For a little change of pace, this past week I replaced our regular lab meeting with a writing exercise. Nearly every person in the lab was thinking about or actively working on a manuscript, so this seemed like a great time to ask people to formally think about how to put together a paper. My plan was to start the meeting with an abstract exercise outlined in this fantastic preprint by Halbisen and Ralston. The preprint provides some artificial figures that can be arranged in order and used to write a manuscript. Rather than asking the lab to write an entire manuscript on the fake data, I asked them to take a few minutes to arrange the figures in a reasonable order and write down their answers. The outcome was so much better than I had hoped. After collecting the responses, I was able to announce that no two people arrived at the same ordering except for myself and the newest undergraduate in the lab! This certainly got everyone’s attention since the least experienced person in the lab (in a room of undergrads, grad students and postdocs) was the only one that arrived at the same solution I did. I told everyone not to jump to conclusions and suggested that maybe we would be able to convince the others of our view or vice versa. A hilarious discussion ensued in which each person vehemently argued their point until we ultimately all agreed upon a final ordering with a common rationale.

After wrestling with the abstract considerations for organizing data, I asked everyone to then think about their own papers and order their data/figures in a way that they thought would be sensible for a manuscript. Some of them previously had been unsure about this task when thinking about their papers, but were now confident and easily able to proceed. If we disagreed, we discussed and were again able to reach a consensus on each ordering. To my delight, this exercise inspired several people to think of great ideas for experiments they could/should do. This is precisely the reason I feel strongly about organizing papers long before all the data are collected, but it was fantastic for all of us to see the benefits of it in real time! I too had some totally new ideas for everyone’s papers after hashing it out with them.

Overall, this combination of an abstract followed by concrete writing exercise was an unqualified success! I think everyone learned something, became empowered to push forward on their manuscripts and had great new ideas for their projects. And importantly, we had a ton of fun!

Update: Some are asking how long the exercise took. We only did the first dataset in the preprint for maybe 10-15 minutes and argued about it for another 20-25 minutes. The entire meeting including going over their own data took just over 2 hours.

It’s almost summer and a new class of graduate students will be starting soon (we usually get at least a few students that do summer rotations). This got me thinking about how students select rotation labs. I put together this list of considerations that I think might help students trying to make decisions on where to rotate.

1. Follow your research interests. If you are truly enthralled by a subject or research area, do a rotation! It might seem obvious, but don’t overthink it. Even if many other students are rotating in the same lab, your genuine enthusiasm will likely make you stand out as a great fit for the lab. Also, don’t agonize about whether the PI has 5-7 years of funding already secured for you (hint: unlikely). These are tough times but there are mechanisms through the graduate program and university to make sure there’s never a lapse in funding that holds back a student.

2. Look for a mentor that will support multiple career paths and create opportunities for you. As a brand new graduate student, it’s likely you’re not sure what you’re going to do yet with your degree. Make sure you pick a mentor that is willing to have career conversations with you often and help you find opportunities that will help you decide or explore your chosen direction.

3. Be fearless! This is a great opportunity to explore and try new things. If you know nothing about a field, it’s hard to know if you would enjoy it. If there’s a lab you’re curious about but have no background in, give it a try! I am always surprised by what piques my interest after learning more about it.

4. Follow your nose to find the right lab team/family for you. It’s not a guarantee that you will get along fantastically with all members of every lab but it’s important that the lab is a place you can imagine being happy for many years. A culture of mutual respect should first and foremost be demonstrated and promoted by the PI. Listen to your fellow students about the reputation of the PI to catch any major red flags, but remember that a poor fit for one student may be an excellent fit for another. You know yourself and if you think you might thrive in a specific environment, give it a shot!

5. Remember, your needs, abilities and confidence will change dramatically throughout grad school. I always tell students that the uncertainty they feel as a first year graduate student won’t be the same when they’re a 4th year. You will learn a great deal and undergo a massive transformation as a scientist throughout graduate school. Look for a PI that will grow with you and is willing to give you more flexibility and freedom as you need/want it.

6. If you’re on the fence about rotating (or even if not), ask to sit in on a lab meeting! I have invited a lot of students to sit in on lab meetings or journal clubs. It’s a great way to learn about the lab culture and see how the lab and PI interact.

7. Picking a rotation lab and ultimately a graduate lab is a big decision, but it’s not terminal. Most places have the option of doing more than the required number of rotations. Even if you join a lab, things go very badly and you need to switch labs, your graduate program will be there to support you. It happens more often than you might think for many different reasons, so don’t let stress and fear rob you of the fun of this great exploratory time in your career!

Every once in a while on New PI Slack we revisit the discussion about lab expectations and onboarding documents. Our lab has a private onboarding document (private because it contains a lot of specific information about our data servers/logistics etc), but I wanted a public expectations document so that current/future lab members could see what we value and see what we think is important for them to succeed. I’m posting it below but it will ultimately appear as a heading on the main menu of this site.

What you can expect from me:

• You can expect me to do everything in my power to find and create opportunities for you that will help you achieve your career goals, whatever those goals may be.
• You can expect me to push you out of your comfort zone towards greater independence.
• You can expect me to meet with you regularly to discuss experiments and future plans.
• You can expect me to help you interpret experimental results, help you decide on next steps and help you prioritize those next steps (but will strive to guide your thinking rather than do it for you).
• You can expect me to help you prepare grants/papers and edit mature drafts of written work (with earliest drafts scanned by labmates).
• You can expect me to go over your career development plan with you at least once per year to see if you are on track for your goals and help you overcome any challenges.
• You can expect me to send you to one conference per year if possible (preferably more if we can afford it or if you get your own funding).

What I expect from you:

• I expect you to conduct yourself with the utmost scientific integrity. Never manipulate data or plagiarize written work (even if it’s your own previous work).
• I expect you to keep a written record of your experiments in the designated electronic laboratory notebook that is updated preferably as you work but no less frequently than once per week.
• I expect you to attend all laboratory meetings. I expect you to tell me in advance if you cannot attend. Experiments should be planned around these events.
• I expect students and postdocs to identify funding opportunities and apply for all relevant/eligible external funding (including travel grants). Let me know of all submissions you have planned for the next 12 months.
• I expect you to be present in the lab for most of the hours between 10am-4pm as that is when most of the lab activities and collaborative discussions occur. The remainder of your work time can be whenever you wish and flexibility in schedule is a perk of the job. Please let me know of any full days missed in advance. Resist the urge to compare your work schedule to others in the lab as the expectations for each career stage and situation are different. Students and postdocs might choose to work more than the 40 hours expected for research assistants because of their additional responsibilities in writing/project design, formal requirements to move to the next career stage and the extra time needed for career development activities. Work hard but stay sane. There will be periods of very high intensity work to meet deadlines/goals and periods you just need to just rest, slow down and reflect (see my previous blog post).
• I expect you to create/revise your career development plan once per year and tell me how I can help you in any ways I haven’t thought of.
• I expect you to treat all of your lab mates with respect, regardless of their career stage. Each person is an important contributor to our team that we can learn from.

I was thinking about putting together a paper writing guide for the lab. Just thinking formally about how I write a paper was a useful exercise. I have lots of ideas on how to deconstruct this process and I started to make a giant list, but one thing I found that sort of encapsulated much of the advice was building a narrative. Let me demonstrate. Below I’ve written a nonsensical summary of a paper.

Title: D+G is the mechanism for how A works.

Summary: Here’s some background on what we’re studying and why it’s important. This is the state of the field/what we know so far. What’s critical, unknown and the question we wanted to ask is A. We figured out the answer by doing clever experiment B and found C. We did multiple orthogonal experiments that confirm that C is indeed true. We think C being true means that the most likely explanation for what is going on biologically is D. There are some alternative explanations for what C implies if not D, but we ruled those out. If D is true, it would predict E and F. We tested E and F and found they are also true but the results are a little unexpected, suggesting that what’s really happening is D+G. D+G would predict that H is true. We tested H and it is indeed true. All our data point to D+G as the mechanism for how A works, finally answering this open question in the field.

My feeling is that many people when starting to write a paper for the first time, can’t see the forest for the trees. It’s easy to get stuck in the details when day to day, you’re thinking at the level of troubleshooting individual experiments. Writing a narrative of the paper will serve as the North star for your paper writing process.  The above is just a stupid example and can vary wildly. When starting a paper, you should know A, have key result C in hand and interpret it to have a proposed idea for D. In my opinion, you don’t really need much else to begin. When writing this narrative, it will become painfully obvious that if you want to make your argument, you’re missing some pieces. That will help you decide which key experiments to add. This is why you shouldn’t wait until you think you’re done collecting data to write a paper. You’ll also notice that I didn’t write the title until I finished the narrative (I never would have known what letters to include!). Again, this highlights the point that you don’t really know what your story is until you go through the iterative process of adding data to round out the narrative.

If you force yourself to build a narrative early and keep revising it, you should eventually get to the finish line on your paper.

All advisors have different mentoring styles and different personalities. Despite these differences, everyone is busy and wants to get the most out of individual meetings with their lab members. Here are a few things that I think would help increase the efficiency of meetings within any lab.

1. Think of meetings as your one-on-one protected time with your advisor. These are (hopefully) free of distraction, so you have the undivided attention of your PI on you and your project. Don’t wait for your PI to initiate the discussion and ask you questions. Take the lead and come to the meeting with your own agenda for what you hope to accomplish.

2. Start the meeting with a recap of what you discussed previously and what you’re currently working on. While you have been thinking and working exclusively on your project, your advisor has been thinking about your project along with projects of at least half a dozen other people. That’s not including percolating grants, teaching and other responsibilities. I often find that I sometimes can’t immediately recall details of what was discussed with someone 24 hours previously. If you start the meeting with a brief mention of the previous discussion and a reminder of the strategy that was put into place, this will prevent your PI from wasting the first half of the meeting trying to get oriented.

3. Keep the amount of time allocated for the meeting in mind. If you have a 30 minute meeting scheduled, it is unlikely that there will be time to discuss everything on your mind. Pick the top three issues that are pressing or holding you back so that those are sure to get covered during the meeting. Important: This will force you to decide what you can/should figure out yourself and what you absolutely need input on from your PI. Never waste your meeting time on asking questions you can easily look up yourself or that you really need to read/learn/understand before discussing with your PI. If you are coming in with data, what is your interpretation and thoughts about how to proceed? If the experiment failed, what is you view about why it failed and what might be improved next time? What are any exciting new ideas you have been thinking about? Be prepared and come to the meeting with an opinion. Your PI may not always agree with you but will be thrilled to argue/discuss with you in detail.

4. In addition to your top scientific questions, remember to bring up any other issues that are pertinent to your progress, career advancement or well being. Are any issues in the lab (lack of resources, disruptive colleagues, logistical issues) slowing you down? This is not a gossip session but if something is affecting your progress or happiness in the lab, your PI wants to know/help. Are you concerned about whether you are on track to achieve the next step in your career path? Do you have a personal issue that your advisor needs to know about? Do you have question on how best to strategize for a particular goal? There may not be time to cover all issues, but you can set up another dedicated meeting if needed.

5. Often, people think of meetings as a time when they update their PI on what they’ve been doing to show they’ve been working/working hard. While your advisor may or may not be making this determination, this is not the purpose of the meeting. The purpose of the meeting is to discuss data and exchange information so that your project can progress more smoothly. Remember you and your advisor are on the same team. You both are excited about your project and want it to succeed. Both learning and progress are goals in the relationship with your mentor. I never recall being disappointed in an experiment that didn’t work if something was learned. I never recall being disappointed in progress if intellectual growth is evident.

In summary, remember that meetings are your protected time with your advisor and that there is much in your control that can make them as useful and efficient as possible.

In the lab, we often have discussions about prioritizing experiments and I usually have a strong opinion about these things. I was starting to realize that being able to set these priorities, feel confident in your decisions and fluidly change course as data and circumstances shift is a learned skill. In an attempt to somewhat formalize some of the considerations that might come up when prioritizing experiments, I came up with the below decision tree to share with the lab. It’s a work in progress and I think the tree very likely looks a bit different depending on your career stage. Missing from the tree is also a bee line for emergency experiments needed for paper revisions, grant applications etc., but those decisions are rarely ambiguous so I omitted them here. I’m not even sure I make decisions this way, but it seemed like a fair place to start for those trying to juggle experiments early in their career. I hope others find it useful and, as always, please contact me on twitter or via email if you have comments or suggestions!

Experimental prioritization decision tree.

I remember often being on autopilot as a grad student and postdoc. I was so focused on ticking off my todo list and trying to make rapid progress that I forgot to periodically step back and re-evaluate whether I was still doing the right experiments. When I gave myself permission to take the extra time to think of ways to complement or improve on current approaches, it resulted in better ideas. I was convinced that carving out time to challenge my assumptions and re-prioritize experiments was important but it didn’t happen as frequently as it should have.

I think this is a fairly common phenotype. There’s a lot of pressure to produce and it’s easier to defer to your own previous judgement (or your advisor’s) than to re-derive the rationale for every experiment in real time. In thinking of how best one might challenge assumptions and experimental path, I propose the following:

1) Question everything. What is the biological question being asked in any given experiment? Why is this experiment the right one to tackle this question or the best thing to do first? What do you think will happen and why? What are some other possibilities for what you might see? What would it mean if these less expected possibilities were the outcome? Is there an orthogonal way you can test the outcome (regardless of the result) to convince yourself that the result is true? You might think you know the answers to all of these questions for your experiments, but as you collect more data, your assumptions and best path forward may shift—sometimes dramatically. If you’re not asking these questions often enough, you might be veering away from your goal.

2) Brainstorm. Set up regular brainstorming sessions to come up with new ideas, even if it’s only a few minutes each day or 10-15 minutes each week. Some of the ideas will be terrible but a few might be worth pursuing. Some might even be groundbreaking. Importantly, the exercise of regular brainstorming can help it become second nature, even when you haven’t carved out time for it. The ideas also don’t have to be limited to a set of experiments, your own project or even necessarily be about work. You’ll get better at this no matter what kind of ideas you’re coming up with.

3) See more things and talk to more people. We all know that going to seminars and conferences is important. We also know that going out of our way to talk science with a broad range of scientists is a good thing. However, when we get busy, these things are easy to drop. We hole up and buckle down, shutting ourselves off from an exchange of ideas. Remind yourself regularly not to do science in isolation and talk to others. Particularly if you’re introverted (as so many of us are), it will be a big effort to put yourself out there and discuss with others. However, your science will improve as a result.

4) Challenge your PI. If you’re not totally convinced about something you’re doing, go back to #1 above and see if you can confirm the rationale. If you think a different approach/experiment is better, challenge your PI and make a case for why your plan is the way to go. There’s a good chance that truly nothing will make your PI happier than you convincing them that your idea is better. Even if you discuss and are now sure that the current path is the right one, both parties will feel better that you’ve reinforced the strategy.

5) Above all, enjoy the sleuthing! The detective work that goes into charting new scientific territory and making sure you’ve got the most clever approach to a tough problem is what makes this job so great. If you’re feeling in an experimental rut, let yourself dig into the problem and come up with new ideas that can reinvigorate your passion for science.

Each year the NFL reviews which of its rules are under-enforced and identifies some points of emphasis for the upcoming season. I opened the doors to my own lab space on September 2nd, 2015 (~16.5 months ago). As we try to grow beyond our infancy, it seemed to be the right time to reflect on what we’ve done and what needs further emphasis at this point.

Reflection (papers):
As of today, we have published 2 preprints, one is entirely the product of our new lab (currently under journal review) and the other is a collaborative paper (soon to be submitted). We also have made significant progress on another major paper that I couldn’t be more excited about. We have definitive data supporting different roles for actin in assembly of the microtubule-based flagellum. This is in the vein of my previous published work but we have some exciting new data using new mutants and using some cool new methods we recently developed to tease apart different trafficking mechanisms. I can’t wait to talk about this work at some upcoming seminars and publish the preprint! I won’t go into all the other ongoing work in the lab but we do have exciting data on several additional projects that are a bit further out from publication, though some are scheduled for submission in 2017.

Reflection (grants):
At some point it stopped making sense to count every foundation grant application and letter of intent I submitted, but of note are the major federal grant submissions. In November 2015, I submitted my first NSF BIO grant, which was scored but not funded. The comments were constructive and helpful. I modified and expanded this grant for an NSF CAREER application in July 2016, which was also given a priority score/level but not funded. A different set of reviewers had entirely different comments, but these were also largely constructive. The NSF program officers have been very helpful in providing feedback during this process. If you haven’t already guessed, I love the NSF culture. I sent in another version of the NSF proposal to the regular grant mechanism last November. Based on the timing from the previous year’s submission, we should hear about this near the end of April. During this time, we also had been developing a different project that got submitted as an R01 to the NIH in October. That gets reviewed (and I hope discussed) next week. I am serving as an NIH Early Career Reviewer and hope this will help the R01 resubmission in July. We have about 18 months before we run out of startup funding (we’re in a surprisingly beneficial ‘use it or lose it’ situation), so the race is on and our foot is on the gas! To self: I’m not panicked. Really, I’m not!

Reflection (training):
We have had a fair amount of growing pains trying to fill the lab with people that buy into what we’re selling. However, I’m thrilled to say that we have a truly amazing group of researchers in the lab that are eager to learn, bright and hard working. Several of these people have applied to medical school and graduate school and I hope that we’ve done everything in our power to get them closer to their goals. The postdocs in the lab are also doing a great job sharing their work. At the American Society for Cell Biology annual meeting last year, we had a total of 5 posters on independent projects and one talk (two posters and a talk from a single very productive postdoc, Soumita Dutta). We held a Career Development Day a couple of weeks ago to review everyone’s individual career development plan and make sure each person was on track to achieve their research and career goals for 2017. I’m really proud of what this thoughtful group has accomplished and are continuing to work towards.

Points of emphasis for 2017:

• Get funded!
  We will continue to push out the publications but put an even more urgent emphasis on applying for all eligible grants, large and small. I’m planning an R21, DP2, and R01 and NSF resubmissions (as needed). We’ll also go after some smaller internal pilots early in the year and re-assess as the year progresses.
• Ownership for lab members!
  @biomickwatson wrote this outstanding piece for trainees. What struck me most was item 1 regarding students taking the “lead” or taking ownership of their own projects, ideas and career outcomes. In our haste to get the lab firing on all cylinders and get something on the books publication-wise, something was definitely lost in trainees having the freedom to fail and find their own way. This is largely my fault for being so openly goal oriented. However, the lab does know that I want them to feel free to fail locally knowing I’ll stop them from failing globally. I am going to make a bigger effort to let go and hope the lab takes advantage of this environment to really take some risks and grow their confidence/independence.

We have a lot of goals for this year but hopefully if we focus on these points of emphasis, we’ll make some progress on those fronts. I’d love to hear about your strategies and what the points of emphasis are for your lab this year. You can reach me most reliably via Twitter or email.

Here’s another BioRxiv submission selfie of authors Jenna Christensen, David Mueller and myself for our joint preprint with the Kovar Lab! We’d love to hear your feedback!

The NIH recently put out a request for information (NOT-OD-17-006) on including preprints and interim research products in NIH applications and reports. ASAPbio is collecting responses but you can read my responses directly here as well.