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.

Students begin by reading Dr. Seuss' "The Lorax" as an example of how overdevelopment can cause long-lasting environmental destruction. Students discuss how to balance the needs of the environment with the needs of human industry. Student teams are asked to serve as natural resource engineers, city planning engineers and civil engineers with the task to replant the nearly destroyed forest and develop a sustainable community design that can co-exist with the re-established natural area.

This unit introduces students to Structure from Motion (SfM). SfM is a photogrammetric technique that uses overlapping images to construct a 3D model of the scene and has widespread research applications in geodesy, geomorphology, structural geology, and other subfields of geology. SfM can be collected from a hand-held camera or an airborne platform such as an aircraft, tethered balloon, kite, or UAS (unmanned aerial system). After an introduction to the basics of SfM, students will design and conduct their own survey of a geologic feature, followed by an optional (but highly encouraged) introductory exploration of SfM data after returning from the field.

Show more about Online Teaching suggestions
Hide
Online teaching: This unit was adapted to an online remote field teaching activity. Getting started with Structure from Motion (SfM) photogrammetry (remote field collection).

(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.)

Field experience using geodetic and geophysical tools provides a unique opportunity for upper-level undergraduates to learn research skills applicable to their future graduate research or career path. This unit introduces students to terrestrial laser scanning (TLS), a ground-based, remote-sensing tool that generates three-dimensional point clouds, that has widespread research applications in geodesy, geomorphology, structural geology, and other subfields of geology. After an introduction to the basics of TLS, students will design and conduct their own survey of a geologic feature, followed by an optional introductory exploration of TLS data after returning from the field.

(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.)

Geodetic survey techniques, such as TLS and SfM featured here, have many applications in sedimentology research, including lithological identification and analysis, sediment surface topography, and sequence stratigraphy. In this unit, students will design a survey of a geologic outcrop to conduct a sequence stratigraphy analysis. After conducting the survey in the field, students will analyze the parasequences found within the outcrop by mapping and measuring section thickness in the point cloud. The goal is to calculate deposition duration and sedimentation rate based on thicknesses extracted from the data.

(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.)

In this unit, students will design a survey (TLS and/or SfM) of a fault scarp. After conducting the survey in the field, students will analyze the data to identify the number and magnitude of possible fault displacement(s) by measuring offsets in the point cloud as well as calculate the recurrence interval of the fault based on either a known age or scarp morphometric age (or both). The goal is to create a brief report summarizing the methods used and Quaternary history of displacements on the fault. An optional extension exercise (Unit 3.5) has the students conduct a hillslope diffusion analysis is using MATLAB. Fault scarps are the topographic evidence of earthquakes large and shallow enough to break the ground surface, and are evidence of Quaternary fault activity. A primary goal of studying exposed scarps is to gain insight into the magnitude and frequency of fault slip. Scarps typically begin as step-shaped landforms and deteriorate with age through erosion. In some cases, the form of the scarp may record evidence of more than one earthquake, distinguished by a change in scarp slope. Assuming the same surface processes, the relative age of fault scarps can be determined by their morphology (shape).

(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.)

Applications of geodetic imaging in geomorphology research often center on monitoring and detecting change within a system over time. Since most geomorphic systems evolve over longer time periods -- months, years, or more -- than available in a typical field course, this unit of the module may be entirely lab-based. Or you may have students collect data -- of a fluvial system, landslide, or other geomorphic feature -- if the feature they survey has a previously collected data set upon which to compare their new data set. The goal of this unit is to teach students to transform point clouds of a feature taken some time apart into DEMs. The DEMs are then subtracted to calculate the elevation change over time.

(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.)

Unit 5 is the summative assessment for the module. This final exercise takes eight to ten hours. The exercise evaluates students' developed skills in survey design, execution of a geodetic survey, and simple data exploration and analysis. This summative assessment is written flexibly so that it can be applied to a variety of potential field sites and associated geoscience research questions. The unit has two parts, like most of the units in the module: Part 1, Geodetic Survey; and Part 2, Data Exploration. In addition, there is an optional Part 3, Data Processing, for students who have done Unit 4. This unit also has a number of prepared data sets for courses not able to collect field data.

(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.)