Exploring Data and Models
- Making sense of data (e.g. summarizing, visualizing, and synthesizing)
- Relating separate types of data/integrating data of varying data types through advanced statistical techniques (e.g.multivariate analysis)
- Developing interfaces using real world data from scientific repositories
- Basic graph interpretation skills (MATLAB helps plot functions and see how they change)
- Contextualized examples to increase engagement
- Comparing output of different numerical/analytical approaches
- Examining simple cause and effect relationships in Geoscience datasets to help motivate student learning
- Using "big data"- Geological data incorporates all types of data across temporal and spatial scales, as well as physical and numerical models. MATLAB provides a platform aggregate, analyze and visualize disparate datasets
Why use MATLAB?
In addition to the overarching benefits of MATLAB, there are specific benefits of exploring data and models using MATLAB, including:
- Data manipulation with minimal coding skills
- An interface that allows for comparison of the data to the visual
- Simultaneously processing and displaying many types of data
- Large online technical and scientific support community
Approaches to Getting Students Started
Many students have previous programming experience using other software, such as Microsoft Excel, or from other contexts, such as a computer science course. This background can be built on, transitioning students to the more complex quantitative analyses using MATLAB. Class instruction may include structured activities such as modeling fundamental Earth Science formulas such as heat flow through oceanic lithosphere, or collecting data in lab and using MATLAB to load, plot, and analyze their data. Using MATLAB's advanced and "easy-to-learn/use" analytical and visualization functions and toolboxes, provides students with a more comprehensive and holistic learning experience. Students benefit from having independent access to MATLAB on their own computers, allowing them to gain familiarity through regular practice and working on their own outside of class.
- Join the Teaching with MATLAB in the Sciences Community to discuss ideas and get expert answers.
- Using Univariate Statistics to Understand Regional Drainage Patterns, Peter Adams. Students use MATLAB to compare two data sets of organic matter content in order to provide quantitative evidence that tests the null hypothesis that sediment samples have the same fluvial source.
- Thermal Evolution of the Oceanic Lithosphere, Mark Behn. Students take real seafloor bathymetry data and determine how well it compares to a simple half-space cooling model.
- Signal processing and earthquake triggering, Jackie Caplan-Auerbach. Students use MATLAB to analyze waveforms from the 2004 Sumatra M9.0 earthquake, as they were recorded on three seismic stations in Alaska.
- Intra-annual variability of Sea Surface Temperature and Data Interpolation, Andrew Fischer. Students will learn to work with scientific data files (e.g. NetCDF) to extract, using Matlab, sea surface temperature data, plot this data and interpolate for missing values.
- Working with scientific data sets in MATLAB: An Exploration of Ocean Color and Sea Surface Temperature, Andrew Fischer. Students will be introduced to the concepts of remote sensing, ocean color and using Matlab to import and manipulate and visualize scientific datasets.
- three-Point Problem by Simultaneous Linear Equations William Frangos. The exercise can also serve as an intuitive springboard for subsequent learning in data fitting, parameter estimation, experiment design, data resolution, and factor analysis.
- Course: Quantitative Data Analysis, Scott Marshall. The main goal of this course is to provide a computational and quantitative skill set relevant for processing, filtering, analyzing, and visualizing quantitative Earth science data efficiently and accurately.
- Course: Data Analysis, David Heslop. The aim of this course is to provide an introduction to statistical and numerical techniques that are useful in the analysis and characterization of geological data.