Writing your thesis

academic
writing
Compilation of ideas and resources intended to assist you in writing your thesis.
Author

Marcel Turcotte

Published

April 19, 2025

Congratulations! You are now at a stage where you are thinking about writing your thesis. Whether you are just commencing your degree or actively embarking on the writing phase, this compilation of ideas and resources is intended to assist you in this scholarly endeavor.

Models

Theses from the University of Ottawa can be accessed online via uO Research. It is highly recommended to review examples to gain a comprehensive understanding of the expected standards. Given that these expectations differ across disciplines, it is crucial to examine theses from your own program or from closely related fields.

For students who are applying machine learning techniques to the fields of bioinformatics or medicine, these examples serve as excellent models. My selection of these particular examples is informed by my experience as a member of those defense committees.

Tools

Scientific writing tools like LaTeX help maintaining consistency in document formatting, particularly in the organization and presentation of references. Both TeX, started in 1978, and LaTeX, released in 1984, are free and compatible with nearly all operating systems. They use text files with markup annotations, ensuring long-term editability and compilability. Unlike traditional word processors, LaTeX’s format enables continued access to documents decades later, for instance I can still edit and compile my PhD thesis from 1995.

Overleaf is a widely used online platform for creating and compiling LaTeX documents. It eliminates the need for local LaTeX installations by providing a cloud-based solution. The platform offers a free version with certain constraints, as well as a discounted subscription plan for students. A significant feature of Overleaf is its facilitation of collaborative work on documents, enabling multiple users to edit and review content simultaneously.

Refer to the following page to download and use LaTeX on Linux, macOS, Windows, and online platforms.

LaTeX provides numerous packages and styles, including specific thesis templates for the University of Ottawa.

Quarto and Typst are powerful tools for preparing documents, especially for incorporating mathematical equations and dynamic content with Python, R, Julia, and Observable. However, I hesitate to recommend them for thesis writing. They are not as established as LaTeX, which may lead to occasional frustration, and it is unclear if they will have the same longevity as LaTeX.

A common problem in thesis drafts is the handling of references, which are often incomplete, incorrect, or inconsistently formatted. LaTeX users can solve these issues by using BibTeX, which manages references and creates bibliographies. BibTeX keeps the bibliography in a separate file with a .bib extension, apart from the main LaTeX document. I personally use a single BibTeX file for all my projects, including academic papers, lecture notes, and my CV. Over 35 years, this file has grown to include over 10,000 references.

Please, please, please, and this is a golden rule: never enter references manually in your bibliography. Errors in entering bibliographic details, such as incorrect page ranges, volume numbers, or publication dates, are inevitable. To mitigate these errors, use a bibliography management tool that interfaces directly with databases like Scopus, Web of Science, Google Scholar, and PubMed. This allows for the seamless importation of references into your bibliography. As a macOS user, I prefer using BibDesk, although JabRef is a commendable alternative.

University of Ottawa

References

Dobin, Alexander, Carrie A Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, and Thomas R Gingeras. 2013. STAR: ultrafast universal RNA-seq aligner.” Bioinformatics (Oxford, England) 29 (1): 15–21. https://doi.org/10.1093/bioinformatics/bts635.
Wang, Jun, Marc Horlacher, Lixin Cheng, and Ole Winther. 2024. DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning.” Bioinformatics 40 (2): btae065. https://doi.org/10.1093/bioinformatics/btae065.