Why I sign reviews.

I have been signing my manuscript reviews since I joined NESCent as a postdoc in late 2011. That organization upheld a strong commitment to open science (as evidenced by their data archival efforts since closing their doors last year), and it was that community of scientists that both introduced me to signed reviews and helped me feel comfortable adopting the practice as my default.

There are multitudes of blog posts and articles about signing reviews. Two sources I find particularly useful include multiple viewpoints from different scientists (from The Molecular Ecologist) and interesting data about frequency of signing in different subdisciplines (from publons).

Many of the arguments in favor of signing reviews refer to improving the quality of the scientific assessment in the review, as well as keeping the tone of the review civil and constructive (as opposed to possibly derogatory). I don't believe the content of my reviews differ based on whether I sign my name to them, because I try hard to be rigorous (and not a jerk). Instead, here are the reasons that resonate with me:

1. Signed reviews are easier for authors to interpret. When I received my first signed review as an author of a manuscript, I realized how much easier it was to understand the reviewer's recommendations. By signing my reviews, I'm offering authors additional context into the comments I've provided. I include my name, title, department, university affiliation, and email, which makes me easily google-able (they could even find this blog post!).
2. Sometimes it's pretty obvious who the reviewer is anyway. On occasion, I've received reviews as an author that are almost laughably transparent as to the identity of the "anonymous" reviewer. The science world isn't so big! If you're a taxonomic expert in a particular group of organisms, it's easy to guess that it's you catching spelling mistakes in names of subfamilies (or suggesting someone cite your papers).
3. It's gratifying to be explicitly included in the process of improving a manuscript. I work hard to be a thorough reviewer, and I hope that my effort may be rewarded by a reputation for fairness and helpful insight. I appreciate my name being included in the acknowledgements (although this did once cause some consternation with a few peers, as it wasn't clear I was a reviewer and they thought I was collaborating with their competitors). In fact, I particularly enjoy reviewing for PeerJ, as my review is published with the early version of the manuscript.

Here are a few things to keep in mind about signing your reviews:

1. Be consistent. It's easy to sign a glowing review of a manuscript that you're accepting without recommendations. It's harder to sign a review of a manuscript you're rejecting, but (for the reasons I mention above), is arguably more important for both you and the author.
2. Ask journals before agreeing to review. Here's a tweet exchange I had with @rmflight about corresponding with journals (pure genius!):

@k8hert that is why I ask the editor first before agreeing to review, and make it a condition of reviewing

— Robert M Flight (@rmflight) January 15, 2016

3. You get used to it quickly. Once I committed to my decision to sign all reviews (for papers I accept AND reject), it only took a few reviews to feel comfortable with it. Nothing disastrous has happened, and reviewing papers feels less like working for free.
4. You can do it! Anonymous peer review is still the default in most areas of academia. In most cases, though, signing is a choice. I've heard early career scientists warned away from the practice, but it hopefully helps you to know that there is, in fact, a community of folks who make transparency in science a priority, and they exist in all career stages.

In short, I've signed ALL my reviews for about five years now. In that time, I've published my own papers, applied for grants, and even landed a tenure track faculty job I'm still quite happy with my decision.

(Thanks to @MusselDS for asking the question and prompting me to finally write this post.)

Managing your academic identity.

A friend from Facebook posted today that she stumbled across another academic with her same first and last name, working on similar research topics, and remarked that mistaken identities were bound to happen.

There are few other Hertwecks out there, but I still do what I can to manage and maintain my academic identity, so that other folks can find this blog, the papers I write, my research website, etc. There are social media style sites for maintaining your research products (Academia.edu, ResearchGate), but here are my favorite alternatives:

1. Get an account on ORCID. It's free, and you'll get a unique identifier to include when you submit grants, manuscripts, etc. This is also a nice segue into checking out your Scopus author identifier (if your organization has access).  
2. Curate your Google Scholar profile. This is another free tool, and there's nice documentation and explanations if you click through the link. Citation tracking tools and the ability to manually adjust which articles show up in your profile makes this a quick go-to for academics, plus there are other functionalities built in (i.e., citation alerts for topics which might interest you).
3. Sign up for Impactstory. This is a great option if you have an active presence in social media or invest energy in other types of non-traditional academic deliverables. Although they recently transitioned to a paid subscription service, they are a non-profit and your money goes towards keeping the data and software open-source.

These were the first options that came to mind, although there are others which might be of interest. What am I missing? Who's got a better resource for maintaining your academic identity online?

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.

Data visualization

I've been thinking lots lately about how we visualize data in biological research. As a student of both communication and science, I find the interplay between the two fields to be especially compelling. This interest was reinvigorated last spring by some meetings with a visiting NESCent scholar, Tyler Curtain, who introduced me to some of the preeminent and current literature on the field. Since then, I've noticed the theme popping up repeatedly in my interactions with colleagues. Folks pop in to my office to ask for advice on figures, and some NESCent-associated projects like Open Tree of Life are particularly interested in advancing visualization abilities for evolutionary biology.

As a result of this convergence of events, I jumped at the chance to take a short workshop on data visualization offered by Duke Libraries Data and GIS Services. I was struck by the breadth of options available for representing data visually, and began thinking a bit about how different figures in evolutionary biology are from other fields. I talked a bit with Angela Zoss, the instructor of the course, who concurred that biological disciplines tend to lag behind other sciences in adopting more effective methods of visualizing data (I would extend that claim and imply that sciences in general lag behind other fields of study as well).

Let's face it, folks. Figures representing biological data are notoriously problematic to not only build, but interpret. Dendrograms (which in the data visualization world include many types of tree structures) are very complicated in evolutionary biology, and our tendency is to cram as much information as possible into each figure. A single diagram may include tree topology, branch lengths/divergence times, taxon names (tree tips AND higher taxonomic groupings), color coding for one or more traits, etc. Additionally, I've long thought that effective visualizations for genomics research, especially in the comparative realm, are notoriously convoluted and difficult to understand.

Why are our figures so complicated? I think it's because biology as a science inherently includes many different variables, each of which includes sometimes large margins for error. As scientists, we want to tell our research narrative in as non-biased a manner as possible, which means including (visually) as much data as possible. This impulse is compounded by a push to streamline figures for publications, which is further complicated by lack of availability of color, space, and resolution.

But let's face it. We're never telling an unbiased story with our figures. We make decisions about inclusion of data in a study and methods of analysis even before we get to the publication stage, and cramming as much summary information as possible into a figure doesn't represent those biases. What is the goal of a figure or representation of biological data? It should be interpretable by an audience, which in journals means scientific peers. When visualizations become so specialized that only a handful of people in a field can understand them, we're working counter to the purpose of the visualization. It's not doing its job.

I advocate striking a balance between the goals of the two paragraphs above. Tell a clear story with the data, but include enough information for the audience to understand associated variance and error. As a result, I'm planning a open discussion at NESCent with folks from our informatics and science groups, in addition to folks who work on data visualization, to see how we can improve our methods of building figures.

Possible topics for contemplation include (but aren't limited to) the following:

• tree visualization
• deep time
• visualizing error
• taxonomic levels
• trait mapping