A constructionist in an objectivist world

I was having a conversation with a scientist friend today about my doctoral research and explaining how part of my dissertation would involve grounding my research in a particular theoretical stance.  He gave me a very perplexed look, which is not really a surprising reaction from someone who is not a social scientist.  I’m sure it sounds very odd to some people to think that one’s findings and data are subject to interpretation based on one’s particular theoretical bent, but I do think it makes some sense when you’re dealing with social phenomena.

Anyway, I had written a blog post for one of my first semester doctoral classes that very much addresses this topic, so I’ve decided to repost it here.  This blog was a response to one of our class readings, so it makes some assumptions about shared vocabulary – in case you’re not familiar, here are some terms you’ll need to understand this:

  • epistemology: an understanding of knowledge and how it is constructed, which informs a theoretical basis, which in turn informs research methodologies and therefore specific methods.  It’s sort of the scaffold on which knowledge is built
  • objectivism: an epistemology that suggests that meaning and knowledge exist apart from human interpretations of things.  In other words, there is one objective Truth that can be discovered.
  • constructionism: an epistemology that suggests that meaning is constructed by human interpretation, in which case there may be multiple valid ways of constructing meaning from the same observations.

So with that in mind, here we go!

As I read Crotty’s description of epistemologies in his Introduction to The Foundations of Social Research, I naturally found myself wondering which epistemological stance most closely fit my natural approach to research and knowledge. On the one hand, objectivism deeply appeals to the scientist in me. Not only that, but I also spend my days surrounded by objectivist research in my work at the NIH. In biomedical research, there’s no room for constructing meaning about research. Either a drug works or it doesn’t; a bacterium is present in the culture or it isn’t; a reaction occurs or it doesn’t. Granted, there are plenty of ways to “game the system” to ensure you get your desired answer out of your data – outcome switching and p-hacking come to mind – but by and large, the professional world I currently inhabit is pretty strictly objectivist.

Nonetheless, I think that constructionism might be a better fit epistemologically speaking when it comes to my own research. The phenomena I’m interested in are highly social – how do researchers’ communities of practice, ways of constructing knowledge, and attitudes and experiences shape the ways that they interact with data, specifically in terms of sharing and reusing data? In fact, I argue that the social factors are possibly the most important barriers to the problem of data sharing and reuse. While it’s true that some technological barriers exist in this problem, at least work is already being done to address some of those problems. On the other hand, the social issues are much harder to quantify and therefore harder to address. I think when it comes to questions like the ones I’m asking, it’s really difficult to talk about objective approaches, so I find myself feeling drawn to constructionism.

So what’s a constructionist to do in an objectivist world? I guess partly I’m trying to situate myself within my appropriate research community and figure out how to interact with other research communities that take different epistemological approaches. Biomedical researchers may not be my research “tribe” insofar as our methods and epistemologies are concerned, but I’m keenly interested in how my research will eventually be perceived by this community because I expect my work will have implications for them, such as in terms of how they train their next generation of researchers to interact with data successfully, how they incentivize sharing and reward certain types of academic labor and research work, and even how they approach their work of data gathering at a very fundamental level.

I was on a conference call at work last week with a group that is charged with evaluating usability of a data catalog. It was easy to tell which of the participants were scientists by training. They were essentially trying to brainstorm how they could come up with a randomized controlled trial and determine some sort of gold standard for data discovery that they could use to compare to the new data catalog. It was obvious from the discussion that they were uncomfortable with the thought of employing what they considered “non-scientific” methods and skeptical about what kind of meaningful results could arise from qualitative approaches. They wanted hard, numeric data, and other types of evidence were not part of their approach.

Moving forward, I will be interested in seeing how I can reconcile the qualitative methods I may take and the constructionist approach I may adopt with the objectivist epistemologies commonly adopted with the biomedical research community.

Some real talk from a very tired PhD student

This post is going to be different from what I normally write.  It’s going to seem a little bleak for awhile, but stick with me, because it’s going to have a happy ending.

You know the way that some girls dream of their wedding day for their whole lives? That’s kind of like me, but instead of with getting married, it was with getting my PhD (I know, I was a weird kid). Starting almost 15 years ago when I was an adjunct professor, and to this day, people will sometimes send me emails that begin “Dear Dr. Federer,” and I think, not yet, but one day.

Eventually that day did come, and I got into this PhD program, working on a topic I’m really fascinated by and I think is pretty timely and relevant.  It was great.  There was the one little catch that I also had a full-time job that I love and a lease on an apartment that was well beyond grad student means, but I’m a pretty motivated person and I figured I could handle working full-time and doing the PhD program part-time.

This plan went fine the first semester.  So fine that I figured, well, why not just go ahead and do a third class in the spring?  Being a full-time PhD student with a high-pressure, full-time job?  Sure!  WHY NOT.  The semester is halfway through now, and I’m not dead yet. So this weekend, when I was looking at the PhD student handbook and I realized that after this semester, I’ll need just 4 more classes to complete my coursework, a cockamamie plan popped into my head.  I had this little conversation with myself:

evil Lisa: what if you did all four classes over the summer?
regular Lisa: I don’t know, while working full-time? That sounds like a bit much.
evil Lisa: but then once you’re done you could advance to candidacy.  Maybe you could finish the whole thing in two years!  I bet no one has ever even done that!
regular Lisa: but this sounds like torture
evil Lisa: why don’t you at least check the summer schedule and see if there are any interesting courses?regular Lisa: hmm, well, some of these do look pretty good.  And they’re online.  Maybe it wouldn’t be so bad.
evil Lisa: REGISTER FOR THEM.

And I did.

To my credit, a part of me knew this plan was not my greatest idea, so today, when I had a meeting with a potential new advisor, since my advisor is leaving for a new position, I said, “I had this idea, but I think it might be a little crazy,” and I told her and she looked at me very patiently, the way you look at a person who has lost all touch with reality and said, “yes, that’s crazy.”

After that conversation, I came back to the graduate student lounge to wait for my class to start, and I looked at the draft of a paper I’m working on, I looked at my slides for a presentation I’m giving in class this afternoon, I looked at my Outlook calendar for work, and I hated all of it.  The presentation looked like garbage and the paper seemed to be going nowhere.  I’d spent hours working on this paper, and it really had seemed like an interesting idea at the time, but now it seemed like a completely pointless waste of time.  The more I thought about data sharing and reuse, the more I hated it.

How could this be?  I love data!  I could talk about data all day!  How could it be that I suddenly hated data?  That was when I realized that I’ve been going about this all wrong and my ridiculous approach was actually ruining the entire experience.  It’s like if you love ice cream and you have a gallon and you try to jut devour the entire thing in one sitting.  Of course it would be a horrible experience.  You’d be sick and you’d hate yourself, and you’d definitely hate ice cream.  On the other hand, if you had a little bit of the ice cream over several days, you’d enjoy it a lot more.

I have this instinct from my days of long-distance running: when I’m many miles in and tired, and I want to slow down, that’s when I push myself to run even faster.  The slower I run, the longer it’ll take me to finish, but if I just run as hard as I can, the run will be over sooner.  I’m not sure about the validity of this approach from a distance running perspective, but I think it’s fair to say it’s a completely stupid idea when it comes to a PhD.

People warned me when I started this program that everyone gets burned out at some point, and I thought, not me, I love my topic, there’s no way I could ever get tired of it.  That’s why it was especially confusing when I sat there looking at my paper draft yesterday and just hating the guts out of data sharing and reuse.  Fortunately, I don’t hate data.  I hate torturing myself.

So, that’s why I’m not going to!  Could I take four courses over the summer?  I suppose.  Could I finish a PhD in two years while working full-time? I guess it’s possible.  But what would be the point, if I emerged from the process angry and tired and hating data?  Time to slow down and enjoy the ride, and de-register for at least two of those summer classes.

Scientific “artifacts” – #overlyhonestmethods and indirect observation

This week I’ve been reading the first half of Bruno Latour and Steve Woolgar’s book Laboratory Life: The Construction of Scientific Facts.  Like many of the other pieces I’ve been reading lately, this book argues for a social contructivist theory of scientific knowledge, which is a perspective I’m really starting to identify with.  What I’m finding most interesting about this book is the ethnographic approach that was taken to observe the creation of scientific knowledge.  Basically, Bruno Latour spent two years observing in a biology lab at the Salk Institute.  Chapter 1 begins with a snippet of a transcript covering about 5 minutes of activity in a lab – all the little seemingly insignificant bits of conversation and activity that, taken together, would allow an outside observer to understand how scientific knowledge is socially constructed.

The authors emphasize that real sociological understanding of science can only come from an outside observer, someone who is not themselves too caught up in the science – someone who can’t see the forest for the trees, as it were.  They even suggest that it’s important to “make the activities of the laboratory seem as strange as possible in order not to take too much for granted” (30).  Why should we need someone to spend two years in a lab watching research happen when the researchers are going to be writing up their methods and results in an article anyway, you may ask?  The authors argue that “printed scientific communications systematically misrepresent the activity that gives rise to published reports” and even “systematically conceal the nature of the activity” (28).  In my experience, I would agree that this is true – a great example of it is #overlyhonestmethods, my absolute favorite Twitter hashtag of all time, in which scientists reveal the dirty secrets that don’t make it into the Nature article.

I’ve been thinking that an ethnographic approach might be an effective way to approach my research, and I’m thinking it makes even more sense after what I’ve read of this book so far.  However, this research was done in the 1970s, when research was a lot different.  Of course there are still clinical and bench researchers who are doing actual physical things that a person can observe, but a lot of research, especially the research I’m interested in, is more about digital data that’s already collected.  If I wanted to observe someone doing the kind of research I’m interested in, it would likely involve me sitting there and staring at them just doing stuff on a computer for 8 hours a day.  So I’m not sure if a traditional ethnographic approach is really workable for what I want to do.  Plus, I don’t think I’d get anyone to agree to let me observe them.  I know I certainly wouldn’t let someone just sit there and watch me work on my computer for a whole day, let alone two years (mostly because I’d be embarrassed for anyone else to know how much time I spend looking at pictures of dogs wearing top hats and videos of baby sloths).  Even if I could get someone to agree to that, I do wonder about the problem of observer effect – that the act of someone observing the phenomenon will substantively change that phenomenon (like how I probably wouldn’t take a break from writing this post to watch this video of a porcupine adorably nomming pumpkins if someone was observing me).

This thought takes me back to something I’ve been thinking about a lot lately, which is figuring out methods of indirect observation of researchers’ data reuse practices.  I’m very interested in exploring these sorts of methods because I feel like I’ll get better and more accurate results that way.  I don’t particularly like survey research for a lot of reasons: it’s hard to get people to fill out your survey, sometimes they answer in ways that don’t really give you the information you need, and you’re sort of limited in what kind of information you can get from them.  I like interviews and focus groups even less, for many of the same reasons.  Participant observation and ethnographic approaches have the problems I’ve discussed above.  So what I think I’m really interested in doing is exploring the “artifacts” of scientific research – the data, the articles, the repositories, the funny Twitter hashtags.  This idea sort of builds upon the concept I discussed in my blog last week – how systems can be studied and tells us something about their intended users.  I think this approach could yield some really interesting insights, and I’m curious to see what kind of “artifacts” I’ll be able to locate and use.

If data sharing is difficult, what can it tell us? An Actor-Network Theory approach

In my ongoing adventures in science and technology studies readings, this week I’ve been reading The Social Construction of Technological Systems.  It diverges a little bit from my interests, strictly speaking, and focuses more on development of technologies rather than more of the laboratory and clinical science that I’m interested in, but I’m still glad I read it because it sparked some thoughts and ideas that I think could be interesting to pursue.

The portions of the collection that I read were rooted in social constructivist theory (as you might guess from the title of the book), specifically Actor-Network Theory (ANT).  The preface to the 25th anniversary edition explores some new developments in the field since the original edition, including “posthuman” approaches that consider nonhuman actants within social systems (xxv).  Scientific researchers operate within a complex system – not only because scientific research is itself often complicated, but also because science happens within a social system involving things like grant funding and scholarly articles and citations and so on.  Data play important roles in that system, as the raw product of scientific research, as evidence for scientific claims, and, now that many researchers operate in fields where data sharing is becoming more expected, something of a commodity.  In ANT, actants can be nonhuman, so I think it would be reasonable to consider data an actant in the social network of scientific research, and potentially one of the more interesting parts of that network, even more so than the humans.

The other avenue this collection sent my mind down had to do with data repositories.  At the start of the chapter “Society in the Making: The Study of Technology as a Tool for Sociological Analysis,” Michael Callon argues that “the study of technology itself can be transformed into a sociological tool of analysis” (77).  To summarize his thesis, essentially he argues that technological systems are created by what he calls “engineer-sociologists,” the designers or creators of the technology, who have had to essentially transform themselves into sociologists to study the intended users in order to develop technologies that will meet their needs.  If this is true, then these new technologies should be able to tell us something about their intended users.

This chapter got me thinking about some of the systems that are in place for data sharing, like some of the major data repositories.  I won’t name any names, but there are a couple of very well-known data repositories that people often complain to me about when it comes to submitting their data.  In some labs, researchers have mentioned that they have one person who knows how to submit the data, and they all have to bug that person because they can’t figure out how to do it properly.  I’ve read some of the help documentation for some of these repositories, and those people weren’t complaining for nothing.  Many of these systems are a big pain – opaque in many of their requirements and onerous to use, yet many researchers are specifically required to put their data there because of grant or journal requirements.

So if we take Callon’s approach and view the system as a tool for sociological analysis, what does it say about the state of data sharing that some of these repositories are so difficult to use?  I can think of possibilities:

  • that the engineers haven’t really been in all that close of contact with the users, so they’ve built a system that doesn’t actually meet their users’ needs;
  • that the needs of the system administrators (good quality data with a minimal amount of effort on their part) are directly at odds with the needs of the data submitters (also a minimal amount of effort on their part) and the administrators’ needs won out;
  • that the engineers are aware of issues but there just isn’t money/time/resources to make the system easier to use.

Another possibility is that sharing data isn’t really that much of a priority for most researchers, so they go along with a hard-to-use system because it’s not worth the trouble to try to get it to change.  It’s sort of like how I feel like it’s really a huge pain to have to deal with the DMV, but I only have to go there once every few years, so I’m not about to start a huge campaign to reform the DMV, especially when there are bigger problems our elected officials should be dealing with.  Maybe sharing your data in some of these systems is like that – an annoyance you deal with because you have to.

This is all entirely speculation on my part, but I do think it’s an interesting approach to take.  It would be interesting to sit down with some of the people who built or who currently run some of these systems and get the story on why things are the way they are.

Flip flop: my failed experiment with flipped classroom R instruction

I don’t know if this terminology is common outside of library circles, but it seems like the “flipped classroom” has been all the rage in library instruction lately.  The idea is that learners do some work before coming to the session (like read something or watch a video lecture), and then the in-person time is spent on doing more activities, group exercises, etc.  As someone who is always keen to try something new and exciting, I decided to see what would happen if I tried out the flipped classroom model for my R classes.

Actually, teaching R this way makes a lot of sense.  Especially if you don’t have any experience, there’s a lot of baseline knowledge you need before you can really do anything interesting.  You’ve got to learn a lot of terminology, how the syntax of R works, boring things like what a data frame is and why it matters.  That could easily be covered before class to save the in person time for the more hands-on aspects.  I’ve also noticed a lot of variability in terms of how much people know coming into classes.  Some people are pretty tech savvy when they arrive, maybe even have some experience with another programming language.  Other people have difficulty understanding how to open a file.  It’s hard to figure out how to pace a class when you’ve got people from all over that spectrum of expertise.  On the other hand, curriculum planning would be much easier if you could know that everyone is starting out with a certain set of knowledge and build off of it.

The other reason I wanted to try this is just the time factor.  I’m busy, really busy.  My library’s training room is also hard to book because we offer so many classes.  The people I teach are busy.  I teach my basic introduction to R course as a 3-hour session, and though I’d really rather make it 4 hours, even finding a 3-hour window when I and the room are both available and people are likely to be able to attend is difficult.  Plus, it would be nice if there was some way to deliver this instruction that wasn’t so time-intensive for me.  I love teaching R – it’s probably my favorite thing I do in my job and I’d estimate I’ve taught close to 500 researchers how to code.  I generally spend around 9 hours a month teaching R, plus another 4-6 hours doing prep, administrative stuff, and all the other things that have to get done to make a class function.  That’s a lot of time, and though I don’t at all mind doing it, I’d definitely be interested in any sort of way I could streamline that work without having a negative impact on the experience of learning R from me.

For all these reasons, I decided to experiment with trying out a flipped classroom model for my introduction to R class.  I had grand plans of making a series of short video tutorials that covered bite-sized pieces of learning R.  There would be a bunch of them, but they’d be about 5 minutes each.  I arranged for the library to get Adobe Captivate, which is very cool video tutorial software, and these tutorials are going to be so awesome when I get around to making them.  However, I had already scheduled the class for today, February 28, and I hadn’t gotten around to making them yet.  Fortunately, I had a recording of a previous Intro to R class I’d taught, so I chopped the relevant parts of that up into smaller pieces and made a YouTube playlist that served as my pre-class work for this session, probably about two and a half hours total.

I had 42 people were either signed up or on the waitlist at the end of last week.  I think I made the class description pretty clear – that this session was only an hour, but you did have to do stuff before you got there.  I sent out an email with the link to the video reminding people that they would be lost in class if they didn’t watch this stuff.  Even so, yesterday morning, the last of the videos had only 8 views, and I knew at least two of those were from me checking the video to make sure it worked.  So I sent out another email, once again imploring them to watch the videos before they came to class and to please cancel their registration and sign up for a regular R class if this video thing wasn’t for them.

By the time I taught the class this afternoon, 20 people had canceled their registration.  Of the remaining 22, 5 showed up.  Of the 5 that showed up, it quickly became apparent to me that none of them had watched the videos.  I knew no one was going to answer honestly if I asked who had watched them, so I started by telling them to read in the CSV file to a data frame.  This request is pretty fundamental, and also pretty much the first thing I covered in the videos, so when I was met with a lot of blank stares, I knew this experiment had pretty much failed.  I did my best to cover what I could in an hour, but that’s not much, so instead of this being a cool, interactive class where people ended up feeling empowered and ready to go write code, I got the feeling those people left feeling bewildered and like they wasted an hour.  One guy who had come in 10 minutes late came up to me after class and was like, “so this is a programming language?  What can you do with it?”  And I kind of looked at him like….whaaaat?  It turned out he hadn’t even registered for the class to begin with, much less done any of the pre-class work – he had been in the library and saw me teaching and apparently thought it looked interesting so he decided to wander in.

I felt disappointed by this failed experiment, but I’m not one to give up at the first sign of failure, so I’ve been thinking about how I could make this system work.  It could just be that this model is not suited to people in the setting where I teach.  I am similar to them – a busy, working professional who knows this is useful and I should learn it, but it’s hard to find the time – and I think about what it would take for me to do the pre-class work.  If I had the time and the videos were decent enough quality, I think I’d do it, but honestly chances are 50-50 that I’d be able to find the time.  So maybe this model just isn’t made for my community.

Before I give up on this experiment entirely, though, I’d love to hear from anyone who has tried this kind of approach for adult learners.  Did it work, did it not?  What went well and what didn’t?  And of course, being the data queen that I am, I intend to collect some data.  I’m working on a modified class evaluation for those 5 brave souls who did come to get some feedback on the pre-class work model, and I’m also planning on sending a survey out to the other 38 people who didn’t come to see what I can find out from them.  Data to the rescue of the flipped class!

Delocalizing data – a data sharing conundrum

This week I’ve been reading the second half of Sergio Sismondo’s An Introduction to Science and Technology Studies and I have been finding myself interested in the question of the universality of scientific knowledge and data.  A single sentence that I think captures the scope of the problem I’m finding interesting: “scientific and engineering research is textured at the local level, that it is shaped by professional cultures and interplays of interests, and that its claims and products result from thoroughly social processes” (168).  That is to say, the output of a scientific experiment is not some sort of universal truth – rather, data are the record of a manipulation of nature at a given time in a given place by a given person, highly contextualized and far from universally applicable.

I was in my kitchen the other day, baking a mushroom pot pie, after reading Chapter 10, specifically the section on “Tinkering, Skills, and Tacit Knowledge.”  That section describes the difficulties researchers were having in recreating a certain type of laser, even when they had written documentation from the original creators, even when they had sufficient technical expertise to do so, even when they had all the proper tools – in fact, even when they themselves had already built one, they found it difficult to build a second laser.  As I was pulling my pie out of the oven, I was thinking about the tacit knowledge involved in baking – how I know what exactly is meant when the instructions say I should bake till the crust is “golden brown,” how I make the decision to use fresh thyme instead of the chipotle peppers the recipe called for because I don’t like too much heat, how I know that my oven tends to run a little cold so I should set the temperature 10 degrees higher than called for by the recipe.  Just having a recipe isn’t enough to get a really tasty mushroom pot pie out of the oven, just as having a research article or other scientific documentation isn’t enough to get success out of an experiment.

These problems raise some obvious issues around reproducibility, which is a huge focus of concern in science at the moment.  Obviously scientific instruments are hopefully a little more standardized than my old apartment oven that runs cold, but you’d be surprised how much variation exists in scientific research.  Reproducibility is especially a problem when the researcher is herself the instrument, such as in the case of certain types of qualitative research.  Focus group or interview research is usually conducted using a script, so theoretically anyone could pick up the script and use it to do an interview, but a highly experienced researcher knows how to go off-script in appropriate ways to get the needed information, asking probing questions or guiding a participant back from a tangent.

More relevant to my own research, thinking about data not as representations of some sort of universal truth, but as the results of an experiment conducted within a potentially complex local and social context, can shared data be meaningfully reused?  How do we filter out the noise and get to some sort of ground truth when it comes to data, or can we at all?  Part of the question that I really want to address in my dissertation is what barriers exist to reusing shared data, and I think this is a huge one.  Some of the problem can be addressed by standards, or “formal objectivity” (140).  However, as Sismondo notes, standards are themselves localized and tied to social processes.  Between different scientific fields, the same data point may be measured using vastly different techniques, and within a lab, the equipment you purchase often has a huge impact on how your data are collected and stored.  Maybe we can standardize to an extent within certain communities of practice, but can we really hope to get everyone in the world on one page when it comes to standards?

If we can’t standardize, then maybe we can at least document.  If I measured in inches but your analysis needs length input in centimeters, that’s okay, as long as you know I measured in inches and you convert the data before doing your analysis.  That seems fairly obvious, but how do I necessarily know what I need to document to fully contextualize the data for someone else to use it?  Is it important that I took the measurement on a Tuesday at 4 pm, that the temperature outside was 80 degrees with 70% humidity, that I used a ruler rather than a tape measure, that the ruler was made of plastic rather than wood?  I could go on and on.  How much documentation is enough, and who decides?

The concepts of reproducibility, standardization, and documentation are nothing new, but the idea of data being inextricably caught up in local and social contexts does get me thinking about the feasibility of reusing shared data.  I don’t think data sharing is going to stop – there are enough funders and journals on board with requiring data sharing that I think researchers should expect that data sharing will be part of their scientific work going forward.  The question then is what is the utility of this shared data.  Is it just useful for transparency of the published articles, to document and prove the claims made in those publications?  Or can we figure out ways to surmount data’s limited context and make it more broadly usable in other settings?  Are there certain fields that are more likely to achieve that formal objectivity than others, and therefore certain fields were data reuse may be more appropriate or at least easier than others?  I think this requires further thought.  Good thing I have a few years to spend thinking about it!

 

Who owns science? Some musings on structural functionalism

This week I’ve been reading Sergio Sismondo’s An Introduction to Science and Technology Studies, which has given me a lot to think about in terms of theoretical backgrounds for understanding how science creates knowledge.  In fact, it’s almost given me too much to think about.  There are so many different theoretical bases brought into the mix here, and I can see the relative merits of each, so I find myself wondering how to make sense of it all, but also what it means to adopt a theoretical underpinning as a social scientist.  Is it like a religion, where you accept one and only one dogma, and all parts of it, to the exclusion of all others?  Or is it more like a buffet, where you pick a little bit of the things that seem appealing to you and leave behind the things that don’t catch your eye?  I’m hoping it’s the latter, and I’m going to go on that assumption until the theory police tell me I can’t do it. 🙂  So, on that assumption, here are some ideas I’ve put on my plate from Sismondo’s buffet.

Structural Functionalism and Mertonian Norms

My favorite theoretical framework I picked up here was structural functionalism, and in particular, Robert Merton’s four guiding norms.  Structural functionalism, as I understand it, argues that society is composed of institutional structures that function based on guiding norms and customs.  Merton suggests that science is one such institution, the primary goal of which is “the extension of certified knowledge” (23).  Merton also outlined four norms of behavior that guide scientific practice, suggesting that those who follow them will be rewarded and those who violate them will be punished.  The norms are universalism (that the same criteria should be used to evaluate scientific claims regardless of the race, gender, etc of the person making them), communism (that scientific knowledge belongs to everyone), disinterestedness (that scientists place the good of the scientific community ahead of their own personal gain), and organized skepticism (that the community should not believe new ideas until they have been convincingly proven).

Of those four norms, communism and disinterestedness speak the most to my interest in data sharing and reuse.  Communism seems the most obviously related.  It’s very interesting to think about what parts of science are typically thought to belong to the community and which are thought to be privately owned.  For example, the Supreme Court unanimously ruled in 2015 that human genes could not be patented, a decision that seems in line with Merton’s communism norm.  On the other hand, plenty of scientific ideas can be and are patented.  While many scientific journals are becoming open access and making their articles freely available, many more work on a subscription model, suggesting that the ideas shared within are available for common consumption – if you are willing and able to pay the fee.

Although this example comes from an entirely different realm than science, thinking about these ideas has reminded me of the case of the artist Anish Kapoor, who purchased the exclusive rights to paint with the world’s “blackest black” so that no other artist can use it.  In retaliation, another artist designed the “pinkest pink” paint and made it available for sale – to any artist except Anish Kapoor. While this episode is somewhat entertaining, it does bring up some interesting ideas about ownership in communities that are generally dedicated to the common good.  Art and science are very different, but they’re also quite alike in some ways that are very relevant to the work I’m doing.  They’re both activities carried out by individuals for their own reasons (artistic expression, scientific curiosity) for the common good (to share beauty with the world, to further scientific knowledge).  We are outraged when we hear of a rich artist laying exclusive claim to the raw materials of art so that no one else can use them.  It feels somehow petty, and it also seems like a disservice to not just the art world, but to all of is.  What could others be creating for us if they had access to that black?  I don’t know if we feel that same outrage when we hear of a scientist trying to lay exclusive claim to data.  Of course this isn’t a perfect analogy – a big part of the work of science is gathering or creating the data, which confuses the concept of ownership.  Still, I think there are some interesting ideas here to explore about how scientists think about common ownership of science – not just the ideas, but the data as well.

I started out this entry saying I was going to dip into some other theories – I have some things to say about social constructionism and actor-network theory, but now I’ve spent a long time going on and on about art and science and this is getting a bit long, so I think I’ll stop here for today. 🙂

 

On data, knowledge, and theory

As I’ve mentioned on this blog before, I recently started a PhD program at the University of Maryland’s iSchool, focusing on scientific researchers’ data reuse practices.  There’s a great deal of attention lately on encouraging, and even requiring, researchers to share their data, but less work has been done on how researchers actually make use of that shared data (or if indeed they do at all).  This semester, I’m doing an independent study with my advisor, Dr. Andrea Wiggins, with the aim of better understanding the theoretical background for this problem.  I have the good fortune of working in a job that involves interacting with researchers on data questions on a pretty much daily basis, so I have plenty of opportunity to observe actual practices, but I have less background on theoretical frameworks for contextualizing and understanding why  these things happen, so that’s my goal this semester!  I’ve picked out several readings and am going to write weekly reflections on what I’ve read and thought, and since I have this blog, I figured, why not inflict all this on you, my readers, as well? 🙂

This week I read Paul Davidson Reynolds’ Primer in Theory Construction, which breaks down the research process and explores the scientific method and all its component parts.  It is described as being designed “for those who have already studied one or more of the social, behavioral, or natural sciences, but have no formal introduction to the way theories are constructed, stated, tested, and connected together to form a scientific body of knowledge.”  While I was reading it, I often was thinking to myself, “well, yeah, obviously…” but after I had a little more time to think about it, it occurred to me that it was useful to really stop and think about why research is done the way it is and what we can really determine using data, inference, and logic.

One of the things I was thinking about as I was reading this book was how we make the jump from data to knowledge, and also how to operationalize terms like “data” and “knowledge.”  The NIH’s big data initiative is called Big Data to Knowledge, but what exactly does it mean to translate “big data” to “knowledge”?  How do we define “big data” (as opposed to small data?) and “knowledge”?  Are the ways that big data become knowledge different than the ways non-big data become knowledge?  There are some good definitions of big data, but how do we define “knowledge” in the scientific, and particularly biomedical, realm?

Thinking about how researchers use data by really breaking things down to their most basic level is a little different from how I’ve thought about things before, but actually makes good sense.  I suggest that the barriers to reuse of shared data are:

  • technological: there aren’t good tools for easily getting/reusing the data, or the data are poor quality or hard to find)
  • social: incentive structures of science often do not reward research that reuses data – take a look at the concept of #researchparasites
  • educational: reusing data involves a different skill set that most researchers aren’t taught

However, I never really thought about one of the most fundamental social factors, which is how researchers in a field conceptualize data and how it is transformed into knowledge.  Are there fundamental differences between the data I gather and data someone else gathers and I reuse?  Obviously if I gather my own data, I know more about its context, quality, and provenance.  If I reuse someone’s shared data, I don’t know how careful they were when collecting it, or other important things I might need to know about how the data were collected to be able to reuse them meaningfully.  For example, I once worked with a researcher on locating a clinical dataset for reuse, and once we got the dataset, the researcher asked how patient temperature had been measured – oral, axillary, rectal?  I got back in touch with the original data owner, and they didn’t know – the person who would be able to answer that question had moved on to a new position.  Apparently that mattered to the methods of the researcher I was working with, so they couldn’t use that dataset.  The sorts of things that seem like little minor details can actually make a big difference, but there’s really no way of knowing that unless you know how a research field works with and understands data.

Some things – like knowing how temperature was measured – are probably pretty specific to a narrow field, or even just a particular research method, and it’s probably not possible to know all of the intricacies of the many fields that comprise biomedical research.  However, I think there are also likely other fundamental qualities of data that would apply more broadly across many research fields, and perhaps that would be a useful approach to this question.

 

Can you hack it? On librarian-ing at hackathons

I had the great pleasure of spending the last few days working on a team at the latest NCBI hackathon.  I think this is the sixth hackathon I’ve been involved in, but this is the first time I’ve actually been a participant, i.e. a “hacker.”  Prior to working on these events, I’d heard a little bit about hackathons, mostly in the context of competitive hackathons – a bunch of teams compete against each other to find the “best” solution to some common problem, usually with the winning team receiving some sort of cash prize.  This approach can lead to successful and innovative solutions to problems in a short time frame.  However, the so-called NCBI-style hackathons that I’ve been involved in over the last couple years involve multiple teams each working on their own individual challenge over a period of three days. There are no winners, but in my experience, everyone walks away having accomplished something, and some very promising software products have come out of these hackathons.  For more specifics about the how and why of this kind of hackathon, check out the article I co-authored with several participants and the mastermind behind the hackathons, Ben Busby of NCBI.

As I said, this time was the first hackathon that I’ve actually been involved as a participant on a team, but I’ve had a lot of fun doing some librarian-y type “consulting” for five other hackathons before this, and it’s an experience I can highly recommend for any information professional who is interested in seeing science happen real-time.  There’s something very exciting about watching groups of people from different backgrounds, with different expertise, most of whom have never met each other before, get together on a Monday morning with nothing but an often very vague idea, and end up on Wednesday afternoon with working software that solves a real and significant biomedical research problem.  Not only that, but most of the groups manage to get pretty far along on writing a draft of a paper by that time, and several have gone on to publish those papers, with more on their way out (see the F1000Research Hackathons channel for some good examples).

As motivated and talented as all these hackathon participants are, as you can imagine, it takes a lot of organizational effort and background work to make something like this successful.  A lot of that work needs to be done by someone with a lot of scientific and computing expertise.  However, if you are a librarian who is reading this, I’m here to tell you that there are some really exciting opportunities to be involved with a hackathon, even if you are completely clueless when it comes to writing code.  In the past five hackathons, I’ve sort of functioned as an embedded informationist/librarian, doing things like:

  • basic lit searching for paper introductions and generally locating background information.  These aren’t formal papers that require an extensive or systematic lit review, but it’s useful for a paper to provide some context for why the problem is significant.  The hackers have a ton of work to fit in to three days, so it’s silly to have them spend their limited time on lit searching when a pro librarian can jump in and likely use their expertise to find things more easily anyway
  • manuscript editing and scholarly communication advice.  Anyone who has worked  with co-authors knows that it takes some work to make the paper sound cohesive, and not like five or six people’s papers smushed together.  Having someone like a librarian with editing experience to help make that happen can be really helpful.  Plus, many librarians  have relevant expertise in scholarly publishing, especially useful since hackathon participants are often students and earlier career researchers who haven’t had as much experience with submitting manuscripts.  They can benefit from advice on things like citation management and handling the submission process.  Also, I am a strong believer in having a knowledgeable non-expert read any paper, not just hackathon papers.  Often writers (and I absolutely include myself here) are so deeply immersed in their own work that they make generous assumptions about what readers will know about the topic.  It can be helpful to have someone who hasn’t been involved with the project from the start take a look at the manuscript and point out where additional background or explanation might be beneficial to aiding general understandability.
  • consulting on information seeking behavior and giving user feedback.  Most of the hackathons I’ve worked on have had teams made up of all different types of people – biologists, programmers, sys admins, other types of scientists.  They are all highly experienced and brilliant people, but most have a particular perspective related to their specific subject area, whereas librarians often have a broader perspective based on our interactions with lots of people from various different subject areas.  I often find myself thinking of how other researchers I’ve met might use a tool in other ways, potentially ones the hackathon creators didn’t necessarily intend.  Also, at least at the hackathons I’ve been at, some of the tools have definite use cases for librarians – for example, tools that involve novel ways of searching or visualizing MeSH terms or PubMed results.  Having a librarian on hand to give feedback about how the tool will work can be useful for teams with that kind of a scope.

I think librarians can bring a lot to hackathons, and I’d encourage all hackathon organizers to think about engaging librarians in the process early on.  But it’s not a one-way street – there’s a lot for librarians to gain from getting involved in a hackathon, even tangentially.  For one thing, seeing a project go from idea to reality in three days is interesting and informative.  When I first started working with hackathons, I didn’t have that much coding experience, and I certainly had no idea how software was actually developed.  Even just hanging around hackathons gave me so much of a better understanding, and as an informationist who supports data science, that understanding is very relevant.  Even if you’re not involved in data science per se, if you’re a biomedical librarian who wants to gain a better understanding of the science your users are engaged in, being involved in a hackathon will be a highly educational experience.  I hadn’t really realized how much I had learned by working with hackathons until a librarian friend asked me for some advice on genomic databases. I responded by mentioning how cool it was that ClinVar would tell you about pathogenic variants, including their location and type (insertion, deletion, etc), and my friend was like, what are you even talking about, and that was when it occurred to me that I’ve really learned a lot from hackathons!  And hey, if nothing else, there tends to be pizza at these events, and you can never go wrong with pizza.

I’ll end this post by reiterating that these hackathons aren’t about competing against each other, but there are awards given for certain “exemplary” achievements.  Never one to shy away from a little friendly competition, I hoped I might be honored for some contribution this time around, and I’m pleased to say I was indeed recognized . 🙂

It's true, I'm the absolute worst at darts.

There is a story behind this, but trust me when I say it’s true, I’m the absolute worst at darts.

A Silly Experiment in Quantifying Death (and Doing Better Code)

Doesn’t it seem like a lot of people died in 2016?  Think of all the famous people the world lost this year.  It was around the time that Alan Thicke died a couple weeks ago that I started thinking, this is quite odd; uncanny, even.  Then again, maybe there was really nothing unusual about this year, but because a few very big names passed away relatively young, we were all paying a little more attention to it.  Because I’m a data person, I decided to do a rather silly thing, which was to write an R script that would go out and collect a list of celebrity deaths, clean up the data, and then do some analysis and visualization.

You might wonder why I would spend my limited free time doing this rather silly thing.  For one thing, after I started thinking about celebrity deaths, I really was genuinely curious about whether this year had been especially fatal or if it was just an average year, maybe with some bigger names.  More importantly, this little project was actually a good way to practice a few things I wanted to teach myself.  Probably some of you are just here for the death, so I won’t bore you with a long discussion of my nerdy reasons, but if you’re interested in R, Github, and what I learned from this project that actually made it quite worth while, please do stick around for that after the death discussion!

Part One: Celebrity Deaths!

To do this, I used Wikipedia’s lists of deaths of notable people from 2006 to present. This dataset is very imperfect, for reasons I’ll discuss further, but obviously we’re not being super scientific here, so let’s not worry too much about it. After discarding incomplete data, this left me with 52,185 people.  Here they are on a histogram, by year.

year_plotAs you can see, 2016 does in fact have the most deaths, with 6,640 notable people’s deaths having been recorded as of January 3, 2017. The next closest year is 2014, when 6,479 notable people died, but that’s a full 161 people less than 2016 (which is only a 2% difference, to be fair, but still).  The average number of notable people who died yearly over this 11-year period, was 4,774, and the number of people that died in 2016 alone is 40% higher than that average.  So it’s not just in my head, or yours – more notable people died this year.

Now, before we all start freaking out about this, it should be noted that the higher number of deaths in 2016 may not reflect more people actually dying – it may simply be that more deaths are being recorded on Wikipedia. The fairly steady increase and the relatively low number of deaths reported in 2006 (when Wikipedia was only five years old) suggests that this is probably the case.  I do not in any way consider Wikipedia a definitive source when it comes to vital statistics, but since, as I’ve mentioned, this project was primarily to teach myself some coding lessons, I didn’t bother myself too much about the completeness or veracity of the data.  Besides likely being an incomplete list, there are also some other data problems, which I’ll get to shortly.

By the way, in case you were wondering what the deadliest month is for notable people, it appears to be January:

month_plotObviously a death is sad no matter how old the person was, but part of what seemed to make 2016 extra awful is that many of the people who died seemed relatively young. Are more young celebrities dying in 2016? This boxplot suggests that the answer to that is no:

age_plotThis chart tells us that 2016 is pretty similar to other years in terms of the age at which notable people died. The mean age of death in 2016 was 76.85, which is actually slightly higher than the overall mean of 75.95. The red dots on the chart indicate outliers, basically people who died at an age that’s significantly more or less than the age most people died at in that year. There are 268 in 2016, which is a little more than other years, but not shockingly so.

By the way, you may notice those outliers in 2006 and 2014 where someone died at a very, very old age. I didn’t realize it at first, butWikipedia does include some notable non-humans in their list. One is a famous tree that died in an ice storm at age 125 and the other a tortoise who had allegedly been owned by Charles Darwin, but significantly outlived him, dying at age 176.  Obviously this makes the data and therefore this analysis even more suspect as a true scientific pursuit.  But we had fun, right? 🙂

By the way, since I’m making an effort toward doing more open science (if you want to call this science), you can find all the code for this on my Github repository.  And that leads me into the next part of this…

Part Two: Why Do This?

I’m the kind of person who learns best by doing.  I do (usually) read the documentation for stuff, but it really doesn’t make a whole lot of sense to me until I actually get in there myself and start tinkering around.  I like to experiment when I’m learning code, see what happens if I change this thing or that, so I really learn how and why things work. That’s why, when I needed to learn a few key things, rather than just sitting down and reading a book or the help text, I decided to see if I could make this little death experiment work.

One thing I needed to learn: I’m working with a researcher on a project that involves web scraping, which I had kind of played with a little, but never done in any sort of serious way, so this project seemed like a good way to learn that (and it was).  Another motivator: I’m going to be participating in an NCBI hackathon next week, which I’m super excited about, but I really felt like I needed to beef up my coding skills and get more comfortable with Github.  Frankly, doing command line stuff still makes me squeamish, so in the course of doing this project, I taught myself how to use RStudio’s Github integration, which actually worked pretty well (I got a lot out of Hadley Wickham’s explanation of it).  This death project was fairly inconsequential in and of itself, but since I went to the trouble of learning a lot of stuff to make it work, I feel a lot more prepared to be a contributing member of my hackathon team.

I wrote in my post on the open-ish PhD that I would be more amenable to sharing my code if I didn’t feel as if it were so laughably amateurish.  In the past, when I wrote code, I would just do whatever ridiculous thing popped into my head that I thought my work, because, hey, who was going to see it anyway?  Ever since I wrote that open-ish PhD post, I’ve really approached how I write code differently, on the assumption that someone will look at it (not that I think anyone is really all that interested in my goofy death analysis, but hey, it’s out there in case someone wants to look).

As I wrote this code, I challenged myself to think not just of a way, any way, to do something, but the best, most efficient, and most elegant way.  I learned how to write good functions, for real.  I learned how to use the %>%, (which is a pipe operator, and it’s very awesome).  I challenged myself to avoid using for loops, since those are considered not-so-efficient in R, and I succeeded in this except for one for loop that I couldn’t think of a way to avoid at the time, though I think in retrospect there’s another, more efficient way I could write that part and I’ll probably go back and change it at some point.  In the past, I would write code and be elated if it actually worked.  With this project, I realized I’ve reached a new level, where I now look at code and think, “okay, that worked, but how can I do it better?  Can I do that in one line of code instead of three?  Can I make that more efficient?”

So while this little project might have been somewhat silly, in the end I still think it was a good use of my time because I actually learned a lot and am already starting to use a lot of what I learned in my real work.  Plus, I learned that thing about Darwin’s tortoise, and that really makes the whole thing worth it, doesn’t it?