INTRODUCING... Christina Laternser has a B.S. and M.S. in Mathematics and an M.S. in Economics. An experienced LaTeX user, she wrote her thesis on hyperbolic geometry using it, as well as her economics thesis. She has worked in data analytics, application development, and lectured in mathematics. She finds some time to do academic research as well. While Christina has developed and managed global intra-company team collaboration tools over LAN, effectively a stripped-down version of Authorea, this is her first introduction to the collaborative platform for research.

Friday, an op-ed piece _actually_ titled “Academic Science Isn’t Sexist” went up on the _New York Times_ blog (a version appeared in the Sunday Review). It was about academic research and the lack of sexism therein. The two editorialists are co-authors on a recently released analysis on the subject (it _is_ beautifully open access, and much of the raw data is available). The piece and the paper claim sexism has largely waned in academic research, the result of shifts from a previously sexist, male-dominated academy. Further, that any remaining incongruities between male and female enrollment, advancement, and achievement are artifacts and anecdotal. Academic research is completely gender-blind now. Any differences are largely the product of society-at-large and earlier life decisions (like the choice to play with dolls/cute animals versus trucks/destructive robots). Huh. The response from the science blogging community and Twittersphere was immediate and is still on-going. Jonathan Eisen responded Halloween night, soon after the piece was posted. His immediate critique was of the acknowledgement of reports of “physical aggression” in the op-ed piece, without ever addressing these in their data or analysis (even the 60+ page research paper is short on coverage). The assumption: they are also anecdotal? So everything is actually fine? Probably not (<- this article details accounts of _sexual misconduct in field work_ involving biology, anthropology, and other social sciences, disciplines the authors above highlight as _largely welcoming and open to women_). Emily Willingham provides excellent analysis of the data presented in the paper and in the broader debate at hand. It turns out there are numerous discrepancies and avoided topics of analysis (e.g. salary figures often had statistically significant differences by gender; women more often reported lack of inclusion; more details in her impeccable post). Likewise, Matthew Francis covered the story, emphasizing the need to actively address these still-existent problems and not ignore them: the importance of even a little explicit encouragement of female students in the face of implicit discouragement (like he sees in his native field of physics) is often all that’s needed. The ever-emphatic PZ Myers rounds out the debate by breaking down the major reasoning and assumptions in the original paper, with characteristic gusto. So what exactly were the original authors thinking? A handful of distributed scientists were able to challenge the key arguments of their paper, using their data and citations, in free time over the weekend. Talk about peer-review. Seriously though, what were they thinking? I would _like to think_ that this was actually a brilliantly orchestrated publicity stunt to get more attention on this critical issue. AFTER ALL, WHO IS GOING TO BLOG/TWEET/COUNTER-OP-ED “ACADEMIC SCIENCE IS SLIGHTLY LESS SEXIST THAN WHEN MALE ACADEMICS COULD STILL SMOKE IN THEIR OFFICES”? Because when you look at the data, the background on this issue, and the immediate response from the community, it’s obvious academic research isn’t now some utopian meritocracy brimming with equality. There is still institutional and systemic biases. Whether its gender, race, sexual-preference, or need related, or tied up in the archaic publishing system that is all too easily gamed, we have a long way to go before things can be considered “fair”. What might a fair system even look like?

INTRODUCTION TO VERSION CONTROL Many scientists write code as part of their research. Just as experiments are logged in laboratory notebooks, it is important to document the code you use for analysis. However, a few key problems can arise when iteratively developing code that make it difficult to document and track which code version was used to create each result. First, you often need to experiment with new ideas, such as adding new features to a script or increasing the speed of a slow step, but you do not want to risk breaking the currently working code. One often utilized solution is to make a copy of the script before making new edits. However, this can quickly become a problem because it clutters your filesystem with uninformative filenames, e.g. analysis.sh, analysis_02.sh, analysis_03.sh, etc. It is difficult to remember the differences between the versions of the files, and more importantly which version you used to produce specific results, especially if you return to the code months later. Second, you will likely share your code with multiple lab mates or collaborators and they may have suggestions on how to improve it. If you email the code to multiple people, you will have to manually incorporate all the changes each of them sends. Fortunately, software engineers have already developed software to manage these issues: version control. A version control system (VCS) allows you to track the iterative changes you make to your code. Thus you can experiment with new ideas but always have the option to revert to a specific past version of the code you used to generate particular results. Furthermore, you can record messages as you save each successive version so that you (or anyone else) reviewing the development history of the code is able to understand the rationale for the given edits. Also, it facilitates collaboration. Using a VCS, your collaborators can make and save changes to the code, and you can automatically incorporate these changes to the main code base. The collaborative aspect is enhanced with the emergence of websites that host version controlled code. In this quick guide, we introduce you to one VCS, Git (git-scm.com), and one online hosting site, GitHub (github.com), both of which are currently popular among scientists and programmers in general. More importantly, we hope to convince you that although mastering a given VCS takes time, you can already achieve great benefits by getting started using a few simple commands. Furthermore, not only does using a VCS solve many common problems when writing code, it can also improve the scientific process. By tracking your code development with a VCS and hosting it online, you are performing science that is more transparent, reproducible, and open to collaboration . There is no reason this framework needs to be limited only to code; a VCS is well-suited for tracking any plain-text files: manuscripts, electronic lab notebooks, protocols, etc.

SO YOU ACTUALLY WANT YOUR RESEARCH READ... Every year in science, tech, and medicine, on the order of 2 MILLION PAPERS are published. That’s a lot of papers. To remain current with their field, physicians must read about 20 PAPERS A DAY. Given the growing “scourge” of cross-disciplinary science and the interconnectivity of life, our world, and everything, 20 papers honestly seems low. How, then, is an average journal article only read by 10 people, or only 20% of _cited_ papers actually read? Maybe it has to do with the overextension of researchers (see Alberto’s post above for massive discipline-spanning course lists). Or maybe it has to do with the way papers are presented. They’re long, in archaic formats, and only accessible with a background in the given discipline (and, critically, freedom from paywalls). Why can’t we - scientists/communicators of knowledge/sharers of discoveries - agree to write clearly, concisely, and for broad impact and appeal? Many universities and other research institutions have press offices that interface with the public for just this reason. This is critical, as institutions’ research and resources help attract more funding and, nobly, should be shared with the world. The problem? You, as the person who did the research, probably know it better! And you (hopefully) won’t oversell it!

INTRODUCTION As we enter the advanced-detector era of ground-based gravitational-wave (GW) astronomy, it is critical that we understand the abilities and limitations of the analyses we are prepared to conduct. Of the many predicted sources of GWs, binary neutron-star (BNS) coalescences are paramount; their progenitors have been directly observed , and the advanced detectors will be sensitive to their GW emission up to ∼400 Mpc away . When analyzing a GW signal from a circularized compact binary merger, strong degeneracies exist between parameters describing the binary (e.g., distance and inclination). To properly estimate any particular parameter(s) of interest, the marginal distribution is estimated by integrating the joint posterior probability density function (PDF) over all other parameters. In this work, we sample the posterior PDF using software implemented in the LALINFERENCE library . Specifically we use results from LALINFERNCE_NEST , a nest sampling algorithm , and LALINFERENCE_MCMC , a Markov-chain Monte Carlo algorithm \citep[chapter 12]{Gregory2005}. Previous studies of BNS signals have largely assessed parameter constraints assuming negligible neutron-star (NS) spin, restricting models to nine parameters. This simplification has largely been due to computational constraints, but the slow spin of NSs in short-period BNS systems observed to date \citep[e.g.,][]{Mandel_2010} has also been used as justification. However, proper characterization of compact binary sources _must_ account for the possibility of non-negligible spin; otherwise parameter estimates will be biased . This bias can potentially lead to incorrect conclusions about source properties and even misidentification of source classes. Numerous studies have looked at the BNS parameter estimation abilities of ground-based GW detectors such as the Advanced Laser Interferometer Gravitational-Wave Observatory \citep[aLIGO;][]{Aasi_2015} and Advanced Virgo \citep[AdV;][]{Acernese_2014} detectors. assessed localization abilities on a simulated non-spinning BNS population. looked at several potential advanced-detector networks and quantified the parameter-estimation abilities of each network for a signal from a fiducial BNS with non-spinning NSs. demonstrated the ability to characterize signals from non-spinning BNS sources with waveform models for spinning sources using Bayesian stochastic samplers in the LALINFERENCE library . used approximate methods to quantify the degeneracy between spin and mass estimates, assuming the compact objects’ spins are aligned with the orbital angular momentum of the binary \citep[but see][]{Haster_2015}. simulated a collection of loud signals from non-spinning BNS sources in several mass bins and quantified parameter estimation capabilities in the advanced-detector era using non-spinning models. introduced precession from spin–orbit coupling and found that the additional richness encoded in the waveform could reduce the mass–spin degeneracy, helping BNSs to be distinguished from NS–black hole (BH) binaries. conducted a similar analysis of a large catalog of sources and found that it is difficult to infer the presence of a mass gap between NSs and BHs , although, this may still be possible using a population of a few tens of detections . Finally, and the follow-on represent an (almost) complete end-to-end simulation of BNS detection and characterization during the first 1–2 years of the advanced-detector era. These studies simulated GWs from an astrophysically motivated BNS population, then detected and characterized sources using the search and follow-up tools that are used for LIGO–Virgo data analysis . The final stage of the analysis missing from these studies is the computationally expensive characterization of sources while accounting for the compact objects’ spins and their degeneracies with other parameters. The present work is the final step of BNS characterization for the simulations using waveforms that account for the effects of NS spin. We begin with a brief introduction to the source catalog used for this study and in section [sec:sources]. Then, in section [sec:spin] we describe the results of parameter estimation from a full analysis that includes spin. In section [sec:mass] we look at mass estimates in more detail and spin-magnitude estimates in section [sec:spin-magnitudes]. In section [sec:extrinsic] we consider the estimation of extrinsic parameters: sky position (section [sec:sky]) and distance (section [sec:distance]), which we do not expect to be significantly affected by the inclusion of spin in the analysis templates. We summarize our findings in section [sec:conclusions]. A comparison of computational costs for spinning and non-spinning parameter estimation is given in appendix [ap:CPU].

_Kepler_ has revolutionized the study of transiting planets with its unprecedented photometric precision on more than 150,000 target stars. Most of the thousands of transiting planet candidates detected by _Kepler_ have been observed as long-cadence targets with 30 minute exposure times, and the upcoming Transiting Exoplanet Survey Satellite (TESS) will record full frame images with a similar integration time. Analytic approximations for the variances and covariances on the transit parameters can be derived from fitting non-binned light curve photometry to a non-binned model. Integrations of 30 minutes affect the transit shape, particularly for small planets and in cases of low signal to noise. We derive light curve models in terms of the transit parameters and exposure time, and we used the Fisher information matrix technique to derive the variances and covariances among the parameters due to fitting these binned models to binned data. We found that binning the light curve can significantly increase the uncertainties and covariances on the inferred parameters. Uncertainties on the transit ingress/egress time can increase by a factor of 34 for Earth-size planets and 3.4 for Jupiter-size planets around Sun-like stars for exposure times of 30 minutes compared to instantaneously-sampled light curves. Similarly, uncertainties on the mid-transit time for Earth- and Jupiter-size planets increase by factors of 3.9 and 1.4, respectively. On the other hand, uncertainties on the transit depth are largely unaffected by finite exposure times (increasing by a factor of only 1.07 under the influence of 30 minute exposure times). While correlations among the transit depth, ingress duration, and transit duration all increase in magnitude with longer exposure times, the mid-transit time remains uncorrelated with the other parameters. We provide code for predicting the variances and covariances of any set of planet parameters and exposure times at www.its.caltech.edu/~eprice.

Still writing your own documents? That’s so 3200 BCE! At Authorea, we take digital publishing so seriously, we want to WRITE YOUR PAPER FOR YOU! Partnering with SCIGEN (who we briefly profiled last week), writing, submitting and disseminating your research will never again require a keyboard. Scholarly research and writing have never been easier. By stitching together a home-brewed soup of technical lingo, a cursory glance at your paper will yield resounding “Oh, hum, yes?”s — we guarantee! Concerned your manuscript won’t get accepted for publication? SCIgen has a proven record that _many_ of their randomly generated manuscripts have made it in to “peer reviewed” journals. And don’t worry, this is only horrifying if you aren’t in on it! Best of all, you can use a private article on Authorea to keep unwanted questions and comments out of the equation. Open and honest peer review is after all too dangerous an experiment, when we have venues ready and capable of accepting your SCIgen derived piece of utter genius. Happy Writing! ;)

This is the abstract. Change test. This wordshould be surrounded by double quotes.

ABSTRACT We describe a scalable automated method for measuring the pharyngeal pumping of _Caenorhabditis elegans_ in controlled environments. Our approach enables unbiased measurements for prolonged periods, a high throughput, and measurements in controlled yet dynamically changing feeding environments. The automated analysis compares well with scoring pumping by visual inspection, a common practice in the field. In addition, we we observed overall low rates of pharyngeal pumping and long correlation times when food availability was oscillated.

PREAMBLE This is an introduction to a series on pushing beyond “The Article”. But where to draw the line? Health economist Christopher Sampson tackles the question head on and makes some fantastic points about the benefits of communicating through alternative channels like blogs and platforms outside of traditional journals. This series is about extending communication even further: outreach, non-traditional dissemination, writing for different audiences, and how to get more eyes on your work without resorting to click-baity titles (full disclosure: title was _almost_ “7 Things You Won’t Believe Increase Your Online Presence and DOUBLE the size of your h-index!”). Simply, these are more ways to extend scientific communication into the 21st century. So, be sure to stick around, follow us (fb, tw, g+) (we’ll also cover social media), and leave comments, questions, and suggestions (we’ll cover interactivity too).

WHAT’S ON YOUR MIND? This is an experimental “article”. I’ll periodically drop citations to papers and news I’m reading, I’ve read, or I’m still thinking about. Dropped citations might have comments, links to related articles/writings, or be effectively untouched. At any point, anyone can comment on anything (please do; it’s encouraged). Maybe you have some insights on a paper, a particular concept, or technique. Maybe you disagree with something. Maybe you have more recommended reading. Maybe you just want to say something is “cool”. Any kind of comment is good because it adds information to the thought-process or discussion. As this is a work very much in progress, organizational changes will be made I’m sure, and if anyone has suggestions or wants to build off anything here, go for it. The goal, while not yet entirely clear, is some kind of radical or innovative collaboration.