A statistical significance may be computed by pointing out those predictions that are very specific to the dataset - slightly changed dataset will no more generate a prediction.
To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway.
These two datasets are viewpoint change datasets widely used in visual SLAM research and in LCD in particular.
More recently, high-resolution land-change datasets, such as the US Geological Survey's Land Cover Trends (LCT) dataset have become available [34, 35].
We have developed (gene set matrix analysis) GSMA as a useful method for the rapid testing of group-wise up- or down-regulation of gene expression simultaneously for multiple lists of genes (gene sets) against entire distributions of gene expression changes (datasets) for single or multiple experiments.
To resolve this question, we used the Landsat-derived Global Forest Change dataset to estimate deforestation rates between 2000 and 2012 inside and outside of 60 nature reserves spread across the tropics.
Deforestation estimates derived from wall-to-wall photointerpretation, official records (e.g. felling licences), the CORINE land-use/land cover changes dataset and a combined approach (hereafter termed "the Deforestation Map") are compared in two regions in Ireland for the period 2000 to 2012.
The USGS topographic change dataset (http://topochange.cr.usgs.gov/) was used to identify mines and other major human-based changes in topography.
Spearman-rank-ordering correlation was additionally performed on the fold changes dataset at each time point as described in Methods.
A Shapiro-Wilk normality test for the fold change dataset was performed prior to the correlation analysis.
