Prioritise Open-Source

Analysing and Interpreting Data

Prioritise Open-Source

In this section, we promote the use of open-source software and provide alternatives to commonly used closed-source options in research.

Introduction

For data analysis to be truly inclusive, it must be open and accessible to everyone. Open-source software, where the source code is freely available for anyone to view, modify, and distribute, plays a crucial role in achieving this goal. In contrast, closed-source or proprietary software restricts access, often requiring paid licenses and limiting transparency.

In research, we often overlook how frequently we rely on proprietary, closed-source tools such as Microsoft Office, MATLAB, or SPSS.

When researchers use closed-source tools or don’t share their code, it creates barriers for others who may struggle to access, learn from, or replicate the work.

This lack of accessibility can disproportionately impact individuals and institutions with limited funding, further reinforcing systemic inequities in research.

The following table highlights common proprietary software and their open-source counterparts, offering more accessible options for researchers across disciplines.

Category Proprietary (Closed-Source) Software Open-Source Alternative(s)
Word Processing & Document Editing Microsoft Word LibreOffice Writer, LaTeX
Spreadsheets & Data Management Microsoft Excel LibreOffice Calc
Presentation Software Microsoft PowerPoint LibreOffice Impress, LaTeX
Statistical Analysis SPSS, Stata, SAS R (with RStudio), JASP, PSPP, Jamovi
Numerical Computing & Data Analysis MATLAB Python (NumPy, SciPy, Matplotlib), GNU Octave, Julia
Reference Management EndNote, Mendeley Zotero, JabRef, BibTeX
Image Editing & Visualisation Adobe Photoshop, CorelDRAW GIMP, Inkscape
Vector Graphics & Data Visualisation Adobe Illustrator Inkscape
Typesetting & Scientific Writing Microsoft Word, Adobe InDesign LaTeX
Version Control & Collaboration GitHub (free but not fully open) GitLab, Gitea
Computational Modelling & Simulation COMSOL, ANSYS OpenFOAM, FEniCS, LAMMPS
Electronic Lab Notebooks LabArchives, Benchling Jupyter Notebook, OpenLabNotebook
Qualitative Data Analysis NVivo, ATLAS.ti Taguette, RQDA (in R), QualCoder
Machine Learning & AI MATLAB toolboxes Scikit-learn, PyTorch, TensorFlow (Google-managed, but open-source)

By adopting open-source alternatives, researchers can foster a more inclusive and collaborative scientific community. Open-access tools lower financial barriers, encourage knowledge sharing, and promote innovation by enabling a broader audience to engage with and build upon existing work. Additionally, open-source software enhances reproducibility, as transparency in code and methodology allows for more rigorous validation of results.

Activity

See page 48 of the workbook.

  1. Complete the table below by listing all the software you use during the research process. This could include text editors such as Microsoft Word, software for data collection (e.g. Inscopix), and software for data analysis (e.g. MATLAB).
  2. Determine whether the software is open- or closed-source.
  3. If the software is closed-source, try to find an open-source alternative.
  4. Consider: Is it feasible for you to switch to the open-source software?
Software This software is… Open-Source Alternative
Closed-Source Open-Source
e.g. MATLAB ✓ Python

Practical Steps and Tools

Read UKRN’s primer on Open Code and Software.

Complete the Open-Source Software activity above and consider the following:

  • How much of the software that you use is open-source and did this amount surprise you?
  • Is it feasible for you to swap any closed-source software for open source and what are the barriers?
  • Can you encourage a general shift towards open-source software in your lab group or with your collaborators?

Clearly document your analysis choices and procedure as you conduct your analyses.

If you use custom analysis scripts, make sure they are well-commented and available for others to view.

Contribute to the Hub

Feedback helps improve research quality, refine methods, and keep insights relevant and impactful. By sharing their perspectives, users help shape future studies, refine methodologies, and contribute to a more dynamic and collaborative research community.

Contribution Submission

The Hub is a living resource. As such, we welcome critical feedback and contributions of all kinds. In particular, we invite feedback on:

  • Concepts or practices we may have missed or under-explained
  • Our use of language, and how it could be clarified or made more inclusive
  • The organisation and presentation of information and resources

We would especially appreciate suggestions for subject-specific case-studies that are relevant to the various sections of the Hub.

Consent

Section 1

Accountable Decision-Making

Section 2

Prioritise Open-Source

Current Section
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