June meeting

Open Research Workshop Series

We’re excited to invite you to a short series of in-person practical workshops at Liverpool John Moores University (LJMU) focused on improving reproducibility, research collaboration, and data management. These sessions are open to all disciplines, including Science, Engineering and beyond, and are designed to be hands-on, accessible, and directly relevant to your work.

Workshop Schedule

Monday 23rd June | 2:00–5:00 PM (with coffee & cake from 2:00 PM) – Tom Reilly building, IT room 146a & b

Aakanksha Chauhan – Reproducible Data Science in Practice: A Hands-On Git Workflow Workshop

Learn how to use Git effectively in your day-to-day research workflows to ensure your work is versioned, collaborative, and fully reproducible.

Aakanksha brings industry experience in applied data science, with a background in analytics, modelling, and machine learning across various sectors.

Wednesday 25th June | 10:00 AM–1:00 PM (light lunch included) - Tom Reilly building, IT room 146a & b

Amie Longthorne & Susi Zajitschek – Research Data: From Funders’ Expectations to the Open Science Framework

Get practical advice on writing data management plans and selecting appropriate repositories to meet UKRI requirements, followed by an introduction to the Open Science Framework (OSF) — a platform to help you plan, manage, and share your research openly and transparently.

Monday 30th June | 10:30 AM–5:00 PM (includes lunch & breaks) – James Parsons building, room 3.10

Friederike Hillemann – Effective Collaboration in Research: Practical Skills for Teamwork and Reproducibility Important – Please bring your own laptop to code along!

Build your skills in collaborative research, from communication strategies to shared documentation and reproducibility tools. Friederike is a behavioural ecologist with a strong commitment to open, collaborative, and reproducible research, drawing on her experience across both human and animal systems. She has contributed to long-term datasets and brings expertise in rigorous data management and transparent analytical workflows.

Resources

Open Science

Our recently created BES Best practise handboook! can be viewed and/or downloaded as pdf from the School’s website.

Information from the library on open datasets , data management plans and info on open publishing assistance

An Introduction to the Open Science Framework to organise your projects and share workflows with collborators: OSF intro

Code review

Best practice for your code, and a workflow to review (yours and other’s) code before publishing: check out Friederieke Hillermann’s work: Githut repository: Shiny app for code review

OSF SORTEE project, collaboration: Code review workshop

Or copy the CODE directly into a new r scrip and execute to open your code review checklist!

Open Data Science

If you’re interested in more advance data science version control, check out Akankshas Github repo