In this manuscript we report on the design, implementation and testing of a new platform, SOCR Data Dashboard, for exploratory querying of heterogeneous and multi-source datasets.
There is a need for cheap methods for digital soil mapping on intermediate scales that make optimal use of existing multi-source datasets on both general and detailed scales.
This article concentrates on the multivariate analysis of multi-way, multi-source datasets.
Armed with a comprehensive understanding from such validations, we can improve and integrate multi-source datasets to inform carbon monitoring efforts.
We formulate the new model for the multivariate analysis of multi-way, multi-source datasets under the so-called 'large p, small n' conditions (high dimensionality p, small number of observations n) as a hierarchical Bayesian machine learning model.
As more and more biological multi-source datasets with paired samples integrating data from different measurement techniques and/or tissues are becoming available, we expect the method to find wide applicability.
Hence, it was striking that the method was capable of finding covariate effects in a real-world lipidomic multi-way, multi-source dataset.
So-called "ontology-based" approaches for the semantic integration of multi-source geographical datasets may be used to give a firm conceptual basis to digital soil mapping from multi-scale, multi-source geographic data.
In order to investigate "ghost cities", we proposed a feasible framework by utilizing multi-source remote sensing datasets, including nighttime light imagery, land cover type products and population grid.
Fig. 1 Overview of the source dataset "contaminated_area".
The source dataset integration augments the network mainly in PPIs.
