Geeking out about acorns on the local news.

My university has a pretty decent relationship with the local news. Reporters fairly frequently contact the public relations office, seeking out an authority (i.e., professor) on the topic of the story they're preparing. A few folks in my department appear in news stories on a semi-regular basis, mostly offering facts and opinions on issues tangentially related to their areas of expertise.

I wasn't entirely surprised, therefore, when I got a phone call from the public relations office a few days ago, asking if I'd be willing to talk to a reporter. I was initially apprehensive, as I'm still trying to get my bearings in my new home state and wasn't sure I wanted to be put on the spot if asked about something controversial. I had no need for fear, though: I'd been recommended as someone who could talk about why it was there seemed to be such a large acorn crop this year.

"Heck yeah!" I thought, and calmly agreed to meet with the reporter that afternoon. I spent a few minutes doing a quick literature search. As I suspected, there's a decent amount of literature on that very topic. I made a list of talking points, including a few general notes about plant biology/ecology:

1. Acorns are the fruit of the oak tree, created by the fertilization of a female flower by pollen from a male flower.
2. Multiple factors can affect the development of an acorn, including how many flowers of each type are produced, effectiveness of pollination, and whether the tree has resources to dedicate to developing lots of fruits.
3. The factors above are, in turn, dependent on temperature, amount of sunlight, and levels of precipitation.
4. Acorn production (masting) also varies temporally (through years), spatially (across geography), and by species (some oak species produce more/larger acorns).
5. This summer has been particularly cool and wet compared to the past (I checked average temperature and precipitation for the month of July for 2011-2014), so perhaps those conditions favor more acorn production.
6. Trees are large and can't move, so resource allocations from previous years can affect acorn production in subsequent years.

The interview took about 15 minutes. We went outside and stood near some conveniently located oaks near my building. Of course, the talking points I had planned were shuffled around and reframed depending on the questions he asked, but I think I covered everything listed above and more (acorns are food for wildlife, they do eventually grow into trees, etc). Then the reporter grabbed a totally awkward shot of me walking down a path looking at leaves.

It's totally cringe worthy, but I know at least my dad will want the link, so you can see how my talking points above translated into the final story here: KLTV News, Why so many acorns this fall?

Lessons learned: keep makeup and a blazer in my office, to prepare for next time. Be a more careful about preparing appropriate sound-bites. I was also surprised to learn how much trouble the reporter had getting someone to talk to him about this; a number of arborists in town completely blew him off. I thought it was super cool! That's probably a good thing, because the public relations representative said at the end of the interview, "Great! I can add you to the list to talk about plant stuff!"

Great, indeed. Let's call this "service to the university." 

A catalysis meeting on long term experimental evolution.

Travel makes it easy to let blog posts slip away without being fully formed, written, and posted. Now that the semester is winding down, I'm going to try and follow through with writing the backlog of posts that have been piling up from my adventures over the last few weeks. Today's report is about the first part of my trip back to NESCent and North Carolina the week before Thanksgiving.

The catalysis meeting I attended on long term experimental evolution (you can read a little more about the meeting and participants here) was not only fantastic but also the last NESCent will host (more on this in a later post). Although the topic is outside of my main research interests, I answered the solicitation to participate in this meeting because of some research I've been doing with Joe Graves, Michael Rose and colleagues on experimentally evolved Drosophila populations. Moreover, there seem to be some really interesting opportunities to explore robustness of analytical methods using experimental evolution data, which is of particular interest to me.

Here are the things I found compelling about this meeting:

1. Meeting organizers set the tone. Rob Lanfear, the main organizer, put together a fantastic webpage and started a Mendeley group so we could share literature beforehand.
2. The participants were diverse. Forty four percent of attendees were female. There were graduate students, postdocs, early career scientists, and senior researchers present, and folks came from all over the world. Model systems included microbes, invertebrates, fish and trees. 
3. We capitalized on the group's diversity. As an early career scientist, I was pleased to develop relationships with a number of other folks starting faculty jobs at similar institutions. As a group, I was gratified to hear well-respected, senior scientists describing junior scientists' research as "brilliant." There were multiple types of interactions incorporated into the meetings such that folks who were hesitant to speak in full-group discussions could still contribute ideas. In short, this meeting exhibited many aspects of scientific discourse that are overlooked, but which I value deeply.
4. Attendees were invested in the meeting itself. Part of the meeting was structured (or perhaps more accurately, unstructured) as an "unconference," with participants determining topics for talks and group discussions on the fly. Despite this free-form format, folks in this group were very interested in talking about broader research ideas, rather than pushing their own agendas.

I left the meeting with a much wider and deeper understanding of experimental evolution as an active field of research, as well as a better grasp on different ways of thinking about the process of science and its limitations. I was also grateful to participate in one last meeting of this type at NESCent...stay tuned for my next post to hear more about that!

Value judgements and scientific results.

As scientists, we like to think that we are objective in the interpretation of data. As humans, our personal value-based judgments creep into these assessments far more than we might realize. I often think of these biases in my career, both as a researcher (when writing papers or reviewing manuscripts) but also as an educator.

I mentioned at the beginning of the semester that I'm obligated to watch student presentations for a class on science communication. As the semester progresses, I'm getting a better handle on how students (at least in our department) are starting to think about scientific arguments. A few students have made what are, at least to me, surprising statements about the strengths and weaknesses of the primary literature they discuss: they relate that failure of results to support a hypothesis is a weakness of the paper (also vice versa, that "supporting the hypothesis" is a strength).

One of the first lessons I learned while doing scientific research is that (more often than not) unexpected results and questions can emerge from our experiments. You can learn something from any experiment, even if it's simply a better way to perform the experiment in the future. Unfortunately, research on science research (I know, so meta) indicates that results which fail to support the hypothesis are often discarded by researchers as "useless" or "no good" (you can read more about this phenomenon in association with the formation of the Journal of Negative Results in Biomedicine here). Albeit unfortunate, this behavior makes sense in the face of the competitive realm of science research, where a nice, concise story can make the difference between publishing or wallowing in academic purgatory.

It gives me pause, however, when students indicate such a value preference for results that support a hypothesis. In my mind, results aren't "good" or "bad," they simply...are. Is devaluation of negative results innate in our educational mindset? On the other hand, students vocalizing negative results as a weakness of the paper may be more semantic than scientific; for example, students aren't nuancing their argument to discuss particular experimental drawbacks, which is a completely justifiable concern. Alternatively, they might just be saying what they think we (the instructors) want to hear, and hoping we'll give them points for covering all the required topics listed on the rubric.

Regardless of the direct causes of such reasoning, I'm going to be keeping an eye (ear?) on such comments as I continue developing classes. When operating in isolation, these judgements about the value of results can be a starting point to some interesting discussions about epistemology and hypothesis testing. More often, though, these mindsets work in concert with other scientific misconceptions, which can result in huge problems for me as an educator. Fortunately, there are sections in my bioinformatics class that I'm explicitly developing so we can have discussions about whether the results we're obtaining are accurate and meaningful. I'm looking forward to embedding these higher-level reasoning skills into the regular content of the class.

The art of asking in science.

I posted some love for Neil Gaiman's spoken words a few weeks back, so it only seems fitting that I follow up with inspiration from his partner, Amanda Palmer. She did a TED talk (the video is slightly NSFW at 10:49) last year that explored the relationship between artist and fan, emphasizing that we need to think less about how to make people pay for music and more about how to let them. She relates that asking people to help us is hard because it makes us feel vulnerable and shameful, but that asking for help can also create a profound connection with other people that allows a mutually beneficial transaction to occur. But watch the TED talk...she says it much better than I ever could.

I am not a professional artist or musician, but life in the ivory tower of academia does not shield me from the need to reach out to other people. I'm lucky that I don't have to ask people for financial help to pay my bills, but there are plenty of other circumstances in which my professional success rests on my ability to appeal to other people for assistance.

Learning to ask other people for help was a major stepping stone for me during graduate school, as I realized the need for assistance in troubleshooting lab techniques, designing experiments, and proofreading papers. As an assistant professor, asking for help is becoming even more of an art. I wouldn't be able to navigate the logistical issues of university bureaucracy without running to the office next door every few days. I belong to a small department, so connecting with other academic units on campus and at other schools is important to have access to resources for teaching and research. An important part of my job right now is applying for grants, which is really just another way of saying "asking for money to fund my research." Some scientists are taking an even more Amanda-style approach to paying for research by launching crowdfunding campaigns for particular projects. Moreover, my job as a bioinformaticist means I rely on other people for data, so asking for research opportunities is essential. I knew my job would require me to engage in asking, and even persuading, my peers to help me. I'm only starting to realize, however, what it means to ask students for help.

Am I really asking students to help me? That seems counter-intuitive to the stereotypical role of a professor, but indeed, we rely on students to take our classes and do research with us. As a newbie, I can't rely on my reputation to attract students to my class or lab. I have to ask students to consider it (hence my post from a few days ago: Student motivation, AKA please take my class). Sometimes that requires educating students about what I have to offer, like the ability to obtain marketable skills. Moreover, it makes me consider another question Amanda ponders: "Is this fair?" For my job, that means asking whether this student has the capacity to succeed, and whether it will be beneficial for them to spend the time, energy, and money to do so.

Framing my professional, scientific interactions as "asking for help" appeals to me on a fundamental level. It gives me the responsibility of finding the things I need. Moreover, it allows choice and freedom on the part of the audience, whether it's a student wanting to work with me or a funding agency deciding to award a grant. We pursue science because we think the work is interesting, but we're fooling ourselves if we think everyone will innately feel the same. Making an art of asking is a way of starting a conversation, and opens the door to persuade without being overbearing.

There is also a benefit in the act of asking a question. I wouldn't've ended up in graduate school if one of my professors hadn't asked me to consider it, and you certainly have to ask (i.e., apply) to receive a grant. I'm practicing asking questions as way to start a conversation about obtaining the things I need.

Can you fund my research?
Would you like to work on a project together?
Have you thought about going to graduate school?
Are you interested in taking my class?

Student motivation, AKA Please take my class.

My current homework for Software Carpentry instructor training is to think about a time when I lost my motivation to learn. I posted my response to the SWC blog, where you can also poke around a see other folks' stories (click on the "Motivation" tag).

The other part of our homework is to work on a three-minute pitch to motivate students to learn a particular topic. The timing of this assignment is fortuitous, as I'm also promoting the bioinformatics class I'm teaching next spring as well as an undergraduate minor in bioinformatics and genomics. I took to twitter with my attempts at persuasion:

Disclaimer: I am not planning on physically harming students if they have a different shell preference than bash, and I don't particularly dislike perl. I'm really just rather fond of word games.

A few other folks chipped in with their own token words of wisdom:

It's not surprising that my motivation for getting students to take bioinformatics differs from their reasons for enrolling. I am personally passionate about teaching next semester because I think I can help students be better scientists and thinkers. I'm hoping to convince them that it will help them be more marketable (taking additional biology and computer science classes will certainly accomplish that). 

At times, these persuasive attempts seem like fighting a rather uphill battle. Convincing students to take extra classes that bridge boundaries between different types of knowledge is difficult, especially when students who might be interested are already quite overwhelmed by courses required for their major. I come across lots of folks who are intimidated by large datasets or using a command line interface. 

I spend a lot of time talking to folks about my experiences, and how I'm planning on teaching. 

Here are my talking points in encouraging folks to step up to the plate and learn some bioinformatics, from the perspective of a biology student who has little computation experience: 

1. You can do this. Not too long ago, I was in your shoes. I didn't know a lot about computers, how they worked, or how I could use them to answer questions. I don't have a ton of formal training in computer science, and my degrees are all in biology. Writing computer scripts may seem really different from other things you've studied, but...
2. Learning a little can be very powerful. Learning to work on the command line and write computer scripts will take work. You will be surprised, however, at how many tedious, mundane tasks you can accomplish much more quickly and efficiently with a little bit of shell scripting. Better yet...
3. These skills are transferrable. You may not end up working in a job where you need to assemble genomes or build phylogenetic trees. It is possible, though, that you'll need to manage large numbers of files or answer questions about large data sets. You can apply these skills to lots of other practical tasks, but in addition...
4. The topics are interesting. Technological advances are producing genomic and other large-scale biological datasets at an unprecedented rate. The applications of these data include empirical research, agriculture, and medicine. 

At the very least, I hope to convince students the first point is true. Nothing is more frustrating than hearing students declare, "I can't do that." A student saying "That's too hard" is a student who's hit a motivational brick wall, and can't even ask themselves whether it might be beneficial for them (or, heaven forbid, that they might enjoy it!). If they can do that, hopefully one of the last three points will be appealing.

On the other hand, I'm still trying to figure out effective ways to appeal to computer science students. The fourth point above definitely still applies, and they can arguably improve (or at least broaden) their job prospects by gaining some understanding of biology. More importantly, they can learn to answer hypothesis-driven questions, which seems to be less of a focus in their curriculum than in biology. 

As always, this is a work in progress. What am I missing? I'm planning a few mini-workshops on for students (graduate and undergraduate) on campus, which will certainly allow me more opportunity to pinpoint more effective pitches. These students are not motivated by the same factors that convinced me to pursue higher education, and bioinformatics for research. I need to find out what they need. However, I struggle with how much I should cater to student interests. That, however, is part of a broader discussion about the purview of higher education, and is perhaps left to another post.

A trip back to my ol' Kentucky (and Indiana) home.

Historic buildings on Franklin Street are framed by neon
lights for one week a year.

As promised in my semester preview, and as a follow up to my Missouri trip a few weeks back, I had the opportunity to head back home to southern Indiana and central Kentucky last week. The timing of my trip was fortuitous, as my hometown of Evansville, Indiana was celebrating a momentous yearly event: the West Side Nut Club Fall Festival. More than a street fair, the Fall Festival marked the official start of autumn during my childhood. It's been probably ten years since I last partook of the food and frivolities. However, I was born and bred on the west side of town (my high school is less than a mile from the festival's location), so I fell back into old celebratory patterns without much trouble.

My happy face at
Fall Festival food.

As a kid, I was pretty invested in the games and rides at the festival. As an adult, I didn't even step foot into those areas, instead preferring to walk along the 4+ blocks of food vendors (each booth is a fundraiser for a group in the community, including several of my old school groups). My mom was kind enough to accompany me on my meanderings, which makes for great food-grazing because we could share twice the number of different menu options. I could wax philosophical about the virtues of various chili and cheese covered, deep fried concoctions, or you could take a look at the index of foods from this year's Munchie Map. Some items, like brain sandwiches and burgoo, are regional specialties. Some booth attract patrons with weirder options, like pickle juice slushies. I stuck with more traditional choices, like turkey burgers, stromboli, and cobbler with ice cream.

Of course, watching people is my favorite pastime at these events. The best overheard comment this year was from a family that stood in a semi-circle sharing corn dogs. One woman remarked (ever so sagely and without accusation), "Well, if one of us has ebola, I guess all of us do now." Indeed.

A rather large gingko tree.

The purpose of my trip was to give a seminar for the Department of Biology at my alma mater, Western Kentucky University. Part of the reason I'm enjoying my new job at UTTyler is because, as a regional school, it feels familiar to WKU. Campus has grown since I left, and the science folks have some great new teaching facilities. Despite these renovations, I was pleased to see this fantastic old ginkgo tree still standing between buildings.

It was a pleasure to meet with both mentors from my undergraduate years as well as with other assistant professors. After my graduate education and postdoc work at R1 universities, it's refreshing to talk to folks who are making a real difference in under-served parts of the country, both by helping students conduct science research as well as teaching classes in evolution.

It was gratifying to see a lecture room near full to bursting with students for my seminar that afternoon. I'm sure it was due in part to extra credit being offered for classes, but I hope a few of them gleaned some sort of benefit from my discussion linking the evolution of organisms to that of genomes and transposons (the powerpoint is available on Slideshare if you'd care to take a gander). After all, I graduated with my bachelor's degree from WKU less than ten years ago; if I were to believe the words of several folks I've encountered lately, I'm a shining example of a "success story."

Historic letterpress prints from Hatch Show Print

Why do I feel the need to qualify my "success" with quotation marks? For now, suffice it to say that I'm way too excited about developing classes and proposing new research to feel like I've accomplished anything more than the promise of more work! As my undergrad advisor, Larry Alice, reminded me when I saw him last week, I also need to enjoy myself. Recalling my previous obsession with mosses, he remarked: "Don't forget to stop and smell the bryophytes." In the spirit of following his advice, I allowed myself to make my travel back to Texas a little less miserable by sipping a caramel latte and perusing the Hatch Show Print display of historic letterpress designs in the Nashville Airport. Mmm...whole hog sausage.

In which I admit new depth to my dorkiness: knitting and teaching.

Greg Wilson did a great job during Software Carpentry instructor training this afternoon of using interesting examples and analogies to explain the psychology of learning. Long time readers of this blog might remember my use of analogies to explain research and teaching (sandwiches and DDR?). This is a habit I picked up from my first class on college science teaching, during my first year as a graduate student. My professor was the incomparable Sandi Abell, without whom I likely would not have achieved my career aspirations. Although she is no longer with us, I think often of her and the time I spent in her classes. One of our course requirements was to write a teaching philosophy using an analogy for teaching. I didn't do very well on the assignment. I tried to compare teaching to the scientific process or ecosystems or something similarly silly, given that the point of the assignment was to make our views on teaching more accessible to a layperson.

When I started applying for jobs a year ago, I pulled that teaching philosophy out of my file archives, blew off the metaphorical dust, and wrapped it in fluffy, wooly goodness. Indeed, I spent a good-sized paragraph describing how teaching and learning are like knitting. I know you're dying to hear how I made that work, so here you go:

Much of my leisure time is spent knitting. I'm not surprised anymore to meet other scientists and educators who play with sticks and string. In fact, knitting possesses many parallels to the process of teaching science in particular. Yarn represents content knowledge like vocabulary that forms the foundation of learning. Knitting needles are like questions offered to students to assist in connecting together loops of yarn to form fabric. A student's misconception is like dropping a stitch in knitting; the instructor must backtrack, correct the mistake, and then pick back up with the previous pattern or lesson. The knitted product represents the goal of learning: a cohesive object which unites together different aspects of knowledge and imparts functionality. In my opinion, science learning is a multidisciplinary, dynamic process that requires flexibility, and instructors who approach science teaching actively and inquisitively are most effective. In the same way, some of the most entertaining knitting projects include many different types of yarn, use interesting stitch patterns, and produce objects which are both functional and beautiful. Both teaching and knitting are skills which require time, effort and continual assessment to yield successful outcomes.

I probably could've written another five pages! Of course, that was just the introductory paragraph to my two-page statement, which was tied up nicely at the end with this hum-dinger of a line:

I hope to continue helping students “knit” scientific concepts together in an interesting, engaging manner.

See what I did there? I'm SO (or maybe just mildly) PUNNY. I'd totally take a class from me.

Gratuitous wool picture, spun and knit by me!

I was halfway through the application season, having submitted at least a dozen applications, before I started to wonder if that was really the best way to present myself as a serious, authoritative scholar. I chalked it up to job-season jitters, though, and persevered. Apart from the analogy method being Sandi-approved, I sincerely believe the analogy to be about as accurate as I could devise. And as I learn more about pedagogy through new faculty orientation and the SC instructor course, I'm finding that things which seem silly to me in the moment are actually an effective way of communicating about science.

I sally forth with my renewed commitment to knitting + teaching. I consider fiber arts and science to be sometimes contradictory but more often complimentary parts of my life. I'm comfortable with that, and I'm extremely grateful to have found a new group of colleagues who can appreciate it as well (I couldn't help it...I knit during my first faculty meeting last week).

The value of a few kind words in academia and science.

At the last Evolution poster session, I came across a first year graduate student whose short research grant I recalled reviewing last spring for a professional society. Like all students (dare I say all scientists?), her writing was a bit rough around the edges, but I could tell she was doing interesting, important research. I paused as she walked another conference attendee through the theory and figures on her poster. It was a delight to hear her explanation of the work she'd accomplished; she was animated, enthusiastic, and clearly invested in the science she was highlighting. Afterwards, I introduced myself and told her I'd enjoyed reading her proposal. Her face lit up and she told me a bit more about future research directions.

I often reflect on the tension in academia (especially science) between being objective researchers but also effective mentors and peers. Science demands we interpret evidence without bias, which requires a cold, detached mindset. However, science is also an intensely personal, and therefore subjective, enterprise (myself and other #ScienceFAIL storytellers highlighted some of these topics). I was lucky to have a graduate advisor who helped me stay optimistic about my research and career trajectory. After leaving his lab and communicating with other early-career scientists, however, I've started to realize how rare that type of support can be.

Let's be honest. The job market sucks. Funding is abysmally competitive. Student evaluations of our teaching can be brutal. There's the cliche about reviewer #3 being a total meanie. We're trained to be skeptical, and that sometimes translates into an unfortunate type of cynicism that permeates our work. The result? The suicide prevention hotline is listed as a "Useful link" on the notorious Ecology Jobs Wiki, and if the comments on the rant page are any indication, there are folks who actually need it.

Don't get me wrong--the evolutionary biology and bioinformatics folks with whom I work are fantastic, happy, awesome people. It just doesn't seem to occur to most of us to offer personal, meaningful words of support. I took the advice of another early-career scientist and started to compile bits of encouraging correspondence to bolster myself in times of self-doubt. I'm shocked at how discouraged some of my peers feel at times. Often, these are people I deeply respect and even view as quite successful. As I've started to transition to my next career stage, I find myself in the position of being able to offer small bits of encouragement to other scientists, too.

The upshot is that not everyone needs this type of encouragement. However, I suspect a few kind words can make a huge difference in improving diversity in science. Women and minorities of any sort (racial, ethnic, sexuality, etc) are already fighting an uphill battle against implicit (and even explicit) bias. It's worth a few moments of my time every once in awhile to help ameliorate those effects.

Con drop and lessons learned from Evolution/iEvoBio 2014

I'm in the midst of processing the aftermath of seven very full days of scientific conference. After almost twelve hours of sleep, I'm feeling a bit of a science hangover.  It occurs to me that this post-conference experience is not unprecedented or unfamiliar. Con drop is pretty well documented by other attendees of a variety of conferences and conventions (look here and here for a few examples), and is generally related to the let-down accompanying the return to real life following submersion in a large group of like-minded people. I don't recall seeing much discussion about post-science con drop, but it makes sense. Especially this year: I was on my home turf, seeing lots of folks I already knew and meeting lots of new people, all of us exuberant on the fumes of scientific progress. In the days following Evolution, I found myself relieved at the relaxation of my scheduled obligations but simultaneously bereft. As I slowly return to "real life," I decided to note a few of the big take-home lessons from Evolution 2014 and iEvoBio. A few other folks have written similar posts (I particularly like Rob's lessons and Jeremy Yoder's tweet synopsis).

First, my time limit for conference science-ing is six hours. I do a passable job of stifling my introverted nature for a time, but it means I need to pace myself. If I do a full morning of talks and lunch meeting, I'll have to take a break mid-afternoon and spend some time along. I'm really glad I have a tangible, quantifiable amount of time that seems to be consistently manageable for me…makes planning easier next time!

Second, I participated in two informal lunch-time gatherings: one tweet-up and one LGBTQ meeting. We tried two different models for these. For the first, we met at the registration desk and wandered elsewhere to find lunch. For the second, we had a lunchtime venue planned. Neither seemed satisfactory, as finding a place for 15+ people to eat is difficult, regardless of the amount of planning involved. I'm thinking about better ways to achieve future meetings, such as contacting conference organizers to have a room assigned. I don't know if I want to manage a formal discussion like the one I attended for AWIS at Evolution, but I like the idea of providing a venue to facilitate interactions between groups of likeminded people.

Of course, there are plenty of other things I'd like to break down in more detail, but fomenting ideas about these main topics seems sufficient navel-gazing for now.

Want someplace to go at Evolution 2014 after regularly scheduled events? Check out my map of downtown Raleigh!

After attending a few large professional conferences, you start to realize that some of the best ideas happen after the scheduled events of the meeting, when folks head off to restaurants and bars to socialize into the wee hours of the morning. I also know how challenging it can be to find someplace suitable when you're just a visitor in a city. The Evolution 2014 website does a great job of highlighting  local restaurants for during the day, but I thought it would be nice to have a reference for late-night socializing (which may or may not include drinking some of the wonderful local beer North Carolina has to offer).

Luckily, I've lived in the RDU area long enough to be able to recommend a few places. I compiled these locations in a handy google map (color-coded red), including the location of the conference center (black), conference housing (yellow), and NESCent/area universities (for reference, in green). I put a screenshot below, but click on the link and you can select each marker for the website (hours, events, etc) as well as a way to estimate distance from wherever you are. I included a variety of establishments: bars with good food, dive bars, LGBTQ-friendly places, and even a basement whiskey bar. If you know the area and want someplace added, drop me a note! Finally, and I hope this is obvious: this resource is in no way endorsed by the conference or NESCent…it's just my attempt to help make your trip a little more fun and relaxing.

Downtown Raleigh after hours

Rebranding scientists: rock stars and princesses

Given my recent (and successful!) job search, I've thought a lot over the last several months about how I'm perceived as a scientist. My musings generally relate to how I explain my own expertise to my peers, both within subfields and to a broader set of academics. There was a certain amount of image revamping involved in my switch from plant systematics as a graduate student to more comparative genomics as a postdoc, and I spent a little time researching methods for effective professional rebranding. For example, I've found it valuable to think about how a professional bioinformaticist spends their time, and made sure I took classes, talked to people, and got a GitHub account. These things are useful as professional development, but also necessary to include on a CV and in other resources, to show that I know what I'm talking about.

While traveling recently, I came across a rather large advertisement in an airport which represents a much more dramatic attempt at rebranding the professional image of scientists. The Geoffrey Beene Rock Stars of Science program has been around a few years, and is aimed at promoting cancer research by bringing together scientists and rock stars. I don't know much more about it than that. Perhaps it was my lack of sleep or travel weariness, but it struck me as a rather comical attempt to merge the high-profile, devil-may-care attitude of the rock world with the meticulous, potentially life-changing realm of cancer research. I was left wondering about the main goal of this attempted merger. More importantly, I pondered what journal I could convince to publish my glam-rock album cover in their next issue.

The effect of this ad struck home when I started to think about larger patterns of how these professions individually are perceived by the public. The union of popular music with philanthropy is not new, and neither is an attempt to show scientists are more diverse than just balding, aging white men who wear lab coats and eyeglasses. I've had a number of articles kicking around my "To Write" list for awhile which address the phenomenon of Princess Scientists, or the representation of scientists who are also beauty queens, cheerleaders, and otherwise covered in lipstick and glitter. Given my choice of Princess Tradescantia as a blog title, the resulting debate is one I've followed with reluctant albeit vested interest. The opinions range from vehemently opposing the princess/scientist mashup to a more thoughtful piece by Stephanie Schuttler, one of my friends from graduate school. She contends that using conventionally feminine imagery to attract girls to science isn't necessarily a bad thing. I concur. To me, the only requirement for being a scientist is for someone to actually think about science. The main point is to eliminate additional expectations while simultaneously allowing for other identities, be it rock star, princess, or whatever your imagination desires.


At the end of the day, I still tend to prefer representations of scientists which encourage the full diversity of outside interests. A good example is This Is What A Scientist Looks Like. It's been awhile since the last update, but it's nice to look through and see scientists pursuing myriad activities, from painting to acting. Encouraging this type of plurality is especially important because of the intersectionality between this type of identity inclusion with acceptance of social, gender, sexuality, and racial minorities.

My identity includes scientist. It also includes fiber artist (knitter/spinner/etc), queer, and science fiction aficionado, among many other recurrent interests. Sometimes that includes princess, and that's OK. I couldn't find a picture of myself in full princess regalia, so please settle for this paltry offering of my wearing a silly hat. It has a little bit of shimmer, so hopefully that will suffice.

Data management and maturing as a scientist.

I attended an awesome workshop last week called Data Carpentry. I briefly mentioned my attendance at a Software Carpentry Bootcamp a few years back, and suffice it to say that I was really excited for another two days of computational bootcamping. I even made a quick little Storify of a few of my tweets if you'd care to take a gander at some highlights.

I've spent quite a bit of time in my professional life learning to manage, analyze, and interpret data. In the 10+ years I've been conducting scientific research, my thought processes about how I relate to data have changed markedly.

1. When I first started learning how to sequence data as an undergraduate, I thought the hardest part of science was collecting data, mostly because that's how I spent most of my time.
2. As I started graduate school, I thought the hardest part of science was getting started on a research project. No doubt this was due in part to my adventures trying to collect plants for my dissertation.
3. As I finished my PhD, I decided the hardest part of science was communicating scientific results to get them published, largely because I was being indoctrinated with publications demonstrating my value as a scientist.
4. During the first part of my postdoc, I thought the hardest part was analyzing data. This is also unsurprising, as my work now is entirely computational (no data collection).
5. The current iteration of my maturation as a scientist? I think the hardest part of science is keeping track of everything you've done and making it accessible to the rest of the community. It's a concept that encapsulates most of the previous "hardest parts," and while it doesn't often deliver tangible rewards like lines of code, pretty figures, or publications, it's satisfying and essential in its own way. Moreover, my career as a data vulture relies on the community's ability to maintain reproducible, open science.

Anyway, that's why I'm so excited about learning better ways to build/query databases, share code, and maintain data. 

Adventures in transitioning from post-doc to professor.

It's that time of year. After dwelling in an esoteric professional purgatory for a few months, I can finally start to visualize where my career will be heading when my time as a post-doc has ended.

In my case, I'm happy to say I'll be joining the Biology Department at the University of Texas at Tyler as assistant professor. My time here at NESCent prepared me well for hitting the job market, but now I'm faced with actually making decisions that up until now were exclusively hypothetical. What is my 5 year research plan? How will I find students? What kind of desk chair should I buy?

I would say that you should expect my blogging to take on a slightly different flavor, but I'm confident in my ability to maintain a consistently inconsistent theme as always. As a quick preview, I'll be thinking lots over the next few months about how to set up computational resources, building collaborations, and teaching things like genomics and bioinformatics. On a more personal level, I'll be contemplating moving across the country, the quasi-nation-state of Texas, and how to explain to my new colleagues that I'll be knitting during faculty meetings.

Onward, towards adventure!

Failure, storytelling, and science.

It's been quiet around here at Princess Tradescantia lately, but I'm excited to report back with a few of my recent adventures!

In January I had an opportunity to participate in a storytelling event co-sponsored by some fantastic folks: The Monti, UNC's Morehead Planetarium, and Scientists with Stories (SwS). The event was called #ScienceFail, and each storyteller shared an experience regarding failure and our careers in science. If you're so inclined, watch this video to learn a bit more about the event and SwS's associated research.

The story I told was from my graduate career; I wrote a bit about it a few years back (I bleed for my thesis: Part I).  I met with Jeff Polish from The Monti several times to craft the details of the narrative, and found the process to be both amusing and intense. The quick version of the story is that I wrecked my truck while doing fieldwork. A longer version of the story can be heard below. The longest version of the story is one I'm still developing, as the repercussions of the events I describe still affect how I pursue my career and think about science. I could write numerous posts about how stories and science reciprocally inform each other, and I'm very grateful to have been afforded the opportunity to participate.

You can also head over to the SoundCloud page to hear the rest of the stories told that night.

Knitting and Science: Molecules!

I thought it was time to take another look at geeky knitting, this time moving away from organisms and more towards molecular biology.

Chromosome hat, Ravelry project,  pattern

I think this hat would be awesome with the chromosomes knit in different colors to mimic the look of chromosome painting (fluorescent in situ hybridization). Ideally, the hat would be black, the chromosomes in blue (DAPI staining), and small dots of green/red/etc embroidered on later…

Genome socks, Ravelry project

The pattern for these socks is actually called "Genome," given the lovely cable twining up the front. I spun the yarn, too, from some lovely wool hand dyed with organic dyes (I'll be talking a  bit more about spinning and dyeing before long!).

DNA scarf,  Ravelry project,  pattern

Finally, a slightly less stylized cable interpretation of a DNA helix, this time in scarf form. There are some really beautiful projects from this same pattern that include color-coding of base pairs.

Other posts in the Knitting and Science series: 

Animals

Plants

Knitbot

Knitting and Science: Plants!

There's a long tradition of incorporating botanical designs into knitting, including many lace patterns inspired by and named after flowers/gardens, and entire books of patterns for creating flowers. Like all other knitted objects, these range from abstractly inspired by plants to fairly realistic representations.

Flower bouquet, Ravelry project

Let's start with the most realistic flowers: in this case, calla lilies and some campanula-style bellflowers. I strove for ease in knitting and aesthetic appeal more than botanical realism, hence the calla lily's paltry spadix and the incredibly droopy, non-differentiated sex organs in the bellflower.

Elodea socks, Ravelry project

Then there are patterns which are inspired by plants. They may or may not actually resemble said plant, but I'm a sucker for inclusion of scientific names into knitting patterns.

Lace stockings with flower motif; Ravelry project

There are plant motifs which occur in lace knitting, often featuring leaves or flowers.  

My Little Flower, Ravelry project

Finally, it's not surprising there are knitters that have contrived disgustingly cute little anthropomorphic flower-people as toys for kids.

I'm looking forward to sharing other plant-related fiber art posts with you soon, including plant-derived yarns and fiber dyes made from native plants!

Other posts in the Knitting and Science series: 

Animals

Knitting and Science: Animals!

Last year, I posted a picture of the giant squid I knitted for Deep Sea News. As a long-time knitter, I have lots of other projects related to science--both directly and tangentially--which I thought might be fun to showcase to a broader community. I thought I'd start with a category of projects which is both related to the squid: animal projects.

I've included a few links in the caption for each project: Ravelry is an awesome resource for fiber folks, and I've included the page on my own profile where each project is described. You can also find the Ravelry pattern page linked from there. The second link (when available) for each project is to the website where the pattern is available. Most of these patterns are freely available online.

Some of these projects aren't that unusual, as many folks like to knit animal toys and whatnot for kids:

Elephant, Ravelry project, pattern

Kitty cat,  Ravelry project, pattern 

Who? hat, Ravelry project, pattern

Instead of knitting 3-D shapes, you can also make interesting animal patterns out of textured stitches, like this owl pattern made from cables:

Dolphin puppet, Ravelry project,
pattern improvised (crochet, not knitting!)

Finally, there are projects relating to animals that I've made for cosplay: the dolphin puppet was part of my costume for Pam Poovey from Archer...

Anatomical heart, 
Ravelry project,  pattern

...and the heart was for Daenerys Targaryen from Game of Thrones. Knitting, science, AND sci-fi/fantasy? Doesn't get much better than that!



Coming up next in this series: plants, molecules, different types of yarn, and more!

Rich with science.

During my graduate studies, I shared the lab with a Chinese postdoc who was an expert at fluorescent in vitro hybridization (FISH). He spent hours at the lab bench and in front of the microscope, attempting to capture the perfect images of chromosomes with important portions of the genome highlighted in glowing dyes (you can see some of his beautiful pictures here). On productive days, he would report back to our advisor by saying "I am rich with chromosomes!" 

I wasn't alone in thinking that an odd turn of phrase, but it seemed such a beautiful way to think about science. Materials which allow accumulation of data and results are indeed worthy of excitement. Lately, as I traverse the boggy grounds of job applications, I find myself sinking down into uncertainty and dissatisfaction as my own scientific bean counting fails to match up to my peers: not enough papers, not enough grants, not enough "deliverables" to show my scientific prowess. 

Occasionally, I just take a deep breath, step back, and think about my favorite parts of my personal scientific process: kicking around wacky ideas about "big picture" science, thinking up ways to tackle interesting research questions, outlining papers before buckling down to write. Those times are my happiest at work, when I feel like I make real progress. 

In reality, I am very lucky and very intellectually "wealthy." I am rich with data. I am rich with ideas. I am rich in the currency of science that is most satisfying to me.

A scary specter of scientific research: When your paper has an error.

This week at work, I encountered something far more terrifying than the costumed ghouls wandering my neighborhood last night for Halloween.

My paper on transposable elements in Asparagales just came out in Genome as a part of a special issue on Genome Size Evolution. Apart from being the first bit of research from what is the main part of my research career these days, it's also the first manuscript on which I'm first author. Even scarier…I'm the only author. Suffice it to say that there is no one to blame but myself for any and all errors which occur in this piece of research and writing. Of course, just after the paper came out in print, I found some problems.

Let me preface this attempt at a self-effacing apology by saying that the NESCent journal club just read and discussed a few real humdingers of articles last week: first, the now-classic paper "Why Most Published Research Findings are False," and second, a recent article from The Economist describing the alarming inefficiency of science (more accurately, the scientific community) at self-correcting errors or misinterpretations. The two articles cited above strike fear into my scientific heart because they indicate our implemented standards for scientific research don't match our expectations as professionals.

This admonition brings me to my own sad confession: there are two problems with my paper that, while not deal breakers for any of my results or conclusions, are bothersome and irritating issues that may cause headaches for other researchers down the line. A good portion of my graduate training was spent reconciling with myself the knowledge that I would eventually be publishing research that was imperfect. It rankles me that those imperfections are going to persist beyond "lack of data" or "insufficient sampling," and perhaps affect priorities near and dear to my heart, like reproducibility and transferability to other systems.

The first issue involved me overlooking a small change while revising the manuscript. The genome size data in Table 1 is actually Mb/1C, instead of pg/1C. Oh, the humanity! Granted, the correction was made in Figure 2, and the error is obvious if you have any frame of reference about genome sizes in plants, but still…I'm quite irked at myself. The second issue is something for which I couldn't have anticipated an problem. The software I used for genome assembly, MSR-CA, has been modified and re-released since I sent back the proofs for the article. My paper includes the website from which I obtained the program, but that website is no longer functional. Moreover, the program name has changed (MaSuRCA), so trying to reproduce my methods would be a bit difficult. However, the paper describing the software is now available, so this will hopefully be less of an issue as I continue to publish.

I understand that these issues like these happen far too commonly in scientific publishing. I've read papers with legends for figures mixed up, obvious typos, and figures where you can't tell if the dot was a data point or printing error. I suppose I feel these errors in my own work more keenly because I am so early in my career. I have more at stake regarding their success, and I'm more invested in their survival and propagation through scientific literature. Are these worth contacting the journal to issue a correction? I'm not sure. It makes me feel a bit better, though, that in the meantime, this post might get picked up by someone trying to figure out what the hell I was talking about with that "MSR-CA" stuff...

Journal club as training for reviewing papers.

The last two posts I've written were basically leading up to this set of musings about how journal club has helped me become a better reviewer of manuscripts. Peer review is one of the most lauded aspects of science, described as the foundation of academic integrity and part of a check-and-balance system of the scientific process. However, I find this particular task of academic life to be problematic, mostly because of the mysterious nature of the process. Moreover, most academics would agree there is a great deal of subjectivity in whether manuscripts are acceptable for publication. How much do these standards vary by scientific discipline (or subdiscipline, or model organism)? What is dealbreaker for a manuscript that is a red flag to keep it from being published?  Does anonymity factor into how we criticize papers?

Part of my impetus for thinking about this topic is a desire to be more effective at reviewing papers, which will hopefully allow me to write better papers myself. Some of these topics have been discussed at length elsewhere, but I'm interested in integrating existing obligations in my weekly schedule to this realm of professional development. I argue here that participating in journal club allows us to attune to how others evaluate manuscripts. Here are a few things I've noticed from attending various journal clubs and reading groups.

1. Some people tend to like most papers, while other people tend to dislike all papers. This goes beyond the ability to critically analyze the content of their papers. Recently during a discussion, a few senior scientists actually related their method of reading papers in exactly those terms. The take home lesson on this point is that the general tone with which someone discusses your manuscript sometimes has little relation to scientific merit of the work, but rather, simply reflects how that person views scientific inquiry.
2. There are vastly different standards for how much information to include in a manuscript. This is especially true when there is an enormous amount of information in supplementary material. Judicious use of methods summary and proper citations can make a huge difference in the reader being overwhelmed by uncertainty or accepting that the facts stated are adequate. These standards also vary substantially by subdiscipline.
3. There is a bimodal distribution of acceptance for ambiguous or unknown statements. Some readers prefer to have caveats, study limitations, and generalized discussion of impacts explicitly stated outright, while others will always view such claims as a liability to the study. 
4. Communicating results from new technologies in scientific manuscripts is a moving target. I know the most about genome sequencing technologies, which are one of the fastest-growing methods in biological sciences. Clear elucidation of the limitations and benefits of these technologies in the manuscript text is essential to reconcile misconceptions other scientists may have about these methods.

Of course, there are a few other ways we receive explicit feedback which helps normalize our standards for peer review. As authors, we receive feedback on manuscripts we've written and submitted. As reviewers, editors may forward the final decision and thoughts from other reviewers of the same manuscript. As editors, we see a wide breadth of submitted articles and have the best idea of publication standards. The problem with each of these viewpoints is that they are highly sample-size dependent. As an early-career scientist, I would be hard-pressed to have a decent representation of solid paper reviews from my own publications. Even as an editor, I imagine it would be easy to fall into a myopic view of science based on the standards of a single journal. Discussion groups with peer scientists, especially from a variety of career stages and subdisciplines, can be one of the best ways to stay abreast of fluctuating standards for scientific inquiry.