Data Analysis and Mining
Data analysis techniques include post-processing (e.g., data statistics) of experimental datasets and/or simulation output, as well as the use of mathematical methods (e.g., filtering data) and statistical tests. Data mining usually refers to the application of more advanced mathematical techniques such as classification, clustering, pattern recognition, etc.
Quick Links: NERSC Tools for Data Analysis and Data Mining
Goals To advance in detection, characterization, analysis and predictions of biological cells and their inherent heterogeneity in terms of shape, texture, color, and correlations with metadata. We seek providing services that will allow measurements using digital images of micrographs. As an example, we will build image analysis algorithms that take time slices of stacks of two-dimensional images, representing three-dimensional structures: these images are taken at key moments in cell… Read More »
Goals * Collect experimental 2D-3D imaging data in order to investigate fluid-fluid and fluid-rock interactions;* Provide algorithms for better understanding of processes governing fluid-fluid and fluid-rock systems, related to geologic sequestration of CO2;* Develop image processing methods for analyzing experimental data and comparing it to simulations;* Detect/reconstruct material interfaces, quantify contact angles, derive contact angle distribution, etc. Impact * Unveil knowledge required… Read More »
We have developed partial differential equations-based tools that perform analysis of porous materials. These tools involve the application of the Fast Marching Method (FMM) to predict if a molecule can traverse through a channel system representing void space of the materials, map accessible parts of these void spaces and calculate accessible volumes and surfaces. (More… Read More »