The VIIRS/NOAA20 Deep Blue Level 3 daily aerosol data, 1x1 degree grid, Short-name AERDB_D3_VIIRS_NOAA20 product is derived from the Version-2.0 (V2.0) L2 6-minute swath-based products (AERDB_L2_VIIRS_NOAA20), and is provided in a 1x1 degree horizontal resolution grid. Each data field, in most cases, represents the arithmetic mean of all the cells whose latitude and longitude coordinates positions them within each grid element’s bounding limits. Other measures like standard deviation are also provided. This aggregated product is derived only using the best-estimate, QA-filtered retrievals. Using only cells that were measured on the day of interest, the algorithm requires at least three retrieved measurements to render a given grid as valid on any given day. This daily product record starts from January 5th, 2018. This L3 daily product, in netCDF, contains 45 Science Data Set (SDS) layers.For more information about the product and Science Data Set (SDS) layers, consult product page at:https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_VIIRS_NOAA20OrConsult Deep Blue aerosol team Page at: https://deepblue.gsfc.nasa.gov

The ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G16, derived by aggregating the L3 daily (AERDB_D3_ABI_G16) input data, each M3 ABI/GOES-16 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the short-name as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Monthly Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO)) instruments.The AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at:https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_ABI_G16Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue

This dataset was created to facilitate field-based invasive plant surveys. Each grid-cell constitutes a possible survey area within which a field surveyor documents the presence/absence of a suite of target species. The grid layer is displayed on a mobile device (smartphone or tablet) allowing the surveyor to navigate to the grid cell and establishes the geospatial boundaries of each survey.

This is a polygon feature data layer of United States National Grid (1000m * 1000m polygons ) for Western Arizona (UTM Zone 11)

The VIIRS/SNPP Water Vapor Level-3 daily 0.5 x 0.5 degree grid Product provide total column water vapor (TPW) properties from merged VIIRS infrared measurements and Cross-track Infrared Sounder (CrIS) plus Advanced Technology Microwave Sounder (ATMS) water vapor soundings to continue the depiction of global moisture at a higher spatial resolution started with MODIS on the Terra and Aqua platforms. Level-3 global 0.5 degree by 0.5 degree spatial resolution daily mean data products (called WATVP_D3_VIIRS_SNPP) is derived by using a gridding software (called Yori) developed at the University of Wisconsin, Madison, Space Science and Engineering Center (Veglio et al., 2018), and implemented by the NASA VIIRS Atmosphere Science Investigator-led Processing System (SIPS). The Yori has been adapted for the VIIRS TPW products and is processed using the VIIRS Level-2 Water Vapor products (WATVP_L2_VIIRS_SNPP) separated by day and night. The mean and the standard deviation of each Level-2 water vapor product are calculated for each grid cell. The sum, the square of the sum of each product, and the number of pixels in the cells are also stored in the Level-3 (daily) output files for further aggregation purposes.

This is a polygon feature data layer of United States National Grid (1000m x 1000m polygons ) constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. For correct display, please set the base coordinate system and projection such that it matches the UTM zone for which these data were constructed using the NAD 83 datum. Further information about the US National Grid is available from http://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, http://www.nationalmap.gov. The name of each dataset has the following format - StateAbbv_USNG_UTMXX. For example, for the UTM zone 15 of Mississippi, the dataset is named MS_USNG_UTM15.

Sourcing accurate and up-to-date GIS data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent GIS data across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

GapMaps GIS data for Asia and MENA can be utilized in any GIS platform and includes the latest Demographic estimates (updated annually) including:

1. Population (how many people live in your local catchment)
2. Census Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

GapMaps GIS Data also includes Point-Of-Interest (POI) Data updated monthly across a range of categories including Fast Food, Cafe, Health & Fitness and Supermarket/ Grocery

Primary Use Cases for GapMaps GIS Data:

1. Retail Site Selection - identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Integrate GapMaps GIS data with your existing GIS or BI platform to generate powerful visualizations.

MISR monthly, global 1 x 1 deg grid 'Clim-Likely' aerosol climatology, derived from 'typical-year' aerosol transport model results available in 1999.

This dataset contains the Coded Surface Bulletin (CSB) dataset reformatted as netCDF-4 files. The CSB dataset is a collection of ASCII files containing the locations of weather fronts, troughs, high pressure centers, and low pressure centers as determined by National Weather Service meteorologists at the Weather Prediction Center (WPC) during the surface analysis they do every three hours. Each bulletin is broadcast on the NOAAPort service, and has been available since 2003.

Each netCDF file contains one year of CSB fronts data represented as spatial map data grids. The times and geospatial locations for the data grid cells are also included. The front data is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded. The front polylines from the CSB dataset are rasterized into the appropriate data grids. Each file conforms to the Climate and Forecast Metadata Conventions.

There are two large groupings of the CSB netCDF files. One group uses a data grid based on the North American Regional Reanalysis (NARR) grid, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.

The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.

There are a number of files within each group. The files all follow the naming convention codsus_[masked]_.nc, where [masked] indicates that the presence of the word masked is optional and is either merra2-1deg or narr-96km. The element is either the word mask or the sequence wide_, where is the front width and is the year for the data stored in the file.

The codsus_mask.nc file is a file containing a single data grid that delineates the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year. The WPC meteorologists don't attempt to provide equal levels of attention to every grid cell displayed on their workstations. The files of the form codsus_masked_wide_.nc have all had the mask described above applied to exclude parts of fronts that extend past the envelope. The files of the form codsus_wide_.nc have no masking applied.

The wide portion of the file names takes two forms—1wide and 3wide. The fronts in the1wide files were rasterized by drawing the front polylines with a width of one grid cell. The fronts in the 3wide files were rasterized by drawing the front polylines with a width of 3 grid cells.

Within each grid group, there are five subsets of files:

codsus_masked_1wide_.nc

codsus_masked_3wide_.nc

codsus_1wide_.nc

codsus_3wide_.nc

codsus_mask.nc

The primary source for this dataset is an internal archive maintained by personnel at the WPC and provided to the author. It is also provided at DOI 10.5281/zenodo.2642801. Some bulletins missing from the WPC archive were filled in with data acquired from the Iowa Environmental Mesonet.

Tomographic data acquired by a combination of a grid scan and half-acquisition scans (offset center-of-rotation in respect to the grid as a whole) was used to demonstrate data-processing methods in the paper:

"Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448

To extract data, you firstly collect all the files together (there are 47 files uploaded to 24 zenodo sections: *Part01, *Part02, ...*Part24) and run "zip -F grid_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"

The grid values ​​of the wind speed and the Weibull parameters were generated from specially quality-checked 218 ground stations with the statistical wind field model (SWM). The height above sea level was taken into account, as was the geographic location, the terrain and the type of land use.
Further information: https://opendata.dwd.de/climate_environment/CDC/grids_germany/multi_annual/wind_parameters/resol_200x200 /BESCHREIBUNG_gridsgermany_resol_200x200_de.pdf

The test section of the rod bundle experimental facility at HZDR consists of a vertically aligned PMMA channel with an upward flow of the working fluid. The cross-section of the channel is quadratic (inner edge length: 37 mm) and contains nine directly electrically heated rods (material: titanium-alloy, diameter: 10 mm, wall thickness: 0.3 mm) which are arranged in an orthogonal 3 by 3 matrix (rod axis distance: 12.8 mm). Circa 190 mm downstream of the start of the heating zone a 30 mm long spacer for the rods with tilted flow guiding vanes is mounted. These vanes are aimed to increase lateral flow velocities within the subchannels. Working fluid was octafluorocyclobutane (CAS 115-25-3, RC318). The experimental facility is comprehensively instrumented for measurement of flow, temperature and pressure/pressure difference. For non-invasive three-dimensional high-resolution measurement of a temporally averaged volumetric void fraction within the working fluid flowing around the heating rods in the subchannels an X-ray computer tomography measurement system was set up.
The presented dataset contains measurement data of the experimental facility's instrumentation and tomographic void fraction data of experiments with four different configurations of the flow guiding vanes (without vanes, 20°, 29°, 40°) for four different flow velocities between 0.4 m/s and 1.3 m/s at a heat flow density of 85.7 kW/m².

The ABI G17 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G17, derived from the L2 (AERDB_L2_ABI_G17) input data, each D3 ABI/GOES-17 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments.The AERDB_D3_ABI_G17 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at:https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_ABI_G17Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue

Mapped Moments to a Cartesian Grid (MMCG), is taking geographic or polar coordinates and mapping the radar data to a Cartesian grid. Such a method, is useful to data users who are not familiar with radar coordinates. It is also useful to map radar data from radar coordinates to Cartesian coordinates, because some schemes for data assimilation require a level of objective analysis.

Blockchain data dashboard: eth(x,y) Grid Plot Progress

This data release contains the North American Bat Monitoring Program (NABat) Master Sampling Grid at the 5 km x 5 km scale with biologically relevant covariates for NABat analyses attributed to each cell of the 5 km x 5 km grid frame for the continental United States. It was created using ArcPro and the 'sf', 'tidyverse', 'dplyr' and 'exactextractr' packages in R to extract covariates from multiple data sources following the 10 km x 10 km attributed grid process as well as adding additional covariates. These covariates include the habitat characteristics such as percent of wetlands, forest, deciduous and coniferous forest, dominant and subdominant oak types, the number of tree and oak species, topographic features such as physiographic diversity, elevation, and the presence of karst terrain features or water feature, climate variables such as mean temperature and precipitation, and subterranean human structures such as the number and length of culverts. This layer provides the predictive covariates used in the integrated species distribution model for tricolored bats (Perimyotis subflavus, see External Related Resources). The attributed grid can also support future modeling efforts and data visualizations.

The HTXP85 TTAAii Data Designators decode as: T1 (H): Grid point information (GRIB) T1T2 (HT): Temperature A1 (X): Global Area (area not definable) A2 (P): 132 hours forecast T1ii (H85): 850 hPa (Remarks from Volume-C: H+ 132 (GLOBAL MODEL) TEMPERATURE 850 HPA)

Data on resilience of wheat yields in England, derived from the annual Defra Cereals and Oilseeds production survey of commercial farms. The data presented here are summarised over a ten-year time-series (2008-2017) at 10km x10km grid cell (hectad) resolution. The data give the mean yield, relative yield, yield stability and resistance to an extreme event (the poor weather of 2012), for all hectads with at least one sampled farm holding in each year of the time-series (i.e. the minimum data required to calculate the resilience metrics). These metrics were calculated to explore the impact of landscape structure on yield resilience. The data also give the number of samples per year per hectad, so that sampling biases can be explored and filtering applied. No hectads are included that contain data from <9 holdings across the time series (the minimum level required by Defra to maintain anonymity is <5). The data were created under the ASSIST (Achieving Sustainable Agricultural Systems) project by staff at the UK Centre for Ecology & Hydrology to enable exploration of the impacts of agriculture on the environment and vice versa, enabling farmers and policymakers to implement better, more sustainable agricultural practices.

This data represents the map extent for current and historical USGS topographic maps for New Mexico, Cell Grid 1 X 2 Degree. The grid was generated using ESRI ArcInfo GIS software.

Tomographic data acquired by a combination of a grid scan and half-acquisition scans (offset center-of-rotation in respect to the grid as a whole) was used to demonstrate data-processing methods in the paper:

"Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448

To extract data, you firstly collect all the files together (There are 47 files uploaded to 24 zenodo sections: *Part01, *Part02, ...*Part24) and run "zip -F grid_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"