Planning a dissertation

Designing a research plan can be a challenging experience, but in the end a well-thought out project that is designed to answer the questions being asked, has properly balanced work expectations, and has contingency plans will be key to a successful research program and excellent preparation for a scientific career.

A good research question

Every dissertation project requires a research question. Sometimes this question is clear and well-defined by your supervisor. Sometimes it's more like a vague idea, a hunch. Sometimes the question proposed by your adviser is manageable and realistic. Sometimes it is huge project that would require you to stay your whole life as a graduate student to complete it. Regardless your particular situation, your very first step should be to immerse yourself in your research question, adapt it and make it your own. But how to know if it a good scientific question?

Let's start with definitions. A good scientific problem is that that allows you to discover new knowledge that you care about by conducting activities that you enjoy doing. However, probably we will not be so fortunate to have it all. Considering this, we can reframe this definition and say that a good scientific problem is that that allows you to discover a new thing about something that you partially care about by conducting at least some activities that you enjoy doing.

Scientific problems can be described through two main parameters: feasibility and interest [1]. Feasibility refers to how easy or hard is to tackle that problem. In general, a problem can be easier or harder to work on depending on the researcher's abilities and the technology and resources available in the lab. Interest refers to how striking the results will be for the the scientific community in your field and/or how interesting the results will be to you. A good scientific problem will have the right combination of all these factors. The worst possible case will be to choose an extremely hard project that will take years and years to complete just to get trivial results that no one cares about. This is certainly and exaggeration and hopefully none of us are in that position. However, a lot of projects have a dangerous resemblance to this caricature. Stay away from them. Run!

Within the good scientific problems, there is a plethora of options involving different combinations of feasibility and impact. If you are starting in a new field, a good project for you will be low impact but extremely feasible. This will allow you to gain key skills in the field while achieving progress which will keep your motivation high. After finishing this project, you will be ready to go for bigger fishes with the experience and confidence gained. Also, consider that time constrains and future career career goals are key when choosing on your right combination of feasibility and impact. As general rule, the more impact, the more difficult a project will be, and in turn, the more time it will take to complete.

Finally, don't rush and get feedback. It takes time to really get to really understand a scientific question to even assess if it is easy or hard to answer and if you care or not about it. Don't rush into a question just to realize half-way that is impossible to answer in ~4 years and you cannot possibly care less about this topic. I've seen a lot of grad students having the need of "make progress" and show to their supervisor that they are committed and responsible. In science, progress (or the illusion of it) is not linear: nothing will seem to happen during the firsts months or even the first year(s). The important part is to keep working consistently and strategically. Also, remind that defining a research question is an iterative process that you will not have to do alone. In fact, it's your advisor's job to advice you on this! Discuss project ideas with your PI and other people in academia – postdocs, senior scientists, colleagues. Take their feedback, but don't fall in the trap of the "new project"! (see next section).

Embrace a project management system

Conducting research is culture medium for procrastination. Big projects, with fuzzy deadlines, no inherent reward structure (lots of effort can be pointless if experiments are unsuccessful), and PIs lacking management skills. So, don't bit yourself if you are performing to the level of your expectations, the system is made for that. We, however, design our plant for our dissertation in such a way that facilitates our works and improves our well-being.

Big projects are overwhelming and offer little actionable steps. Crafting a project management plan is an excellent starting point, something you can do even as you are refining your proposal. More often than not, students seriously underestimate the amount of time required to complete a thesis or dissertation. You'll find it helpful, therefore, to make specific time estimates of various stages of your work, even if your estimates are subject to change. Here is how you do it: after you define your big picture question, break it down iteratively up to a point where specific, concrete actions are defined. You can do this by generating an outlines as nested bullet points, from more general to very specific actionable steps. Then estimate how much time each step will take and... multiply everything by 1.5 (yes, this step is necessary, humans are terrible estimating how much time actions take). Look at your outline and the list with action items and ideate one or two backup plans for each one of these points. You can organize all these information using project management techniques such as Gantt chart, a project timeline, a mindmap a kanban board, a flowchart, or a simple project checklists.

Each sub-question will require you to accomplish a set of activities like literature search, fieldwork/placement, survey(s), interviews, desk research, and lab work. When these headlines are defined, then enumerate all the steps needed to achieve that goal. These set of actions not only need to be small but tiny. For example, "literature research" must be divided into reading X, Y and Z papers; "conduct experiment A" must be detangled into read paper with protocol, download/print protocol, study protocol, check for supplies, order supplies, etc. After these, set a clear timeline for each one of them. If you have a record of not fulfilling personal deadlines in the past, then recruit an accountability partner. It doesn't need to be your PI! It could a lab mate, a friend in our cohort, your significant-other, your mother, anyone that you know are interested in your progress and wellbeing (and that understands to a certain extent what you are doing). More importantly, when you accomplish your goals –no matter how tiny they are – don't forget to celebrate! Little wins brighten our lives.

Communicate these plan with with your supervisor and timetable meetings with your them at the start of your project/research. Timetable meetings with your supervisor at the start of your project/research (See Navigating Lab Relationships). Be specially aware of the trap of the "new project". Some PIs are really eager scientist that address every question with curiosity and enthusiasm. This is generally a good thing, however, if your advisor proposes you a new dissertation project every month, do not go on board without giving it a thought. In some cases it will be a good idea, but more often than not, it will be distracting, if not confusing and/or overwhelming. Take the same approach discussed in "A good research question" section. Take your time, don't rush.

Even if you follow all these advice, procrastination will find its way. One of the worse kinds of procrastination is the disguised one. It consumes your time and energy, it distracts you from your real goals, but you don't even notice! It can be disguised as a new project, a new collaboration, as administrative work, as a sudden desire to organize your desk or all the lab supplies... it's the kind of procrastination that looks like work, but it doesn't help you move forward towards your most significant objectives. But even worse than these, is the motivation procrastination. Some people call it "waiting for the motivation fairy" [2]. This is the annoying voice that says "you are not ready to do that experiment yet", "you are not ready to write yet", "you need to do this one extra thing". Here is the secret: the motivation fairy strikes when you see progress. But to see progress, you need to start now. So go!

... and a knowledge management system

A frequently overseen aspect of personal development is the crafting of a personal knowledge system. This is specially important for us, because our work and performance depends on the knowledge we are able to capture, integrate and create through our work. Knowledge management involves four pillars: knowledge discovery, capture, sharing and application.

In scientific research, there are fundamental activities that we all need to accomplish. These include: reading literature and citation, track experiments and protocols, track data analysis, code and data sources, write collaboratively manuscripts and grants, and take notes of meetings, lectures, seminars, and readings. Think about a plan for discovery, capturing, sharing and applying knowledge in each one of these activities.

For reading scientific literature and managing citations, consider using a reference manager such as Zotero, Mendeley or PaperPile. These useful tools allow you to organize your articles into folders, highlight and annotate articles, and integrate a bibliography manager into multiple platforms including Google Docs and Microsoft Word. Some of them even have apps so you can sync your library with your phone to read articles on the go.

We all understand the importance of keeping a clear and organized lab notebook. This is the most standard knowledge management techniques in scientific research. Allocate time to organize your lab notebook, if not daily, at least once a week. To get better at writing notes in your notebook, you can check what techniques other people use.

In addition to your lab notebook, there is another increasingly relevant notebook that you should keep: your data analysis and bioinformatics notebooks. The idea of merging code with careful explanations of the goals, results and the meaning of the code itself is known as literature programming. It is incredibly important to develop an habit of literate programming, your future self will be grateful. To do this you can use any text editor, but tools like Jupyter Notebooks and R Markdown greatly facilitate this process. The next step is to keep track of the changes you have made to your code and coding notebooks. Gladly, software engineers have already created tools to handle this process. The most popular of these tools is GitHub, which uses a version control system called Git to track changes and offer a cloud service to backup all your code. Give it a try!

The tools used for manuscripts and grant writing can differ depending on your PIs preferences. Ideally, the tool that you use allow other people to add suggestions in real time, avoiding emailing back a forth the classic document_final.docx, document_final_final.docx, and so on so forth. Google Docs, Microsoft Word in Office 365 and even some GitHub tools allow you to do that.

Finally, you must define a note taking system. This is the most personal knowledge management decision and there is no "one size fits all" solution. Popular note taking apps with a hierarchical (folder, subfolders) structure include Evernote (Mac/Windows), OneNote (Mac/Windows), Apple Notes (Mac) and Bear (Mac); popular note taking apps with a network structure (and bidirectional linking) are Obsidian, Roam Research, and RemNote; the best outlining apps are Workflowy and Dynalist; and the most popular note taking apps that can be designed to fulfill all of the above paradigms (a movement called NoCode) are Notion and Coda. Regardless of the tools, the important part is to actually design a system that suits you where you can discover, capture, share and create knowledge, even if it is as simple as a bunch of text files.

References

[1] Alon, Uri. “How to Choose a Good Scientific Problem - PubMed.” PubMed, 1 Jan. 2009, https://pubmed.ncbi.nlm.nih.gov/19782018/.

[2] Kearns, Hugh. “Waiting for the Motivation Fairy.” Nature, 6 Apr. 2011, https://www.nature.com/articles/nj7341-127a.