This document provides an overview and onboarding information for instructors of Data Carpentry workshops. It describes how Data Carpentry started by focusing on teaching foundational data skills to researchers who need to manage and analyze data but have little computational experience. Workshops cover the full data lifecycle through hands-on lessons and target domain researchers. Current domains include ecology, genomics, and spatial data. The document outlines workshop structure, curriculum, logistics, how to schedule and set up workshops, and how to get help from Data Carpentry.

1. Data Carpentry Instructor Onboarding
@datacarpentry
http://www.datacarpentry.org

2. Overview
• Overview of Data Carpentry
• How it started
• Focus of workshops
• Target audience
• Goals for workshops
• Workshop overview
• Workshop structure
• Curriculum
• Workshop logistics
• Scheduling workshops
• Setting up workshops
• How to get help with lessons or workshops

3. Overview of Data Carpentry

4. With the emergence of new technologies generating large
datasets in all domains of research, data management and
analysis is no longer the domain of specialists and is instead
widely done by all researchers.
Training is the missing piece between data and data-driven discovery

5. Biggest Bioinformatics Difficulty Most useful thing BRAEMBL could do
Survey by Bioinformatics Resource Australia – EMBL
Researchers view the major limiting factor in
research progress as a lack of expertise in how
to handle and analyze data

6. How it started
• Idea to focus on materials for these researchers who had little
computational experience, but now needed to manage and
analyze data, came from an NSF BIO Center meeting in 2013
where we identified this shared need for training
• Wanted to use the same hands-on collaborative approach that
Software Carpentry was using
• After developing and teaching a few of these workshops in
2014, we saw there was broader interest and after talking
with Greg Wilson, Data Carpentry began as a sibling
organization to Software Carpentry

7. Workshop focus
Workshops are focused on data – teaching researchers the
foundational skills that will let them effectively manage,
organize, analyze and visualize data.
Workshops follow the data lifecycle, starting with ‘so, you just
got your data’
Foundational skills:
- Project organization
- Data organization in spreadsheets
- Data cleaning and wrangling
- Data analysis in a scripting language
- Data visualization in a scripting language

8. Target audience
• Current audience is domain researchers with little to no
computational experience. This is advertised on every
workshop website, to manage expectations of the
participants.
• This doesn’t mean there won’t be some people there with
more experience, but we restate this at the beginning of the
workshop to help manage expectations, and encourage those
with some experience to help their neighbors

9. Workshop goals
• Primary goal is increased confidence in a learners ability to do
computational work and continue to learn more.
• Lower the activation barrier for people to get started working
with data. Often people don’t know where to start.
• The skills we teach should be ones they can immediately apply
to their research.
• People should learn the things we’re teaching.
• We should see a shift in perspective in the value of skills like
scripting for better research and reproducible research.
• People should have a positive workshop experience that
empowers them to do better work with data.

10. Deliver domain-specific hands-on intensive
workshops covering the full lifecycle of data-
driven research. Current workshops are
designed for people with little to no prior
computational experience.

11. Workshop Overview

12. Workshop structure
• Workshops are domain-specific, so participants are working
with the type of data they use in their research
• Workshops use one dataset from start to finish to go through
the whole process of the data management and analysis
• Workshops follow a narrative structure, again going through
the process in the workshop that is what a researcher will go
through in their data processing.
• Data sets are teaching datasets of real data and are publicly
available for reuse

13. Workshop Introduction
• Because this audience is people newer to computation, setting up a
friendly environment for learning from the start is very important!
• For an overview on introductions, see An Introduction to
Introductions
https://github.com/tracykteal/instructors-introduction/tree/gh-pages
• Introduce yourself highlighting your experience, but also your
interest and enthusiasm for teaching this group. You want to show
that you know what you’re talking about, but also that you’re
accessible and approachable.
• Highlight the Code of Conduct http://www.datacarpentry.org/code-
of-conduct/ pointing out that it mainly is ‘Be respectful of each
other’.

14. Workshop curriculum
• Current domains are ecology, and genomics, and spatial data
under development with NEON
• Lesson templates are slightly different than SWC (we just use
Markdown and Rmarkdown and no pandoc), but module and
lesson structure is essentially the same
• Because the workshops are a narrative approach, it’s
important to use the specified lessons

15. Lessons
Ecology:
https://github.com/datacarpentry/ecology-workshop
Genomics:
(stilll in beta; will be 1.0 in early 2016)
https://github.com/datacarpentry/genomics-workshop
Spatio-temporal lessons
(Developed in collaboration with NEON, alpha phase)
http://github.com/data-lessons/NEON-R-Spatial-Raster
https://github.com/data-lessons/NEON-R-Tabular-Time-Series
https://github.com/data-lessons/NEON-R-Spatial-Vector

16. Lessons
• Bringing in Reproducible Research curriculum
https://github.com/Reproducible-Science-
Curriculum/workshop-
planning/blob/master/workshopOverview.md
Contributing to lessons is still being done through github, but if
you are not familiar with github and want to contribute to
lesson development, get it touch and we’ll help. Don’t let the
technology be a barrier to participation if you’re interested.

17. Ecology lessons
Points of interest
• Spreadsheet lesson
http://www.datacarpentry.org/spreadsheet-ecology-lesson/
• OpenRefine lesson
http://www.datacarpentry.org/OpenRefine-ecology/
Even if ecology is not your field of study, these are lessons you
can teach. They are focusing on using tabular data.

18. Genomics lessons
Points of interest
• Amazon EC2 or a local HPC is used for these lessons. I need to
spin up instances for a workshop, if you’re using EC2
• These lessons go through getting data back from a sequencing
center, understanding the FASTQ file type, connecting to
remote resources, using the command line and running a
bioinformatics pipeline on remote computers, data transfer
and data analysis and visualization in R.
Some genomics or bioinformatics background is needed to
teach this workshop.

19. Workshop logistics

20. Scheduling workshops
Hosts request workshops through a request form
http://www.datacarpentry.org/workshops-host/
Maneesha Sane is our Program Coordinator as well as Software
Carpentry’s and she will put out calls for instructors for requested
workshops. If you are interested and available to teach, please sign up!
We are making efforts to match experienced instructors with newer
instructors for each workshop.
As a newer and smaller organization, Data Carpentry does not have
the same process for self-organized workshops as Software Carpentry.
We’re updating our workshop request form and documentation to
reflect this, so better details soon, but if you want to run a self-
organized workshop, you need to put in a workshop request form and
request a fee waiver. We will grant waivers if self-organized criteria are
met.

21. Setting up a workshop
• Workshop templates are like Software Carpentry
https://github.com/datacarpentry/workshop-template
If the workshop is not self-organized, you can also ask us to set up the
workshop web site.
• Like for Software Carpentry, there will be a lead instructor. That
instructor will communicate with the host and other instructors and
helpers in preparation for the workshop with the instructor
checklists as a guide.
• http://software-
carpentry.org/workshops/checklists/lead_instructor.html
• http://software-
carpentry.org/workshops/checklists/instructors.html
• For all workshops, we will set up pre and post workshop surveys and
send them to you to distribute

22. • If you need help going through lessons or with workshop
questions or logistics, you can contact
admin@datacarpentry.org and we’ll make sure the right
person gets it.
• For lessons, each module has (or will soon have) topic
maintainers. They are people you can ask questions about
individual modules.
• The SWC discuss list is a place to ask questions
• We also just set up a Slack channel! Request to join.
https://carpentries.slack.com
• There are regular debriefing meetings being run by the
Mentoring Subcommittee. Come there before a workshop to
ask questions about lessons.
How to Get Help

23. After a workshop
• After a workshop we will:
• Ask for the number of participants you had in your workshop
• Share the post-workshop survey results with you
• After a workshop you can:
• Attend a Mentoring Subcommittee debriefing meeting
• Let us know if you had any suggestions for the workshop,
whether that’s materials, organization or structure or anything
else
• Contribute back changes you made to the lessons. If you made
changes that you thought improved the lessons, please put in a
Pull Request or talk to us about those changes. It’s the only way
the lessons get better!

24. Guiding Data Carpentry
Data Carpentry Steering Committee:
Karen Cranston (NESCent / OpenTree of Life)
Hilmar Lapp (NESCent / Duke)
Aleksandra Pawlik (Software Sustainability Institute)
Karthik Ram (rOpenSci / Berkeley Institute of Data Science Fellow)
Ethan White (University of Florida / Moore DDD Investigator)
Greg Wilson (Software Carpentry)
Hiring an Associate Director
http://www.datacarpentry.org/blog/associate-director-posting/

25. Data Carpentry support

26. Questions?