Project MOSAIC is a community of educators working to develop a new way to introduce mathematics, statistics, computation and modeling to students in colleges and universities.

Our goal: Provide a broader approach to quantitative studies that provides better support for work in science and technology. The focus of the project is to tie together better diverse aspects of quantitative work that students in science, technology, and engineering will need in their professional lives, but which are today usually taught in isolation, if at all.

• Modeling. The ability to create, manipulate and investigate useful and informative mathematical representations of a real-world situations.
• Statistics. The analysis of variability that draws on our ability to quantify uncertainty and to draw logical inferences from observations and experiment.
• Computation. The capacity to think algorithmically, to manage data on large scales, to visualize and interact with models, and to automate tasks for efficiency, accuracy, and reproducibility.
• Calculus. The traditional mathematical entry point for college and university students and a subject that still has the potential to provide important insights to today’s students.

The name MOSAIC reflects the first letters — M, S, C, C — of these important components of a quantitative education. Project MOSAIC is motivated by a vision of quantitative education as a mosaic where the basic materials come together to form a complete and compelling picture.

OpenIntro was started with one goal in mind: create a free and open-source introductory textbook. 

The mission of OpenIntro is to make educational products that are free, transparent, and lower barriers to education.

This website features an array of short video tutorials on ideas covered in Radford University's Math4Bio and Stats4Bio courses. Some videos cover concepts, while others cover the logistics of using software to accomplish certain tasks (e.g., graphing in Excel).  The main purpose of these videos was to provide a reference for students to return to, to review ideas they need to apply in their courses. 

Article abstract:  "Galileo wrote that “the book of nature is written in the language of mathematics”; his quantitative approach to understanding the natural world arguably marks the beginning of modern science. Nearly 400 years later, the fragmented teaching of science in our universities still leaves biology outside the quantitative and mathematical culture that has come to define the physical sciences and engineering. This strikes us as particularly inopportune at a time when opportunities for quantitative thinking about biological systems are exploding. We propose that a way out of this dilemma is a unified introductory science curriculum that fully incorporates mathematics and quantitative thinking."

This is an article in MAA's Innovative Teaching Exchange that describes strategies to get students past thoughtless number crunching, guessing, and intuition when it comes to solving probability problems.  

From the project website "

Workshop Statistics:  Discovery with Data, A Bayesian Approach, Key College Press; ISBN: 1930190123 (coauthored with Allan J. Rossman of Dickinson College) is a collection of classroom and homework activities designed to introduce the student to concepts in data analysis, probability, and statistical inference.  Students work toward learning these concepts through the analysis of genuine data and hands-on probability experiments and through interaction with one another, with their instructor, and with technology.  Providing a one-semester introduction to fundamental ideas in statistics for college and advanced high school students, this text is designed for courses that employ an interactive learning environment by replacing lectures with hands-on activities.

The complete book can be downloaded from http://bayes.bgsu.edu/nsf_web/workshop.bayes.pdf 

This text is distinctive with respect to two aspects --- its emphasis on active learning and its use of the Bayesian viewpoint to introduce the basic notions of statistical inference."

This is a workbook of solved problems created by Dr. Juergen Gerlach at Radford University, for  students in Math 119 - Mathematics for Biologists.   

Using natural history collections, both physical specimens and archive data, promises to connect classroom learning with the real world of science. 

An NSF-funded Research Coordination Network (DEB 0956129), the goal of AIM-UP! is to bring museum archives and natural history collections to the forefront of biology education. 

An article exploring the use of visual datasets as an educational aid for biology students. 

Modules designed to help students understand the math behind the material learned in introductory ecology courses. 

In this issue of the journal Peer Review, the authors discuss the issues surrounding quantitative literacy. 

This packet contains a set of 24 practice questions and 2 full-length practice assessments for the Quantitative Reasoning Assessment for Wellesley College. 

An online journal focusing upon generally-applicable studies of cell biology.

This course in the Numbers Count! curriculum introduces the basics of statistical analysis, including basic definitions, data visualization, and application. 

In this activity, students learn about ancient plant life and climate change by examining coal balls. In addition to the lesson itself, information is provided on where to purchase coal ball peel kits for hands-on experience. 

This lesson introduces the use of GIS (geographic information systems) in the study of biological questions, using bats as the model organism. This activity requires access to GIS software. 

Designed to span 4 one-hour lectures, this module focuses upon developing the skill of thorough and critical reading of scientific literature. 

Module Summary: The production of crops is essential to human life. Producing these crops, however, comes with it a cost to the environment. The commonly used agricultural techniques in place in the United States rely on machinery and chemicals in order to keep up with the demand. However, over time, these methods strip the soil of nutrients which in turn require added inputs to produce the crops in the first place. In order to continue to produce crops at current levels of need and, by extension, to increase production, methods to counter these deficiencies must be utilized. One of these methods, used in both organic and 4conventional" agriculture, is that of crop rotation. Crop rotation may be as simple as the current corn/bean rotation in the conventional system, or more elegantly implemented with organic techniques such as intercropping, 4green manure,! and 4catch cropping.! Certain crops have characteristics which lend themselves to rotation. These crops are then rotated with crops which provide income or fodder for animals. However, the creation of a rotational schedule over a plot of subdivided land is a challenge which faces the agriculturist in planning the most efficient use of land so that demand is met, soils are sustained, and economic impact is reduced when leaving land off of a 4production schedule.! The module develops discrete mathematics models that allow for creation of schedules meeting a variety of needs.

In this module, we integrate the context of carbon emission and human consumption into an introductory lesson on units and estimation. Background information is provided to familiarize students with the science of carbon emissions as well as greenhouse gas effects on mean global temperatures.

In this module the students will learn some basic concepts in statistical thinking about data, with emphasis on exploratory data analysis. The module will analyze daily temperature data collected over 55 years at a single location. The analysis explores the question, 4Is there any observable temperature trend over this time period at this location?! The challenge is to see a potentially small change within a data set that has both seasonal variability and high daily variability. Basic plots are done to help the students view data in different ways, introduce methods for removing seasonality, and use averaging to reduce day-to-day variability. This module might be viewed as a case study! in data analysis. It will give students a taste of what it's like to do real world data analysis. Students will work with a large noisy data set and look at it in different ways to try to answer a specific question.

This module uses a variety of available datasets to explore methods of calculating biodiversity and measuring landscape as well as the relationship between those. These points are then used to teach logarithms by estimating slopes and intercepts from a log-log plot of the number of species in a given location against a variety of metrics including island size and distance from mainland. Optionally, this could then be adapted to fragmented habitats near a national park or the like. Finally, the plots are used to estimate the level of fragmentation that would push the system to a given level of species loss.

We based our instructional unit on the premise that many people have, that one does not need mathematics if the they do not plan on attending college or pursuing careers known for involving mathematics. A huge misconception many students have is that mathematics has no relevance in the blue-collar industry. The goals of this lesson are to dispell this myth, to demonstrate, explore and discuss mathematics through a field operation, and through investigative activities to extend the concepts from the farm to general mathmematics with the use of technology. We will use a field trip to a local Dairy Farm, Gilbert and Sons Dairy Farm, to achieve these goals.

Lecture  Slides

This is a Powerpoint presentation which motivated a panel discussion on "What Makes Networks Work?" at the RCN-UBE Summit at AAAS in January 2016.