OBW is a community effort to produce complete course material for a series of short workshops (1 day to 1 week each) on applied bioinformatics. Instead of (or in addition to) the traditional online courseware offerings of slides, syllabi, and/or video lectures, each OBW course provides all the material an instructor needs to teach an interactive workshop, including exercises, example data sets, and machine images (such as Docker files) with all required software.
Both the overall philosophy of OBW and the design of these workshops are heavily inspired by Software Carpentry and Data Carpentry.
All course materials are freely available under a CC0 license, meaning that you may use and modify them as you wish without cost. Note that copyrighted images are used in some slides; if you will be using these for commercial purposes, you must acquire permission from the copyright holder or replace them with unrestricted alternatives. Although not required, we do appreciate when you acknowledge OBW, and we encourage you to submit suggestions and improvements as GitHub Issues and/or Pull Requests.
- Beginning Genome-scale Sequence Analysis
- Advanced Genetic Variant Analysis
- RNA-seq Analysis
- Data exploration and visualization: This would be an advanced R workshop focusing specifically on tidy data (tidyr), data exploration (dplyr), and data visualization (ggplot2). It would focus on a single dataset (gapminder?) and the goal would be to explore the data in many ways, including plotting the data many ways (e.g. http://flowingdata.com/2017/01/24/one-dataset-visualized-25-ways/).
- The Canadian Bioinformatics Workshops: This is a fantastic series of workshops going back more than 10 years. Approximately 10 new workshops are hosted each year at various sites around Canada. These workshops run anywhere from 1-5 days. The slides are available for all workshops, and videocasts are also available for most workshops held since 2012. The content of these workshops serves as a good model of what we are trying to develop with OBW.
- David B Searls published an excellent curriculum guide for a comprehensive online computational biology education. This curriculum spans entry-level through graduate-level courses, and is designed to be accomplished over a longer term -- at least 6 months, and probably more like 2 years, depending on your background how motivated you are.
- A suggested bioinformatics curriculum collaboratively developed by attendees at the 2016 Biological Data Science meeting
- A semester-long Data Carpentry course for biologists
- Suggestions for the minimal bioinformatics education every scientist should receive (tl;dr: data discovery, data management, data munging)
- Workshop run by C Titus Brown
- 10 rules for collaborative lesson development
- Greg Wilson's book on teaching
- How to teach Data Science: https://arxiv.org/abs/1612.07140
- A guide to teaching data science
- UiO has adapted software carpentry to focused, single-day workshops, e.g Reproducible Analysis in R, and Collaboration With GitHub.
- Aaron Quinlain's Applied Computational Genomics course: https://github.com/quinlan-lab/applied-computational-genomics
- SeqAcademy Jupyter Notebooks: http://www.seqacademy.org/?m=1