1. The pillars of science are I) fundamental theory, II) experimentation, III) observation and IV) mechanistic modelling. Students should strive to prominently feature creative applications of at least two of these pillars in their research.
2. We are happy to guide student’s research down the paths that we feel are most promising but students are encouraged to propose their own ideas and to follow their curiosity and intuitions. We find that people are happiest and most productive when they are allowed to take ownership of their projects and feel a high level of agency.
3. Mental health is critical to student happiness and research productivity. Unfortunately, graduate students are at higher risk of anxiety and depression than the general population. We encourage students to take steps to stay mentally healthy. These include maintaining an appropriate work-life balance, making time to exercise, practicing mediation, and engaging with a supportive community of fellow graduate students and faculty. Cognitive behavioral therapy applied to the stresses of graduate school can also be helpful.
1. As a first step in their research, students must gather an appreciation for what is known and what is unknown in their subject matter. A possible pathway to build this knowledge foundation is as follows:
a. Read textbook chapters that underlie the research topic
b. Read published “review” articles on the topic
c. Read seminal journal articles on the topic
d. Read contemporary journal articles on the topic
When students read journal articles, they should take the time and effort to truly understand what they are reading. Casually reading 10 papers for 30 minutes each, will amount to 5 hours of reading papers but true understanding will be very limited and students will forget most of what they read in a matter of days to weeks. Thus, it is better to spend those 5 hours trying to understand a single paper (or section of a paper) as completely as possible. This will allow the information to penetrate into the student’s long-term memory and add analysis tools to the student’s repertoire.
2. Students must obtain the computer skills necessary to perform data analysis. This does not require a degree in computer science but it does require an initial familiarity with a high-level programming language (i.e., Python, Matlab, R, IDL, etc.). Once they have an initial familiarly with a language, further learning can mostly come about “on the job” (in the service of their actual research goals).
3. Once the student has achieved a sufficient level of proficiency in the subject matter and is able to conduct some data analyses and produce plots, they have the tools necessary to create new knowledge and insights! We encourage students to make as many curiosity-inspired plots as possible. At some point they will inevitably stumble upon an interesting result that shows a relationship that is either unknown or at least underappreciated. This is the seed of a section of a master’s thesis or a journal paper. After these initial results are found, one possible means of progression is as follows:
a. Write a Nature-style intro paragraph according to this guide. This helps keep the focus on a relatively narrow question instead of a general area of inquiry. This is also a check on whether the student knows the existing literature well enough to begin writing a paper or the thesis section.
b. Perform additional related analyses that the student is curious about or that we anticipate a reviewer might ask for. This entails making dozens of figures and having some system for organizing them (this system can be as simple as putting them in a ~50-slide PowerPoint document). If any of these results fundamentally undermine the initial results or if any of these results are more promising than the initial results, the student should start over at step “a” above and dive deeper into their new findings.
c. Write the paper or the thesis section, keeping in mind the big picture and narrow focus (from the Nature-style paragraph) and referencing the most relevant results from the large set of analyses conducted. We encourage students to avoid being too perfectionistic and just get words down at first. Re-writing poorly formed sentences is much easier than writing well-constructed sentences from the outset.
d. Make publication-quality figures (i.e., all the panels/labels neat, with consistent text-figure nomenclature etc.). These figures should be pulled from the most relevant results in the large set of analyses conducted. Some people will recommend that the main figures should be decided upon before the writing begins. This is up to the student but sometimes it is not clear what the main figures should be until one starts writing.
We believe that the best way to learn something is to teach it. Thus, in our lab we will take turns teaching each other aspects of our projects or useful tools for analysis. We have weekly lab meetings with one person in the group in charge of engaging in one of the following activities:
1. Teach a textbook chapter on a subject related to your research project
2. Teach the methods/results of a published paper related your research project
3. Teach the group about a new set of results that you have recently produced (we will discuss the potential next steps as a group).
We welcome anyone in the department to attend our group meetings as we feel that progress is made easier when there is intellectual diversity amongst the discussants. We especially encourage undergraduates to attend so that they can get a view of how the research process works.