Reproducible Quantitative Methods
Spring Semester 2017
Part 1: Data
How to handle your data to make your work more efficient and reproducible, how to handle common problems with data coming from other sources
Jan 10-12 - Introduction to reproducibility and open science frameworks
Jan 17-19 - Best practices for spreadsheets/ Learning to use data produced by others
Jan 24-26 - Introduction to Metadata / Data and scientific authorship
Jan 31-Feb 2 - Cleaning up messy data / Identifying 'grey' data sources
Part 2: Analysis
Applying reproducibility principles to common statistical and visualization approaches.
Feb 7-9 - Intro to scripting in R/ Version control in R with Github
Feb 14-16 - Programming in R / Licensing data and software for reuse
Feb 21-23 - Programming in R, continued /Authorship and citation practices for non-manuscript research products
Feb 28-Mar 2 - Student-directed processing and analysis of project data /Data and code sharing challenges
Part 3: Communication
Using technology to make our work accessable to others and to work better together.
Mar 14-16 - Making better plots / Visualization for outreach and communication
Mar 21-23 - Project workshop time, Github for project management / Scientific publication and accessibility
Mar 29-30 - Project workshop time
April 4-6 - Project workshop time / Scientific collaboration
Part 4: Opening Your Work
Inviting the world to contribute to the scientific enterprise
April 11-13 - Project workshop time / Science and technology in a connected world
April 18-20 - Preparing a paper for publication / The future of open science and reproducible research
April 25-27 - Wrap up!
The RQM CourseHere's a bit more about the course. We can start with a talk I gave about the first offering of the course, my motivations, and our results.
Almost every graduate student has a “Now what?” moment during their thesis, and this moment often occurs after a student has collected data and now has to analyse it. Additionally, new (or newly enforced) requirements from federal funders are holding our scientific outputs, including data and code, to more rigorous reproducibility standards, but has offered little guidance on how individual labs and research projects should change their workflows.
Because of poor quality in many data sources, data scientists estimate they spend up to 80% of their time ‘data munging’- that is, cleaning, quality checking, and documenting data that they’re trying to use for their insights. The reality is, most data producers (a group which includes most experimental scientists) do not have specific training in data handling. This leads to decision paralysis, inefficiency, and the potential for incredible losses of information at the interface between observations and analysis- and takes the joy out of data-driven discovery. Training initiatives that address these issues are in high demand- workshops for Software Carpentry and Data Carpentry -organizations that offer workshops to train scientists in efficient software and data science skills- are usually at capacity and waitlisted within days of initial advertising.
This course directly builds on the principles laid out in Software Carpentry and Data Carpentry workshops, but provides students with a more immersive, long term experience in the form of a project-based learning approach. Project-based learning hybridizes a traditional lecture with a student-led working group, which allows the course to be effectively customized to directly apply the principles to real data and real problems. We provide the added incentive of including the students on a publication resulting from their work- giving them concrete training in applying these skills in a way that is relevant to their field. The course takes a two-pronged approach- approximately 2/3 of class time is given to applied tools training using a project data set, and the remaining 1/3 of class time is used to discuss the more philosophical aspects of modern, technologically- enabled science (e.g. how do we handle authorship on manuscripts supported by data compiled from a variety of sources? Is software a research product?).
How to use this website
You: a student of ecology or environmental sciences, interested in becoming better at data and computational applications in your research! This is a living document which we will be collectively modifying as we work through the materials outlined. It is adapted directly from an instructor guide on this subject, available here. If you find typos, broken links, or want to suggest changes, please submit an issue or pull request to this repo.
About this guide
This website was adapted (by Christie Bahlai!) from the instructor guide created by Christie Bahlai, a quantitative ecologist at Michigan State University, while supported by a fellowship from the Mozilla Science Lab.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Here we'll hopefully answer questions you might have. We aren't old enough to have a FAQ :)
A friendly introduction to githubWhat the git? This course relies very heavily on github as a collaboration platform. It's got a learning curve that most closely resembles a cliff, so here's a resource that you can go back to again and again if you get stuck.
This workshop is the friendliest of friendly introductions. Git it.