Roadmapping + Journal Club Lesson
- Read over this roadmapping tutorial from Mozilla Open Leaders & check out some of the examples. We won’t be following this exactly, but it is a good overview. https://mozilla.github.io/open-leadership-training-series/articles/opening-your-project/start-your-project-roadmap/
- The syllabus can be thought of as the roadmap for the programme, but the point of this exercise is for each of you to map these goals to your own timeline.
- Take the syllabus, and map out the main goals, or “milestones”. These include things like blogs, learning goals, and workshops. (This can be taken pretty much straight from the syllabus)
- Keep in mind dates and events for the upcoming 9 months. Will you need to change the dates for a specific milestone due to your schedule (work event, school exam, vacation)?
- Think about your personal goals for this fellowship. What are your short term, medium term, and long term goals? Are there milestones that reflect these goals?
- Are there additional milestones you want to add to your roadmap? For example, writing additional blogs, giving additional workshops? These can also be added later.
- Using project management tools: I recommend using project management tools like Trello, GitHub projects, or simply a pen + paper planner to write down your main milestone tasks to help you keep track of your progress. If you want to use Trello or GitHub projects and need help, Lilly can help you.
- We’ll be reading “Do you speak open science? Resources and tips to learn the language” by Paola Masuzzo and Lennart Martens https://peerj.com/preprints/2689/
- You will all meet virtually to answer the discussion questions and talk about the paper
- Discussion questions:
- The authors identify four "pillars" of open science: data, code, papers, and review. Do you agree with these? Are there aspects of open science that you think are missing?
- When sharing raw data, what other information should also be released?
- What are some ways that researchers can openly release their data?
- Why is it useful to share the code used in an experiment?
- What are some ways to openly share code or software?
- What are preprints and how are they different from traditional publications?
- What are some reasons why researchers might not publish in open access journals? What are your thoughts on those reasons?
- What are some models of open peer review?
- What are some benefits and risks of open peer review?
Data Packages Lesson
Data Packages - using Data Package Creator
- Work through this tutorial on well-packaged datasets https://frictionlessdata.io/field-guide/well-packaged-datasets/
- Watch this video on packaging data https://www.youtube.com/watch?v=VrdPj28-L9g
- In Slack, answer these questions:
- What makes up a data package?
- What kind of metadata would you include for the data you work with?
OPTIONAL - Data Packages - using our Python datapackage.py library
- Use these instructions to import datapackage.py and create a new datapackage http://frictionlessdata.io/docs/creating-tabular-data-packages-in-python/
- Follow these instructions to work with an existing Data Package in Python - note: stop at the 'Loading into an SQL database' step. http://frictionlessdata.io/docs/using-data-packages-in-python/
- Play around with this Jupyter notebook
OPTIONAL - Data Packages - using our R datapackage.r library
- Use these instructions to install datapackage.r and create a new datapackage http://frictionlessdata.io/docs/creating-tabular-data-packages-in-r/
- Follow these instructions to work with an existing Data Package in R - note: stop at the 'Loading into an SQL database' step. http://frictionlessdata.io/docs/using-data-packages-in-r/
Using Specs and Schemas
- Read through this introduction to JSON schemas by json-schema.org
- Read through the Frictionless Data Specs
- Focus on the DataPackage specs & the csv dialect
Goodtables Data Validation Lesson
Validate your data with the Goodtables browser tool and with the command line interface
- Start by reading Understanding JSON Schemas, by json-schema.org
- Next, learn about how to validate data with Goodtables by going through the following tutorial and video (I recommend starting by first reading the tutorial, then watching the video, then actually trying to do the tutorial):
- Follow the first half of this tutorial to learn (1) how to validate data with the goodtables browser tool, and then continue on to the second half to learn (2) how to use the goodtables command line interface https://frictionlessdata.io/field-guide/validated-tabular-data/
- Follow along with this video on validating on the command line with goodtables: https://www.youtube.com/watch?v=PqtM6d696eY&list=LLQe-pXn0XZzmRzvyOMZpfEg Note: this video has some Python installation tips, so if you are stuck on the Python part, watch the video (and ask in Slack if you are stuck!).
- note: We can have a session on 27 January to make sure everyone has the correct Python environment and make sure that everyone's code is working.
- Once you are comfortable with the idea of validating your data with Goodtables, learn about continuous validation by:
- Reading through this guide on how to continuously validate your data with goodtables https://frictionlessdata.io/field-guide/automatically-validated-tabular-data/
- Watching this video to see continuous validation in action: https://www.youtube.com/watch?v=9YHwu34D_vM&list=LLQe-pXn0XZzmRzvyOMZpfEg
- For an recap, read through this documentation about goodtables https://frictionlessdata.io/docs/validating-data/
- In Slack, answer these questions:
- What is one reason why you should validate your data?
- What information does goodtables use to "know" the correct data type?
Audience Mapping Lesson
Knowing your audience
- Read through this information on audience mapping
- Make a copy of this template and fill it out yourself
Open Access Week Lesson
Blogging for Open Access Week 2019
- Open Access Week 2019 has the theme of 'Open for whom?'
- We will collaboratively write a blog reflecting on the question 'open for whom?'
- The next week, each Fellow will be assigned a day to post their blog text to the Fellows blog by doing a pull request to the GitHub repo (Lilly will help with this). So this way, we will have new content each day of Open Access Week.
- GitHub instructions (Lilly will help you with this):
- The repository is here https://github.com/frictionlessdata/fellows
- The blog content can be found within the following folder: fellows -> content -> blog -> oa-week -> contents.lr
- Step 1, Pull down the master branch. Since you will all be editing the same blog file, you will need to pull any changes onto your local machine. Do this before you make your edits. Type 'git pull origin master'
- Step 2, Create a new branch: in the terminal, write 'git checkout -b [your-branch-name]' (replace with your name & remove the brackets)
- make sure you don't edit the master branch You can check with 'git status'
- Step 3, Add your paragraph to the blog: You will be editing the 'contents.lr' file within the oa-week folder
- Step 4, Add and commit your changes: Once you finish editing the file, in your terminal, type:
- git status
- make sure everything looks OK
- git add content/blog/oa-week/contents.lr
- git commit -m "adds [your name]'s blog"
- git status
- Step 5, Push your changes to your branch and open a Pull Request
- git push origin [your-branch-name]
- in GitHub, open a pull request (PR) + tag @lwinfree for review
- Lilly will merge your PR
- ask Lilly if you need help!