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].

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.

Bill Gates has some thoughts about education. Specifically, how its future might look. He recently visited Arizona, where Rio Salado College and University of Phoenix are broadening access to education. improvements include: 1. LOW COSTS (compare Rio Salado’s $84/credit-hour vs the 2011 average $250/credit-hour for in-state, public tuition); 2. FLEXIBILITY (many classes start new sections every week); 3. online and MOBILE INTEGRATION (U of Phoenix offers an app for studying and course management from anywhere, anytime). These innovative offerings help solve practical problems for modern education. Given the 40% college dropout rate, ever-rising costs of tuition, associated increases in post-college debt, the need to stay competitive, and the desire to explore new areas of knowledge, anything that lowers friction is certainly welcome. Given that these two institutions alone serve over 350k students, you also can’t argue with demand that’s clearly there.

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?

In this week’s _Nature_, a special issue on the evolution of modern academic institutions, Arizona State University (ASU) President Michael Crow and his vision of the New American University are profiled. Appointed President in 2002, previously Executive Vice Provost at Columbia University, Crow began restructuring ASU. His goal: to shape it into A HUB OF MULTIDISCIPLINARY RESEARCH, ENTREPRENEURSHIP, AND INNOVATION. 12 years into Crow’s tenure, ASU has expanded its campus, forming and constructing new research institutes like the Biodesign Institute, School of Earth and Space Exploration, and the School of Human Evolution and Social Change. The University’s growth in funding and collaboration are also remarkable. From the _Nature_ piece: ASU’s funding numbers show that grant-givers find the cross-disciplinary approach attractive. From 2003 to 2012, the university’s FEDERALLY FINANCED RESEARCH PORTFOLIO GREW BY 162%, vastly outpacing the average increase seen at 15 similar public institutions. ... The number of funded projects with principal investigators in two or more departments rose by 75% between 2003 and 2014. While the article further notes that ASU’s publication rate has more than doubled, it asserts its scientific profile has hardly been raised. Citing largely unchanged proportions of publications in high-profile journals or with high numbers of citations, this analysis doesn’t account for some important factors. 1. Since ASU’s funding at present has more than doubled, one should expect an explosive BOOM in publications and citations over the next few years; 2. Collaborating, whether in the same institution or across the world is fraught with challenges, so pace may lag; 3. The research from ASU’s new institutes and far-reaching collaborations is inherently different (innovation is, by definition); like Crow said, “We don’t want to ask the same questions as other institutions,” so there aren’t yet large circles to cite these early works; 4. Building off the above point, it can take years for journal articles to accumulate even a portion of their lifetime citations (an argument against impact factor); Even without these caveats, ASU’s progression and THE ESSENCE OF CROW’S NEW AMERICAN UNIVERSITY MODEL PRESCIENTLY ANTICIPATED RECENT DEVELOPMENTS IN THE MODERN UNIVERSITY. Observing ASU’s emphasis on cross-disciplinary entrepreneurship and innovation (E&I) through research, one notes similar trends at top-tier universities across the world. With job markets in flux, a rapidly changing economy, and an ever-increasing focus on science and technology, schools attract students and build connections to business through E&I hubs, an explicit goal of Crow’s vision for ASU. Rethinking and reimagining research and education at academic institutions is critical for universities and their students to remain competitive. Best of all, science and society will both benefit. Here’s to hoping the New American University expands beyond Phoenix, Arizona.

WHO GAVE SUMMER BACK TO CHILDREN OF THE 50S AND 60S? Yesterday was Dr. Jonas Salk’s 100th birthday. The Google Doodle celebrating it was profiled in The Guardian, which acknowledged: The story of Salk’s search for a vaccine isn’t one that should be told in isolation, stopping with the elimination of polio in the US. Instead, it sits within a rich tapestry of stories about scientific discovery and progress. Except that Salk’s treatment _wasn’t_ responsible for eradicating polio in the US. His treatment was too expensive for millions of Americans at a time when children were kept indoors during summer to prevent infection. Despite the oft-repeated Salkian quote, “There is no patent. Could you patent the sun?”, adminstrative powers above Salk (his involvement is unclear) determined they COULD NOT LEGALLY PATENT THE VACCINE, given previous works. Still, three vaccine shots and a booster priced polio protection only within the reach of middle class Americans and above. So infection rates dropped among demographics that could afford the Salk vaccine, while rates expanded in lower income communities, especially among under-served minority groups. This was an economic as well as access problem: pediatricians could command higher prices as there was high demand for the multi-course regimen. Dr. Albert Sabin, another polio researcher, knew that Salk’s vaccine was not the best possible solution or even sufficiently safe. His arguments for impartiality and caution were largely ignored by the council backing Salk’s vaccine (see link below), yet Sabin labored on. He developed a single orally-dosed drug that allowed low-cost, wide-scale distribution of this life-saving treatment. As described in the aptly titled review, The Myth of Jonas Salk, Sabin’s treatment was truly responsible for ending polio in the US (and is currently the one in use to eradicate polio across the globe): Beginning in January 1962, pediatricians in two Arizona counties ... conducted separate but similar voluntary mass immunizations using Sabin’s vaccine. “Previous programs using the Salk vaccine had failed to bring polio immunization to a satisfactory level,” they reported a year later in the Journal of the American Medical Association.... More than 700,000 people were immunized – 75 percent of the total population in both counties. The vaccine was given at the cost of 25 cents, for those who could pay. It was given to population groups that were socially, racially, and culturally diverse, on Indian reservations and military posts and in urban and rural areas. The program became a model for subsequent U.S. mass-immunization programs. By the mid-1960s, Sabin’s vaccine was the only one in use in the United States. It was the Sabin vaccine that closed the immunity gap and effectively put an end to polio in the States. Of course aspects of Sabin’s work were built off the work of Salk - all of science is inherently iterative. But we as a community and society at large need to have systems in place to ensure credit is given where credit is due. Thanks to a half-century of good PR and first-mover advantage, Dr. Salk is heralded as the vanquisher of polio, while it was Sabin’s dogged persistence at achieving a better solution that tipped the scales (not to mention countered an economically and racially-biased course of treatment). There is an entire subset of scientists who passed through history largely undetected, while making tremendous impacts. Female scientists have made up a disproportionate amount of this subset, routinely discouraged from research (e.g. see comments in this post). For every triumphant Salk, there is almost always a Sabin (or Rosalind Franklin, et al) who deserves equal if not more recognition. Hopefully with improved access and documentation, the scientific community can better allocate credit, resources, support, and realize improvements faster. Share this on Facebook to extend Salk’s celebration to Sabin (and the other researchers who contributed to the polio vaccine), and to serve as a reminder of the less-than-famous scientists who are giants in their own right.

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.

The traditional way to publish scientific work is to write a narrative describing the performed experiments and related conclusions. Nowadays, the pressures for funding and journal impact factor generate a vicious circle promoting, at the very least, an increase in the minimum number of relevant findings required for publication and the over-stretching of claims. As a consequence, the problem of reproducibility in science has surged to the attention of the media, including The New York Times and The Wall Street Journal.

In a breakthrough victory for open access, the EU’s European Medicines Agency (EMA) approved a system last week that provides researchers and the public with the vast majority of data from clinical trials. While generally resistant to such developments, some pharmaceutical companies are already opening up their data to scrutiny. In the US, NIH’s clinicaltrials.gov hosts a similar database for voluntary submission of public and private clinical trial results. By January 1, 2015, however, all companies in the EU will be REQUIRED BY LAW to submit trial data for newly approved drugs. FDA is considering adopting such a policy, with safety and efficacy data-mining projects in mind. As the analysis from ScienceInsider notes: Published journal articles often contain the main outcomes, . . . but lack detailed data and information about study design, efficacy, and safety analysis, which might shed a different light on the results when analyzed by others; moreover, some trials aren’t published at all. The AllTrials campaign has argued that the details of every trial should be publicly available for anyone to study. Traditional publication formats disconnected from modern needs? A move toward data-rich scientific content? Opening up the process of verification and analysis to a wider audience? It’s as if science was always meant to be open, or something. Naturally, there are caveats to the ruling. Only identified researchers can download searchable trial results and data, while registered public users can only view results on-screen. Further, certain types of commercially relevant data may be redacted by companies, with the EMA providing an 18 month window before completed trial results are finalized and posted. Still, however, this represents a huge step forward for widespread access to and synthesis of information that could be critical for improving patient outcomes. Long-sought by many researchers, the beneficial network effects of open trial data have been lauded in the literature, with comparisons made to successes in the open-source community . In one example, data-sharing led to rapid analysis and determination of treatment for a deadly _e coli_ outbreak in 2011. By broadly applying standard protocols to ease the access and use of clinical trial information, researchers contend we will see huge health care improvements. Results include learning what treatments are best in which circumstances, determining contraindications faster, and increasing adoption and innovation rates in treatments. Here’s to hoping EMA’s actions are successful, FDA approves similar measures, and science in aggregate opens up to take advantage of these synergetic network effects. EMA Q&A release

Simple, mundane tasks can take huge tolls on our productivity. A growing body of research is quantitatively demonstrating the existence of willpower depletion, so don’t be a statistic. Authorea helps cut out friction associated with academic writing and editing, so you can get back to doing what you love instead of just writing about it. HERE’S THE SITUATION: You’ve spent months (or even years) running and reproducing experiments, keeping meticulous notes, collecting and annotating data, writing code, writing grant applications, presenting your progress and, occasionally, sleeping. All for that slam-dunk publication. Which, finally, you are writing up. BUT THERE ARE SOME PROBLEMS. From stylistic preferences and building consensus with your colleagues, to back-up paranoia, to re-re-re-formatting your article for the ~~reach~~, ~~next best~~, ~~achievable~~, journal of last resort, Authorea has you covered. AN AUTHOREA-TATIVE DIFFERENCE As the recent over 200-author CERN paper demonstrates, Authorea can really kill it when it comes to collaborating, on any scale. This is particularly powerful, given the well-feared notion that communication complexity increases as the SQUARE of the number of people on a project. Regardless of your level of distribution (worldwide or just down the hall), with Authorea, everyone you care to include can view, edit, comment, commit, and even upload and review data, code, and figures. Let’s consider that last point for a minute. No longer will you have to fumble for flash drives, attach and contextualize via email, or compile, edit, and view in separate programs. All the data and code associated with your figures is online in the upper left-hand folder for your collaborators to play with. Further, if your document is public, members from the wider Authorea community can comment, verify, and even fork it - increasing your FF and contribution to science. Pretty sweet. Authorea, given the oft-made comparisons to GitHub and Google Docs, also helps with versioning updates and the distributed editing of your manuscript. Let’s say your PI isn’t thrilled with your phrasing or explanation in the Discussion. With Authorea, you can: lock the section while you edit it (i.e. no one is looking over your shoulder, judging); commit the update for all to see (oh, how they will marvel); get real-time feedback through additional comments and edits; and, when your PI has a change of heart, you can easily revert back to the section’s previous version (by clicking on that handy “History” clock icon). So, Authorea provides a platform for collaborative writing and review of your manuscript, an easy and automated citation mechanism, a one-stop repository for all your figures’ data, code, and editing, and even lets you get pre-publication feedback from your peers. What’s more, Authorea will also format your manuscript for the journal of your choice - text, figures, bibliography and all - at a click of a button. TWO QUESTIONS: 1. Why _wouldn’t_ you use Authorea for your next collaborative publication? 2. _What would you do with the time saved_ when you’d otherwise be emailing around drafts and data, sharing and modifying code, clarifying, citing, and formatting? Let us know in the comments!

_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.