The purpose of the SNF study was to improve our understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology. Parameters studied were biomass, leaf area index, above ground net primary productivity, bark area index and ground coverage by vegetation. Thirty two quaking aspen and thirty one black spruce sites were studied. Sites were chosen in uniform stands of aspen or spruce. The dominant species in the site constituted over 80 percent, and usually over 95 percent, of the total tree density and basal area. Aspen stands were chosen to represent the full range of age and stem density of essentially pure aspen, of nearly complete canopy closure, and greater than two meters in height. Spruce stands ranged from very sparse stands on bog sites, to dense, closed stands on more productive peatlands. Use of multiple plots within each site allowed estimation of the importance of spatial variation in stand parameters. Within each plot, all woody stems greater than two meters in height were recorded by species and the following dimensions were measured: diameter breast height, height of the tree, height of the first live branch, and depth of crown. For each plot, a two meter diameter subplot was defined at the center of each plot. Within this subplot, the percent of ground coverage by plants under one meter in height was determined by species. These data, averaged for the five plots in each site, are presented in this data set (i.e., SNF Forest Understory Cover Data (Table)) in tabular format, e.g. plant species with a count for that species at each site. The same data are presented in the SNF Forest Understory Cover Data data set but are arranged with a row for each species and site and a percent ground coverage for each combination.

Summary Data Table for Land Cover in AG and Rural Residential zones. Data created 3/2018 by Spatial Alternatives for The Ad-Hoc Committee on Auburn's Agriculture and Natural Resource. Data source: 2013 Aerial Imagery. Source 2013 Aerial Imagery.See also Land Cover PDF Maps.

The data is published using the Summary Data Template, Version 1.1 as of 05 March 2015. According to the EITI Standard 5.3.b: "Summary data from each EITI Report should be submitted electronically to the International Secretariat according to the standardised reporting format provided by the International Secretariat" This template should be completed in full and submitted by email by the national secretariat to the International EITI Secretariat following the publication of the report. The data will be used to populate the global EITI data repository, available on the international EITI website. NB: The data available on ResourceData is republished from the EITI API and covers one section of the Summary Data, Part 3 which is comprised of data on government revenues per revenue stream and company. Notes for consideration: Disclaimer: The EITI Secretariat advice that users consult the original reports for detailed information. Where figures are not available in US dollars, the annual average exchange rate is used. Any questions regarding the data collection and Summary Data methodology can be directed to the EITI Secretariat: data@eiti.org or by visiting eiti.org/summary-data Data and Resources

CVS_GNDCOVER_TBL:

The ground cover data table contains vegetative and non-vegetative cover data from a ground layer and an aerial layer, measured along a sample plane consisting of five consecutive 10-foot linear segments. This data was collected during the initial installation projects only.

A complete list of live websites using the Responsive Data Table technology, compiled through global website indexing conducted by WebTechSurvey.

A complete list of live websites using the angular-data-table technology, compiled through global website indexing conducted by WebTechSurvey.

SNF study location measurements of percent ground coverage provided by each understory species; percentages are averages of five 2-meter-diameter subsamples in each site (presented in table format)

This table contains estimates for upland sediment erosion and delivery to the stream estimated using a modified Revised Universal Soil Loss Equation 2 (RUSLE2) approach (USDA-ARS, 2013). Upland erosion (Eu) was calculated as the sum of the product of the erosivity factor (Rm) estimated for each month, the soil erodibility factor (K), the length-slope factor (LS), C-factor (Cm) for each month, farming support practices (P) assumed to be 1, and the area of each land cover grouping in acres. Rasters with 10-m resolution were created for these five variables in the modified RUSLE2 equation to create an upland erosion (Eu) raster for the study area. Data were calculated for each National Hydrography (NHD) Dataset Version 1 catchment in the Difficult Run watershed.

These data are points depicting the location of each camera managed by CitiWatch.

Attribute information includes camera number and nearest street intersection or street address.

Data updates on a rolling basis as new cameras are brought online or old cameras are decommissioned. To leave feedback or ask a question about this dataset, please fill out the following form: CitiWatch Camera Locations feedback form.

For predict expected size of the crops in a future date, predict for batter yield and profited, we use a dataset containing various information in the four XLSX file, the first XLSX file contain information about plants with 4895 rows and 15 columns of numerical along with column.The second flight data, which contain batch Number and Flight Date which date measurements were taken from the drone, the third sheet planting file from which we take planting date. And the fourth sheet on plants and weather conditions. In the plant chart, we want to predict the Head Weight, Radial diameter, Polar diameter under certain weather condition. We have some missing values in the plant table. So we deal by fill these values by taking mean. We add flight date column from flight date sheet. The flight data table contains batch numbers and flight data along with numeric values. The flight data table has 50 rows of data and 2 columns named batch number and flight date. No value is missing in this table. Adding 1 table of data, we have a total of 2373 rows of data and 10 columns of data. In this data table, some data values are missing in all the columns. There are 2556 rows and 14 columns of numeric data. The values of Dew Point [avg], Dew Point [min] and ET0 [result] columns are missing in this data table

Dataset extracted from the post Exit Load in Mutual Funds: What Every Investor Must Know on Smart Investello.

Fig 1 Complete Data Table from Conservation of Breast Cancer Molecular Subtypes and Transcriptional Patterns of Tumor Progression Across Distinct Ethnic Populations

The data sets in the data table have been collected as part of a project to understand how reduced sea ice cover in the Arctic will impact polar bear populations. Bears that stay ashore in summer have almost no access to food and tend to be inactive. Those that stay on the ice, however, have continued access to prey and make extensive movements. Over a three year period, scientists from the University of Wyoming and the United States Geological Survey (USGS) followed the movements of bears in both habitats and monitored their body temperature, muscle condition, blood chemistry, and metabolism. The physiological data will be added to spatially-explicit individual-based population models to predict population response to reduced ice cover.

This dataset links to the Long Term Ecological Research (LTER) Florida Coastal Everglades (FCE) Core Research Data Table of Contents (DTOC). The DTOC contains links to 173 individual datasets, which may be queried from the DTOC page. FCE Core Research Data are long-term data sets that address FCE LTER objectives and hypotheses, and that are supported primarily by LTER funds. All data are provided with accompanying metadata. Metadata includes details about the data including how, when and by whom a particular set of data was collected, and information regarding the data format. The FCE practice of dataset versioning has been discontinued as of March 2013. All long-term data will have new data appended to the file and the accompanying metadata will be updated. FCE data may be freely downloaded with as few restrictions as possible. Consultation or collaboration with the original investigators is strongly encouraged. Please keep the dataset originator informed of any plans to use the dataset, and include the dataset's proper citation and Digital Object Identifier (DOI) found under the 'How to cite these data' on the dataset's summary table. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/FlCoastalEverglades_eaa_2015_March_19_1527

This data release contains files that are associated with the publication "Understanding and predicting iron fouling associated with drainage from roadway sites constructed with rock-fill in New Hampshire, USA". Each zipped file contains a data table or raster file and an associated metadata file. IronFoulingCutFillData.zip contains a .csv file and .xml metadata file related to the roadcut and rock-fill locations used to develop a statistical model to predict the probability of iron fouling at roadway sites constructed with rock-fill in New Hampshire. Water_Quality_Measurements.zip contains a .csv file and .xml metadata file for water quality field measurements made at drainage areas from a subset of the rock-fill locations in the IronFoulingCutFillData files. The NHIronFoulingProb.zip file contains a geotiff raster of the predicted probability of iron fouling occurring at any _location within New Hampshire where rock-fill from that _location is used and contacts drainage. This is the output from the statistical model prediction. NHIronFoulingProb.zip also contains an .xml metadata file.

No description is available. Visit https://dataone.org/datasets/6cf3ed81b1a3bc298499a0e214efe70f for complete metadata about this dataset.

Dataset extracted from the post 7 Powerful Reasons Term Insurance Remains the Purest Life Cover in 2025 on Smart Investello.

Dataset extracted from the post Complete Mutual Fund Guide 2025: 10 Essential Lessons Every Beginner Must Learn on Smart Investello.

Marsh Distribution (2010)This dataset was created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer depicting potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The purpose of this data is to highlight marsh type distribution. Tiles have been cached down to Level ID 15 (1:18,055). This initial (2010) land cover condition is derived from the Coastal Change Analysis Program (C-CAP) land cover (http://www.coast.noaa.gov/digitalcoast/data/ccapregional/), and is dependent upon the accuracy of that classification. The dataset should be used only as a screening-level tool for management decisions. As with all remotely sensed data, all features should be verified with a site visit. The dataset is provided "as is," without warranty to its performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of this dataset is assumed by the user. This dataset should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes. For more information visit the Sea Level Rise Impacts Viewer (http://coast.noaa.gov/slr).

Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps:

* Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years.
* Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years.
* Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added.
* The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years.
* Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities.

As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.