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Our findings highlight emergent synergies between complementary data sources that foster the generation of new information products for forest resources management decision support.
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The goal was to discern dependencies between the gene expression patterns and the physical inter-molecular interactions revealed by complementary data sources.
The addition of complementary data sources may improve the accuracy and usefulness of data from any one source.
That is, the company went from offering dashboards to offering insights by adding complementary data sources and enabling online-to-offline attribution and engagement in physical locations.
Examples of complementary data sources are community surveys, census data from local governments, police information on geocoded offenses, and Systematic Social Observations (SPAN data sources are described in Bruinsma et al. [2013] and Janssen et al. [2014]).
We employ a number of complementary data sources, some covering the entire country and some being based on subsets of cantons.
Such agricultural modelling and simulations need access to elaborated geolocated genetic-trait information as well as complementary data sources coming from geospatial data providers and geospatial data hubs, e.g. soil moisture data, climate data.
Relying on distributed hosting of the complementary data sources compounds the management of multiple permissions even further because it also involves multiple permission management systems.
Complementary data sources can provide different prevalence estimates of chronic medical conditions in the general population.
By combining these two complementary data sources, the method is expected to uncover the TF-target relations.
Gene perturbation experiments (e.g. transcription factor (TF) knock-out) and ChIP-chip experiments serve as complementary data sources.
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