Michael S Vogeley

We study how the void environment affects the chemical evolution of galaxies by comparing the metallicity of dwarf galaxies in voids with dwarf galaxies in denser regions. Using spectroscopic observations from SDSS DR7, we estimate oxygen and nitrogen abundances of 889 void dwarf galaxies and 672 dwarf galaxies in denser regions. A substitute for the [OII] lambda 3727 doublet is developed, permitting oxygen abundance estimates of SDSS dwarf galaxies at all redshifts with the direct method. We find that void dwarf galaxies have about the same oxygen abundances and slightly lower N/O ratios than dwarf galaxies in denser environments. The lower N/O ratios seen in void dwarf galaxies may indicate both delayed star formation and a dependence of cosmic downsizing on the large-scale environment. Similar oxygen abundances in the two dwarf galaxy populations might be evidence of larger ratios of dark matter halo mass to stellar mass in voids.

User-generated Structured Query Language (SQL) queries are a rich source of information for database analysts, information scientists, and the end users of databases. In this study a group of scientists in astronomy and computer and information scientists work together to analyze a large volume of SQL log data generated by users of the Sloan Digital Sky Survey (SDSS) data archive in order to better understand users' data seeking behavior. While statistical analysis of such logs is useful at aggregated levels, efficiently exploring specific patterns of queries is often a challenging task due to the typically large volume of the data, multivariate features, and data requirements specified in SQL queries. To enable and facilitate effective and efficient exploration of the SDSS log data, we designed an interactive visualization tool, called the SDSS Log Viewer, which integrates time series visualization, text visualization, and dynamic query techniques. We describe two analysis scenarios of visual exploration of SDSS log data, including understanding unusually high daily query traffic and modeling the types of data seeking behaviors of massive query generators. The two scenarios demonstrate that the SDSS Log Viewer provides a novel and potentially valuable approach to support these targeted tasks.

In the new data-intensive science paradigm, data infrastructures have been designed and built to collect, archive, publish, and analyze scientific data for a variety of users. Little attention, however, has been paid to users of these data infrastructures. This study endeavors to improve our understanding of these users' data usage models through a content analysis of publications related to a frequently cited project in the data-intensive science, Sloan Digital Sky Survey (SDSS). We find that 1) Content analysis of scientific publications could be a complementary method for researchers in HCI community; 2) although SDSS produced a large volume of astronomical data, users did not fully utilize these data; 3) users are not only consumers of scientific data, they are also producers; and 4) studies that can use multiple large scale data sources are relatively rare. Issues of data provenance and usability may prevent researchers from doing research that combines such data sources. Further HCI study of detailed usability issues associated with data infrastructures in the new paradigm is eagerly needed.

We introduce a new visual analytic approach to the study of scientific discoveries and knowledge diffusion. Our approach enhances contemporary co-citation network analysis by enabling analysts to identify co-citation clusters of cited references intuitively, synthesize thematic contexts in which these clusters are cited, and trace how research focus evolves over time. The new approach integrates and streamlines a few previously isolated techniques such as spectral clustering and feature selection algorithms. The integrative procedure is expected to empower and strengthen analytical and sense making capabilities of scientists, learners, and researchers to understand the dynamics of the evolution of scientific domains in a wide range of scientific fields, science studies, and science policy evaluation and planning. We demonstrate the potential of our approach through a visual analysis of the evolution of astronomical research associated with the Sloan Digital Sky Survey (SDSS) using bibliographic data between 1994 and 2008. Implications on methodological issues are also addressed.

We describe our experience of developing scientific software in an ongoing multidisciplinary research project participated by information scientists and astronomers. In particular, we reflect on how the interdisciplinary collaboration is facilitated by the development of a unique boundary object - a hybrid map of the Universe and scientific discoveries. With reference to theories of interdisciplinary communication, we underline that information Visualization serves a fundamental role in scientific software development. The impact of information visualization can go far beyond the content representation, and can facilitate communications across distinct disciplines.

We propose a graph decomposition algorithm for analyzing the structure of complex graph networks. After multi-word term extraction, we apply techniques from text mining and visual analytics in a novel way by integrating symbolic and numeric information to build clusters of domain topics. Terms are clustered based on surface linguistic variations and clusters are inserted in an association network based on their intersection with documents. The graph is then decomposed based on atom graph structure into central (non-decomposable) atom and peripheral atoms. The whole process is applied to publications from the Sloan Digital Sky Survey (SDSS) project in the Astronomy field. The mapping obtained was evaluated by a domain expert and appeared to have captured interesting conceptual relations between different domain topics.

The Sloan Digital Sky Survey (SDSS) is the largest ongoing sky survey. It regularly makes data releases to the astronomical community. From a macroscopic point of view, a profound question is: what is the role of SDSS data releases in the evolution of the relevant scientific fields? In this paper, we introduce an integrated approach by combining statistical, information-theoretical, and symbolic methods for text data analysis and show how this combined approach can distinguish thematic variations associated with the different data releases.

We discuss here the spatial clustering of Seyferts and LINERs and consequences for their central engines. We show that Seyferts are less clustered than LINERs, and that this difference is not driven by the morphology-density relation, but it is related to the difference in clustering as a function of level of activity in these systems and the amount of fuel available for accretion. LINERs, which are the most clustered among AGN, show the lowest luminosities and obscuration levels, and relatively low gas densities, suggesting that these objects harbor black holes that are relatively massive yet weakly active or inefficient in their accretion, probably due to the insufficiency of their fuel supply. Seyferts, which are weakly clustered, are very luminous, show generally high gas densities and large quantities of obscuring material, suggesting that in these systems the black holes are less massive but abundantly fueled and therefore accrete quickly and probably efficiently enough to clearly dominate the ionization.

Recent optical studies of QSOs/AGN have revealed the unexpected result that the amplitude of QSO clustering does not evolve with redshift. The likely dependence of AGN clustering on the host halo mass and heterogeneity in the events that trigger the efficiency of feeding the central engine imply that the observed clustering may strongly vary with wavelength and other selection criteria. However, progress on these issues has been limited by the small size of redshift samples of AGN at other than optical wavelengths. Here we describe a unique project that combines X-ray, UV, IR, radio and optically selected data to study the clustering of AGN. This will allow us to study samples of 10(4) AGN across a wide range of wavelengths, addressing many key questions about AGN.