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.

_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.

We combine _Kepler_ photometry with ground-based spectra to present a comprehensive dynamical model of the double red giant eclipsing binary KIC 9246715. While the two stars are very similar in mass ($M_1 = 2.171 \ M_{\odot}$, $M_2 = 2.149 \ M_{\odot}$) and radius ($R_1 = 8.37 \ R_{\odot}$, $R_2 = 8.30 \ R_{\odot}$), an asteroseismic analysis finds one main set of solar-like oscillations with unusually low-amplitude, wide modes. A second set of oscillations from the other star may exist, but this marginal detection is extremely faint. Because the two stars are nearly twins, KIC 9246715 is a difficult target for a precise test of the asteroseismic scaling relations, which yield M = 2.17 ± 0.14 M⊙ and R = 8.26 ± 0.18 R⊙. Both stars are consistent with the inferred asteroseismic properties, but we suspect the main oscillator is Star 2 because it is less active than Star 1. We find evidence for stellar activity and modest tidal forces acting over the 171-day eccentric orbit, which are likely responsible for the essential lack of solar-like oscillations in one star and weak oscillations in the other. Mixed modes indicate the main oscillating star is on the secondary red clump (a core-He-burning star), and stellar evolution modeling supports this with a coeval history for a pair of red clump stars. This system is a useful case study and paves the way for a detailed analysis of more red giants in eclipsing binaries, an important benchmark for asteroseismology.

ABSTRACT. ρ Ophiuchii is a group of five B-stars, embedded in a nearby molecular cloud: Ophiuchus, at a distance of ∼ 119 pc. A “bubble”-like structure is found in dust thermal emission around ρ Oph. The circular structure on the Hα map further indicates that this bubble is physically connected to the source at the center. The goal of this paper is to estimate the impact of feedback from these embedded B-stars on the molecular cloud, by comparing the energy associated with the material entrained in the bubble to the total turbulent energy of the cloud. In this paper, we combine data from the COMPLETE Survey, which includes ¹²CO (1-0) and ¹³CO (1-0) molecular line emission from FCRAO, an extinction map derived from 2MASS near-infrared data using the NICER algorithm, and far-infrared data from IRIS (60/100 μm) with data from the Herschel Science Archive (PACS 100/160 μm and SPIRE 250/350/500 μm). With the wealth of data tracing different components of the cloud, we try to determine the best strategy to derive physical properties and to estimate the energy budget in the shell and in the cloud. We also experiment with the hierarchical Bayesian-fitting technique introduced by in an effort to eliminate the bias in the derived column densities and/or temperatures induced by noise in the far-IR data. We find that the energy entrained in the bubble is ∼ 12 % of the total turbulent energy of the Ophiuchus molecular cloud. This fraction is similar to the number give for the Perseus molecular cloud, and it suggests the non-negligible role of B-stars in driving the turbulence in clouds. We expect that a complete survey of “bubbles” in the Ophiuchus cloud will reveal the importance of B-star winds in molecular clouds.

ABSTRACT. Here we will explore various aspects of long term and high volume data curation and analysis. What has worked in Astronomy? What has not? What does it mean to say something has worked? What do astronomers think? What are the financial, social, technical, and practical issues that limit how data can be used in the future?

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.

ABSTRACT. Broadly, we seek to understand the role of pressure in star forming molecular clouds. We examine molecular line data of the Perseus region from the COMPLETE survey alongside radiative transfer-processed ‘observations’ of the turbulent simulations of S. Offner to try to (1) understand to what extent we can actually measure pressure through observations, and (2) study how pressure changes within a cloud’s substructure.

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.

If you are a scholar and haven't spent the last ten years in a vacuum, you have heard of Open Access: the emerging practice of providing unrestricted access to peer-reviewed scholarly works, such as journal articles, conference papers, and book chapters. Open Access comes in two flavors, _green_ and _gold_, depending on how an article is made available to the public. The "green road" to Open Access involves an author making her article publicly available after publication, e.g. by depositing the article's post-print in an open institutional repository. According to many, the preferred avenue to achieve Open Access, however, is the "golden road" which happens when an author publishes an article directly in an OA journal. THE FACT THAT OPEN ACCESS, REGARDLESS OF ITS FLAVOR, HAS INNUMERABLE BENEFITS FOR RESEARCHERS AND THE PUBLIC AT LARGE IS BEYOND DISCUSSION --- even the most traditional scholarly publishers would have to agree. Importantly, the vision of universal Open Access to scholarly knowledge, i.e., the idea that the entire body of published scholarship should be made available to everyone free of charge, is not too far fetched. In practice, by a combination of green and golden OA practices, this vision is already a reality in some scientific fields, such as physics and astronomy. So: Open Access is both fundamentally necessary and bound to happen. BUT, WHETHER OPEN ACCESS, ALONE, CAN GUARANTEE REPRODUCIBILITY AND TRANSPARENCY OF RESEARCH RESULTS IS A DIFFERENT AND COMPELLING QUESTION. Do research articles contain enough information to exactly (or even approximately) replicate a scientific study? Unfortunately, very often the answer to this question is no. As science, and scholarship in general, become inevitably more computational in nature, the experiments, calculations, and analyses performed by researchers are too many and too complex to be described in detail in a research article. As such, the minutiae of research activity are often hidden from view, making science unintelligible and irreproducible, not only for the public at large, but also for scientists, experts and, paradoxically, even for the same scientists who conducted the research in the first place, who may have not documented their exact workflows elsewhere. A parallel movement to Open Access --- Open Science --- is building up momentum in scholarly circles. Its mission is to provide open, universal access to the full sources of scientific research.

Instructions for Co-Authors The full file repository for this paper is at a shared Google Drive directory, https://drive.google.com/#folders/0BxIRxiTe1u6BcGlnUGt2ckU1Vms, shared with all co-authors. NOTE: THE “AAS” (PRESS CONFERENCE) SLIDES AT HTTPS://DRIVE.GOOGLE.COM/#FOLDERS/0BXIRXITE1U6BRKLQRZLUAUNUUUU GIVE A BETTER IDEA OF WHERE THIS DRAFT IS GOING THAN THE TEXT/FIGURES HERE AS OF NOW... AG WILL UPDATE ALL BY C.1/1/13! The Mendeley Library “Nessie and Friends” used to house references used in this work, at: http://www.mendeley.com/groups/2505711/nessie-and-friends/, but since Authorea works more directly with ADS links, we’ll use the ADS Private Library at http://adsabs.harvard.edu/cgi-bin/nph-abs_connect?library&libname=Nessie+and+Friends&libid=488e32b08b instead. The Mendeley library is the source of the nessie.bib file in the “Bibliography” folder here on Authorea, but I am not sure how to get the ADS references out as a .bib file. xxAlberto?xx The Glue software used to intercompare data sets used in this work is online through: http://glue-viz.readthedocs.org/en/latest/ We are using Authorea.com as an experimental platform to compile this paper. The manual steps we will need to take before submission include: - download LaTeX file - modify LaTeX file to use aas macros - insert needed information (e.g. about authors, running header) into was version of LaTeX manuscript - extract needed figures from relevant folders here & bundle them with LaTeX manuscript & macros - create .bib file from ADS Private Library - add .bib file to folder with manuscript & figures - fix in-line referencing so that $\citet$ and $\citep$ commands work