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...

Dreary weather getting you down? Have a picture of a rose.

You're welcome.

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.

How to think about research much different from your own.

As a follow up to my post last week about the value in learning about a breadth of topics, I thought it apropos to briefly describe some of the most profound ways in which my scientific thinking has been altered because of talking about disparate research.

Here's a bit of context. I was trained as an undergrad in molecular systematics of plants. I knew a lot about plant evolution and a little about molecular genetics. What did I learn when I started grad school and had to attend seminars about cellular pathways in mice, or behavior in insects? Here are a few examples.

1. Researchers trained in particular fields approach the narratives of science from different perspectives. The way we ask scientific questions, design experiments, and convey our results differs widely depending on the biological scale and phenomena we're addressing. For example, my tendency towards thinking about organismal evolution is strikingly different from a reductionist view of molecular developmental pathways. These ways of thinking are not mutually exclusive, but sometimes it seems we get stuck in thinking about science the same way. One of my current officemates was trained as a physicist, which results in some pretty eye-opening revelations about biological complexity and uncertainty.
2. You can do really cool science by applying methods from one theoretical background to a novel question from another field. There are some obvious examples of the success of these mash-ups. The modern synthesis, evolutionary development, and systems biology are all examples of uniting previously disparate fields of research. My personal favorite is the application of ecological principles to genomics (some examples are here, here and here).
3. Cross-talk assists in uniting themes in biology that are exclusive of model system. A great example of this point comes from journal club last week. Metagenomic methods borrow largely from those developed by ecologists to evaluate how diversity and abundance of organisms differs between ecosystems. It's pretty obvious to ecologists who work on macro-organisms that the average size of species can factor heavily into their influence on an ecosystem. The same argument can apply for microbes that differ widely in average size, yet biomass is rarely considered in microbial studies. Talking about vastly different study systems helps remove model-system specific bias.

Of course, those are just a few of my favorite vignettes to validate the time I spend thinking about research that isn't directly related to my own. Dare I also say that such thought experiments are also simply fun? Basically, I refuse to let myself be impatient about attending seminars or meeting with visiting scientists if their work is very different from my own. I had a great one-on-one meeting with Darwin historian Alistair Sponsel  a few weeks back when he visited NESCent. We only spoke for half an hour, but the time was constructively spent talking about visualization of different types of data as conveyed across a time scale: certainly important insight for both historians and biologists.

My last point is that understanding a breadth of research helps make your own research deliverables more appealing to a broader audience. Some practical applications are obvious: how to communicate in a seminar to a broad audience, how to convince a panel of experts your grant is worth funding. I'll continue this thought in a few days, focusing on one particular part of our job: peer review.

Journal club and breadth of research

As someone interested in research synthesis, it's not surprising I have an appreciation for a wide breadth of biological investigations. My PhD training was in a biology department which spanned the gamut of research, from neuroscience to cell/molecular to ecology/evolution. As a result, the peers with whom I interacted often possessed research which was only distantly related to my interests in plant evolution. Fellow graduate students working on mouse stem cells, molecular pathways in fungus, and katydid behavior offered some of the best insights into formation of my dissertation questions and analysis.

The result of this training is acceptance that I will often be drawn into discussions about science which does not include direct application to my personal expertise. Rather than bemoan the time "wasted" by these "distractions," I instead use them as a way to improve the overall efficacy of my scientific thinking.

Take, for example, this selection of articles, each of which was discussed at NESCent's journal club sometime during the last few months (our journal club basically invites all NESCent scientists to read a journal article prior to an hour-long meeting where we talk through the paper).

1. Dung Beetles Use the Milky Way for Orientation
2. Gut Microbiota from Twins Discordant for Obesity Modulate Metabolism in Mice
3. Is there Room for Punctuated Equilibrium in Macroevolution?
4. Genomic Evolution and Transmission of Helicobacter pylori in two South African families
5. The Tragedy of the Commons

NESCentians are all evolutionary biologists, but that's where the generalities end. That sampling of articles also handily describes the variation in research from participating scientists. Molecular to organismal, animals to bacteria, theory and empiricism. While most of the papers are current (2013), the fifth article is from 1968. The really beautiful part is that some articles don't even mention evolution at all! 

Somehow, we still manage to find plenty of things to discuss (sometimes rather heatedly). This model for journal club does well to expand our brains and promote novel research questions by taking advantage of the variety of expertise here at NESCent. There's generally one person in attendance who knows something about the model system or experimental approach, who then answers basic questions about the whys and hows of the methodology. The goal is for us to not understand every nuanced detail of the paper's analysis, but to focus on the parts in which we're interested. 

The moral of the story: it's not necessary to have a super-specific focus for a discussion group to still have meaningful and interesting discourse. The particular benefits gleaned from these interactions, however, will have to wait for another post.