Not all grad school all the time

Since I’m a day late, I’m gonna talk a bit about the fact that I don’t do grad school stuff all day every day. I actually tend to work in the 40-50 hours per week range. And part of that is because the type of project I have right now is fairly time-dependent, so there’s just not much I can do at 2am. Each experiment I do takes a few hours (inject embryos), then 24 hours later a few more hours of work (dissect spinal cords and culture* them), then 24 hours later a few more hours (fix and stain the spinal cord cultures).

*culturing is basically the process of growing cells or tissue outside of the organism (i.e. in vitro). For instance I grow chunks of spinal cord on glass dishes (that way they’re near other cells, which helps the neurons grow better, but they extend out axons so I can image individual axons without other cells getting in the way).

So my work isn’t super conducive to staying in lab all night (it may happen, and I’ve done it before, but it’s far from the norm). Also when picking my program and picking rotations, that was a huge thing I was looking for: an expectation of work/life balance. Because I love science. I love doing research. I love being in lab. But I want to keep loving it. I’ve talked to several people with PhDs who said that by the time they finished, they hated research and regretted getting their PhD (or at least weren’t entirely sure they’d do it again if they could go back). And my career goal is to actually stay in research doing some amount of bench work rather than get out of it (typically faculty move out of the lab the further they get in their career; but that’s not the track I’m interested in).

Also I’ve seen no evidence that putting in 80 hours per week makes you twice as productive as someone who works 40 hours per week. Heck, it doesn’t even seem like it makes you any more productive at all. Especially if that extra stress caused me to question being there or to potentially drop out. I’d rather focus on using my time wisely and enjoying my life. Because yes, I have career goals, and I love what I do, but my primary goal is to be happy.

And there are grads students who definitely work 60+ hours every single week, and if that works for them, that’s great. But that doesn’t work for me, which is something I realized awhile ago.

So for anyone who’s afraid that grad school necessarily demands all of your time: it can if you pick the wrong lab or program for you, but it doesn’t have to. And you’re not a failure or inferior if you don’t want to spend every waking moment in lab. In fact, I think knowing how to prioritize activities and be productive with limited time are far more important than working constantly. So take that extra time to eat your lunch outside and take your Saturday off to hang out with friends and pet their cats and dog! That’s what I did today and it was great!


So how LGBTQ-friendly is science anyway?

Before going to grad school, I had heard about the dismal representation of LGBTQ faculty in STEM: science faculty are overwhelmingly straight, white, cisgender, men. Women are underrepresented; lesbian, gay, and bisexual people are underrepresented; and transgender people are really underrepresented. Which is pretty similar to most fields.

And I had gone to an incredibly LGBTQ friendly undergraduate institution, so I expected to be thoroughly disappointed in graduate school. Sure, I was applying to schools in liberal cities (San Francisco, Seattle, Madison, Minneapolis), but I knew it would be nothing like my undergrad (and especially not like my friend group in undergrad, which was majority LGBTQ).

And I have to say, I was actually pleasantly surprised to find out how many grad students I know who are LGBTQ. Maybe there’s some confirmation bias, but I feel like I know a fairly large number of grad students (including several people in my program and in labs I rotated in; people I see fairly often).

But I can’t name a single LGBTQ faculty member at UW-Madison. Which is not at all to say there are none. In my program (Cellular and Molecular Biology), there are almost 200 faculty trainers, so I would be incredibly surprised if none of them were LGBTQ. And part of this is that being LGBTQ is largely invisible. Other than a few cultural cues (which some people adopt and others don’t; and which tend not to be 100% exclusive to LGBTQ people anyway) or someone saying they’re LGBTQ, there is absolutely no way to know. As Dr. Jane Rigby (a gay astrophysicist at NASA) points out in this article about LGBTQ representation in science presentations “while a student attending a talk on Queer theory will suspect the speaker is queer, the same is not true of a student attending a great talk on AGN [Active Galactic Nuclei] by a queer scientist.”

This article “Why is Science so Straight?” delves a bit more into this topic of LGBTQ invisibility. And while the author Dr. Manil Suri’s experience was that personal lives were rarely talked about, I’ve found that less true of my graduate experience. And this may be another reason that I know many LGBTQ grad students and no LGBTQ faculty: I don’t talk about personal lives as much with faculty.

And I think times are definitely changing. Certainly LGBTQ youths’ experiences today are drastically different than youths’ 20, 30, 50 years ago.

Today there’s also institutional support and organizational support that just didn’t exist, even 15 years ago. For instance, Out in STEM is a relatively young national organization that is rapidly spreading to Universities across the country. There are now scholarships such as the Out to Innovate Scholarship for LGBTQ undergrad and grad students in STEM. And at least at UW-Madison, there are also purely social groups for LGBTQ grad students to connect.

And while things are definitely better than they used to be, are better than in many other fields, and are better than I had expected, there’s definitely still tons of room for improvement, especially in terms of making science welcoming to transgender researchers.

Gene Editing – CRISPR/Cas9

I saw this list of 13 female scientists who are doing major things in biotech in Silicon Valley and #1 is Dr. Jennifer Doudna, a faculty member at UC Berkeley who helped develop a technology you may have heard about: CRISPR/Cas-9. It’s the hot, new thing in biology research (for good reason) and could have implications for medicine as well (though they say that about every new biological and chemical tool and nothing ever works quite like we expect it to when we try working with humans).

So I wanted to explain a bit about what exactly it is beyond a “gene editing technique.” While I’m not using this tool right now, I had a related project in one of my rotations, and I may end up using it in the future (frogs have extra copies of chromosomes [humans have 2 of each] which makes genetic modification more difficult than in mice or fruit flies, but CRISPR/Cas9 works well and could be really useful for us frog people).

So what is it? First of all, in case you hear it spoken, it’s pronounced “Crisper Cass Nine”. The CRISPR part of it refers to the process (it stands for “Clustered Regularly-Interspersed Short Palindromic Repeats” but I actually had to google that because I didn’t know what it stood for, so not too important): it’s basically an immune system for bacteria (and archaea). Using this system, they can recognize foreign DNA and chop it up so it can’t do anything (such as be used to make proteins that infect or kill the cells). So CRISPR is the name of the process and Cas9 is the name of one of its effectors. Cas9 is the protein that does the chopping. It’s called a nuclease (nucle=related to nucleic acids like DNA and RNA and ase=it cuts something).

Now your cells are filled with nucleases. They’re used in all kinds of processes, including totally normal functions, but they’re tightly regulated so they can’t start chopping up your DNA (or to at least only chop it up when you want it to). So what makes Cas9 special is that it incorporates a piece of RNA that can specifically bind a certain sequence of DNA. If you remember from biology classes, DNA and RNA have bases that can pair with each other in certain ways. So As and Ts base pair and Cs and Gs base pair (see the adorable comic from Beatrice the Biologist below, whose name I just realized I totally ripped off, haha):

So Cas9 is a protein, but it can hold a piece of RNA that basically acts as an address. That piece of RNA can bind to the matching piece of DNA. And Cas9 will only cut when it’s RNA (called the “guide RNA”) matches the DNA (it doesn’t have to be a perfect match, but it has to be pretty close). And in its natural setting (bacteria and archaea), the guide RNA comes from foreign DNA that gets into the cell, so Cas9 targets this foreign DNA and chops it up. But in model systems in the lab, we can put in our own guide RNA.


It’s very easy to put DNA or RNA into cells. You can inject it (which is what you often do for frog embryos), you can electroporate it (electrically shocking the cells so that the cell membrane opens up and lets things outside the cell [such as the DNA you put in the media] in; this is used a lot with cell lines), you can introduce a virus (retroviruses will insert DNA sequences randomly into the genome). We’ve been able to do that for a long time. The problems were that either the DNA wouldn’t actually get incorporated into the genome (and DNA not in the genome sometimes doesn’t work as well as you’d like, and it won’t get copied and passed on if the cell divides), or it would get incorporated into a random part (which could disrupt other genes or cause other unintended problems).

But with CRISPR/Cas9 and a guide RNA for a specific sequence, you can make sure the DNA gets cut exactly (well, almost) where you want it to. Then the cell tries to correct that cut and it can do that in a couple of ways. It can try to just stick the two ends back together (which often leads to insertion or deletion of a few bases which can break the gene; this is called non-homologous end-joining). It can also try to repair the broken spot by using a template to remake what was damaged (this is called homologous recombination). And you can inject (or electroporate) a piece of DNA that provides a template that includes a new piece of DNA with whatever you want (for instance the blue “DNA” in the cartoon below).

This is one of the easiest and most effective ways we have of introducing a specific DNA sequence into a specific part of the genome, which is what makes it so exciting.

And from a medical standpoint, this could be really cool. A lot of diseases are caused by problems in single genes. If you can replace the bad gene with a good copy, the disease may go away (or at least not get worse). And with CRISPR/Cas9, you could potentially very faithfully insert a good copy in place of the old one (you can have two guide RNAs to cut on either side of the bad gene to remove the whole thing).

The first trick with doing this in humans is that you have to still get the guide RNA, the template DNA, and the Cas9 protein (or the DNA to encode it) into all (or at least many) of the person’s cells. And there isn’t a super great way to do that yet (viruses are the best, but they never seem to work as well as they “should”).

The second is arguably a much more difficult problem: ethics. One of the things that makes CRISPR/Cas9 so valuable for research is that it changes the DNA in the genome for the vast majority of the cells: including germ line cells. Germ line cells are the cells that become gametes, i.e. eggs and sperm. Which means that if you use CRISPR/Cas9 to treat someone, you will not only be changing their DNA, but the DNA of any children they may potentially have. Which doesn’t seem like a terrible idea for most heritable diseases, but we as a society have no idea where to draw the line. What even counts as a disease? That’s something that changes more than you might think. What’s the difference between a difference and disease when we define a lot of diseases (particularly neurological/social ones) as a difference from “normal”?* So until we can answer those basic questions, most people agree we probably shouldn’t mess too much with genome editing in people.

*I personally think the desert island test is a handy rule of thumb. If someone with a given condition would be perfectly happy alone on a desert island with nobody to treat them badly for being different, it’s probably just a difference. If they would be unhappy because the condition itself causes pain or suffering, then it might be a disease. Of course a lot of it is squishy and difficult and impossible to separate from society and culture.

I was just about to publish this post when I saw this article on CRISPR/Cas9 being used to modify (nonviable) human embryos. Talk about good timing. As the article mentioned, this is the second publication in which nonviable human embryos have been modified with CRISPR/Cas9. These two articles’ goals were proof-of-principle for therapeutic editing, so even though the embryos were nonviable and a product of normal IVF procedures, many scientists think these proof-of-principle studies should not be done until we’ve decided whether therapeutic editing is ethical (or under what conditions it’s ethical). As the article mentions, a UK group has also gotten approval (but not published results yet) to use CRISPR/Cas9 to study development in viable embryos. However the goal of the UK group is to study developmental processes that contribute to miscarriage (and their protocol has strict rules about the timeline of experiments so the embryos will get no further than a roughly spherical ball of cells about the size of a poppy seed), which is a fundamentally different goal. What do you think? Should we explore the use of CRISPR/Cas9 for explicitly medical purposes? Should it remain a research tool? Should it be used only for certain types of medicine? Only certain types of research? Plenty of existential questions to think about this weekend!

Name Brand Hype

Scientists are just as prone to following fads and overvaluing (or undervaluing) certain brands as anyone else. We get superstitious about which companies or brands (or journals or labs) are the best, often based on almost nothing (or absolutely nothing), just like everyone else.

But there’s one huge place where this can be a serious problem: publications.

The way science gets disseminated is through publishing articles in peer-reviewed journals*. And people talk about prestige of journals, which is in large part based on impact factor, a single number (with plenty of flaws). And that number largely determines whether it’s a “good” or “bad” journal. Additionally certain labs are known for being published in “good” vs “bad” journals, and where someone publishes (or where a lab typically publishes) is seen as a mark of the quality of the scientist.

*there’s plenty of debate about whether this is even the best way to disseminate information. See my previous postthis blog devoted to publishing research in real timethis article which gives you a taste of how difficult articles can be to read and understand; and this article about the challenges of open access.

Now there are a few obvious problems with this.

  1. Impact factor is a flawed measure. It’s just the number of times a paper gets cited divided by the total number of papers published. But people give highest weight to the journals with the highest impact factors, so they get cited more. So you get a positive feedback loop that overestimates the value of the “good” journals.
  2. We tend to assume that just because something is published in a “good” journal, it must be a good article. And this isn’t actually true. The best journals (Science, Nature, Cell) are also generalists. They tend to publish more flashy, exciting research that is broadly applicable or of interest to a larger variety of people. That means a paper could get rejected from one of those journals, even if it is incredibly high quality research, simply because it’s too specific or not (obviously at the time) applicable to broader research areas. This contributes two sub-problems.
    1. When someone looks at this researcher’s CV or resume, they only see that they published in a lower impact journal unless they take the time to read the actual article (and are familiar enough with the area to realize that it was excellently done). This can hurt researchers who do excellent work. Especially since the whole point of research is that you don’t know the answer, so it can sometimes be difficult to tell whether or not what you’re studying will be broadly impactful or not.
    2. The high impact journals are more likely to have retractions. Part of this is that they’re read more, so they get more feedback and criticism, so errors are more likely to be caught. But this is also because the pressure to publish in a good journal (and to publish the new, flashy finding before someone else beats you to it) means that sometimes this research can be rushed, causing flaws in the methodology or conclusions drawn to be overlooked.
  3. Certain people or labs begin to be associated with quality of research based on where they publish. But then evaluation of future publications get based on the prestige of the lab, which was based on where they have published before. So you get this circular thinking related to lab quality research, which may not be based on the quality of the current research quality at all. I have definitely read articles before in top journals by labs known for publishing in top journals (and therefore assumed to be doing good science) that are actually terrible! And it has really solidified in my mind that lab prestige and publication prestige isn’t correlated highly with the quality of the research at all. (I should be reading and thinking critically about the actual methods and results anyway, but realistically I can’t read everything, and too often we fall into a trap of choosing what to read based on these baseless ideas about prestige….)

Of course we also get overly invested in certain research trends (we follow fads in topics to study and methods to use [though really, optogenetics is just too cool :P]). There are definitely cool and uncool things to study (though I’ve never actually heard someone say, “That’s so 10 years ago” about a research topic, I’ve definitely heard the sentiment expressed). And people get defensive about their pipette brands or reagent brands (or sometimes there’s collective hatred for one; I can’t even count the number of times I’ve heard “Don’t get Santa Cruz antibodies; they never work”**). And sometimes this is harmless (I use certain pipettes preferentially for no real reason), but when it comes to something like publications (which are the biggest determinant of your career and funding opportunities) this can be a huge problem

**Santa Cruz Biotechnology is a the largest producer of antibodies. The thing about antibodies though is that to be most useful, they need to do 2 things: 1) bind to the thing they’re supposed to bind to and 2) not bind to anything else. There are ways to test that this is true for your antibody, but most companies don’t verify the antibodies themselves. They typically leave that up to the researchers (and then if you’re lucky someone will have published with it or will have posted a review so you know whether or not it works before you order it yourself). So because Santa Cruz Biotech produces so many antibodies, but doesn’t verify them, there are a lot out there that are unverified. You basically end up with a higher likelihood of getting one that doesn’t work just because they make so many. But then they get the reputation of being unreliable.

Proteins as tools for research

I’m gonna talk a little bit about the new project I’m taking on (which I was going to start working on this past week, but a nasty cold set me back a bit, so I haven’t actually done anything with it yet. I’m almost fully recovered though!).

Basically this new project is testing a super cool tool, and that tool hinges on the fact that proteins can do things based on their shape. What we’re doing is shining light at proteins to make them change shape and do something we want. And because we control when the light goes on and off, we can control the precise moment that this protein starts doing its job, and the precise moment when it stops. Which is pretty useful for studying how things work and interact in a cell! Also this concept has actually been around and used for awhile, and I’m just testing the tool being used in a slightly new way. And I didn’t develop or create it, I’m just testing it in a slightly new application to see if it works in our frogs.

Okay, so why are proteins so amazing? Proteins are what actually do most things in your cells (and outside them). They do this by generating chemical environments that facilitate certain reactions, they bind molecules to recognize them or change what they’re doing, and they provide structure and support and sometimes elasticity or shape.

A quick refresher for people who don’t know/remember: proteins are made up of amino acids. Each amino acid has a certain shape and certain chemical properties (some are acidic, some are basic, some are hydrophobic, some are hydrophilic, some are big and take up a lot of space, some are tiny and can tuck into small pockets, some are very flexible, and others are very rigid and are locked at certain angles). The protein is sort of like a string of beads, where the amino acids are the beads. This string then gets arranged to form a dense ball in a certain shape, and this shape (along with the locations of the amino acids within it) determine the protein’s function. That shape can determine whether or not the protein can bind other things (imagine puzzle pieces fitting together), and how well it can bind those things.

But these shapes aren’t static. They can be changed by other proteins or molecules binding. They can also be changed by the chemical environment (because it changes how the amino acids interact with each other and their surroundings), which is influenced by electromagnetic forces such as light as well. Basically, certain wavelengths of light can change certain proteins’ shapes. And this can cause all sorts of things to happen depending on what the protein is.

For an example, this is what a tool called optogenetics does. My project isn’t optogenetics exactly (I’m working with different proteins to do a different thing in the cell, but this is something you may have heard of on the news, and it’s really cool).

First a quick neuroscience lesson. Neurons can send electrical signals, and what starts these electrical signals is ions (which have a charge) moving into or out of the neuron. This happens when ion channels (they’re proteins that act as tunnels with gates that allow certain ions through when open) change shape to cause a pore to form. Two common examples of what could cause this shape change in neural ion channels is a molecule (termed a ligand) binding or by a voltage change. Below is a great image demonstrating how a voltage-gated ion channel works:


But green algae and some bacteria also have ion channels that can be induced to change shape with certain wavelengths of light (actually some of your retinal cells can change shape in response to light, which is how you see! They’re called rhodopsins, but they aren’t ion channels and cause the light signal to be sent in a slightly different way).

That means that now you have a protein in one species that you can shine light on and cause it to allow the flow of ions. And you have neurons which “turn on” and “turn off” in response to ion flow. And with some genetic techniques that have been around for decades, we can actually cause these algal and bacterial proteins to be expressed in specific neurons in the lab! Which means you have precise control over when a certain group of neurons turn on or off! Below is a video of that in action. Note that when the blue light comes on, the mouse begins to run around the edges, which is very different from its behavior before and after.

Now this may seem a little creepy and over the top from just that video. But it’s not like we’re just around the corner from mind control. But it does allow us to study the neural circuits in lab animals. Because trying to study how all the billions of neurons in your brain work to coordinate movements and thoughts is incredibly difficult. Being able to turn on a specific group of neurons can begin to tell us which behaviors different ones are involved in, and therefore will help people piece together how they’re interconnected.

This tool has also been discussed in the treatment of diseases such as Parkinson’s. One issue in Parkinson’s is that you lose a certain group of neurons, but we know from other treatments (deep brain stimulation) that keeping the remaining neurons firing helps in managing the disease. So if you could implant a light to periodically turn on the neurons on the appropriate time, it could be therapeutic. The hard part is actually getting the proteins to be expressed (while we’re very good at genetic manipulations in research animals, gene therapy for humans isn’t quite here for the masses yet).

If you want to hear a little more (and put much more eloquently, haha), watch this Ted talk about optogenetics!

So that’s a popular example of how we can use proteins to precisely control a certain cellular function in order to study how something works! Which is basically what my project is doing, except we’re using light to control the production of proteins instead of using it to turn neurons on and off. But both are tools based off the fact that proteins have functions that are related to their shape, and we can use things like light to control the shape of certain proteins. Yay for biological tools! And yay for proteins!

Quick correction: I said what I’m doing is a bit different than optogenetics, which is actually incorrect. It falls under the heading of the term optogenetics. I had just only ever heard the term be used to specifically describe the method of using genetics to express light-sensitive ion channels, specifically. I wasn’t aware that it is used more generally to refer to using genetics to express any exogenous* protein you can manipulate with light.

*exogenous means non-native to the tissue/cell/organism. basically that the protein you’re expressing is not normally found there. Also just a reminder that if I ever use a term that makes you go, “wait, what the heck does that mean?” feel free to aks! Especially if googling isn’t helpful because 1) sometimes the use of a term in a specific sub-field is different than the common definition and 2) I use words wrong all the time, so it’s entirely possible I misused it. As in the case of optogenetics!

Misogyny, Racism, and the importance of diversity in STEM

I actually have a fun, new project, but I’m gonna save that for next week’s post (it’s based on changing protein’s shape with light!)

But today I’m gonna instead highlight a couple of great articles pointing out misogyny and racism in STEM and why diversity is important. There are several articles, but they’re all great and talking about a very important problem that not only hurts the individual women and people of color (and LGBTQ people, more on that specifically at a later date) in science, but hurts all of science, and by extension all of society that’s missing out on the amazing research and progress that those people could be contributing if their time and energy weren’t taken up with dealing with discrimination.

First of all, it’s amazing (in a deeply disappointing way) that there are still people who believe discrimination doesn’t happen in the sciences. But it absolutely does. And the fact that it isn’t believed and isn’t taken seriously, is a huge part of why it’s so hard to combat. Because nowadays, we don’t have departments actively excluding women, and I’ve never been explicitly told that I shouldn’t be in science. But the methods by which we exclude women (and people of color, though that article focuses on gender), are much subtler and more insidious, making it easy to write off differences in female enrollment or accomplishment or matriculation rates as just being due to “personal choice,” or (even worse) “fundamental differences between men and women” (the day you show me a study demonstrating differences between men and women where all environmental and societal factors have been controlled for is the day I’ll start considering any conclusions based on this premise). Here’s another article about misogyny in the sciences.

Okay, I’m sad that I even have to bring up this point, but some people may ask, “Well, so what? Maybe women aren’t as good at/as interested in/necessary in sciences.” But science is fundamentally creative and therefore thrives off of diversity. We can’t have scientific progress without diversity of thought. Sticking to the same old ideas (and people) is detrimental to progress. The larger the pool of scientists (and specifically the pool of backgrounds and thought patterns and experiences), the better off we all will be. Because sometimes you need someone thinking a little differently (sometimes questioning ideas we’ve taken for granted for decades) to move forward or find something new.

Finally, I want to leave you with two things. The first is a great little list calling into question the idea that academia should or is best as it is, and instead encouraging a more “gentle academic” which I think addresses some of the issues I have found with grad school and academia and are important to strive for to create a scientific culture more open to new, better ideas (by the way, this comes from but tumblr makes it very difficult for me to post a link to this):

“towards a gentle academic

  1. be up front and honest about the things you do not know
  2. acknowledge the intrinsic value of others’ knowledge bases, even if they do not seem important to you from your institutional context
  3. do not feign mastery where you have none
  4. respect the gaps in others’ knowledge bases
  5. be generous, not only with others
  6. but also with yourself
  7. you overwork yourself at the risk of legitimizing a culture of overwork
  8. privilege voices and perspectives that have historically been left out of the academy
  9. nothing is ever neutral or apolitical
  10. support the progress of other scholars
  11. collaboration over competition”

And the second thing is this great post from “10 Black Scientists You Should Know” (via which is an excellent blog I highly recommend!). If you follow that link, there are pictures and blurbs of the scientists, but below is just a list of the names and their field (how many of them have you heard of? And how many could you say something about them or their work? I’ll admit I’d only heard of 3, and 30% is a pretty abysmal rate):

1. Ernest Everett Just – developmental biologist (fertilization and cell division)
2. Patricia Bath – opthamologist (cataract treatment)
3. Marie Maynard Daly – biochemist (cholesterol and sugar in disease)
4. David Harold Blackwell – statistician (game theory)
5. Neil deGrasse Tyson – astrophysicist (and science educator)
6. Percy Julian  – chemist (synthetic progesterone and cortisone)
7. Mae Jemison – astronaut (and chemical engineer and doctor and dancer!)
8. Charles H. Turner – animal behaviorist (insect perception, memory, and learning)
9. James West – engineer (invented technology still used in most microphones)
10. George Washington Carver – botanist (invented hundreds of uses for soybeans, peanuts, and sweet potatoes)

Open Access Science

One of the reasons I started this blog was because I think it’s important for scientific research to be accessible to everyone. This sounds simple, but actually requires several things:

  1. Research results need to be easily accessed by the public.
  2. How the research was conducted needs to be accessible.
  3. The public needs to be able to understand it.

Now to talk a bit about each of these:

  1. With modern technology, that means it needs to be online and it needs to not be behind a pay wall. For those who aren’t familiar with the term “pay wall,” it’s basically when a publisher requires you pay some amount of money (often as much as $50) to read an article. In practice, nobody gets single articles, and instead universities and large research groups will buy subscriptions to databases that have access to articles from a variety of publishers, but these can cost thousands of dollars a year and so are only available to people affiliated with universities and research groups.
  2. Research is only as good as the way in which it was done. Due to publications requiring increasingly extensive data to be considered acceptable but simultaneously placing increased restrictions on article length, “materials and methods” sections (which is where this information is located) is increasingly being reduced or relegated to “supplemental materials,” which is published online only and often buried slightly.
  3. This is one of my most important and most difficult parts. If people don’t understand, then they don’t actually have access to the information. The problem is that the complex terminology really does serve a purpose. Much of the language we use in science has very specific definitions that make it difficult to communicate as efficiently and effectively without it. And I don’t actually think that should change among scientists. But it absolutely needs to for communication with the public. You shouldn’t need a college degree to understand the research being done with your money (because most research is funded by government grants). And I think the solution to this needs to be better simplified communication by the researchers themselves. Science media could serve this purpose, but it can’t possibly account for all the research being published every day.

I was recently shown this website: Dr. Rachel Harding is publishing what she’s doing and her data in real time, which I think is awesome! That is what I wish this blog could be. I love that Dr. Harding is not only using her blog to make her data accessible to the research community, but to the broader public as well. And it’s useful to get feedback in real time (which comments and emails allow when you publish what you’re doing) because it can keep you from spending months doing research based on a faulty premise or incorrect technique. So why am I not publishing my data? Why am I being secretive about what exactly I’m doing?

I’m in my first year and have no publications. That means I have at least 4 more years, and in that time I need to publish in a respectable journal. You’ve probably heard the term “publish or perish.” And it is absolutely true. It is extremely difficult (and not advised) to get your PhD without publishing on your work. The number of publications you have is one of the major numbers that gets looked at for hiring. If I were to graduate with no publications, I would have an extremely difficult time finding a job. Because publication is viewed as verification that you have done work worth reading.

But many journals will not publish any data that’s been published before (which seems like it makes sense at first because you shouldn’t just copy what you’ve done before to make it look like you’ve done more than you have), but that includes things that have been published online. Which means if I put out my data on this blog, it may be incredibly difficult for me to publish on it, which could seriously hurt my future. And I’m nowhere near established or experienced enough for my career to survive that.

Another big problem with publishing data online is “scooping.” This is when another lab publishes on your research before you. Journals will only publish new research (which is one of the reasons we have a big replication problem). So if another lab already published experiments similar to mine, I won’t be able to publish my own. In actuality, two labs typically aren’t doing exactly the same thing, so the lab that publishes later probably will still get a publication, but it will be in a “lesser” journal (journals are ranked by how good they are; I have plenty of complaints about that if you want to hear them). So how does this happen? Usually it just happens that labs pursue the same questions by accident because they’re both interested in similar topics (which means making your research public is actually great because it means you could see what other people were working on and work with them or go in a slightly different direction!) But occasionally you get people who specifically try to scoop someone. I’ve heard numerous stories of labs that send grad students to scope out posters and talks in order to steal ideas and then try to finish the project first and beat them to publication. Which is a waste of everyone’s time and money, but because all that matters is publications, people have incentive to cheat like that.

So I’m not in a place in my career where I can feel secure putting my data out there. Dr. Harding already has her PhD, a post doc position, and several publications. And because most scientists would love if research were more open access, her blog could even be used as a selling point for hiring committees. But I’m just not there yet. And as much as I agree that the publication and research access system is deeply broken, I’m not yet in a position where I can afford to not work within it.

Fortunately the vast majority of researchers agree that data should be more open access, so this will hopefully change in the future. And of course, when I do get a publication (still working on the getting data part though, so it could be awhile), I will definitely let you know and provide explanations so you can understand what my data actually mean, because that’s the most important part.

As always, if you have any questions about anything, let me know!