Tag: research

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Storry Telling – Part I

When you are a scientist, there’ll be countless times when you’ll be required to “communicate your work”. It’s necessary, it’s woven into the current system, and it makes sure people don’t have to reinvent the wheel (or at least that’s the point).

But sciieeeence… is too technical for good communication. And when it gets too specific (as in a PhD research), then it appears very far off from being relevant to general life. This means that often when science IS being communicated, it may not be reaching the ears it should be reaching.

Now that doesn’t matter in a lot of cases, because, after all, it is science and not a novel that we want to sell a 100 million copies of. It’s for other scientists, more specifically for those who are related to your own field of work… And even in there, people who really read your writings will be people who are almost exactly working on the same thing as you (how many people in the world does that mean, especially for a junior scientist? 10? Maaaay be 20, if they find your article)… And even they will not be reading it word to word, but mostly just skimming it, and extracting those precious couple of sentences that they’d find most relevant for their own work (and that you, by the way, spent months working to get).

So yeah, it is okay if it is boring and technical, because it is not meant to torture a whole lot of people (just the ones who might really need to read it).

Now this may seem sloppy on behalf of a scientist (although most of us are making a sincere effort to put forth that gibberish-to-your-ears in the most understandable way possible). And then, of course, we need to interact with people from other disciplines of science, and no matter how big of a scientist you are, it still helps that people from other fields can break their stuff down to bare basics for you to understand.

So good communication is still the king.

One of the key ways of communicating effectively is to tell it as a story (and this fad has been going on for a while now). And the thing, which I have always had a hard time understanding, is: how do you tell a scientific work as a story?

Story telling has so many elements, and so many styles: so which one is most suitable for the communication of a scientific work?

Okay, so you can give a good introduction. Start off by introducing the theme and “characters” of your story. If you give your audience (or readers) a really good introduction to ground them in understanding, it becomes easier (on both sides) as you progress.

Another aspect of this can be to tell your story with the story of your work. So you tried something, and it didn’t work (or, surprise!, this time it did work), and that’s how you jumped on this new idea that you’ll be telling people about (but hang on! you shall not overdo it, because, after all, thou shalt talk about science more).

Because story telling involves a lot of elements that can directly affect the integrity of your science. Like exaggerations. Or sometimes unnecessary and frilly details. It often also requires knowledge of the complete picture (which you never have in science until may be after you have gone through everything).

 

In science communication, I find this quite interesting as a scientist myself: What limits can you test and how far can you push the boundaries of science communication by applying principles of story telling?

(And how sorry should you be if you fail at it).

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What Doesn’t Make It To THE Thesis

I recently attended a PhD seminar of a student at the Physics department. One interesting question that came up was: What didn’t make it to your thesis?

After deep thought and much analysis, I have realized this is such a good question. It let the candidate show other things he did and learnt in his PhD that didn’t make it to his thesis, because they may have been slightly irrelevant or might have been disregarded as not-important-enough.

But with this question, he could highlight this stuff because he still did those during his PhD (or more likely, he could highlight this stuff well if he’d been prepared for such a question to come up – this may not be your average kind of opponent-speak).

But hang on, is it just this stuff that doesn’t make it to your thesis?

When I was doing my Masters, we’d always joke about how everybody should write 2 theses: One the more formal, “required” document; the other, a document of “failures”: the questions that we asked, and what we tried in the lab, that then didn’t work out, and that we then dropped… which then obviously wouldn’t make it to the thesis. The kind of information that you jot down in the side margins of your lab notebook, along with angry and frustrated emoticons and hashtags and exclamation marks.

Jargon that only the person who owns the lab notebook may understand – And now I realize how I don’t make much useful or interesting notes in my lab notebook :'(

The kind of information that doesn’t make it anywhere.

Because that information is also useful, although it mostly just tells you what NOT to do (which is quite the time-saving information). But this kind of information doesn’t have much space in the scientific world. Like people in all other fields, we just want to make noise about the best work that we do, disregarding the important stepping stones that our failures were that got us there.

So yeah, a lot of stuff doesn’t make it to the thesis at any level (or any important document, for that matter – except may be some internal lab communication).

There’s also a lot of other stuff that doesn’t make it to the thesis: the hardwork and energy that you put in, how you picked yourself up after repeated failures, how your informal collaborations helped you (and the people around you)…

… Although if you end up doing really good work, the thesis can become a reflection of it.

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Fast Lessons

So for the last month, I have not been blogging much.

I’m a Muslim, and we recently ended our month of fasting, in which we don’t eat or drink from pre-dawn to a little after sunset. In Finland, particularly in Åbo, that makes about 20 hours. So while we are fasting, we often switch to this energy saving mode to get through the day with grace, while not completely going into some kind of hibernation mode (which we (most of us) do go into if we are on vacation).

So, for reasons mentioned above, I was not much into anything apart from food thoughts and how-to-get-through-the-day-while-keeping-some-lab-work-going techniques (and managing my sleep deprivation issues).

But all this down-time also helps you reflect on what you are doing, and why you are doing it, anywhere from life in general to specific PhD related stuff. And this year, Ramadan (that’s what the fasting month is called) taught me something that I think I’ll be needing much reminding of in the four years of my PhD:

That you think you cannot do it, but you can.

Whenever this thing came up about me (or us Muslims) not eating or drinking anything for 20 hours straight, people would mention how they cannot go through without food and water for this long. Even we, Muslims, who get this training repeatedly every year for 30 days, cannot think of going without food and water for this long when we are not fasting.

But when we are fasting, we do.

And this is quite strange for me, because now that Ramadan is coming in summers*, we always have this big worry on our minds about how we will get through it this time, specially without water (I, in particular, whine a lot about this one). It sometimes seems so hard, and at times, so impossible.

And yet, when we start fasting, we do. Every time. The whole month.

And this year, I realized, how we are always making assumptions and excuses about how we cannot do something, without really trying it out. We don’t take into account the fact that the human mind and body are very flexible and adaptable, and we become what we make out of ourselves.

 

In my PhD, I know that I’ll come across multiple instances when I’ll think I cannot do this anymore, or how I am not able to try any further with a particular experiment, or when taking the next step forward will seem like the most difficult thing to do.

But all that will just be stuff in my head unless I try and find out that I was able to do that all along (or not).

And that, when the start is difficult, it only becomes easier as you move forward.

—

* Ramadan shifts by 10 days every year because we follow a Lunar calendar for this, which is 355 days long compared to the more-common 365-day solar calendar.

 

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Blocked

I find writer’s block to be quite a fascinating thing.

It’s really a great reason for an author to be not-writing (read reason as excuse). And the best part is, it is considered a valid one, too. It’s the exact reason I am not writing a blog post every day.

In fact, it’d be pretty great to come up with terminologies for every kind of block that a PhD student might face. Like the “lab-experiment-block” or the “reader’s-block”. Or just plain old “work-block” that could fill in every time we don’t feel like working.

However, I do think even the far-better-evolved excuse of writer’s block would stop working so well for me once I start writing my PhD thesis (like everything else in science that doesn’t work).

Anyway, the other day, I was listening to this podcast “No Such Thing (As Writer’s Block)“. It tells, among other things, the routine of Isaac Asimov, the big sci-fi writer. He got up early morning, sat down and wrote… whether or not he felt like it. Every day.

Writing daily is a little daunting. I have given it a try in the recent past, and well, you can write daily if you want to, but you surely wouldn’t be producing your best work every day. Some days, yes, but not every day. However, statistically speaking, if you write something daily, then you increase your chances of producing something worthy at least some of the days.

Just as doing science daily is a little daunting, and somewhat demotivating if you are not seeing immediate results at least some of the times. But every experiment that you run, adds up, whether it was a failure or moderately successful. The more you put in, the better chances you have of seeing patterns.

Although you can sometimes get lost in “the cloud”, as this professor notes in this rather interesting TED Talk (that you might want to listen to if you are in science, or a problem solver in general).

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Buried Under Literature

I recently learned I could get updates on recently published research of my particular field by creating alerts in Google Scholar.

After “learning” this, I realized I had to have it. What was I doing, did I want to get out of my hole after four years of my PhD and realized I didn’t know where the world was going? That was not the way to go.

I should know what the current trends are in my field, where the research is going, and how many people (and which groups) are publishing work that is identical to my own (also so that I can send them hate mail if they publish something I was just about to submit).

So I did that. I followed some of the most notable names in my field, created alerts for their works, and also for articles related to their expertise. Breezy easy. I now just had to follow through.

The thing is, if you have very famous people, they seem to be publishing something every other day (probably some kind of “you become famous, you publish more, you become more famous, you get published even more” infinite loop). And the mails I receive every other day have something from a couple to around ten or eleven titles. But of course, if you have related people in your alerts, then some papers are obviously going to overlap.

Now I am blindly downloading papers I know I’m not even going to read half of – there are lots of interesting titles that I would like to get to know more, but then again, they are not related to the “core” of what I’m doing, and having so much to read already ensures that I’m probably never going to read most of them. But that does not deter me from the simple act of saving all pdfs to my downloads folder, with arbitrary, unintelligible titles, further decreasing the probability of me ever reading them, or even knowing why I downloaded them in the first place.

The only fear I have, is downloading the same paper more than once. I do not want to have it so that I read a paper twice, despite all my precautions, because I apparently had two copies ( one of which did not land in my “Read” folder after I’d read it).

I do not want to waste my precious time on a single paper, time that I could have easily wasted elsewhere, in more fun manner.

Sigh. I should now get back to my other form of wasting time, cataloging all literature in my computer to see if I have a second copy of something lurking in my hard drive (I seem to only have downloaded papers in the past four months rather than reading actual literature).

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Very Superstitious

Scientists are the last kind of people you’d expect to have superstitions.

But it’s fairly true – science comes with its own set of superstitions.

Which, if you are new or entirely foreign to the science world, wouldn’t make sense at first. But trust me, as you start delving deeper in there, everybody (or so I think) starts developing some strange set of beliefs that have no reasonable explanation.

For example, you have been trying something that has not been working for you, and you are baffled. It seems to work fine for all those other research groups that have a research publication out on the same topic every couple of months. So it definitely works, but what in the world are you doing wrong?

Then you either realize yourself that it must be your “handling”, or you talk to someone who is doing this already and they tell you something particular they do when they do it (in other words, it must be your handling). So you start doing some ritualistic “prep” steps before you begin the process (nothing creepy like animal sacrifice or the kind, but more related to some general lab washing protocols, or how many gloves you should set aside for a protocol, or some equipment settings).

So now, if it works, it was due to this “new” thing, although it doesn’t make any sense why that should have affected the procedure to such a great extent, if at all.

And if it still doesn’t work, then no idea why it didn’t. But congratulations, you have still added a dubious step to your protocol, which you are not sure of, but that’s how it is done, so that’s how it is going to be for all future generations of researchers (and let’s keep it, because it is “good” practice, after all).

So in a science lab, you’ll probably find lots of things that have no logic. People do specific things because they believe that if they don’t wash their slides exactly 4 times, in the right sequence, for the exact amount of seconds, then 11 steps later, they’d discover it didn’t work – again (and well, we are methodical). Or they have procedures that seem to have this seasonal dependency on what month they are being done in (which, again, is not related to position of stars in the sky or if Mars is in retrograde or not). And of course, if that machine is not starting up, drat! That’s because it’s Friday. It’d be fine on Monday.

When you are working in areas that you don’t know anything about, when you are wandering into the unknown (that is the whole point of getting a PhD: to add something new to the knowledge existing in the world), then it sometimes becomes an emotional need to hold onto some explanation before we find out the real one.

So we develop theories on why something isn’t working. We make links where none exist. And this is where we are supposed to be good at, too, because ultimately, on the verge of discovery, this exact habit comes into play, when a reason finally clicks into place for some phenomena that’s been defying all explanations so far.

And in the world of science, some things do work without you knowing why. And often, that’s just how it is (until someone writes a paper about it).

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That Doesn’t Work

For the last four months, my PhD has accumulated into wonderful experiences of “okay so that doesn’t work”.

Which is good, because every time one thing doesn’t work, there’s one thing less to discover that doesn’t work, getting you one step closer to the thing that does work.

So you have this recipe/procedure/protocol that you need to optimize, or get the best out of. You start off with rather good energies, thinking the most it will take you is, what, three weeks? It seems pretty simple. You’ll easily find the best way to do it in that much of time. 

As you try on and on, you realize there are so many factors and parameters that are affecting the whole process. And to get to the best possible option, you will need to twiddle with all of those (ALL of those, one at a time. And then of course you have to make sure that once you are checking one factor, something else doesn’t meddle in and give you some kind of false positive or negative).

But…

… Do you even have all the options jotted down? There might be more. There must be something you are missing.

… What if option G was the best recipe? It did give some results but you probably went ahead trying H, I, J, K, L because you may discover something better (there might be no “better”, but you’ll need to find that out for yourself, now, won’t you?).

… Would “better” pass off as “best”? What even is meant by “best-possible recipe/procedure/protocol”? Is there a “better” than what you are considering “best”?

After everything, you end up with lots of data, most of which is just proof of how, mostly, it doesn’t work (if you are lucky, it may be otherwise). Obviously, there’s always a chance that nothing is going to work. And it might be some time before you realize that you need to change the whole game plan.

I mean, a PhD is like life in real time. So you want to learn about life, go get a PhD: It will teach you things in a couple years that you might take decades to learn otherwise.

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Codes For Your Samples

If you’ll come to my lab, you’d see strangely labelled glass vials of different shapes and sizes, with all kinds of materials in them.

That is also probably one reason why I couldn’t initially find my way in the lab. Someone might tell me to use a particular solution and there I would be, lost in the freezer, trying to tell one solution apart from another by its color and consistency, or by the fact that there was p10 written on top of it (which I was later told was actually “old” – I’d just been reading it upside down).

The codes, of course, only serve their masters. All I can tell by those are that this is definitely something not I labelled.

Now, may be I find it strange and may be you wouldn’t, because perhaps you yourself have used similar systems, and lived in the same habitats, as my current lab.

I haven’t ever been a part of this kind of system. I have never had so many samples to deal with in my previous research experiences that I’d need to evolve complicated naming systems and rituals for my samples. So in the past, I had been naming my samples with their original name, and putting an underscore/hyphen/period/slash for labeling them specifically for whatever needed specification. The names can get a little long, but it had been working for me just fine.

But now, that I’m going to have lots of samples and solutions to keep track of, and following lab culture, I needed to come up with my own system of naming. And, hey! that system had to be good enough for at least four years for me: What if I stopped liking it halfway past my second year? I’d be stuck and miserable for the rest of my time here! (If not for my whole future career that hopefully lay before me). So I had to be very, very careful.

So how should I carry out the naming of my samples? The alphabets and numbers may make sense to a lot of people, but I am pretty sure that after two weeks of sample initiation, I’d most probably forget what it was and why I had a particular number assigned to it (and what good would be those alphabets and numbers to me then, if I cannot tell what they mean?).

To be able to tell which sample is which, you need to develop an emotional connection with your solutions and samples. So perhaps, you can name them like that as well, like you would name pets, or people.

So, my first sample in the series I did today could be Linda (and Linda, I’m afraid, is not doing too well in the lab). And Scruffy died a couple weeks ago, when I put a colder glass slide on a hot surface and cracked it all the way through (I still have his remains in my drawer). But Sunshine, Sunshine is looking good so far, I hope I can show her to my supervisor sometime in the near future and then take her down to the Physics department. If only Joe, Cookie, and Cheeseburger, will learn something from Sunshine, and keep to their good behavior for just a couple more experiments, It’d be great to take them down, too.

This can be pretty practical but what I particularly love about this system is it’s versatility. So many options and I doubt that I’d ever forget my best sample, Mikasa, from the Tape Effect series.

I guess I should wind up this post now… I have to go put Linda, Joe, Tin Tin, Sunshine, Cookie, Moon Crater, Bloo, and Cheeseburger in the oven.

P. S. I, obviously, also have a more professional, alphanumeric system in place to show the world.

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Of Experiments That Go Wrong

Work in the lab is a slow, agonizing grind (looking forward to that in the four years of my PhD). It’s not like everything works perfectly (except may be on some lucky days).

In fact, behind each successful attempt, there may have been tens or hundreds of experiments gone wrong, done by God-knows-how-many people.

I think I don’t mind my experiments going wrong so much if they can still tell me something I can work on (and they are telling me something new everyday). But I have been so far doing this for only three months in the recent past so sure I don’t mind my experiments going wrong (yet, but ask me in a couple months and I am sure I’ll give you a different answer then).

So I have been trying to get some thick metal oxide coatings (thick, so to speak, but they will still be thinner than half a millimeter by a factor of 1000). For the past three tries, I have ended up destroying the coatings completely.

This time around, I decided to try everything I could think of, so that may be one out of my eight samples might make it to the finish line (I have three back up glass slides just in case things start going south for all eight of them). And one of the coatings, which already appears to have gone every which way, left this sparkly substance on all my gloves.

And a strange happening as this, in a lab, gets all these questions popping in your head: Was that really fine glitter? Did it just happen or did I make it? Did I just discover a method to make, I don’t know, plenty of glitter? Do I now want to drop out of my PhD and open a glitter factory? (after obviously I have done some tests and made the process sustainable).

But it’s a real comfort to me, that I might have possibilities of alternate career paths in the world of glitter-making if my PhD doesn’t go as planned, thank you very much thick-metal-oxide-coatings. However, I need to remember that all that glitters is not gold, and a little, one-time glitter in the lab most certainly doesn’t mean I will also get it once I actually start to aim for it (in fact, more reason for the process to completely stop working).

But back to the topic of experiments that go wrong, sometimes those are the ones that really tell you what’s happening. If everything was going all right, all the time, well then, there wouldn’t be any reason to dig deeper, no need to understand the process behind it. So it’s kind of important to fail in science and learn something from it every time.

But when a series of failures, going on for months, makes you finally realize that what you have been trying may not have been even possible from the beginning, that can be quite frustrating (happens in science all the time and doesn’t feel good to be the one that’s happening to).