Reproducible Quantitative Methods

RQM Handbook- Lesson 2

Topics and Resources

1. Slide deck

   Here's a link to our daily agenda slide deck.

2. Overview of skills for collaborative science practice

   Let's start by deciding how we want to write about our projects. There's many tools you can use to work on collaborative products together- today we'll focus on writing platforms. Here are some things to get you started:

   On research workflows and technology: Changing the Research Workflow with Innovations in Scholarly Communication

   Nature article on collaborative writing tools

3. Using other people’s data

   Introduction to the project data set

   You should have a data set selected by this class meeting. Discuss with your group members what the data documents. Ask yourself:

   Where and when was it collected?

   What was measured?

   Were there any complementary data sets collected that might provide context (for example, weather data collected by a weather station near to where the experiment was performed)?

   
   

   ProTip

   
   

   A helpful hint from those that came before

   Start thinking about meaningful, but simple questions you can ask. Long time-series are a favorite of mine- they're awesome because they invite questions of how processes are changing over time. But anything where you can ask a simple question about a process, a change, a characterization are good. We're mostly interested in a descriptive approach in this class, because we want a project that's doable in a semester. We'll begin to articulate these questions more in the exercise below.

4. Spreadsheet best practices

   Ever notice how all well-formatted data sheets look pretty much the same, but poorly formatted data is poorly formatted in any number of ways? We're going to explore the concept of Tidy data, but from the grass roots on up in this class. Where grass roots == spreadsheets.
   
   For this exercise, the class will work through the Data Carpentry lesson on spreadsheets for ecology. A link to a deliciously messy data set is here.

Exercises

1. Brainstorm questions for project data set

What you'd like to ask of your project data set? What questions can be answered with the data set directly? What questions can be answered with additional contextual data? Ask a variety of questions. Write down questions and hypotheses in your blog/etherpad so you have them on record.




ProTip




A helpful hint from those
that came before

Be creative here. At the end of the brainstorming session, discuss the practicality of answering each question, and aim for questions that will require skills or statistical tests commonly used in your field to answer in this class. If you do the legwork on it for this class, you'll have a great template for solving similar problems in the future!

2. Selecting collaborative writing tools

Search the web with your group for collaborative writing tools and platforms. Some places to start: Overleaf, Authorea, Google Docs...even a Word file in a shared Dropbox… really, anything that would allow multiple people to have edit access to a document.

List selection criteria that are important to you, and evaluate each option according to those criteria. Suggestions for criteria include:

User friendliness of interface

Integration with referencing software

Familiarity to students

Apparent learning curve

Costs

Set up a shared document in whichever platform you decide to use

Possibilities for writing include: Authorea, Overleaf, GoogleDocs, GitHub, Fiduswriter, sharelatex and any others you might suggest. As a group, decide which platform you'd like to use for drafting.

You'll also want a separate space to log progress, discussion that may not fit in the main document. An etherpad or blog might work here. Free blogs are available everywhere, but try Wordpress- it's pretty easy to set up a group blog.

For both of these documents, please provide a link to each of these in the README.md file in your project repo. You can edit the file directly on github- use github flavored markdown for formatting (it's pretty quick and easy to learn- it's essentially regular markdown with a few useful github-associated features like the ability to tag other users, give task lists, etc.). You'll want to use this README file as a guide to all your project materials, including linking those not hosted on github.

Discussion

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