Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct
research. Its target audience is researchers who have little to no prior computational experience,
and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly
apply skills learned to their own research.
Participants will be encouraged to help one another
and to apply what they have learned to their own research problems.
Requirements: Participants must bring a laptop with a
Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).
Code of Conduct: Everyone who participates in Carpentries activities is required to conform to the Code of Conduct. This document also outlines how to report an incident if needed.
Accessibility: We are committed to making this workshop
accessible to everybody.
The workshop organizers have checked that:
The room is wheelchair / scooter accessible.
Accessible restrooms are available.
Materials will be provided in advance of the workshop and
large-print handouts are available if needed by notifying the
organizers in advance. If we can help making learning easier for
you (e.g. sign-language interpreters, lactation facilities) please
get in touch (using contact details below) and we will
attempt to provide them.
Most research projects start with collecting data in a spreadsheet, and we tend to organize data in spreadsheets based on how we want to work with the data. In this lesson, you will first learn best practices for entering data and formatting data tables in spreadsheets.
This lesson will go over using a relational database for the effective storage and use of your data. Using databases for your data storage ensures that your stored data isn’t affected when you run analyses on it. SQL (Structured Query Language) allows you to create databases as well as run queries on it. Learning SQL and the concepts behind relational databases are at the core of understanding how to do similar tasks using programming languages such as R or Python.
The second portion of the lesson will focus on generating reports using R. Reports are a way to make your research more reproducible, whether that’s for you and your research group or for peer researchers whom you’ve made the data available to.
Open Coding Lab (optional)
Do you have questions about any of the tools that you’ve learned about over the last four weeks or how to apply them to your research? This week’s session will allow you to come to work on applying what you learned to your own data. The instructors from the previous weeks’ lessons will be available to help answer your questions and work with you on any issues. This week’s session is optional.
For the spreadsheets lessons you will need to have a spreadsheet program,
such as Microsoft Excel or
the Calc program in OpenOffice .
If you do not already have one of these programs installed, please follow the
links above to install.
R is a programming language
that is especially powerful for data exploration, visualization, and
statistical analysis. To interact with R, we use
Install R by downloading and running
this .exe file
Also, please install the
Note that if you have separate user and admin accounts, you should run the
installers as administrator (right-click on .exe file and select "Run as
administrator" instead of double-clicking). Otherwise problems may occur later,
for example when installing R packages.
You can download the binary files for your distribution
from CRAN. Or
you can use your package manager (e.g. for Debian/Ubuntu
run sudo apt-get install r-base and for Fedora run
sudo dnf install R). Also, please install the
SQL is a specialized programming language used with databases. We
use a simple database manager called
SQLite in our lessons.