Alberto Pepe

and 1 more

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.

Hope How-Huan Chen

and 1 more

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.

Alyssa Goodman

and 10 more

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.

Alyssa Goodman

and 10 more