Tuesday, September 26, 2023

“Refusing the call” and presenting a scientific story

 When scientists present in an informal setting where questions are expected, I always have an internal bet with myself as to how long until some daring person asks the first question, after which everyone else joins in and the questions rapidly start pouring out. This usually happens around the 10 minute mark. This phenomenon has gotten me wondering what this means for how best to structure a scientific talk.

I think this “dam breaking” phenomenon can be best thought of in terms of “refusal of the call”, which is a critical part of the classic hero’s journey in the theory of storytelling. The hero typically is leading some sort of hum-drum existence, until suddenly there is a “call to adventure”. Think Luke Skywalker in Star Wars (Episode IV, of course) when Obi Wan proposes that he go on an adventure to save the galaxy, only for Luke to say “Awww, I hate the empire, but what can I do about it?”. (Related point, Mark Hamill sucks.) Usually, shortly afterwards, the hero will “refuse the call” to adventure—usually from fear or lack of confidence or perhaps just from having common sense. This refusal involves some sort of rejection of the premise of the proposed adventure, which then needs to be overcome.

I think that’s exactly what’s going on in a scientific talk. As Nancy Duarte says, in a presentation, your audience is the hero. You are Obi Wan, presenting the call to adventure (an exciting new idea). And, almost immediately afterward, your audience (the hero) is going to refuse the call, meaning they are going to challenge your premise. In the context of a scientific talk, I think that’s where you have to present some sort of data. Like, I’ve presented you with this cool idea, here’s some preliminary result that gives it some credibility. Then the hero will follow the guide a little further on the adventure.

The mistake I sometimes see in scientific talks is that they let this tension go on for too long. They introduce an idea and then expound on the idea for a while, not providing the relief of a bit of data as the audience is refusing the call. The danger is that the longer the audience's mind runs with their internal criticism, the more it will forever dominate their destiny. Instead, spoon feed it to them slowly. Present an idea. Within a minute, say to the audience “You may be wondering about X. Well here is Y proof.” If you are pacing at their rate of questioning, perhaps a little faster, then they will feel very satisfied.

For instance:

“You may think drug resistance in cancer is caused by genetic mutations and selection. However, what if it is non-genetic in origin? We did sequencing and found no mutations…”

Friday, July 16, 2021

Confusion and credentials in presenting your work

Just listened to a great Planet Money episode in which Dr. Cecelia Conrad describes how she dealt with some horrible racist students in her class who were essentially questioning her credentials. She got the advice from a senior professor to be less clear in her intro class:

This snippet reminded me of some advice I got from my postdoc advisor about giving talks: "You don't want everything to be clear. You should have at least some part of it that is confusing." This advice has really stuck with me through the years, and I have continued to puzzle over it for a long time. Like, it should all be clear, no? I always felt like the measure of success for a presentation should on some level be a monotonically increasing function of its clarity.

But… for a while before the pandemic, I was doing this QR code thing to get feedback after my talks on both degree of clarity and degree of inspiration, and I have to say I feel like I noticed some slight anti-correlation: when I gave a super clear talk, it was seemingly less inspiring, but when I got lower marks for clarity, it was somehow more inspiring. Huh.

Nancy Duarte makes the point that in any presentation, the audience is the hero, and you as the presenter are more like Yoda, the sage who leads the audience on their heroic adventure. Perhaps it is not for nothing that Yoda speaks in wise-seeming syntactically mixed-up babble. Perhaps you have to assert credentials and intellectual dominance at some point in order to inspire your audience? Thoughts on how best to accomplish that goal?

Friday, July 31, 2020

Alternative hypotheses and the Gautham Transform

As I have mentioned several times, having Gautham in the lab really changed how I think about science. In particular, I learned a lot about how to take a more critical approach to science. I think this has made me a far better and more rigorous scientist, and I want to impart those lessons to all members of the lab.

The most important thing I learned from Gautham was to consider alternative hypotheses. I know this sounds like duh, that’s what I learn in my RCR meetings, “expected outcomes and potential pitfalls” sections of grants, and boring classes on how to do science, but I think that’s because we so rarely see how powerful it is in practice. I think it was one of Gautham’s favorite pastimes, and really exemplified his scientific aesthetic (indeed, he was very well known for demonstrating some alternative hypotheses for carrier multiplication, I believe). There were many, many times Gautham proposed alternative hypotheses in our lab, and it was always illuminating. Indeed, one of the main points of his second paper from the lab was about how one could explain “fluctuations between states” by simple population dynamics without any state switching—a whole paper’s worth of alternative hypothesis!

Why do we generally fail to consider alternative hypotheses? One reason is that it’s scary and not fun. Generally, the hypothesis you want to consider is the option that is the fun one. It is scary to contemplate the idea that something fun might turn out to be something boring. (Gautham and I used to joke that the “Gautham Transform” was taking something seemingly interesting and showing that it was actually boring.) The truth of it, though, is that most things are boring. Sure, in biology, there are a lot more surprises than in, say, physics, but there are still far fewer interesting things than are generally claimed. I think that we would all do better to come in with a stronger prior belief that most findings actually have a boring explanation, and a critical implementation of that belief is to propose alternative hypotheses. Keep in mind also that when we are trained, we typically are presented with a list of facts with no alternatives. This manner of pedagogy leaves most of us with very little appreciation for all the wrong turns that comprise science as it’s being made as opposed to the little diagrams in the textbooks.

The other reason we fail to consider alternatives is that it’s a lot of work. It’s always going to be harder to spend as much time actively thinking of ways to show that your pet theory is incorrect, and so in my experience it’s usually more work to come up with plausible alternative hypotheses. Usually, this difficulty manifests as a proclamation of “there’s just no other way it could be!” Thing is… there’s ALWAYS an alternative hypothesis. All models are wrong. You may get to a point where you just get tired, or the alternatives seem too outlandish, but there’s always another alternative to exclude. I remember as we were wrapping up our transcriptional-scaling-with-cell-size manuscript, we got this cool result suggesting that transcription was cut in half upon DNA replication (decrease in burst frequency). I was really into this idea, and Gautham was like, that’s really weird, there must be some other explanation. I was like, I can’t think of one, and I remember him saying “Well, it’s hard, but there has to be something, what you’re proposing is really weird”. So… I spent a couple days thinking about it, and then, voila, an alternative! (The alternative was a global decrease in transcription in S-phase, which Olivia eliminated with a clever experiment measuring transcription from a late-replicating gene.) Point is, it’s hard but necessary work.

(Note: I’m wondering about ways to actively encourage people to consider alternatives on a more regular basis. One suggestion was to stop, say, group meeting somewhere in the middle and just explicitly ask everyone to think of alternatives for a few minutes, then check in. Another option (HT Ben Emert) is to have a lab buddy who’s job is to work with you to challenge hypotheses. Anybody have other thoughts?)

So when do you stop making alternatives? I think that’s largely a matter of taste. At some point, you have to stand by a model you propose, exclude as many plausible alternatives as you can, and then acknowledge that there are other possible explanations for what you see that you just didn’t think of. Progress continues, excluding one alternative at a time…

“Hipster” overlay journals

Been thinking a lot about overlay journals and their implications these days. For those who don’t know, an overlay journal is sort of like a “meta-journal” in that it doesn’t formally publish its own papers. Rather, it provides links to other preprints/papers that it thinks are interesting. On some level, the idea is that the true value of a journal is to serve as a filter for what someone thinks is science worth reading so that you don’t have to read every single paper. An overlay journal provides that filter function without the need for the rest of the (costly) trappings of a journal, like peer review and, uhh, color figures ;). 

There is one very interesting aspect of an overlay journal that I don’t think has been discussed very much: in contrast with regular journals, they are fundamentally non-exclusive, meaning that ANY overlay journal can in principle “publish” ANY paper. What this non-exclusivity means is that there is no jockeying between journals to publish the “obviously important” papers, which have a perhaps slightly elevated chance of actually being important. You know, like “we sequenced 10x more single cells than the last paper in a fancy journal” kind of papers. If you run an overlay journal, you never have to gaze longingly at those “high impact” papers—if you want to publish it, just add it to your overlay!

What are the consequences of non-exclusivity? Primarily, I think it would serve to diminish the value of “obviously important” papers. Everyone can identify them based on authors and number of genomes sequenced or whatever, so there’s really not that much value in including them per se. It would be like saying “Here’s my playlist, it’s like a copy of the Billboard Top 40”. Nobody’s going to look to your overlay journal for that kind of stuff (which you can readily get from CNS or Twitter). Rather, the real value would be in making lists of papers that are awesome but might otherwise be overlooked—essentially a hipster playlist. As an editor, your cache would be in your ability to identify these new, cool papers and making Michael Cera-esque mixtapes out of them. Can leave the Hot 100 to Casey Kasem/Spotify algorithms.

Measuring the importance of an overlay journal would also be interesting. Clearly, impact factor is not a useful metric, since anybody can make their impact factor as high as they want by including highly cited papers. I would guess a far more sensible metric would be number of followers of the journal (which makes more sense anyway).

Another interesting aspect of an overlay journal is that it can be retrospective. You could include old papers as well, highlighting old gems that may have been forgotten.

Of course, an interesting question is whether there is any difference between an overlay journal and someone’s Twitter feed. Not sure, actually…

Also, thoughts on existing journals that have hipster qualities to them? I vote Current Biology, my lab votes eLife.

Friday, July 17, 2020

My favorite "high yield" guides to telling better stories

Guest post by Eric Sanford

In medical school, we usually have five lectures’ worth of new material to memorize each day. Since we can’t simply remember it all, we are always seeking “high yield” resources (a term used so often by med students that it quickly becomes a joke): those concise one or two-pagers that somehow contain 95 percent of what we need to know for our exams. My quest of finding the highest yield resources has continued in full force after becoming a PhD student.

A major goal of mine has been to improve my scientific communication skills (you know, writing, public speaking, figure-making… i.e. those extremely-important skills that most of us scientists are pretty bad at), and I’ve come across a few very high yield resources as I’ve worked on this. Here are my favorites so far:

Resonate, by Nancy Duarte:

  • The best talks are inspiring, but “be more inspiring” is not easy advice to follow.

  • This book teaches you how to turn your content into a story that inspires an audience.

  • I received extremely positive feedback and a lot of audience questions the first time I gave a talk where I tried to follow the suggestions of this book.

  • This was both the most fun and the most useful of all my recommendations.

The Visual Display of Quantitative Information, by Edward Tufte:

  • Tufte is probably the most famous “data visualization” guru, and I think this book, his first book, is his best one. (I’ve flipped through the sequels and would also recommend the chapter on color from “Envisioning Information.”)

  • This book provides a useful framework for designing graphics that convey information in ways that are easy (easier?) for readers to understand. Some pointers include removing clutter, repeating designs in “small multiples”, labeling important elements directly, and using space consistently when composing multiple elements in the same figure.

The Elements of Style, by Strunk and White, pages 18-25:

Words to Avoid When Writing, by Arjun Raj

Raj Lab basic Adobe Illustrator (CC) guide, by Connie Jiang

There are many other great resources out there that are also worth going through if you have the time (Style: Lessons in Clarity and Grace by Bizup and Williams is another excellent writing guide), but for me these ones above had the highest amount-learned-per-minute-of-concentration-invested. 

Guest post by Eric Sanford

Wednesday, August 21, 2019

I <3 Adobe Illustrator (for scientific figure-making) and I hope that you will too

Guest post by Connie Jiang

As has been covered somewhat extensively (see here, here, and here), we are a lab that really appreciates the flexibility and ease with which one can use Illustrator to compile and annotate hard-coded graphical data elements to create figures. Using Illustrator to set things like font size, marker color, and line weighting is often far more intuitive and time-efficient than trying to do so programmatically. Furthermore, it can easily re-arrange/re-align graphics and create beautiful vector schematics, with far more flexibility than hard-coded options or PowerPoint.

So why don’t more people use Illustrator?

For one, it’s not cheap. We are lucky to have access to relatively inexpensive licenses through Penn. If expense is your issue, I’ve heard good things about Inkscape and Gimp, but unfortunately I have minimal experience with these and this document will not discuss them. Furthermore, as powerful and flexible as Illustrator is, its interface can be overwhelming. Faced with the activation energy and cognitive burden of having to learn how to do even basic things (drawing an arrow, placing and reshaping a text box without distorting the text it contains), maybe it’s unsurprising that so many people continue to use PowerPoint, a piece of software that most people in our lab first began experimenting with prior to 8th grade [AR editor’s note: uhhh… not everyone]. 

Recently, I decided to try to compile a doc with the express purpose of decreasing that activation energy of learning to use Illustrator to accomplish tasks that we do in the lab setting. Feel free to skip to the bottom if you’d just like to get to that link, but here were the main goals of this document:
  1. Compile a checklist to run through for each figure before submission. This is a set of guidelines and standards we aim to adhere to in lab to maintain quality and consistency of figures.
  2. Give a basic but thorough rundown of essentially everything in Illustrator that you need to begin to construct a scientific figure. Furthermore, impart the Illustrator “lingo” necessary to empower people to search for more specific queries.
  3. Answer some of what I feel to be the most FAQs. Due to my love of science-art and general artistic/design experimentation, I’ve spent a lot of time in Illustrator, so people in lab will sometimes come to me with questions. These are questions like: “my figure has too many points and is slowing my Illustrator down: how can I fix it?” and “what’s the difference between linked and embedded images?”. Additionally, there are cool features that I feel like every scientist should be able to take advantage of, like “why are layers super awesome?” and “how can I select everything of similar appearance attributes?”.
Finally, a disclaimer: This document will (hopefully) give you the tools and language to use Illustrator as you see fit. It does not give any design guidance or impart aesthetic sense (aside from heavily encouraging you to not use Myriad Pro). Make good judgments~

Full Raj lab basic Illustrator guide can be found here.

Sunday, August 4, 2019

I need a coach

I’ve been ruminating over the course of the last several years on a conversation I had with Rob Phillips about coaches. He was saying (and hopefully he will forgive me if I’m mischaracterizing this) that he has had people serve the role of coach in his life before, and that that really helped push him to do better. It’s something I keep coming back to over and over, especially as I get further along in my career.

In processing what Rob was saying, one of the first questions that needed answering is exactly what is a coach? I think most of us think about formal training interactions (i.e., students, postdocs) when we think of coaching in science, and I think this ends up conflating two actually rather disparate things, which are mentoring and coaching. At least for me, mentorship is about wisdom that I have accumulated about decision making that I can hopefully pass on to others. These can be things like “Hmm, I think that experiment is unlikely to be informative” or “That area of research is pretty promising” or “I don’t think that will matter much for a job application, I would spend your time on this instead”. A coach, on the other hand, is someone who will help push you to focus and implement strategies for things you already know, but are having trouble doing. Like “I think we can get this experiment done faster” or “This code could be more cleanly written” or “This experiment is sloppy, let’s clean it up”. Basically, a mentor gives advice on what to do, a coach gives advice on how to actually do it.

Why does this decoupling matter, especially later in your career? When in a formal training situation, you will often get both of these from the same people—the same person, say, guiding your research project is the same person pushing you to get things done right. But after a few years in a faculty position, the N starts to get pretty small, and as such I think the value of mentorship per se diminishes significantly; basically, everybody gives you a bunch of conflicting advice on what to do in any given situation, which is frankly mostly just a collection of well-meaning but at best mildly useful anecdotes. But while the utility of mentorship decreases (or perhaps the availability of high quality mentorship) decreases, I have found that I still have a need for someone to hold me accountable, to help me implement the wisdom that I have accumulated but am sometimes too lazy or scared to put into practice. Like, someone to say “hey, watch a recording of your lecture finally and implement the changes” or “push yourself to think more mechanistically, your ideas are weak” or “that writing is lazy, do better” or “finish that half-written blog post”. To some extent, you can get this from various people in your life, and I desperately seek those people out, but it’s increasingly hard to find the further along you are. Moreover, even if you do find someone, they may have a different set of wisdom that they would be trying to implement for you, like, coaching you towards what they think is good, not what you yourself think is good (“Always need a hypothesis in each specific aim” whereas maybe you’ve come to the conclusion that that’s not important or whatever). If you have gotten to the point where you’ve developed your own set of models of what matters or doesn’t in the world, then you somehow need to be able to coach yourself in order to achieve those goals.

Is it possible to self-coach? I think so, but I’ve always struggled to figure out how. I guess the first step is to think about what makes a good coach. To me, the role of a good coach is to devise a concrete plan (often with some sort of measurable outcome) that promotes a desired change in default behavior. For example, when working with people in the lab in a coaching capacity, one thing I’ve tried to do is to propose concrete goals to try and help overcome barriers. If someone could be participating more in group meeting and seminars, I’ll say “try to ask at least 3 questions at group meeting and one at every seminar” and that does seem to help. Or I’ll push someone to make their figures, or write down their experiment along with results and conclusions. Or make a list of things to do in a day and then search for one more thing to add. Setting these sorts of rules can help provide the structure to achieve these goals and model new behaviors.

How do you implement these coaching strategies for yourself? I think there are a few steps, the first of which are relatively easy. Initially, the issue is to identify the issue, which is actually usually fairly clear: “I want to reduce time spent on email”, “I want to write clean code”, “I want to construct a set of alternative hypotheses every time I come up with some fun new idea”, “Push myself to really think in a model-based fashion”. Next, is reduction to a concrete set of goals, which is also usually pretty easy: “Read every email only once and batch process them for a set period of time” or “write software that follows XYZ design pattern” or “write down alternative hypotheses”. The biggest struggle is accountability, which is where having a coach would be good. How do I enforce the rules when I’m the only one following them?

I’m not really sure, but one thing that works for me (which is perhaps quite obvious) is to rely on something external for accountability. For example, I am always looking for ways to improve my talks, and value being able to do a good job. However, it was hard to get feedback, and even when I did, I often didn’t follow through to implement said feedback. So I did this thing where I show the audience a QR code which leads them to a form for feedback. Often, they pointed out things I didn’t realize were unclear, which was of course helpful. But what was also helpful was when they pointed out things that I already knew were unclear, but had been lazy about fixing. This provided me with a bit of motivation to finally fix the issue, and I think it’s improved things overall. Another externalization strategy I’ve tried is to imagine that I’m trying to model behavior for someone else. Example: I was writing some software a while back for the lab, and there were times where I could have done something in the quick, lazy, and wrong way, rather than in the right way. What helped motivate me to do it right was to say to myself, “Hey, people in the lab are going to look at this software as an example of how to do things, and I need to make sure they learn the right things, so do it right, dummy”.

Some things are really hard to externalize, like making sure you stress test your ideas with alternative hypotheses and designing the experiments that will rigorously test them. One form of externalization that works for me is to imagine former lab members who were really smart and critical and just imagine them saying to me “but what about…”. Just imagining what they might say somehow helps me push myself to think a bit harder.

Any thoughts on other ways to hold yourself accountable when nobody else is looking?