and Agriculture Organization of the United Nations (FAO) data and German Cosmetic, Toiletry, Perfumery and Detergent Association (IKW) data that underlie the dataset.</p> <h4><strong>Data description</strong></h4> <p>The dataset includes estimates of nitrogen (N) and phosphorus (P) emissions from wastewater in Germany (1950-2019) at (1) grid level, and at different levels of aggregation, namely (2) at Nomenclature of Territorial units for statistics level 1 (NUTS-1), that correspond to the 16 German federal states (for this, we used the 2020 NUTS classification; BKG, 2020) and (3) at river basin level for 3778 river basins of the HydroBASINS v1.c of the HydroSHEDS database (HydroSHEDS, 2014; Lehner and Grill, 2013). It also includes the input and calibration data (at NUTS-1 and grid level) that were used to estimate the N and P emissions.</p> <ol> <li> <p>Input and calibration data at NUTS-1 level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: NUTS-1<br><em><strong>time period</strong></em>: 1950-2019 (input data), 1987-2019 (calibration data)<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: input data, calibration data, parameter sample<br><strong><em>file format</em></strong>: CSV<br><em><strong>number of files</strong></em>: 3<br><br></p> </li> <li> <p>Input data at grid level:<br><strong><em>spatial extent</em></strong>: Germany<br><em><strong>spatial resolution</strong></em>: 0.015625°<br><strong><em>time period</em></strong>: 1950-2019 (population data), 2020 (NUTS-1 map)<br><strong><em>frequency</em></strong>: annual<br><strong><em>variables</em></strong>: urban and rural population counts, NUTS-1 map<br><em><strong>file format</strong></em>: netCDF<br><strong><em>number of files</em></strong>: 2<br><br></p> </li> <li>Emission data at grid level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: 0.015625°<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: <br>- N and P wastewater treatment plants (WWTPs) outgoing emissions (treated point sources)<br>- N and P emissions collected in the public sewer system that are not treated in WWTPs (untreated point sources)<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 200 realisations corresponding to 100 different parameter sets and 2 spatial disaggregation methods for the treated point sources<br><em><strong>file format</strong></em>: netCDF<br><em><strong>number of files</strong></em>: 100<br> </li> <li>Emission data aggregated at NUTS-1 (federal state) level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: NUTS-1<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: <br>- N and P gross emissions<br>- N and P total point sources (sum of treated and untreated components)<br>- N and P treated point sources (WWTPs outgoing load)<br>- N and P untreated point sources (emissions collected in the public sewer system that are not treated in WWTPs)<br>- N and P emissions that are removed during treatment in WWTPs<br>- N and P emissions lost during wastewater collection and transport<br>- N and P emissions applied to agricultural soils in sewage farms<br>- N and P emissions that are not collected in the sewer system nor treated in WWTPs<br>- N and P incoming WWTPs load<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 100 realisations corresponding to 100 different parameter sets<br><em><strong>file format</strong></em>: CSV<br><em><strong>number of files</strong></em>: 18<br> </li> <li>Emission data aggregated at river basin level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: river basins from the HydroBASINS v1.c of the HydroSHEDS database (HydroSHEDS, 2014; Lehner and Grill, 2013).<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: N and P WWTP outgoing emissions (treated point sources), N and P emissions collected in the public sewer system that are not treated in WWTPs (untreated point sources)<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 200 realisations corresponding to 100 different parameter sets and 2 spatial disaggregation methods for the treated point sources<br><em><strong>file format</strong></em>: CSV<br><em><strong>number of files</strong></em>: 600</li> </ol> <h4><strong>Acknowledgements and underlying datasets</strong></h4> <p>Partial support for this work was provided by the Global Water Quality Analysis and Service Platform (GlobeWQ) project financed by the German Ministry for Education and Research (grant number 02WGR1527A). We thank Olaf Büttner for providing the WWTPs data that were collected from the authorities of the German federal states (<a href="https://www.hydroshare.org/resource/2f2c2fa04e6e417ba0eb7b0fb14b1090/">Büttner et al., 2020</a>). The dataset produced in this work builds on the NUTS map of the German Federal Agency for Cartography and Geodesy © GeoBasis-DE/BKG that is under a <a href="https://www.govdata.de/dl-de/by-2-0">dl-de/by-2-0 license</a>; the History Database of the Global Environment (HYDE) dataset available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>; protein data provided by the Food and Agriculture Organization of the United Nations © FAO provided under a <a href="https://creativecommons.org/licenses/by-nc-sa/3.0/igo/">CC BY-NC-SA 3.0 IGO license</a>; detergent data from the German Cosmetic, Toiletry, Perfumery and Detergent Association © IKW (<a href="https://www.ikw.org/impressum">license here</a>); data from the statistical offices of Germany and the federal states and the German and federal state authorities (details on data sources in the publication reported above: Sarrazin et al., submitted to Earth System Science Data); WWTP data available in the Waterbase dataset from the European Environment Agency © EEA under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>. The river basins come from © HydroSHEDS (<a href="https://www.hydrosheds.org/terms-of-use">license here</a>).</p> <h4><strong>Contact</strong></h4> <p>Fanny Sarrazin (<a href="mailto:fanny.sarrazin@ufz.de">fanny.sarrazin@inrae.fr</a>)<br>Rohini Kumar (<a href="mailto:rohini.kumar@ufz.de">rohini.kumar@ufz.de</a>)</p> <h4><strong>References</strong></h4> <p>BKG (Bundesamt für Kartographie und Geodäsie) (2020), NUTS regions 1 : 250 000, 31.12.2020, GeoBasis-DE [data set], Leipzig, Germany, https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html (last access: 1 November 2022).</p> <p>Büttner, O. (2020), DE-WWTP - data collection of wastewater treatment plants of Germany (status 2015, metadata), HydroShare [data set],<br>https://doi.org/10.4211/hs.712c1df62aca4ef29688242eeab7940c.</p> <p>HydroSHEDS (2014), HydroBASINS v1.c, https://www.hydrosheds.org/products/hydrobasins (last access: 23 October 2023).<br><br>Lehner, B. and Grill, G. (2013), Global river hydrography and network routing: baseline data and new approaches to study the world's large river<br>systems, Hydrological Processes, 27, 2171–2186, https://doi.org/10.1002/hyp.9740.</p> <p><strong>Changes compared to v1.0 dataset version</strong></p> <p>Compared to the version 1.0 of the dataset, this version v1.1 contains the input and calibration data at NUTS-1 level and the input data at grid level used to calculate the emissions.</p>
Martinelli Snowfield Grid Points, Niwot Ridge LTER Project Area, Colorado
A point coverage for the Martinelli grid. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.
H2020 Platone German Demonstrator - Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are measured values of active power , measured at the low voltage busbar of the medium voltage (MV)/low voltage (LW) grid connection point (secondary substation). The data are provided as 15-minutes mean values in kilowatt.</p> <p><strong>Data Description</strong></p> <ul> <li>p_tei_mean = arithmetic mean of p_tei measured in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_min = the minimum value (1-minute mean) measured within the period of p_tei_mean (15-minutes)</li> <li>p_tei_max = the maximum value (1-minute mean) measured within the period of p_tei_mean period (15-minutes)</li> <li>p_tei_count = the number a 1-minute mean values available for each15-minute interval.</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The substation includes a transformer feeding a low voltage grid, consisting of 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
West Africa Coastal Vulnerability Mapping: Point and Gridded Locations of Fatalities, 2008-2013
The West Africa Coastal Vulnerability Mapping: Point and Gridded Locations of Fatalities, 2008-2013 data set consists of two layers: points representing the location of conflict events with fatalities within 200 kilometers from the coast during the time period from 2008 to 2013, and a raster layer created from the points using a kernel density interpolation of the number of fatalities. These layers were created from the Armed Conflict Location and Event Dataset (ACLED), which codes the dates and locations of all reported political violence events in over 50 developing countries. Political violence includes events that occur within civil wars and periods of instability. Armed conflict reduces human security and increases the sensitivity of populations to climate stressors.
Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates, Revision 11
The Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates, Revision 11 consists of UN WPP-adjusted population estimates and densities for the years 2000, 2005, 2010, 2015 and 2020, as well as the basic demographic characteristics (age and sex) for the year 2010. The data set also includes administrative name, land and water area, and data context by administrative Unit center point (centroid) location. The center points are based on approximately 13.5 million input administrative Units used in GPWv4, therefore, these files require hardware and software that can read large amounts of data into memory.
ASO L4 Lidar Point Cloud Digital Terrain Model 3m UTM Grid V001
This data set provides 3 m gridded, bare-earth elevations (excluding trees) that are used as the baseline for the Airborne Snow Observatory (ASO) snow-on products. The data were collected during snow-free conditions as part of the NASA/JPL ASO aircraft survey campaigns.
A grid point-based zonal displacement approach in building generalization
<p>Data used in the research</p>
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