We present ammonia observations of 193 dense cores and core candidates in the Perseus molecular cloud made using the Robert F. Byrd Green Bank Telescope. We simultaneously observed the NH 3 (1,1), NH 3 (2,2), C 2 S (2 1 ! 1 0 ), and C 34 2 S(2 1 ! 1 0 ) transitions near ¼ 23 GHz for each of the targets with a spectral resolution of v % 0:024 km s À1 . We find ammonia emission associated with nearly all of the (sub)millimeter sources, as well as at several positions with no associated continuum emission. For each detection, we have measured physical properties by fitting a simple model to every spectral line simultaneously. Where appropriate, we have refined the model by accounting for low optical depths, multiple components along the line of sight, and imperfect coupling to the GBT beam. For the cores in Perseus, we find a typical kinetic temperature of T k ¼ 11 K, a typical column density of N NH 3 % 10 14:5 cm À2 , and velocity dispersions ranging from v ¼ 0:07 to 0.7 km s À1. However, many cores with v > 0:2 km s À1 show evidence for multiple velocity components along the line of sight.

Fig. 1. Left:The top twelve overall most memorable visualizations from our experiment (most to least memorable from top left to bottom right). Middle: The top twelve most memorable visualizations from our experiment when visualizations containing human recognizable cartoons or images are removed (most to least memorable from top left to bottom right). Right: The twelve least memorable visualizations from our experiment (most to least memorable from top left to bottom right).Abstract-An ongoing debate in the Visualization community concerns the role that visualization types play in data understanding. In human cognition, understanding and memorability are intertwined. As a first step towards being able to ask questions about impact and effectiveness, here we ask: "What makes a visualization memorable?" We ran the largest scale visualization study to date using 2,070 single-panel visualizations, categorized with visualization type (e.g., bar chart, line graph, etc.), collected from news media sites, government reports, scientific journals, and infographic sources. Each visualization was annotated with additional attributes, including ratings for data-ink ratios and visual densities. Using Amazon's Mechanical Turk, we collected memorability scores for hundreds of these visualizations, and discovered that observers are consistent in which visualizations they find memorable and forgettable. We find intuitive results (e.g., attributes like color and the inclusion of a human recognizable object enhance memorability) and less intuitive results (e.g., common graphs are less memorable than unique visualization types). Altogether our findings suggest that quantifying memorability is a general metric of the utility of information, an essential step towards determining how to design effective visualizations.

We present an overview of data available for the Ophiuchus and Perseus molecular clouds from ``Phase I'' of the COMPLETE Survey of Star-Forming Regions. This survey provides a range of data complementary to the Spitzer Legacy Program ``From Molecular Cores to Planet Forming Disks.'' Phase I includes: Extinction maps derived from 2MASS near-infrared data using the NICER algorithm; extinction and temperature maps derived from IRAS 60 and 100um emission; HI maps of atomic gas; 12CO and 13CO maps of molecular gas; and submillimetre continuum images of emission from dust in dense cores. Not unexpectedly, the morphology of the regions appears quite different depending on the column-density tracer which is used, with IRAS tracing mainly warmer dust and CO being biased by chemical, excitation and optical depth effects. Histograms of column-density distribution are presented, showing that extinction as derived from 2MASS/NICER gives the closest match to a log-normal distribution as is predicted by numerical simulations. All the data presented in this paper, and links to more detailed publications on their implications are publically available at the COMPLETE website.Comment: Accepted by AJ. Full resolution version available from: http://www.cfa.harvard.edu/COMPLETE/papers/complete_phase1.pd

We present a study on the impact of molecular outflows in the Perseus molecular cloud complex using the COMPLETE survey large-scale 12 CO(1-0) and 13 CO(1-0) maps. We used three-dimensional isosurface models generated in RA-DEC-Velocity space to visualize the maps. This rendering of the molecular line data allowed for a rapid and efficient way to search for molecular outflows over a large (∼ 16 deg 2 ) area. Our outflow-searching technique detected previously known molecular outflows as well as new candidate outflows. Most of these new outflow-related high-velocity features lie in regions that have been poorly studied before. These new outflow candidates more than double the amount of outflow mass, momentum, and kinetic energy in the Perseus cloud complex. Our results indicate that outflows have significant impact on the environment immediately surrounding localized regions of active star formation, but lack the energy needed to feed the observed turbulence in the entire Perseus complex. This implies that other energy sources, in addition to protostellar outflows, are responsible for turbulence on a global cloud scale in Perseus. We studied the impact of outflows in six regions with active star formation within Perseus of sizes in the range of 1 to 4 pc. We find that outflows have enough power to maintain the turbulence in these regions and enough momentum to disperse and unbind some mass from them. We found no correlation between outflow strength and star formation efficiency for the six different regions we studied, contrary to results of recent numerical simulations. The low fraction of gas that potentially could be ejected due to outflows suggests that additional mechanisms other than cloud dispersal by outflows are needed to explain low star formation efficiencies in clusters.

In this paper we move beyond memorability and investigate how visualizations are recognized and recalled. For this study we labeled a dataset of 393 visualizations and analyzed the eye movements of 33 participants as well as thousands of participant-generated text descriptions of the visualizations. This allowed us to determine what components of a visualization attract people's attention, and what information is encoded into memory. Our findings quantitatively support many conventional qualitative design guidelines, including that (1) titles and supporting text should convey the message of a visualization, (2) if used appropriately, pictograms do not interfere with understanding and can improve recognition, and (3) redundancy helps effectively communicate the message. Importantly, we show that visualizations memorable "at-a-glance" are also capable of effectively conveying the message of the visualization. Thus, a memorable visualization is often also an effective one.