Some of my favorite meta-science posts from the blog

I recently was asked to join a faculty panel on writing for Penn Bioengineering grad students, and in doing so, I realized that this blog already has a bunch of thoughts on "meta-science", like how to do science, manage time, give a talk, write. Below are some vaguely organized links to various posts on the subject, along with a couple outside links. I'll also try and maintain this Google Doc with links as well.

Time and people management:
Save time with FAQs
Quantifying the e-mail in my life, 1/2
Organizing the e-mail in my life, 2/2
How to get people to do boring stuff
The Shockley model of academic performance
Use concrete rules to change yourself
Let others organize your e-mail for you
Some thoughts on time management
Is my PI out to get me?
How much work do PIs do?
What I have learned since being a PI

How to do science:
The Shockley model of academic performance
What makes a scientist creative?
Why there is no journal of negative results
Why does push-button science push my buttons
Some thoughts on how to do science
Storytelling in science
Uri Alon's cloud
The magical results of reviewer experiments
Being an anal scientist
Statistics is not science
Machine learning, take 2

Giving talks:
How to structure a talk
http://www.howtogiveatalk.com/
http://www.ibiology.org/ibioseminars/techniques/susan-mcconnell-part-1.html
Figures for talks vs. figures for papers
Simple tips to improve your presentations
Images in presentations
A case against laser pointers for talks
A case against color merges to show colocalization

Writing:
The most annoying words in scientific discourse
How to write fast
Passive voice in scientific writing
The principle of WriteItAllOut
Figures for talks vs. figures for papers
What's the point of figure legends?
Musing on writing
Another short musing on writing

Publishing:
The eleven stages of academic grief
A taxonomy of papers
Why there is no journal of negative results
How to review a paper
How to re-review a paper
What not to worry about when you submit a manuscript
Storytelling in science
The cost of a biomedical research paper
Passive-aggressive review writing
The magical results of reviewer experiments
Retraction in the age of computation

Career development:
Why are papers important for getting faculty positions?
Is academia really broken? Or just really hard?
How much work do PIs do?
What I have learned since being a PI
Is my PI out to get me?
Why there's a great crunch coming in science careers
Change yourself with rules
The royal scientific jelly

Programming:
The hazards of commenting code
Why don't bioinformaticians learn how to run gels?

When to say yes

As a junior PI, you get a lot of advice about when to say “no”. One PI I know told me that he and his other junior PIs have a rule that they have to say no to at least one thing a day. And it is sage advice. The demands on our time are huge, and so every minute counts.

Sometimes I worry, though, that the pendulum may have swung too far in the other direction, to the point where the received wisdom to say no to everything prevents us from saying yes every once in a while. Say yes and you might just end up on a new adventure you may not have anticipated with cool and interesting people. Say no and you will never know.

I started thinking about this when I read this excellent blog post with advice for new PIs. All great tips, and one that really resonated with me was the tip to “Be a good colleague”. Basically, the point is that while there are some reasons you might think it a wise to do a bad job on something so that nobody asks you again, it’s far better to do a good job. I think the same holds for interpersonal interactions. I think it’s important to make time for the people you care about in your work life. Sometimes you might do a favor for a senior (or junior) colleague. Then you might have lunch and end up with an awesome collaboration. Or maybe the favor doesn’t get repaid. That’s okay, too, happens. And some people are just not going to make fun collaborators, and you might get burned. It takes time to get better at identifying those beforehand, and I know I still have much to learn about that. But I’m also learning not to be quite as suspicious of every request, and also trying to just go with the flow a bit. It’s led to some really great collaborations from which I've learned a lot.

My point is that by reflexively saying no to everything, I think we’re denying ourselves some of the richness of the life of a PI that comes through interactions with colleagues and their trainees, which I’ve found to be very valuable. And enjoyable. That’s the point, right?

Time-saving tip–make a FAQ for almost anything

One of the fundamental tenets of programming is DRY: Don’t Repeat Yourself. If you find yourself writing the same thing multiple times, you’re creating a problem in that you have to maintain consistency if you ever make a change, and you’ve had to write it twice.

In thinking about what I have to do in my daily life, a lot of it also involves repetitive tasks. The most onerous of these are requests for information that require somewhat length e-mails or what have you. Yet, many times, I end up answering the same questions over and over. Which brings up a solution: refer to a publicly available FAQ.

I first did this for RNA FISH because I was always getting similar questions about protocols and equipment, etc. So I made this website, which I think has been useful both for researchers looking for answers and for me in terms of saving me time writing out these answer for every person I meet.

I also recently saw a nice FAQ recently (can’t find the link, darn!) where someone had put together a letter of recommendation FAQ. As in, if you want a letter of recommendation from this person, here’s a list of details to provide and a list of criteria to determine whether they would be able to write a good one for you.

Another senior professor I met recently said that she got sick of getting papers from her trainees that were filled with various errors. So she set up a list of criteria and told everyone that she wouldn’t look at anything that didn’t pass that bar. Strikingly, she said that the trainees actually loved it–it made a nice checklist for them and they knew exactly what was expected of them.

I think all of these are great, and I think I might make up such documents myself. I’m also thinking of instituting an internal FAQ for our data management in the lab. Any other ideas?

Verdict on a (mostly) Bacn-free week of e-mail: totally awesome!

It’s been one week since I tabulated my e-mail and decided to run a few experiments based on the results. Quick recap: I found that I got a lot of Bacn (solicited but often unimportant e-mail, like tables of contents and seminar announcements), and this was contributing to a sense of being overwhelmed by e-mail. So I resolved to do the following:

1. Filter out primary conveyors of Bacn to a Bacn folder that I would skim through rapidly just a few times a day.
2. Deal decisively with the e-mail when I read it–either reply or get off the pot, so to speak.

Quick summary is that this experiment has been a great success! I feel much more efficient, less overwhelmed, and less likely to miss important things. Highly recommended.

Here’s a few more details. So I have two e-mail addresses. For the most part, one of them gets all my work e-mail, and the other one is mostly personal, but has a lot of Bacn and spam in it. Before, I had been combining both into my inbox. So that was easy: just check my work e-mail and separate out the personal one to check over on an as needed basis. Of course, I’m still getting a lot of Bacn on my work e-mail, so I then made filters to automatically file Bacn into a separate folder. I initially thought this was going to be super simple. Turns out it was a bit more work than I thought: there are MANY different Bacn providers at Penn. So it took a while to set up a filter for each of them. But it worked: almost all the Bacn went to a specific folder.

The results were glorious! I found I spent much less time looking through all these unimportant e-mails during the day, and then I could batch process them much more efficiently during a period of downtime. There is little better than selecting a huge block of e-mail and deleting them all at once! A few times, I would get a real e-mail from a Bacner that I needed to respond to, but it turns out that they were never urgent nor terribly important, and I could deal with them during this downtime period (which is probably when I should be dealing with them anyway).

I didn’t anticipate how much this e-mail filtering would engender peace of mind. I guess I was expending more mental energy that I thought processing all these different e-mails in a single stream. The steady stream of notifications that we all know we should ignore but don’t thinned out considerably, and I felt like my focus was better. I didn’t quantify actual productivity gains there may have been (although I suspect there was some), but I can definitely say that the perceived quality of e-mail life went up considerably. Definitely felt like I was in much more control over what I was doing. Basically, it made it much easier to process e-mail the way I always knew I should in theory but rarely actually did in practice.

I think this filtering also really helped with the other aspect of my experiment, which was to be decisive (actually something I have been working on in general). The idea here was to read each e-mail only once before doing something with it, which means either marking as read or replying. Or at least getting as close to this ideal as possible. Since all the e-mails in front of me now have a similar status, I found it a bit easier to do this, because I’m not changing “modes” from one e-mail to the next.

Decisiveness is hard, and something I’ve struggled with for a long time, both in the context of e-mail or otherwise. And being deliberate is not necessarily a bad thing. But I think most of us tend to undervalue our time, and I feel like being decisive is making a tradeoff between making the best possible decision slowly and making a good enough decision quickly. Or, as is more often the case, making the best possible decision slowly and making the best possible decision quickly–indeed, I feel like much of the time, the “decision making process” is really more like a slow process of rationalizing a decision you’ve already essentially made. So I’m trying to just go with my instincts and then thinking, well, if I made a mistake, so be it. The key thing is to think to myself “Well, am I going to get any new information that might change my decision? If not, then go for it.” That actually takes care of a lot of situations, e-mail or otherwise.

UPDATE: Forgot to mention that I got two e-mails this past week from close collaborators with the subject line "Not Bacn". :)

A week in my e-mail life

[Follow up post here: Verdict on a (mostly) Bacn-free week of e-mail: totally awesome!]

[Note: This is a longish post, so here’s an “abstract” that gets across the main points: Academics get a lot of e-mail. I decided to catalog my e-mails for the week to see if I could identify any patterns. I found that a large amount of my e-mail was “Bacn”, meaning e-mails that I am in some way supposed to get, but are typically not very important, like seminar announcements, etc. A lot of the more research-oriented e-mail was related to logistics, like shipping, etc. As for what to do about it, I think the number one thing is to pre-filter a bunch of the Bacn, which typically just comes from a relatively limited number of easily identified people and only very very rarely requires any sort of immediate action. This will help make it easier to process it in batch mode, which is another area where I could really improve how I handle e-mail, rather than replying in a more "real time" fashion. And I will try to be more decisive in handling e-mail. An update on how all this worked next week.]

As is the case for most academics these days, I get a lot of e-mail. And as is the case for most academics, I love to complain about how much time it takes up. I was thinking about this recently when I came across the line “E-mail is everyone else’s to do list for you.” Which I thought was an interesting way of thinking about it. I mean, just because someone has my e-mail address doesn’t necessarily give them the right to command my attention, right? But then I thought a bit more, and I wondered if my attention really is being dragged unnecessarily in unwanted directions, or is it primarily spent on things that I want to pay attention to. Are there ways that I can make myself more efficient?

So I decided to catalog all the e-mail I got in the last week. First, a couple notes on methodology. I basically just looked through my e-mail for the past week and tried not to delete anything (which I normally don’t do, except for spam). Going through, I categorized the e-mail (more on that later), kept track of whether I replied or forwarded the e-mail, and how long it took me to reply. I also kept track of whether the e-mail was initiated by myself or came from someone else and whether the e-mail was directed to me specifically or whether it was just a general broadcast (some judgement calls in this).

Here's what I found:

Good news is that I don't instigate a lot of e-mail, which makes me feel better about myself–in fact, so few that I didn’t really think it was worth doing a similar analysis on my sent e-mail. But I did reply to a relatively large number of e-mails. But now that I think about it, I would guess this is the case for most academics. Most of their e-mail misery comes from others randomly bugging you, and I think it’s usually just a handful of others.

As for speed of reply, I’m generally quite fast, but there’s a long tail:

Zooming in on the short time-scale:

A pretty substantial number of replies actually happened within minutes, sort of like texting or something, then a tail of longer times to reply.  I actually expected this to be a bit more bimodal, but it's pretty unimodal, but with a long tail. I did notice that I have chunks of reply e-mail at the beginning and end of the day, which is good–my intention lately has definitely been to try and do as much batch processing as possible. I think I could be more disciplined about this, though.

Of course, the key piece of data is what different sorts of e-mail I get. Here’s how I broke it down:

1. Spam
1. Spam spam. Like, Nigerian Bankers who have a great deal on Viagra for you. 
2. Science spam. This is various marketing for HPLC equipment or strange journals or whatever. I get a lot of this, presumably because various vendors have sold my e-mail to direct marketers.
2. Bacn. Bacn is a very interesting category. It is like spam, but a level up: it’s something where there is some sort of relationship there, including perhaps direct solicitation of the e-mail. Here is how I broke that down:
1. Personal. e.g. NYtimes.com table of contents.
2. TOC. Tables of contents of various journals.
3. Science. ResearchGate, Nature Publishing Group
4. Penn Bacn. Seminar announcements, thesis defenses, visitors, latest fund-raising drive.
3. Scheduling. This includes setting up a meeting or lunch or whatever with someone, thesis committee meeting times, etc.
1. Scheduling Bacn. These are scheduling e-mails in which you’re just sort of along for the ride. You don’t have to do anything, but the e-mail is there, perhaps asking you if you want to meet with so and so.
4. Teaching. Students asking for help or whatever.
5. Evaluations and Letters. Someone asking for you to evaluate a person or paper or whatever in some way, shape or form. An important part of our lives. I’m of course happy to do this for people who have been in my life in the lab. Less exciting is...
1. Evaluations and Letters Bacn. This is any sort of evaluation of someone or something from outside. This includes, but is not limited to, reviewing papers.
6. Research. This is what we’re supposed to be doing, right? Well, that all depends…
1. Logistics. This is all stuff about orders, handling of manuscripts, lab organization, etc.
2. Collaborations. This is managing various collaborations with other groups. This does not include close collaborators with whom we are doing real science together with. It’s more just like people whom we’re doing a one-off experiment with. Often, there is overlap with the Logistics category.
3. Research Bacn: Seems like a weird category, right? These are what I would consider relatively unsolicited e-mails that are random and tangential to your research effort, but are science related. Like, someone sends you a link to a paper they wrote. Or someone had a thought after meeting with you. Or something. This is not quite Bacn in the sense that you may not necessarily be able to ignore all of it, but it’s not quite important enough not to be Bacn.
4. Actual Research: This is, you know, actual research. Also a proxy for what I consider the most important to me. Mostly conversations with people whom we are working with closely about science. This can include making decisions about scientific goings-on in the lab, or thoughts on an experiment, or how to interpret something–basically, the fun part of it.

So what’s the breakdown? Here are some pie-charts (I’ll get to strategies I’m thinking about implementing later).








Let’s start with spam. Turns out I don’t get that much of it. It certainly doesn’t take that long to get rid of them. In fact, I have to say that I sometimes rather enjoy them for their humorous qualities. Here are four of my favorite examples:

Message 1:
Subject: ВОССТАНОВИМ ЗАПУЩЕННЫЙ УЧЕТ

Вы руководитель от Вас внезапно ушел бухгалтер!

Вас предали? Вы подставлены? Завтра налоговая?

БУХГАЛТЕРСКИЙ БЕСПРЕДЕЛ!!!

Message 2:

Subject: Лучший Новогодний подарок - безопасность ваша и ваших близких!

Message 3:
Subject: Your  Account Was Banned

This is a joke :)

Than trying to work mounted on clumsy, long webfeet by the

ecriture artiste which the french writers that hears. Similarly,

employing the eye, it is a moment without devoting his heart

upon mahadeva. Towards the abode of bhishma, casting aside

their.

Message 4:

Subject: Mandy - 100% results.

Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lolGy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.Gy, lol.

I think the Gmail spam filters do a pretty good job of getting rid of most of this cruft.

Bacn. This was perhaps the biggest surprise. Most of what I get is Bacn. And it’s super annoying to sort through, due primarily to the very nature of Bacn, which is something that you might conceivably be interested in. And one of the worst offenders is Penn! The amount of Penn Bacn I get is crazy. It’s primarily seminar announcements (and reannouncements (and re-reannouncements)) and various other random stuff that I may in theory want to know about, but I typically won’t. And it typically comes from a few prime Bacn distributors. The only problem is that I will sometimes get something important from these Bacners, and so I can’t just automatically filter them out into the trash. Hmm. These typically come mostly in the morning, which is when I try and get real work done.

Funny note about Bacn: I made some Bacn myself! Had to send out an e-mail to the graduate group about something or other. I feel sort of bad about it now. Even funnier, I even managed to send research-related Bacn to myself in the form of an e-mail to myself of a paper I thought I should read. Of course, I paid it about as much attention as all my other research-related Bacn… :)

Scheduling. Surprisingly large amount of e-mail just to schedule appointments. This was actually a relatively tame week in that regard, so I was sort of surprised how much e-mail circulated about that.

Research. Large number of logistic e-mails, often about shipping, etc. The shipping and ordering stuff doesn’t take up too much time, honestly, perhaps because we have a relatively small operation. It was interesting to see how much Research “collaborations” took up. To me, this is partly a matter of how much you invest in your scientific community, sort of like being a good citizen. That said, it is clear that this can suck your brain quite easily. Research Bacn is I think something that I get a lot more of than I imagine most people getting, for various reasons. Surprisingly (unsurprisingly?) little time spent on actual Research Research e-mails. Which I actually regard overall as a good thing: for most research discussions, I talk with the people in my lab directly. I think that is a far more efficient way to get things done, generally, and avoids those super long e-mails that take hours to craft.

So what to do with this data? I think I came to a few primary conclusions:

1. I need to organize my e-mail so that the Bacn is out of sight most of the time. I try my best to ignore Bacn most of the time, but in practice, it takes a lot of discipline to avoid looking at all those e-mails during the day, especially when there are sometimes other interesting e-mails that interspersed in my inbox as well that I may very well want to deal with. To do this, I’ve implemented filters on Gmail to just send most of these to a specific folder that I will check once a day or so, hopefully in a really fast batch mode. There is some slight chance that I might miss a timely e-mail, but whatever. Looking at it now, perhaps this is obvious, but somehow I just didn't think of it before.
2. I get a lot of research-related logistical e-mails that I should probably be delegating about ordering and the such. These are not quite Bacn, because I (or someone in the lab) do need to give some input or really read them, sometimes in a timely manner. But just as often not. I also noticed I got a few more of these this week than usual.
3. Teaching: I didn’t get a lot of teaching e-mail this week, which is nice, but somewhat unusual. I actually have a specific teaching gmail account that I ask students to send to–this organization is very useful, and it allows me to make others do some of the organizing for me. Of course, you have to actually tell your students about it, which I of course forgot to do this term in my grad class. But I will definitely remember next term in my big required undergrad class. I will also be sure to have a policy that I only respond to student e-mails on one particular time of the week, no exceptions.
4. Perhaps the most important lesson is to BE DECISIVE. Someone (and I’m so sorry, I forget who, and the comments got deleted) left an awesome comment on the blog somewhere about a simple rule, which is read each e-mail only once. I think that’s absolutely right. I definitely found myself reading an e-mail and then mulling it over and then mulling it over again. I have to not do that. If it requires thought, I should just make a (prioritized) to-do list item for it and then mark it as read and be done with it. Otherwise, I’m just cycling over and over again.

Anyway, those are some thoughts. I will try and implement this this week and post again once the results of this reorganization are in.

My favorite quote about LaTeX

Argh, just finished struggling through submitting a LaTeX document to a journal. And I think I still screwed up and will have to do some more fussing. My only hope (and a fading one at that) is that things will not devolve to the point where I just have to copy the whole damn thing into Google Docs, where you can actually spend your time on, you know, doing real work.

So I just Googled around and found the following page, which has my new favorite quote about LaTeX:

Latex ("LaTeX" if you're pretentious as hell) is the biggest piece of shit in the history of both pieces and shit.

Yes.

(And yes, before you say it, I know what you are going to say.)

A proposal for controlling the amount of paperwork

As anyone who’s tried to submit a grant knows, there is an absolutely enormous amount of paperwork involved. Budgets, front matter, various other little bits and pieces and forms. It’s so much paperwork that it’s basically impossible to apply without a professional grants administrator, which most universities have. In fact, I was recently working with someone who didn’t have access to a grants administrator, and I wanted to have him participate in a grant, and he said that he couldn’t because he didn’t have the time to figure out how to fill out all the forms. Yipes!

I’m sure there are plenty of studies about how paperwork tends to proliferate, but here’s my take on it and a potential solution. My feeling is that every bit of new paperwork comes from some sort of new initiative in which the new paperwork serves to encourage that goal. Like, “We want to promote diversity, so now include a minority involvement plan.” Or, in a recent grant, I had to include a Research Leadership Plan, presumably to encourage thinking about how the PIs will collaborate together. All laudable goals, so it’s sort of hard to argue with these being a good thing, right?

Well, the problem is that this leads to more and more paperwork as these encouraged goals pile up over the years. Here’s a solution, inspired, ironically enough, by the NIH. When we submit a grant, we have a page limit, right? This means that we have to make decisions–if you want to include a particular piece of additional data, then it must come at the expense of another. So why not have a paperwork limit? Like, you can have a certain number and length of forms and no further. Any increase in the amount of paperwork must come at the expense of some other paperwork. Any new form means you have to remove some older form. That would have the added benefit of forcing the paperwork producing bodies to think carefully about what forms are the most important.

Of course, this still has the flaw that people can change the paperwork required, which is annoying to keep up with–take for instance the updated NIH Biosketch. Ugh, annoying. But I guess we should be thankful they didn’t make us submit an additional Biosketch! :)

How much do PIs work?

Just read this very astute blog post by Meghan Duffy about how much academics say they work, how much they actually work, and how much they should work. The gist of it is that there’s a myth that you need to work 80 hours a week to get tenure, that virtually all academics don’t work that much, and that working that much would be counterproductive anyway. I agree completely! I personally don’t work 80 hours a week, and I don’t think I’m working particularly more or less than anyone else. The upshot of the post is that we should stop promulgating the myth of 80 hours a week, and stop believing it when other people say it.

Hmm. I’m not 100% sold that it’s the myth itself that’s really the problem. In talking with my junior prof friends, we don’t really talk about how many hours we work or anything like that, and I certainly haven’t had anyone tell me they work 80 hours a week. But I think it’s safe to say that most of us feel overwhelmed by our jobs–it FEELS like we’re working 80 hours a week. I really think it is the perception that we’re up to our eyeballs that creates problems more than the reality of the number of hours we work. Why do we have this feeling?

I think there are two reasons (aside from the obvious one that yes, this is objectively a busy time of life). One is that once I started as a PI, my time was no longer my own in the same way it was before. As a postdoc, it’s very easy to measure your daily productivity: I needed to collect this data, and I collected it; write this paper, and I wrote it–checkbox ticked. Now, my day is filled with a large number of diffuse scientific tasks and specific administrative tasks, neither of which yield the same sort of fulfillment that actually doing science itself has. I think this contributes to the feeling of not “getting anywhere”, which leads to a constant sense that we need to do something, hence always feeling busy.

What to do about that? I don’t really do many (if any) experiments myself in lab anymore–once I started teaching, I was too overwhelmed to keep it up, and once that became more manageable, it was hard to get back on the experimental treadmill (mostly, I just fix stuff around the lab now). I was lamenting this fact with a friend who started his lab around the same time as me, and he said something that really stuck with me: “The best use of our time is to help our students get THEIR experiments working.” I think that’s very true, and that sentiment really helped me get over the guilt associated with not doing experiments anymore. But what it doesn’t help with is generating the feeling of accomplishment that let’s you sleep well at night knowing that you, personally, made some tangible contribution to the progress of humanity (or whatever it is that we do).

So lately, I’ve tried to assign myself a reasonable set of science tasks to do, like analyze a dataset or solve some computational problem. This has given me a real feeling of satisfaction, and has made me feel more productive. The amazing thing is that it also makes me feel less harried at the end of the day, because I can point to something I care about and say “I did that!”, so I feel much less like I’m behind on every single thing (although I’m still just as behind as ever). I guess the point was to inject a little bit of positive reinforcement into my life, and I think it’s given me more energy to tackle all the other stuff I need to get done. Of course, the key is to set reasonable expectations and set aside some time to complete the task, but that’s a whole other blog post… :)

The second reason that it feels like we’re working 80 hours a week is that on some level, we actually are–I think it’s just the nature of being a scientist. I’m into what I do, and I think about it all the time. I often think about ideas and projects in the lab before I go to sleep and when I wake up. Not every time, but a lot of the time. I’m running into the lab right now on Sunday morning because someone smelled burning plastic and I want to make sure the lab doesn’t burn down (update: issue with fan coil). Sometimes I’ll meet a colleague for lunch. We might talk about kids or teaching or research. Does that count as work? Maybe I’ll have an idea that springs from that conversation. Maybe get an idea in the shower. Is that work? What about writing this blog post?

Don’t get me wrong, it’s not like science is the sole purpose of my existence. I have small children, am married to a non-scientist, have non-scientist friends, and watch tons of crummy action movies. All I’m saying is that I love science. It is a pervasive part of my life, and I don’t feel a need to apologize for that–certainly no more than a need to apologize for watching virtually every Steven Seagal movie ever made (check out this one, where he plays a Russian mobster named–wait for it–Ruslan!). Perhaps this leads to feeling a bit overwhelmed sometimes, but overall, I am passionate about what I do and enjoy it tremendously.

Anyway, I think what I’m trying to say is that I feel like it’s the perception of how busy we are that matters more than the actual hours, and maybe the best way to improve our well being in that regard is to not focus on how long we work but rather how to make those hours as meaningful and fulfilling as possible.

How to get people to do boring stuff they don’t want to do in lab

Lately, we’ve been working on a lot of infrastructure and process-oriented aspects of our work in lab, like a complete overhaul of our RNA FISH analysis software (now in sufficiently good shape to be publicly available to everyone), a probe database, and thinking about how best to organize our growing RNA-seq datasets. Once we have established what we believe to be best practice, though, the next issue is compliance. It’s one thing to tell people what they should do, quite another to actually get them to do it. For instance, we can all say “if you’re going to analyze your RNA-seq data, you should use this data organization scheme”, but there’s a natural entropy at play when people actually do work in the lab, and non-compliance is a natural by-product. How can you enforce best practice?

Well, actually, before starting to think about enforcement, I think it’s worth making sure that whatever scheme you put in place has actual, real benefits to people in the lab. I’ve come to realize that process, while it can enable science, is not science in and of itself, and it’s not always worth the effort. It’s a fine line, and perhaps somewhat a matter of personal taste; I think some folks are just fussier about stuff than others.

So what are the benefits? For our lab, I feel like there are three main benefits to building process infrastructure:

1. Error reduction: To me, the most useful benefit to having a standardized and robust data pipeline is that it can greatly reduce errors. The consequences of mixing up your datasets or applying the wrong algorithm can be absolutely devastating in a number of ways.
2. Reproducibility/documentation: For data, I feel, as do many others, that it is imperative to be able to reliably (and understandably) reproduce the graphs and figures in your paper from your raw data. Frankly, in this day and age, there’s no excuse not to be able to do this. Documentation is just as important for other things we do in lab, whether it’s how we designed a particular probe or what the part number is for some kit we ordered 3 years ago and is about to run out.
3. Saving people time and facilitating their work: Good infrastructure can save time in a number of ways. Firstly, it hopefully leads to less wheel-reinvention, which I’ve seen all the time in other labs. Another way it saves time is by (hopefully) leaving a data trail; i.e., “That data point looks funny, can you show me the image it came from?” Good infrastructure makes it easy to answer that question, and makes it much easier to explore your data in general. If getting answers is easier, you will ask more questions, which is always a good thing.

So what’s the problem? Well, for points 1 and 2, the issue is that error reduction, reproducibility and documentation are just not that exciting, at least not to people who are more interested in doing science. That, and the payoff is typically a sigh of relief a couple years down the line. My experience thus far has been that most systems for documenting lab stuff, no matter how sound the rationale, just don’t stick without some serious effort. For instance, we have a probe “database” (i.e., spreadsheet) that is woefully out of date. And we have a number of protocols that are fairly out of date, and an orders spreadsheet that is out of date, you get the idea. Same for RNA-seq and RNA FISH datasets, at least at high level data organization. You know the feeling: “No, not that transcription inhibition dataset, that’s the one that came out funny because of the cells acting weird, use this one instead…” The only way to enforce in these cases is to create a punitive rule, something like "no more orders placed until you update the ordering sheet". Sucks, but I guess that works.

But point 3, saving time and facilitating work, that’s something everyone can all get behind without any prodding. And then there's never any issue of compliance. For instance, our software provides all the backend to make sure that our data is fully traceable from funny outlier data point to the raw images of a particular cell. But it also provides all the tools to analyze data and use all the latest tricks and tools for image analysis that we have developed in the lab. For this reason, it's essentially inconceivable that anyone would spend any time writing their own software and doing anything else, the benefits are big and, importantly, immediately realizable future.

So what I’m thinking is that we somehow have to structure all the boring lab documentation tasks so that there is some immediate gratification for doing so. What can that be? I’m not sure. But here’s an example from the lab. We’re working on having our probe database automatically generate identifiers and little labels that we can print out and stick on the tube. Not a huge deal, but it’s sort of fun and certainly convenient. And it’s something you can enjoy right away and only get if you access the probe database. So I’m hoping that will drive the use of the database. A more ambitious plan is to develop similar databases for experiments and consequent datasets that would enable automatic data loading. This would be both important for reproducibility, but would also be enormously convenient, so I’m hoping people in the lab would be excited to give it a whirl.

Anybody else have any thoughts about how to encourage people to participate in lab best practices?

Simple rules for when not to send an e-mail

E-mail, despised and scorned as it may be, is still pretty awesome. I think it's safe to say that it has had a transformative (and largely positive) effect on how we do work. Seriously, try to imagine living without e-mail for a while. Yes, there are people who have gone just to Twitter or have found other ways to manage their life without e-mail. I don't know how they do it.

That said, there are of course situations in which e-mail just doesn't work, and it makes much more sense to pick up the phone, like if previous e-mails have been confusing or talking about something sensitive in which you need to gauge the other person's feelings. How do you know? Here are some signs I watch out for:

1. If it's taking you more than 10 minutes to write the e-mail, call.
2. If you rewrite a particular sentence 5 times, call.
3. If the thought runs through your mind about how someone will interpret your words, call.
4. If there is any likelihood that your e-mail will make someone angry, call.
5. If you are angry, call.

Basically, e-mail works great if the message is unambiguous and not inflammatory. If it's not, chances are it's a bad idea. You can spend eons crafting the perfect message and still piss someone off, or just call and maybe smooth everything over in 5 minutes. Which reminds me that I should get a cell phone.

Change yourself with rules

Can a person change? Loaded question, one with many answers. I have vacillated on this many times myself, but I think I have an answer now, and that answer is yes. The question is how. I think the most effective thing for me has been to just find a rule that makes concrete a particular principle you want to abide by and then stick to it. Simple as that. I think it's much more effective than big sweeping generalities, because it's quantifiable: Did I break my rule? Did I not break my rule? If you have to will to stick to the rules, then you can change. And I feel like simple rules can have profound effects.

Here's a little example from my own experiences. At some point, I saw one of Uri Alon's tips on how to give a good talk, which is that every slide must have a title that is a complete sentence–subject, verb, object. I found this transformative in putting together my talks, because now every slide has a point, one to the next. And it's just such a simple rule. At first, I found myself fighting this rule, because it was a big change. But now I can't even imagine preparing a talk any other way. It's the perfect example of a rule: quantifiable, actionable, consequential.

Lots of other rules, many of which are well known. Another rule I try to follow is to avoid the use of "you" language in my interactions, which is bad for communication (e.g. compare: "You are doing that wrong" to "I usually do that differently"). For a while, I thought, "Hmm, I can't think of how to say this without saying the word you". But I followed the rule and just kept my mouth shut, and afterwards I realized that I was honestly just better off not saying anything. It worked! Over time, it just becomes second nature. And that is real change.