Behind the scenes of “Data sharing in PLOS ONE: An analysis of Data Availability Statements”

Recently some colleagues and I published a paper in PLOS in which we analyzed about 47,000 Data Availability Statements as a way of exploring the state of data sharing in a journal with a pretty strong data availability policy. The paper has gotten a good response from what I’ve seen on Twitter, and I’m really happy with how it turned out, thanks in part to some great feedback from the reviewers. But I also wanted to tell a few more things about how this paper came about – the things that don’t make it into the final scholarly article. A behind the scenes look, if you will.

The idea for this paper arose out of a somewhat eye-opening experience. I needed to get a hold of a good dataset – I forget why exactly, but I think it was when I was first starting to teach R and wanted to some real data that I could use in the classes for the hands-on exercises. Remembering that PLOS had this data availability policy, I thought to myself, ah, no problem, I will find an article that looks relevant to the researchers I’m teaching, download the data, and use it in my demo (with proper attribution and credit, of course). So I found an article that looked good and scrolled down to the Data Availability Statement.  Data available upon request.  Huh. I thought you weren’t allowed to say that, but okay, I guess this one slipped through the policy.  Found another one – data is within the paper, it said, except the only data in the paper were summary tables, which were of no use to me (nor would they be of use to anyone hoping to verify the study or reanalyze the data, for example).

What a weird fluke, I thought, that the first two papers I happened to look at didn’t really follow the policy. So I checked a third, and a fourth. Pretty soon I’d spent a half hour combing through recent PLOS articles and I had yet to find one with a publicly available dataset that I could easily download from a repository. I ended up looking elsewhere for data (did you know that baseball fans keep surprisingly in-depth data on a gazillion data points?) but I was left wondering what the real impact of this policy was, which was why I decided to do this study.

I’ll let you read the paper to find out what exactly it is that we found, but there’s one other behind-the-scenes anecdote that I’ll share about this paper that I hope will be encouraging. Obviously if you’re going to write critically about data availability, you’re going to look a little hypocritical if you don’t share your own data. I fully intended to share our data and planned to do so using Figshare, which is how I’d shared a dataset associated with another publication I’d previously published in PLOS. When I shared the data from the first article, I set it to be public immediately, though I didn’t expect anyone to want to see it before the paper was out. Unexpectedly, and unbeknownst to me, someone at Figshare apparently thought this was an interesting dataset and decided to tweet it out the same day I submitted the paper to PLOS, obviously well before it was ever published, much less accepted.

While the interest in the dataset was encouraging, I was also concerned about the fact that it was out before the paper was accepted. I figured I was flattering myself to think that someone would want to scoop me, but then, I got an email from someone I didn’t know, who told me that she had found my dataset and that she would like to write an article describing my results, and would I mind sharing my literature review/citations with her to save her the trouble? In other words, “hi, I would like to write basically the paper that you’re trying to get accepted using all of the work you did.” I want to be clear that I am all for data sharing, but this situation bothered me. Was I about to get scooped?

Obviously our paper came out, no one beat us to it, and as far as I know, no one has ever written another paper using that dataset, but I was thinking about it when I was uploading the data for this most recent paper.  This dataset was way more interesting and broadly applicable than the first one, so what if someone did get a hold of it before our paper came out? So what I decided to do was to upload it to Figshare, have it generate a DOI, but keep the dataset listed as private rather than publicly release it. Our data availability statement included the DOI and was therefore on the surface in compliance, but I had a feeling that, if you went to the DOI, it would tell you that the dataset was private or wasn’t found. Obviously I could have checked this before I submitted, but to be totally honest, I just left it as it was because I was genuinely curious whether any of the reviewers would try to check it themselves and say something.

To their credit, all three of the reviewers (who by the way, were incredibly helpful and gave the most useful feedback I’ve ever gotten on peer review, which I think significantly improved the paper) did indeed point out that the DOI didn’t work. In our revisions, our Data Availability Statement included a working link to not only the data, but also the code, on OSF. I invite anyone who is interested to reuse it and hope someone will find it useful. (Please don’t judge me on the quality of my code, though – I wrote it a long time ago when I was first learning R and I would do it way better now.)

 

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!

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?

Who Am I? The Identity Crisis of the Librarian/Informationist/Data Scientist

More and more lately, I’m asked the question “what do you do?” This is a surprisingly difficult question to answer.  Often, how I answer depends on who’s asking – is it someone who really cares or needs to know? – and how much detail I feel like going to at the moment when I’m asked.  When I’m asked at conferences, as I was quite a bit at FORCE2016, I tried to be as explanatory as possible without getting pedantic, boring, or long-winded.  My answer in those scenarios goes something like “I’m a data librarian – I do a lot of instruction on data science, like R and data visualization, and data management.”  When I’m asked in more social contexts, I hardly even bother explaining.  Depending on my mood and the person who’s asking, I’ll usually say something like data scientist, medical librarian, or, if I really don’t feel like talking about it, just librarian.  It’s hard to know how to describe yourself when you have a job title that is pretty obscure: Research Data Informationist.  I would venture to guess that 99% of my family, friends, and even work colleagues have little to no idea what I actually spend my days doing.

In some regards, that’s fine.  Does it really matter if my mom and dad know what it means that I’ve taught hundreds of scientists R? Not really (they’re still really proud, though!).  Do I care if my date has a clear understanding of what a data librarian does?  Not really.  Do I care if a random person I happen to chat with while I’m watching a hockey game at my local gets the nuances of the informationist profession?  Absolutely not.

On the other hand, there are often times that I wish I had a somewhat more scrutable job title.  When I’m talking to researchers at my institution, I want them to know what I do because I want them to know when to ask me for help.  I want them to know that the library has someone like me who can help with their data science questions, their data management needs, and so on.  I know it’s not natural to think “library” when the question is “how do I get help with finding data” or “I need to learn R and don’t know where to start” or “I’d like to create a data visualization but I have no idea how to do it” or any of the other myriad data-related issues I or my colleagues could address.

The “informationist” term is one that has a clear definition and a history within the realm of medical librarianship, but I feel like it has almost no meaning outside of our own field.  I can’t even count the number of weird variations I’ve heard on that title – informaticist, informationalist, informatist, and many more.  It would be nice to get to the point that researchers understood what an informationist is and how we can help them in their work, but I just don’t see that happening in the near future.

So what do we do to make our contributions and expertise and status as potential collaborators known?  What term can we call ourselves to make our role clear?  Librarian doesn’t really do it, because I think people have a very stereotypical and not at all correct view of what librarians do, and it doesn’t capture the data informationist role at all.  Informationist doesn’t do it, because no one has any clue what that means.  I’ve toyed with calling myself a data scientist, and though I do think that label fits, I have some reservations about using that title, probably mostly driven by a terrible case of imposter syndrome.

What’s in a name?  A lot, I think.  How can data librarians, informationists, library-based data scientists, whatever you want to call us, communicate our role, our expertise, our services, to our user communities?  Is there a better term for people who are doing this type of work?

So you think you can code

I’ve been thinking about many ideas lately dealing with data and data science (this is, I’m sure, not news to anyone).  I’ve also had several people encourage me to pick my blog back up, and I’ve recently made my den into a cute and comfy little office, so, why not put all this together and resume blogging with a little post about my thoughts on data!  In particular, in this post I’m going to talk about coding.

Early on in my library career when I first got interested in data, I was talking to one of my first bosses and told her I thought I should learn R, which is essentially a scripting language, very useful for data processing, analysis, statistics, and visualization.  She gave me a sort of dubious look, and even as I said it, I was thinking in my head, yeah, I’m probably not going to do that.  I’m no computer scientist.  Fast forward a few years later, and not only have I actually learned R, it’s probably the single most important skill in my professional toolbox.

Here’s the thing – you don’t have to be a computer scientist to code, especially in R.  It’s actually remarkably straightforward, once you get over the initial strangeness of it and get a feel for the syntax.  I started offering R classes around the beginning of this year and I call my introductory classes “Introduction to R for Non-programmers.”  I had two reasons for selecting this name: one, I had only been using R for less than a year myself and didn’t (and still don’t) consider myself an expert.  When I started thinking about getting up in front of a room of people and teaching them to code, I had horrifying visions of experienced computer scientists calling me out on my relative lack of expertise, mocking my class exercises, or correcting me in front of everyone.  So, I figured, let’s set the bar low. 🙂  More importantly, I wanted to emphasize that R is approachable!  It’s not scary!  I can learn it, you can learn it.  Hell, young children can (and do) learn it.  Not only that, but you can learn it from one of a plethora of free resources without ever cracking a book or spending a dime.  All it takes is a little time, patience, and practice.

The payoff?  For one thing, you can impress your friends with your nerdy awesome skills!  (Or at least that’s what I keep telling myself.)  If you work with data of any kind, you can simplify your work, because using R (or other scientific programming languages) is faaaaar more efficient than using other point and click tools like Excel.  You can create super awesome visualizations, do crazy data analysis in a snap, and work with big huge data sets that would break Excel.  And you can do all of this for free!  If you’re a research and/or medical librarian, you will also make yourself an invaluable resource to your user community.  I believe that I could teach an R class every day at my library and there would still be people showing up.  We regularly have waitlists of 20 or more people.  Scientists are starting to catch on to all the reasons I’ve mentioned above, but not all of them have the time or inclination to use one of the free online resources.  Plus, since I’m a real human person who knows my users and their research and their data, I know what they probably want to do, so my classes are more tailored to them.

I was being introduced to Hadley Wickham yesterday, who is a pretty big deal in the R world, as he created some very important R packages (kind of like apps), and my friend and colleague who introduced me said, “this is Lisa; she is our prototypical data scientist librarian.”  I know there are other librarian coders out there because I’m on mailing lists with some of them, but I’m not currently aware of any other data librarians or medical librarians who know R.  I’m sure there are others and I would be very interested in knowing them.  And if it is fair to consider me a “prototype,” I wonder how many other librarians will be interested in becoming data scientist librarians.  I’m really interested in hearing from the librarians reading this – do you want to code?  Do you think you can learn to code?  And if not, why not?