A two-day introduction to modern evidence-based teaching practices, built and maintained by the Carpentry community.

This tombstone weathering lab is designed to provide students with tangible understanding of chemical weathering and weathering rates. To prepare for this lab, students will have learned in previous labs to identify common minerals and rocks and will have attended lectures about the process of chemical weathering. During the first part of the lab we travel to the city cemetery to collect data on the age and extent of chemical weathering of tombstones that are made of limestone and igneous rocks. After collecting data for ~1 hour, we return to the computer lab where students use Microsoft Excel to analyze and interpret their data. Their task is to calculate a chemical weathering rate for limestone for our region and compare that rate to those from other regions. This activity gives students experience in the process of scientific inquiry: data collection, data analysis and data interpretation. Students develop Microsoft Excel skills: writing formulas, producing charts, understanding trendlines and R2 values.

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Here you’ll find extensive support for APA, MLA, and Chicago documentation styles. This section features instructional videos that show you how to set up your papers in APA, MLA, and Chicago formats, interactive checklists, and visual support for both in-text documenting and referencing at the end of your paper. If you’re new to documentation or just need a refresher, the Citations & Documentation area can help.

This transect across northern Washington State was one of our most geologically and biologically diverse field trips. The trip started with a drive across the relatively uniform basalt flows of the Columbia plateau and then traversed the extremely geologically complex North Cascades accessible from a scenic route through the small, and relatively less-traveled, North Cascades National Park. Steep gradients in elevation annual precipitation and winter temperatures revealed equally dramatic changes in vegetation from cold desert shrub lands to temperate coastal rain forests. Like previous trips, this one allowed students to observe glacial processes up close and trace the history of plant succession as glaciers retreat.

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This trip followed the Columbia River across the basalt flows of the Columbia Plateau through the Columbia Gorge that bisects the Cascade Range and then turned south along Oregons spectacular coastline. The opportunities to integrate biology and geologic processes were limited only by time as students explored the plant and animal life of rocky and sandy beaches dune fields and coastal forests. The southernmost portion of the trip extended from Crescent City, CA (site of the 1964 tsunami) through the Klamath Mountains on the Oregon/California boundary one of the most geologically dynamic landscapes in North America. The tectonic history of the region with its resulting shifts in climate patterns and merging of previously isolated land forms along with an unusual abundance of ultramafic rocks have driven the evolution of one of the most diverse floras in North American populations of carnivorous Darlingtonia (Pitcher plants) provided a dramatic example of the unusually large number of plant species endemic to the serpentine soils of this region.

(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.)

The midterm represents the final week of working in Word. You will be asked to complete documents that demonstrate that you understand basic rules and best practices to ensure your online research is reliable as well as demonstrate skill in the proper use of Word features covered during the first 5 weeks of the course.

Skills & Knowledge Attained:
*Time management – You were asked to think about your midterm topic in week 1 and declare it in a post in week 2 and given several weeks to prepare and do the necessary research. Research document should demonstrate the time provided was used to spread out the work so that it was not done in a rush and/or at the last minute.
*Best practices on how to check a website for accuracy and truth as well as appropriateness as research source.
*Proper application of MLA requirements using Microsoft Word Reference features, such as adding footnotes, citations, and generating a bibliography from correctly added citations as well as placement and content of appropriate header and footer.
*The paper should be an original piece of writing based on properly cited online research, that demonstrates understanding of the topic researched and should explain in your own words, using proper spelling and grammar, what you have learned about your chosen topic.

Active learning modules to help students understand the role of cover crop species selection and design of mixed species cover crop plantings on multiple ecosystem services. Our current goal is to integrate lessons-learned from 8 years of research and extension activities into undergraduate education modules that can be widely distributed. Students completing these modules would be able to describe why cover crops are used, how different species of cover crop affect an array of ecosystem functions, how mixtures can be used to increase the multifunctionality of cover cropping systems, and factors that control mixture growth across sites. If the modules are delivered in the following order then these concepts build sequentially.

An introductory cultural anthropology textbook covering all major topics and produced by Lumen Learning.

Abstract
Training materials. The DATUM for Health training programme covers both generic and discipline-specific issues, focusing on the management of qualitative, unstructured data, and is suitable for students at any stage of their PhD. It aims to provide students with the knowledge to manage their research data at every stage in the data lifecycle, from creation to final storage or destruction. They learn how to use their data more effectively and efficiently, how to store and destroy it securely, and how to make it available to a wider audience to increase its use, value and impact.

Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in one and a half days (~ 10 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.

Data Carpentry lesson from Ecology curriculum to learn how to analyse and visualise ecological data in R. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecology data in R. This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from R.

Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.

The Biology Semester-long Course was developed and piloted at the University of Florida in Fall 2015. Course materials include readings, lectures, exercises, and assignments that expand on the material presented at workshops focusing on SQL and R.

A part of the data workflow is preparing the data for analysis. Some of this involves data cleaning, where errors in the data are identified and corrected or formatting made consistent. This step must be taken with the same care and attention to reproducibility as the analysis. OpenRefine (formerly Google Refine) is a powerful free and open source tool for working with messy data: cleaning it and transforming it from one format into another. This lesson will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you make. Many people comment that this tool saves them literally months of work trying to make these edits by hand.

Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.

This is an alpha lesson to teach Data Management with SQL for Social Scientists, We welcome and criticism, or error; and will take your feedback into account to improve both the presentation and the content. Databases are useful for both storing and using data effectively. Using a relational database serves several purposes. It keeps your data separate from your analysis. This means there’s no risk of accidentally changing data when you analyze it. If we get new data we can rerun a query to find all the data that meets certain criteria. It’s fast, even for large amounts of data. It improves quality control of data entry (type constraints and use of forms in Access, Filemaker, etc.) The concepts of relational database querying are core to understanding how to do similar things using programming languages such as R or Python. This lesson will teach you what relational databases are, how you can load data into them and how you can query databases to extract just the information that you need.

Good data organization is the foundation of any research project. Most researchers have data in spreadsheets, so it’s the place that many research projects start. We organize data in spreadsheets in the ways that we as humans want to work with the data, but computers require that data be organized in particular ways. In order to use tools that make computation more efficient, such as programming languages like R or Python, we need to structure our data the way that computers need the data. Since this is where most research projects start, this is where we want to start too! In this lesson, you will learn: Good data entry practices - formatting data tables in spreadsheets How to avoid common formatting mistakes Approaches for handling dates in spreadsheets Basic quality control and data manipulation in spreadsheets Exporting data from spreadsheets In this lesson, however, you will not learn about data analysis with spreadsheets. Much of your time as a researcher will be spent in the initial ‘data wrangling’ stage, where you need to organize the data to perform a proper analysis later. It’s not the most fun, but it is necessary. In this lesson you will learn how to think about data organization and some practices for more effective data wrangling. With this approach you can better format current data and plan new data collection so less data wrangling is needed.

Data Carpentry lesson to learn how to use command-line tools to perform quality control, align reads to a reference genome, and identify and visualize between-sample variation. A lot of genomics analysis is done using command-line tools for three reasons: 1) you will often be working with a large number of files, and working through the command-line rather than through a graphical user interface (GUI) allows you to automate repetitive tasks, 2) you will often need more compute power than is available on your personal computer, and connecting to and interacting with remote computers requires a command-line interface, and 3) you will often need to customize your analyses, and command-line tools often enable more customization than the corresponding GUI tools (if in fact a GUI tool even exists). In a previous lesson, you learned how to use the bash shell to interact with your computer through a command line interface. In this lesson, you will be applying this new knowledge to carry out a common genomics workflow - identifying variants among sequencing samples taken from multiple individuals within a population. We will be starting with a set of sequenced reads (.fastq files), performing some quality control steps, aligning those reads to a reference genome, and ending by identifying and visualizing variations among these samples. As you progress through this lesson, keep in mind that, even if you aren’t going to be doing this same workflow in your research, you will be learning some very important lessons about using command-line bioinformatic tools. What you learn here will enable you to use a variety of bioinformatic tools with confidence and greatly enhance your research efficiency and productivity.

Software Carpentry lesson that teaches how to use databases and SQL In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and Valentina Roerich led expeditions to the Pole of Inaccessibility in the South Pacific, and then onward to Antarctica. Two years ago, their expeditions were found in a storage locker at Miskatonic University. We have scanned and OCR the data they contain, and we now want to store that information in a way that will make search and analysis easy. Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets. These lessons will show how to use a database to explore the expeditions’ data.

This project involves a field trip to the Jordan Formation in Winona, MN. Student teams are assigned a section of the outcrop from which they are to determine a stratigraphic column. The class then performs a lateral analysis and builds a composite stratigraphic column for the formation. As a final product, the students write up the class's observations about the formation.

Project Webpages

Project Summary and Write-up Outline (Acrobat (PDF) PRIVATE FILE 115kB Jul7 05)
Instructor Notes for Project (Acrobat (PDF) PRIVATE FILE 91kB Jul7 05)
Outlines and Notes (Acrobat (PDF) PRIVATE FILE 1.1MB Jul7 05) for each class session for this project

(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.)