Coding for Reproducible Research (CfRR)#
Coding for Reproducible Research (CfRR) is the University of Exeter’s training hub for programming, data science, and reproducible research practice. The programme combines hands-on workshops, open self-study materials, and an active contributor community so that researchers at every career stage can develop the technical skills they need.
Mission: CfRR supports Strategy 2030 by fostering an open, ethical, and reproducible research culture. See the full statement on the home page.
Who it is for: All University of Exeter staff, postgraduate researchers, and doctoral students who want to strengthen their coding and analytical practice, whether they have never coded before or are looking to sharpen advanced skills.
How it works: Attend live workshops, follow curated learning pathways, explore rich self-study modules, and connect with a community of researchers who share good practice.
Contact: Reach the team via codingforreproducibleresearch@exeter.ac.uk.
Getting Started#
Read the orientation guide – How to Use This Website explains navigation, learning modes, and where to find support.
Plan your learning – Browse the Workshop Schedule and Sign-up page for upcoming sessions and registration links.
Check your starting point – Use the ‘Where Is My Understanding?’ quizzes to identify the right course level.
Follow a pathway – Select one of the curated learning pathways that stitch together workshops and self-study material for a specific goal (e.g. Python for data science, R for data wrangling).
Give feedback – Share your experience after any activity via Course Feedback so the programme keeps improving.
Programme Pillars#
1. Coding Languages#
The CfRR language stream takes learners from absolute basics to intermediate practice across several ecosystems. The umbrella page Coding Languages links to the full catalogue.
Ecosystem |
Workshop Overviews |
Self-Study Collections |
|---|---|---|
Julia |
Julia course homepage with notebooks such as variables, control flow, and visualisation. |
|
Python |
Python overview → Introduction to Python and Python for Data Analysis |
Python course homepage plus in-depth modules covering NumPy, Pandas, Matplotlib, and Scikit-learn. |
R |
R overview → Introduction to R, Regression Analysis with R, Advanced Regression Analysis, and Working with Data in R. |
R course homepage with notebooks on basic commands, manipulating data, plots, and domain-specific topics such as mixed effects models. |
Unix & Shell |
Unix course homepage and step-by-step notebooks for navigating files, finding data, and scripting. |
2. Coding Practices and Research Workflows#
These courses help researchers design robust, scalable, and collaborative workflows. Explore the Coding Practices Overview.
Computational Thinking – Conceptual foundations for algorithmic problem solving, with pathways to self-study (resources).
High Performance Computing (HPC) – Understand cluster environments, job schedulers, and parallel paradigms (intro to HPC, parallel computing).
Software Development Best Practices – Version control, testing, documentation, and reproducibility for research code (resources).
Version Control with Git & GitHub – Essential to advanced workflows (introductory course, intermediate course).
GPU Programming – Foundations for accelerated computing (resources).
3. Drop-in Support & Community Learning#
Languages drop-in and Practice drop-in sessions provide informal support (drop-in overview, languages drop-in, practice drop-in).
4. Self-Assessment and Guided Planning#
Where Is My Understanding? – Interactive quizzes map your current skill level and recommend next steps (landing page plus language- and practice-specific quizzes).
Learning Pathways – Ready-made curricula such as Python for Data Science, R for Data Science, Research Software Best Practices, and more.
5. Self-Study Library#
Course Homepages offer curated sequences of notebooks and exercises for each discipline (for example, the Version Control course homepage and the High Performance Computing course homepage).
Individual Modules dive into specific skills, with resources for Python, R, Julia, Unix, HPC, software engineering, and markdown production.
Short Courses compress skills into focused sessions for rapid upskilling (overview, Python short courses, R short courses).
6. Contribute and Collaborate#
CfRR thrives because researchers share improvements and co-deliver workshops:
Learn how to get involved on the Contributing page.
Discover available roles (workshop leader, helper, content developer) in Roles and Responsibilities.
Refer to Developing a Course Guidance if you want to author new materials.
Policies, Accessibility, and Support#
Code of Conduct – Expectations for inclusive behaviour during every session (Code of Conduct).
Reporting Mechanism – How to flag incidents confidentially (Report Code of Conduct Violations).
Accessibility Statement – Programme commitments and adjustments available on request (Accessibility Statement).
Programme Policies – Registration rules, waiting lists, and data handling (Programme Policies).
Licensing & Attribution – Course materials cite data sources and licences within each module; see CITATION.cff for how to acknowledge CfRR.
Putting CfRR Into Practice#
Pick a focus area. Do you need foundational coding skills, domain-specific workflows, or advanced reproducibility techniques?
Assess yourself. Use the quizzes and pathway recommendations to start in the right place.
Blend formats. Combine live workshops, self-study notebooks, and drop-ins to reinforce learning.
Apply immediately. Every module includes practical exercises using real research scenarios.
Share your journey. Contribute improvements, co-lead sessions, and help grow the community.
Whether you are launching your first data project or scaling an established research programme, CfRR provides the structure, resources, and community to embed reproducibility into everyday practice. Explore the links above, sign up for your next workshop, and become part of the CfRR network.