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Since then, we have continued to merge these data with other data sets, so that we can better understand the demography of displacement, spatiotemporal concentrations of loss, speculators and developers profiting from dispossession, infrastructural relations, as well as more intimate experiences of loss and resistance.
However, because the nature and number of deficits differed between the studies, it was not feasible to merge these data.
It was not obvious that scientists would seek to share and merge these data sets into immense collections of publicly available data.
Computational constraints linked to the software used for the Ecological Niche Factor Analysis (below), led us to merge these data into 5 different land cover types including: (1) dense evergreen forest, (2) deciduous woodland, (3) forest/savanna mosaic, (4) dry savanna and (5) croplands.
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We merged these data with information on personal characteristics from municipal registrations (GBA) and data on jobs involved in firm bankruptcies in the period 2000 2009.
We merged these data to perform the meta-analysis as follows.
We merged these data with the MS5795 human data set at the 246 loci shared by the chimpanzee data set.
We then merged these data with facility-level data on use of RHIS information and its potential determinants.
We then merged these data into a grid of 37 427 800 × 800 m cells using ArcGIS 9.0 (ESRI 2009).
We set the tumor sample to (-1) and the normal samples to (+ 1), and we then merged these data sets together for the 8 methods.
We merged these data as follows: if a given sample was present on both platforms, we kept only the HiSeq version.
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CEO of Professional Science Editing for Scientists @ prosciediting.com