Midterm examination for a class at MIT covering game theory and its applications to economics. The one-hour-and-twenty-minute open book examination asks open ended theoretical questions. The exam contains questions and solutions.

This OER explores the basic organization of the Pythagorean Solids. It contains both an activity as well as resources for further exploration. It is a product of the OU Academy of the Lynx, developed in conjunction with the Galileo's World Exhibition at the University of Oklahoma.

In this activity, students work with paleoclimate proxy data (d18O, CH4, CO2)from the Byrd and GISP2 ice cores to investigate millennial-scale climate changes during the Last Glacial/Deglacial time periods. Students must prepare a publication quality plot of the data and answer several questions about the similarities and differences between the time-series (north-south phasing, amplitude, symmetry) and use this information to assess the bipolar see-saw mechanism for abrupt climate changes. Students are encouraged to read two journal articles for more information and to synthesize their results with other information from lectures and earlier readings.

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The solutions unit consists of the following: General points for discussion relating to the teaching of the mathematical content in the activities. Step-by-step mathematical solutions to the activities. Annotations to the solutions to assist teachers in their understanding the maths as well as teaching issues relating to the mathematical content represented in the activities. Suggestions of links to alternative activities for the teaching of the mathematical content represented in the activities.

The solutions unit consists of the following: General points for discussion relating to the teaching of the mathematical content in the activities. Step-by-step mathematical solutions to the activities. Annotations to the solutions to assist teachers in their understanding the maths as well as teaching issues relating to the mathematical content represented in the activities. Suggestions of links to alternative activities for the teaching of the mathematical content represented in the activities

At the beginning of the course, each student is assigned a unique blob - or a piece of material of a particular shape with specific material properties (density, bulk modulus, composition, viscosity, volatile content, etc) that is residing within the mantle at a specific environment (depth, pressure, temperature). Then as the semester continues as a topic is covered the student must assess (either quantitatively or qualitatively) what observable would be associated with their blob (for example, gravity anomalies, geoid anomalies, surface expressions, seismic tomography, phase transition topography). The student then develops a portfolio of their blob and its observables to then present at the end of the course with an explanation/interpretation for the source of the blob culiminating at building a geo-story around their anomaly.

Some blobs could be amorphous anomalies whereas other could have physical significance (though best not to tell the students ahead of time so they can make their own discovery as to what the blob is or isn't) such as subducted slabs at the CMB (or 660 km), plumes, lithospheric drip, lithospheric root, or a boring typical piece of the mantle.

(Note: this resource was added to OER Commons as part of a batch upload of over 2,200 records. If you notice an issue with the quality of the metadata, please let us know by using the 'report' button and we will flag it for consideration.)

An open-source textbook covering vector calculus, ordinary and partial differential equations, and Fourier series. The textbook is used in a first-year graduate level course in the Department of Mechanical Engineering at the Colorado School of Mines. It undergoes extensive revisions annually, but is relatively complete.

Learning and Understanding Mathematical Concepts in the Areas of Water Distribution and Water Treatment

In this video, we explore some concepts fundamental to algebra. To streamline the discussion of relationships between physical quantities, we introduce variables, functions, composition, and inverse. By thinking about the concept of an inverse function, we obtain our first glimpse of the imaginary root (i.e. square-root of -1) and the complex plane.

This book aims to be an accessible introduction into the design and analysis of efficient algorithms. Throughout the book we will introduce only the most basic techniques and describe the rigorous mathematical methods needed to analyze them.

The topics covered include:

The divide and conquer technique.
The use of randomization in algorithms.
The general, but typically inefficient, backtracking technique.
Dynamic programming as an efficient optimization for some backtracking algorithms.
Greedy algorithms as an optimization of other kinds of backtracking algorithms.
Hill-climbing techniques, including network flow.

The goal of the book is to show you how you can methodically apply different techniques to your own algorithms to make them more efficient. While this book mostly highlights general techniques, some well-known algorithms are also looked at in depth. This book is written so it can be read from "cover to cover" in the length of a semester, where sections marked with a * may be skipped.

Esta guía práctica acompaña la serie de videos Poder estadístico y tamaño de muestra en R, de mi canal de YouTube Investigación Abierta, que recomiendo ver antes de leer este documento. Contiene una explicación general del análisis de poder estadístico y cálculo de tamaño de muestra, centrándose en el procedimiento para realizar análisis de poder y tamaños de muestra en jamovi y particularmente en R, usando los paquetes pwr (para diseños sencillos) y Superpower (para diseños factoriales más complejos). La sección dedicada a pwr está ampliamente basada en este video de Daniel S. Quintana (2019).

Python es un lenguaje de programación general que es útil para escribir scripts para trabajar con datos de manera efectiva y reproducible. Esta es una introducción a Python diseñada para participantes sin experiencia en programación. Estas lecciones pueden enseñarse en un día (~ 6 horas). Las lecciones empiezan con información básica sobre la sintaxis de Python, la interface de Jupyter Notebook, y continúan con cómo importar archivos CSV, usando el paquete Pandas para trabajar con DataFrames, cómo calcular la información resumen de un DataFrame, y una breve introducción en cómo crear visualizaciones. La última lección demuestra cómo trabajar con bases de datos directamente desde Python. Nota: los datos no han sido traducidos de la versión original en inglés, por lo que los nombres de variables se mantienen en inglés y los números de cada observación usan la sintaxis de habla inglesa (coma separador de miles y punto separador de decimales).

Spreadsheets across the Curriculum Activity. Student build spreadsheets that allow them to estimate pi using the same iterative process as Archimedes.

The Authenticity and Agency rubrics are based on elements from two frameworks: Student as Producer and Social Pedagogies. The rubrics were created for instructors and instructional designers to use as they develop authentic learning experiences in the course design process.

A Software Carpentry lesson to learn how to use Make Make is a tool which can run commands to read files, process these files in some way, and write out the processed files. For example, in software development, Make is used to compile source code into executable programs or libraries, but Make can also be used to: run analysis scripts on raw data files to get data files that summarize the raw data; run visualization scripts on data files to produce plots; and to parse and combine text files and plots to create papers. Make is called a build tool - it builds data files, plots, papers, programs or libraries. It can also update existing files if desired. Make tracks the dependencies between the files it creates and the files used to create these. If one of the original files (e.g. a data file) is changed, then Make knows to recreate, or update, the files that depend upon this file (e.g. a plot). There are now many build tools available, all of which are based on the same concepts as Make.

In the master-equation formalism, a set of differential equations describe the time-evolution of the probability distribution of an ensemble of systems. This can be used, for example, to describe the varied mRNA copy numbers found in individual cells in a population.

The stochastic simulation algorithm (SSA, Kinetic Monte Carlo, Gillespie algorithm) produces an example trajectory for a particular member of a probabilistic ensemble by looping over the following steps. The current state of the system is used to determine the likelihood of each possible chemical reaction in relative comparison to the likelihoods for the other possible reactions, as well as to determine when the next reaction is expected. Pseudo-random numbers are drawn to "roll the dice" to determine exactly when the next reaction will proceed, and which kind of reaction it will happen to be.

Companion Site for Harvard Medical School Canvas Network MOOC Best Practices for Biomedical Research Data Management. This Open Science Framework project site includes all the materials contained in the Canvas course including: readings and resources; slide presentations; video lectures; activity outlines; research case studies and questions; and quiz questions with answer guide.

Biomedical research today is not only rigorous, innovative and insightful, it also has to be organized and reproducible. With more capacity to create and store data, there is the challenge of making data discoverable, understandable, and reusable. Many funding agencies and journal publishers are requiring publication of relevant data to promote open science and reproducibility of research.

In order to meet to these requirements and evolving trends, researchers and information professionals will need the data management and curation knowledge and skills to support the access, reuse and preservation of data.

This course is designed to address present and future data management needs.