How I Organise My PhD Research Project Notes in Notion - Thesis Planning Notion Template

A structured Notion system is helping a PhD student keep thesis writing from turning into a scavenger hunt: project notes are organized by chapter-level workstreams, then dragged through a “not started → in progress → done” workflow so every decision, dataset step, and meeting takeaway stays attached to the right research question. The payoff is practical—when it’s time to write, the notes for each project are already packaged for that chapter, reducing the need to rebuild context from scratch.

The setup begins with a thesis dashboard that acts like a roadmap. It includes a “PhD statement on purpose” area with guidelines for writing and an abstract-writing reference, a “literature” section designed to track literature notes, a “research plan” area built to create a Gantt chart for the PhD timeline, and an “upcoming publications” space to log ideas and targets. A global inbox captures stray ideas anywhere in the system, which can later be sorted into the correct place. There’s also a “research diary” for thesis notes and meeting notes, plus a projects area that functions as the hub for chapter-linked work.

Projects move through clear status lanes. When starting something new, the project is moved from “not started projects” into “in progress,” and once finished it’s dragged into “done.” The student emphasizes that this approach matters because thesis writing depends not only on the final paper content, but on the full trail of what was tried, decided, and learned. Earlier in the PhD, notes were managed differently (paper planning and iPad planning), leaving roughly a year and a half without the same level of captured detail—an omission the system is meant to prevent going forward.

Inside each project folder, the notes follow a repeatable structure. Early sections typically include motivation (why the project matters), methods (including data cleaning and dataset summaries), and then sections aligned to individual research questions. For supervisor meetings, the system is used to present organized, readable summaries, often alongside computational artifacts like Jupyter notebooks—while avoiding the fragility of notebooks where reruns can disrupt plots. The student also adds project-specific literature directly into the project folder when new questions emerge, then later merges those findings back into the general literature review.

A concrete example focuses on “training break summarization” in marathon training plans, examining whether training disruptions affect marathon performance. One methodological concern is selection bias: analyzing only runners who finish the marathon can exclude people who dropped out due to training disruptions from time constraints or injury. To address this, the student looks for studies estimating dropout proportions—an issue that didn’t surface during broad literature searching but becomes central once the project narrows.

For a second project view—geared toward paper writing—the folder includes the same core components (motivation, data, methods, evaluation) plus a “paper plan” section. That plan uses bullet points and checkboxes to track what goes into each paper section and to manage last-stage graph fixes. The overall result is faster writing because the system provides ready-to-use outlines and meeting-ready notes, rather than forcing the student to start from zero when assembling a manuscript for publication.