A collection of basic information about teaching online.

The Floe Inclusive Learning Design Handbook is a free Open Educational Resource (OER) designed to assist teachers, content creators, Web developers, and others in creating adaptable and personalizable educational resources that can accommodate a diversity of learning preferences and individual needs. 

At this link is a version of the guidelines with clickable headers and checkpoints. More information is provided for each guideline.

The goal of the Accessibility Toolkit – 2nd Edition is to provide resources for each content creator, instructional designer, educational technologist, librarian, administrator, and teaching assistant to create a truly open textbook—one that is free and accessible for all students.

A three-minute video on how to use Zoom and Google Slides captioning to live captioning a lecture and record it to share.

Article from Aimi Hamraie on the Mapping Access blog on ways to improve accessibility when transferring your typically in-person teaching to an online-environment.  I found this short article to have great ideas about how to incorporate students into the redesign of the course if needed and other ways to help from everyone feeling disconnected when now in an online environment.   

"Each day during the summer of 2019 we intend to add a new entry to this blog on a given topic of interest to educators teaching data science and statistics courses. Each entry is intended to provide a short overview of why it is interesting and how it can be applied to teaching. We anticipate that these introductory pieces can be digested daily in 20 or 30 minute chunks that will leave you in a position to decide whether to explore more or integrate the material into your own classes. By following along for the summer, we hope that you will develop a clearer sense for the fast moving landscape of data science."

This group is back at it with ideas for teaching online.

Avida-ED is an award-winning educational application developed at Michigan State University for undergraduate biology courses to help students learn about evolution and scientific method by allowing them to design and perform experiments to test hypotheses about evolutionary mechanisms using evolving digital organisms.

Check out the blog posts by Jim Smith (posted 3/13/20) and Rob Pennock (posted 4/1/20).

Activity about chili pepper ecosystems to introduce students to reading scientific literature and interpretation of graphs.

Click on the tags below to filter the list of workshops recommended by GEA members:

Project EDDIE (Environmental Data-Driven Inquiry and Exploration) is a suite of education projects composed of STEM disciplinary and educational researchers. We develop flexible classroom teaching modules using large, publicly available datasets to engage students in STEM and improve their quantitative reasoning. Teaching modules span topics such as ecology, limnology, geology, hydrology, and environmental sciences. EDDIE also helps build the associated professional development needed to ensure effective use of the teaching modules.

Handelsman, J., Miller Laufer, S., Maidl Pribbenow, C., Pfund, C.

Also available through HHMI: https://www.hhmi.org/sites/default/files/Educational%20Materials/Lab%20Management/entering_mentoring.pdf

Braun, D.C.,  Gormally, C., Clark, M.D. CBE LSE 2017

Mentorship is a catalyst capable of unleashing one's potential for discovery, curiosity, and participation in STEMM and subsequently improving the training environment in which that STEMM potential is fostered. Mentoring relationships provide developmental spaces in which students' STEMM skills are honed and pathways into STEMM fields can be discovered. Because mentorship can be so influential in shaping the future STEMM workforce, its occurrence should not be left to chance or idiosyncratic implementation. 

National Academies of Sciences, Engineering, and Medicine. 2019. The Science of Effective Mentorship in STEMM. Washington, DC: The National Academies Press. https://doi.org/10.17226/25568.

National Academies of Sciences, Engineering, and Medicine. 2015. Integrating Discovery-Based Research into the Undergraduate Curriculum: Report of a Convocation. Washington, DC: The National Academies Press. https://doi.org/10.17226/21851.

Bangera, G and Brownell, S 2014 CBE LSE

• Data Carpentry
• Online workshops through Amazon Web Services
• Workshop web site

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. This workshop teaches data management and analysis for genomics research including: best practices for organization of bioinformatics projects and data, use of command-line utilities, use of command-line tools to analyze sequence quality and perform variant calling, and connecting to and using cloud computing. This workshop is designed to be taught over two full days of instruction.

• DataCamp Inc.
• Online
• Course web site

Learn Data Science Online

The skills people and businesses need to succeed are changing. No matter where you are in your career or what field you work in, you will need to understand the language of data. With DataCamp, you learn data science today and apply it tomorrow.

• Springboard
• Online
• 6-month program
• Course web site

What you'll learn

We partnered with industry insiders, so you can learn the skills that employers look for. The curriculum is split into 5 units covering the topics below:

• Framing structured thinking
• Analyzing business problems
• Connecting data using SQL
• Visualizing data with Python
• Communicating your analysis

Build industry-level projects

In addition to small projects designed to reinforce specific concepts, you'll complete two capstone projects focused on a realistic data analytics scenario that you can show to future employers.

Work 1:1 with a mentor

Mentor-guided learning not only helps you build skills faster, but also enables career growth.

Get the perfect job with unlimited 1:1 career coaching

Career-focused course material is paired with personal coaching calls to help you land your dream job. You'll have 6 scheduled calls, with unlimited access to more. And full career support continues for 6 months after completing the program.

Is this program right for me?

This data analytics bootcamp is designed for people who demonstrate an aptitude towards critical thinking and problem solving, and have two years of work experience.

Prerequisites

• Strong critical thinking and problem-solving skills
• 2 years of professional work experience working regularly with office, design or programming tools
• Fluency in English (written and spoken), as determined by initial interactions with the Admissions team

Tuition

The full tuition for the program is $6,600. If you pay upfront, you get a 17% discount. Remember, if you don't get a job within 6 months of completion, you'll receive a full refund.

• Pittsburgh Supercomputing Center, PA
• TBA
• Workshop web site

This two-week intensive training workshop provides a robust background in bioinformatics suitable for teaching and research. Every day participants complete hands-on exercises to practice the concepts learned during lectures using various of the Pittsburgh Supercomputing Center's massively parallel computers and various software tools such as the Galaxy web-based biomedical research tool.

A Typical Summer Institute Schedule

Week 1

• Introduction to the Computing Environments at the Pittsburgh Supercomputing Center
• Bioinformatics Databases
• Models and Significance in Searching Bioinformatics Databases
• Sequence Alignment Algorithms (NW, SW, Fasta, BLAST, BW+FM, "Seeded" SW)
• Multiple Sequence Alignment & Mapping Realignment
• Computational Tools: Analyzing Data Using Relational Databases & SQL
• Next Generation Sequencing (NGS) Technologies
• Pattern Identification
• Preparing NGS Datasets for Assembly/Mapping
• Phylogenetics and Reconciliation with Notung
• De Novo Genome Assembly
• The R System for Statistical Analysis

Week 2

• Functional Annotation for Assembled Genomes
• Predicting Genes, Identifying Functions
• Mapping Genome Assemblies
• RNAseq: De Novo Assembly of RNA Data
• Identifying Single-nucleotide Polymorphisms (a.k.a. SNPs) and Other Variants
• RNAseq: De Novo Functional Annotation and Other Post-Assembly Analyses
• Gene Annotation
• Ribosomal Profiling: Genome-wide Measurements of mRNA Translation Rates

• UC Davis Bioinformatics Core
• UC Davis, CA
• November 30 - December 4, 2020
• Workshop web site

This workshop will include a rich collection of lectures and hands-on sessions, covering both theory and tools associated with the analysis of data generated by several common types of epigenetic experiments, primarily data from the Illumina platform. Participants will explore experimental design, cost estimation, data generation, and analysis of DNA sequence data. Participants will explore software and protocols, create and modify workflows, and diagnose/treat problematic data utilizing high performance computing services. Exercises will be performed with provided datasets, using command-line interaction on the Genome Center Compute Cluster, which will be available to you to use for a week after the workshop, so you can continue to practice these skills.

• CSHL Bioinformatics Workshop
• Online
• November 16-20, 2020
• Application Deadline: September 15, 2020
• Course web site

Instructors

• Obi Griffith, Washington University School of Medicine
• Malachi Griffith, Washington University School of Medicine
• Elaine Mardis, Nationwide Children's Hospital Research Institute
• W. Richard McCombie, Cold Spring Harbor Laboratory
• Aaron Quinlan, University of Utah

Course Description

Over the last decade, massively parallel DNA sequencing has markedly impacted the practice of modern biology and is being utilized in the practice of medicine. The constant improvement of these platforms means that costs and data generation timelines have been reduced by orders of magnitude, facilitating investigators to conceptualize and perform sequencing-based projects that heretofore were time-, cost-, and sample number-prohibitive. Furthermore, the application of these technologies to answer questions previously not experimentally approachable is broadening their impact and application. However, data analysis remains a complex and often vexing challenge, especially as data volumes increase.

This intensive two week course will explore use and applications of massively parallel sequencing technologies, with a focus on data analysis and bioinformatics. Students will be instructed in the detailed operation of several platforms (Illumina, PacBio, Nanopore, Etc.), including library construction procedures, general data processing, and in-depth data analysis. Students will be introduced to Unix command-line, important file formats, alignment, data visualization, basic scripting in R, bash and other program languages, cluster job submission and bioinformatics pipeline development. A diverse range of the types of biological questions enabled by massively parallel sequencing technologies will be explored such as bulk transcriptome profiling (RNAseq), single-cell transcriptome/proteome profiling (scRNAseq, CITEseq), epigenome profiling (ATAC-seq), small variant discovery and interpretation, structural variant discovery, long read applications, probability and statistics for genomics analysis, and others that are tailored to the student's research areas of interest.

Cloud-based computing will also be explored. Guest lecturers will highlight unique applications of these disruptive technologies.

We encourage applicants from a diversity of scientific backgrounds including molecular evolution, development, neuroscience, medicine, cancer, plant biology and microbiology.

Support & Stipends

Major support provided by: National Human Genome Research Institute.

Access to cloud computational resources may be supported by an AWS in Education Grant award from Amazon.

Stipends are available to offset tuition costs as follows:

• US applicants (National Human Genome Research Institute).
• Interdisciplinary Fellowships (transitioning from outside biology) & Scholarships (transitioning from other biological disciplines) (Helmsley Charitable Trust).
• International applicants (Howard Hughes Medical Institute).

• UC Davis Bioinformatics Core
• Online
• October 5-9, 2020
• Workshop web site

This workshop will cover the basic computational and statistical concepts needed before performing bioinformatics analysis:

• Access to a multi-core (24 cpu or greater), "high" memory 64Gb or greater Linux server.
• Familiarity with the 'command line' and constructing scripts/pipelines.
• Basic knowledge of how to install software
• Basic knowledge of R (or equivalent) and statistical programming
• Basic knowledge of Statistics and model building

The course will include experimental data organization, basic command line and high performance computing concepts, how to install software, use help, run applications, and the basics of building scripts and pipelines. Also covered will be basic R programming, working with data tables and generating figures, basic statistical concepts, and statistical model building. There are no prerequisites for this workshop other than an interest in bioinformatics! The workshop will be on the UC Davis campus, and will run from 9am to 5:00pm each day and include light breakfast, lunch, and snacks.