From MIT OpenCourseWare: “This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables.”
Explore sampling and the Fourier spectrums of signals by remotely operating equipment in the OpenEngineering Lab. The equipment contains a rotating disc, photodiodes and red LEDs. Requires a free Open University account.
Gravity and Orbits (PhET) (CC BY)
Illustrates the paradigm shift of Newton’s law of gravity and inertia that form circular orbits. Everyone loves to watch the planets move off in a straight line when you turn gravity off. A great activity or short “lab.”
From Go-Lab: “This lab can be used to grasp the concepts of power generation in an osmotic power plant. It is based on a simple model which incorporates geographical parameters. Students can choose a location for their osmotic power plant and compare it to the prototype in Norway.”
From MERLOT: “Software simulations of a variety of chemical reactors. Some modules have quizzes that are scored by the software. English, Spanish, and Portuguese language versions available.” In development by the same people are interactive simulations of physical systems.
A virtual lab using real data from a torsion testing machine. From the lab manual: “In this module, you will perform data reduction and analysis for circular cross section aluminum samples. By plotting the torque vs. twist data for aluminum and performing analyses of the data, you can infer relations between sample geometry, strength, and stiffness. Seeing how the samples deform and fail will also help you to better understand material behaviour under torsional loading.”