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More and more scholars are leaving their academic posts (see [1] [2] [3]). As it turns out, it’s not possible to fully leave academia unless you write a detailed blog post about it. So, here’s mine. I resigned from my postdoctoral position at Harvard two months ago. My academic career was fairly typical. I spent the last twelve years doing research. After college, I worked at CERN for a few years, then pursued a Ph.D. at UCLA and a 3-year Postdoc at Harvard. During my Ph.D. and Postdoc I did not even apply to a single tenure-track job. Why? My research background is very (maybe, way too) interdisciplinary: B.Sc. in Astronomy, M.Sc. in Computer Science, at CERN I did Data Science (basically working in Tim Berners-Lee former group), my Ph.D. is in Information Science, and my Postdoc in Astrophysics. Who the hell is going to hire me? While many praise academic interdisciplinarity as an asset, at the end of the day TO GET TENURED YOU NEED TO BE ABLE TO TEACH CORE CLASSES IN ONE DISCIPLINE. So, even though I was working in an amazing research group and my publication record was just fine, I decided to leave. Leaving a postdoc at a top institution was a hard and risky decision to make. Yet, with so many PhDs and postdocs leaving academia today, I certainly don’t feel alone. But, HOW COMMON (OR RARE) IS IT TO LEAVE ACADEMIA? Last week I attended the ScienceOnline conference and in a session called _Alternative careers in science_, Eva Amsen discussed the infographic below.

This document shows the required format and appearance of a manuscript prepared for SPIE journals. It is prepared using LaTeX2e with the class file spieman.cls. The abstract should consist of a single paragraph containing no more than 200 words. It should be a summary of the paper and not an introduction. Because the abstract may be used in abstracting and indexing databases, it should be self-contained (that is, no numerical references) and substantive in nature, presenting concisely the objectives, methodology used, results obtained, and their significance. A list of up to eight keywords should immediately follow, with the keywords separated by commas and ending with a period. The body of the manuscript should be double-spaced and fully justified.

One of the questions we get more often from our users at Authorea is: Why is my LaTeX command not working? The SHORT ANSWER to that is: Because that LaTeX command was not intended for a webpage; it was intended for the printer. :( THE LONGER ANSWER. Authorea understands and renders markup languages such as Markdown, and LaTeX. But it does not rely on a compiler which takes TeX and spits out PDF. All the content created on Authorea is web-native. As we create more and more content on the web, we think that SCHOLARLY ARTICLES, TOO, SHOULD LIVE ON THE WEB. That said, we do enjoy and use LaTeX frequently at Authorea. This post for example, was written in LaTeX! Want to see? Let’s grab a pre-baked equation, for example this Fourier transform and render it below: a \Leftrightarrow 2\pi a\sum^\infty {\delta (\omega + 2\pi k)} ,( - \infty < n < \infty ) a \Leftrightarrow 2\pi a\sum^\infty {\delta (\omega + 2\pi k)} ,( - \infty < n < \infty ) We decided to support LaTeX from the very beginning, as it is the document preparation toolkit of choice for many (most?) researchers in the hard sciences. We think LaTeX is still the best programming language to tell a computer how to place text on a page. But the TeX project started pre-web, in 1978, and its scope and function are tightly linked to the PRINTED PAGE, not the WEBPAGE. Take, as an example a table definiton that begins with [ht]. This table command instructs TeX to put the table in the page, here, where the table is declared (h) AND at the top of the page (t). The list of examples could go on and on — think of minipage environments, page margins, text width parameters... all LaTeX notation that does really not make sense for a webpage. IS CSS THE NEXT LATEX?. What does the future hold for academic writing? We like to think that a few years from now we will format our research papers with the web version in mind, rather than the printed PDF. And we are not alone! LaTeX will very likely be used many years from now, but, we think, in a much more stripped-down, web-friendly incarnation, like the subset that Authorea currently supports. (We use some amazing tools like Pandoc and MathJax to convert between formats and render equations). Or maybe someday we will just format papers using CSS stylesheets?

INTRODUCTION We are happy to announce a partnership between Plotly and Authorea that gives you a free suite of powerful, collaborative tools for doing your analysis, graphing, coding, and publication. AUTHOREA AND PLOTLY, TOGETHER, CAN POWER YOUR RESEARCH COLLABORATION. WANT TO INSERT A PLOT.LY GRAPH IN YOUR OWN DOCUMENTS? FOLLOW THESE INSTRUCTIONS

THEORY OF EVOLUTION AND DEATH OF MASSIVE STARS Overview of the material that will be covered at the “Look & Listen” School 2014 Abstract We will first cover some basic principles of stellar evolution, with a particular emphasis on the physics of massive stars. Then we will focus on the phenomena that are currently believed to prominently influence the life of massive stars and determine the conditions at the pre-SN stage. Informed by the most recent observational results, we will also focus on some unsolved problems in stellar physics and how they could impact the death of massive stars and their stellar remnants. Schedule 1. Tuesday 14 Jan - Principles of stellar evolution (20+10) - Massive Stars evolution: Main Sequence and He-Burning (20+10) - Massive Stars evolution: Late evolutionary stages (20+10) 2. Thursday 16 Jan - Stellar Rotation (20+10) - Stellar Rotation and Magnetic Fields (20+10) - Mass loss (20+10) 3. Friday 17 Jan - Massive Binary Stars (20+10) - Very Massive Stars (20+10) - Progenitors of NS/BH, SNe, PISNe, GRBs (20+10)

In the last portion of _Sidereus Nuncius_, Galileo reported his discovery of FOUR OBJECTS that appeared to form a straight line of stars near JUPITER. The first night, he witnessed a line of three little stars close to Jupiter parallel to the ecliptic; the following nights brought different arrangements and another star into his view, totaling four stars around Jupiter. Throughout the text, Galileo gave illustrations of the relative positions of Jupiter and its apparent companion stars as they appeared nightly from late January through early March 1610. The fact that they changed their positions relative to Jupiter from night to night, but always appeared in the same straight line near Jupiter, brought Galileo to deduce that they were four bodies in orbit around Jupiter. On January 11 after 4 nights of observation he wrote: “I therefore concluded and decided unhesitatingly, that there are three stars in the heavens moving about Jupiter, as Venus and Mercury round the Sun; which at length was established as clear as daylight by numerous subsequent observations. These observations also established that there are not only three, but four, erratic sidereal bodies performing their revolutions round Jupiter...the revolutions are so swift that an observer may generally get differences of position every hour.”

INTRODUCTION History shows Galileo to be much more than an astronomical hero. His clear and careful record keeping and publication style not only let Galileo understand the Solar System, it continues to let _anyone_ understand _how_ Galileo did it. Galileo’s notes directly integrated his DATA (drawings of Jupiter and its moons), key METADATA (timing of each observation, weather, telescope properties), and TEXT (descriptions of methods, analysis, and conclusions). Critically, when Galileo included the information from those notes in _Siderius Nuncius_ , this integration of text, data and metadata was preserved, as shown in Figure 1. Galileo’s work advanced the “Scientific Revolution,” and his approach to observation and analysis contributed significantly to the shaping of today’s modern ”Scientific Method” . WANT TO INSERT A PLOT.LY GRAPH IN YOUR OWN DOCUMENTS? FOLLOW THESE INSTRUCTIONS

JAVASCRIPT, D3.JS, AND D3PO.JS Javascipt offers many ways to create data-driven graphics. A popular solution is D3.js, a JavaScript library to create and control web-based dynamic and interactive graphical forms. A gallery of some beautiful d3.js plots can be found here. Authorea now supports most Javascript-based data visualization solutions. The example below - Figure [fig:1] - is a plot generated using D3po.js which is a javascript extension of d3.js. D3po allows anyone with no special data visualization skills, to make an interactive, publication-quality figure that has staged builds and linked brushing through scatter plots. What’s even cooler is that the plot below is based on actual data (astrophysics data, yay!). The figure describes how metallicity affects color in cool stars. It is based on work of graduate student Elizabeth Newton and others . TRY CLICKING AND DRAGGING IN THE SCATTER PLOTS to understand the power of linked brushing in published figures. You should know that THIS ENTIRE VISUALIZATION IS RUNNING WITHIN AUTHOREA. The Javascript, HTML, CSS and ALL THE DATA associated with this image are all part of this blog post. They are individual files which can be found by clicking on the folder icon on the top left corner of this page.

This is an example article showing Authorea’s new IPYTHON INTEGRATION features. Take a look at the figures in this paper. When you hover on a figure, a “LAUNCH IPYTHON” button should appear. Click it and you’ll be spinning a virtual machine which takes all analysis (iPython notebooks, makefiles, etc) and data behind that plot and runs it for you to play with. Enjoy!

INTRODUCTION In the early 1600s, Galileo Galilei turned a telescope toward Jupiter. In his log book each night, he drew to-scale schematic diagrams of Jupiter and some oddly-moving points of light near it. Galileo labeled each drawing with the date. Eventually he used his observations to conclude that the Earth orbits the Sun, just as the four Galilean moons orbit Jupiter. History shows Galileo to be much more than an astronomical hero, though. His clear and careful record keeping and publication style not only let Galileo understand the Solar System, it continues to let _anyone_ understand _how_ Galileo did it. Galileo’s notes directly integrated his DATA (drawings of Jupiter and its moons), key METADATA (timing of each observation, weather, telescope properties), and TEXT (descriptions of methods, analysis, and conclusions). Critically, when Galileo included the information from those notes in _Siderius Nuncius_ , this integration of text, data and metadata was preserved, as shown in Figure 1. Galileo's work advanced the "Scientific Revolution," and his approach to observation and analysis contributed significantly to the shaping of today's modern "Scientific Method" . Today most research projects are considered complete when a journal article based on the analysis has been written and published. Trouble is, unlike Galileo's report in _Siderius Nuncius_, the amount of real data and data description in modern publications is almost never sufficient to repeat or even statistically verify a study being presented. Worse, researchers wishing to build upon and extend work presented in the literature often have trouble recovering data associated with an article after it has been published. More often than scientists would like to admit, they cannot even recover the data associated with their own published works. Complicating the modern situation, the words "data" and "analysis" have a wider variety of definitions today than at the time of Galileo. Theoretical investigations can create large "data" sets through simulations (e.g. The Millennium Simulation Project). Large scale data collection often takes place as a community-wide effort (e.g. The Human Genome project), which leads to gigantic online "databases" (organized collections of data). Computers are so essential in simulations, and in the processing of experimental and observational data, that it is also often hard to draw a dividing line between "data" and "analysis" (or "code") when discussing the care and feeding of "data." Sometimes, a copy of the code used to create or process data is so essential to the use of those data that the code should almost be thought of as part of the "metadata" description of the data. Other times, the code used in a scientific study is more separable from the data, but even then, many preservation and sharing principles apply to code just as well as they do to data. So how do we go about caring for and feeding data? Extra work, no doubt, is associated with nurturing your data, but care up front will save time and increase insight later. Even though a growing number of researchers, especially in large collaborations, know that conducting research with sharing and reuse in mind is essential, it still requires a paradigm shift. Most people are still motivated by piling up publications and by getting to the next one as soon as possible. But, the more we scientists find ourselves wishing we had access to extant but now unfindable data , the more we will realize why bad data management is bad for science. How can we improve? THIS ARTICLE OFFERS A SHORT GUIDE TO THE STEPS SCIENTISTS CAN TAKE TO ENSURE THAT THEIR DATA AND ASSOCIATED ANALYSES CONTINUE TO BE OF VALUE AND TO BE RECOGNIZED. In just the past few years, hundreds of scholarly papers and reports have been written on questions of data sharing, data provenance, research reproducibility, licensing, attribution, privacy, and more--but our goal here is _not_ to review that literature. Instead, we present a short guide intended for researchers who want to know why it is important to "care for and feed" data, with some practical advice on how to do that. The set of Appendices at the close of this work offer links to the types of services referred to throughout the text. BOLDFACE LETTERING below highlights actions one can take to follow the suggested rules.

HOW TO USE THIS TEMPLATE Please use this template for preparing your article for _F1000Research_ in conjunction with our (author guidelines). You may not need to complete all the sections below depending on the article type. Please edit the sections below to add your article content and delete any sections that are not required for your article type.

ABSTRACT. Research and scholarship lead to the generation of new knowledge. The dissemination of this knowledge has a fundamental impact on the ways in which society develops and progresses, and at the same time it feeds back to improve subsequent research and scholarship. Here, as in so many other areas of human activity, the internet is changing the way things work: it opens up opportunities for new processes that can accelerate the growth of knowledge, including the creation of new means of communicating that knowledge among researchers and within the wider community. Two decades of emergent and increasingly pervasive information technology have demonstrated the potential for far more effective scholarly communication. However, the use of this technology remains limited; research processes and the dissemination of research results have yet to fully assimilate the capabilities of the web and other digital media. Producers and consumers remain wedded to formats developed in the era of print publication, and the reward systems for researchers remain tied to those delivery mechanisms. Force11 (the Future of Research Communication and e-Scholarship) is a community of scholars, librarians, archivists, publishers and research funders that has arisen organically to help facilitate the change toward improved knowledge creation and sharing. Individually and collectively, we aim to bring about a change in scholarly communication through the effective use of information technology. Force11 has grown from a small group of like-minded individuals into an open movement with clearly identified stakeholders associated with emerging technologies, policies, funding mechanisms and business models. While not disputing the expressive power of the written word to communicate complex ideas, our foundational assumption is that scholarly communication by means of semantically-enhanced media-rich digital publishing is likely to have a greater impact than communication in traditional print media or electronic facsimiles of printed works. However, to date, online versions of ’scholarly outputs’ have tended to replicate print forms, rather than exploit the additional functionalities afforded by the digital terrain. We believe that digital publishing of enhanced papers will enable more effective scholarly communication, which will also broaden to include, for example, better links to data, the publication of software tools, mathematical models, protocols and workflows, and research communication by means of social media channels. This document highlights the findings of the Force11 workshop on the Future of Research Communication and e-Scholarship held at Schloss Dagstuhl, Germany, in August 2011: it summarizes a number of key problems facing scholarly publishing today, and presents a vision that addresses these problems, proposing concrete steps that key stakeholders can take to improve the state of scholarly publishing. More about Force11 can be found at http://www.force11.org. This White Paper is a collaborative effort that reflects the input of all Force11 attendees at the Dagstuhl Workshop [1], and is very much a living document [2] . We see it as a starting point that will grow and be updated and augmented by individual and collective efforts by the participants and others. We invite you to join and contribute to this enterprise. [1] https://sites.google.com/site/futureofresearchcommunications/home/attendees [2] Citation: Bourne P, Clark T, Dale R, de Waard A, Herman I, Hovy E and Shotton D, on behalf of the Force11 community (2011).  Force11 White Paper: Improving the Future of Research Communication and e-Scholarship. 27 October 2011.  Available from http://force11.org/ Copyright: © 2011 The authors. License: This is an open-access article distributed under the terms of the Creative Commons Attribution License (v3.0, unported: http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

Asteroseismology of 1.0 − 2.0M⊙ red giants by the _Kepler_ satellite has enabled the first definitive measurements of interior rotation in both first ascent red giant branch (RGB) stars and those on the Helium burning clump. The inferred rotation rates are 10 − 30 days for the ≈0.2M⊙ He degenerate cores on the RGB and 30 − 100 days for the He burning core in a clump star. Using the MESA code we calculate state-of-the-art stellar evolution models of low mass rotating stars from the zero-age main sequence to the cooling white dwarf (WD) stage. We include transport of angular momentum due to rotationally induced instabilities and circulations, as well as magnetic fields in radiative zones (generated by the Tayler-Spruit dynamo). We find that all models fail to predict core rotation as slow as observed on the RGB and during core He burning, implying that an unmodeled angular momentum transport process must be operating on the early RGB of low mass stars. Later evolution of the star from the He burning clump to the cooling WD phase appears to be at nearly constant core angular momentum. We also incorporate the adiabatic pulsation code, ADIPLS, to explicitly highlight this shortfall when applied to a specific _Kepler_ asteroseismic target, KIC8366239.

Overview This open document is being used to describe and record the events at the Radcliffe Exploratory Seminar on Data Curation and Analysis, to be held at the Radcliffe Institute for Advanced Study, May 9-10 2013. This Google Drive Directory should be used to deposit all files contributed by participants before and during the meeting. (Click "Open in Drive" on your browser to make a new folder, e.g. with your name as its name.) This Google Doc is used for collaborative real-time note-taking. ABSTRACT: Rapid advances in technology have allowed us to collect vast amounts of data in myriad fields and forms, but our ability to manage and analyze these data has not kept pace. As a result, the amount of data collected far exceeds what can be analyzed and, often, what can be archived. These issues only become more pressing as data collection accelerates. Astronomers and astrophysicists, for example, collect terabytes of data per night; the phrase “drowning in a data tsunami” is increasingly used to describe this situation. The issues of what to keep and what to distribute are surprisingly complex, even when we put aside technological issues such as long-term storage and retrieval. A central challenge is the fundamental conflict between reducing the size of data and preserving information for future scientific inquires and statistical analyses. Complicating matters further, the parties/teams involved in the entire data collection, curation, and analysis process often have only limited communication with each other owing to the sequential nature of this process. This seminar brings together a core group of leading experts and emerging scholars in information and natural sciences to discuss, debate, and design principles and strategies to address this grand challenge, which increasingly affects almost every aspect of science and society. GOAL: By gathering experts from information and natural sciences, we aim to start building a set of principles and methods that will allow us to understand such problems and to provide better preprocessing, analyses, and data preservation, especially in the context of the natural sciences. The ultimate goals of this research include providing methods for assessing the validity of such collaborative analyses, guidance on statistically-principled preprocessing, and a rich new theory of statistical learning and inference with multiple parties. We believe that this collaboration will simultaneously sow the seeds for innovative mathematical theory and shed light on directly usable guidelines for the construction and curation of scientific databases.

Dedication The Transforming Scholarly Communication workshop was largely the brainchild of Lee Dirks, of the Microsoft Research "Connections" group. Lee passed away in August 2012, and we hope that all of the good outcomes of this workshop serve forever as a tribute to Lee.

We analyze data sharing practices of astronomers over the past fifteen years. An analysis of URL links embedded in papers published by the American Astronomical Society reveals that the total number of links included in the literature rose dramatically from 1997 until 2005, when it leveled off at around 1500 per year. This rise indicates an increased interest in data-sharing over the same time period that the web saw its most dramatic growth in usage in the developed world. The analysis also shows that the availability of linked material decays with time: in 2011, 44% of links published a decade earlier, in 2001, were broken. A rough analysis of link types reveals that links to data hosted on astronomers’ personal websites become unreachable much faster than links to datasets on curated institutional sites. To gauge astronomers’ current data sharing practices and preferences further, we performed in-depth interviews with 12 scientists and online surveys with 173 scientists, all at a large astrophysical research institute in the United States: the Harvard-Smithsonian Center for Astrophysics, in Cambridge, MA. Both the in-depth interviews and the online survey indicate that, in principle, there is no philosophical objection to data-sharing among astronomers at this institution, and nearly all astronomers would share as much of their data as others wanted if it were practicable. Key reasons that more data are not presently shared more efficiently in astronomy include: the difficulty of sharing large data sets; over reliance on non-robust, non-reproducible mechanisms for sharing data (e.g. emailing it); unfamiliarity with options that make data-sharing easier (faster) and/or more robust; and, lastly, a sense that other researchers would not want the data to be shared. We conclude with a short discussion of a new effort to implement an easy-to-use, robust, system for data sharing in astronomy, at theastrodata.org, and we analyze the uptake of that system to-date.

ABSTRACT The very long, thin infrared dark cloud Nessie is even longer than had been previously claimed, and an analysis of its Galactic location suggests that it lies directly in the Milky Way’s mid-plane, tracing out a highly elongated bone-like feature within the prominent Scutum-Centaurus spiral arm. Re-analysis of mid-infrared imagery from the Spitzer Space Telescope shows that this IRDC is at least 2, and possibly as many as 8 times longer than had originally been claimed by Nessie’s discoverers, ; its aspect ratio is therefore at least 150:1, and possibly as large as 800:1. A careful accounting for both the Sun’s offset from the Galactic plane (∼25 pc) and the Galactic center’s offset from the (lII, bII)=(0, 0) position defined by the IAU in 1959 shows that the latitude of the true Galactic mid-plane at the 3.1 kpc distance to the Scutum-Centaurus Arm is not b = 0, but instead closer to b = −0.5, which is the latitude of Nessie to within a few pc. Apparently, Nessie lies _in_ the Galactic mid-plane. An analysis of the radial velocities of low-density (CO) and high-density (${\rm NH}_3$) gas associated with the Nessie dust feature suggests that Nessie runs along the Scutum-Centaurus Arm in position-position-velocity space, which means it likely forms a dense ‘spine’ of the arm in real space as well. No galaxy-scale simulation to date has the spatial resolution to predict a Nessie-like feature, but extant simulations do suggest that highly elongated over-dense filaments should be associated with a galaxy’s spiral arms. Nessie is situated in the closest major spiral arm to the Sun toward the inner Galaxy, and appears almost perpendicular to our line of sight, making it the easiest feature of its kind to detect from our location (a shadow of an Arm’s bone, illuminated by the Galaxy beyond). Although the Sun’s (∼25 pc) offset from the Galactic plane is not large in comparison with the half-thickness of the plane as traced by Population I objects such as GMCs and HII regions (∼200 pc; ), it may be significant compared with an extremely thin layer that might be traced out by Nessie-like “bones” of the Milky Way. Future high-resolution extinction and molecular line data may therefore allow us to exploit the Sun’s position above the plane to gain a (very foreshortened) view “from above" of dense gas in Milky Way’s disk and its structure.