The Sloan Digital Sky Survey has validated and made publicly available its Second Data Release. This data release consists of 3324 square degrees of five-band (u g r i z) imaging data with photometry for over 88 million unique objects, 367,360 spectra of galaxies, quasars, stars and calibrating blank sky patches selected over 2627 degrees of this area, and tables of measured parameters from these data. The imaging data reach a depth of r ~ 22.2 (95% completeness limit for point sources) and are photometrically and astrometrically calibrated to 2% rms and 100 milli-arcsec rms per coordinate, respectively. The imaging data have all been processed through a new version of the SDSS imaging pipeline, in which the most important improvement since the last data release is fixing an error in the model fits to each object. The result is that model magnitudes are now a good proxy for point spread function (PSF) magnitudes for point sources, and Petrosian magnitudes for extended sources. The spectroscopy extends from 3800 A to 9200 A at a resolution of 2000. The spectroscopic software now repairs a systematic error in the radial velocities of certain types of stars, and has substantially improved spectrophotometry. All data included in the SDSS Early Data Release and First Data Release are reprocessed with the improved pipelines, and included in the Second Data Release. The data are publically available as of 2004 March 15 via the web sites http://www.sdss.org/dr2 and http://skyserver.sdss.org .Comment: 24 pages, submitted to AJ. See ftp://ftp.astro.princeton.edu/strauss/sdss/dr2.ps for high-resolution figure
The Sloan Digital Sky Survey has validated and made publicly available its First Data Release. This consists of 2099 square degrees of five-band (u, g, r, i, z) imaging data, 186,240 spectra of galaxies, quasars, stars and calibrating blank sky patches selected over 1360 square degrees of this area, and tables of measured parameters from these data. The imaging data go to a depth of r ~ 22.6 and are photometrically and astrometrically calibrated to 2% rms and 100 milli-arcsec rms per coordinate, respectively. The spectra cover the range 3800--9200 A, with a resolution of 1800--2100. Further characteristics of the data are described, as are the data products themselves.Comment: Submitted to The Astronomical Journal. 16 pages. For associated documentation, see http://www.sdss.org/dr
Abstract. We introduce Science Object Linking and Embedding (SOLE), a tool for linking research papers with associated science objects, such as source codes, datasets, annotations, workflows, packages, and virtual machine images. The objective of SOLE is to reduce the cost to an author of linking research papers with such science objects for the purpose of reproducible research. To this end, SOLE allows an author to use simple tags to delimit a science object to be associated with a research paper. It creates an adequate representation of the science object and manages a bibliography-like specification of science objects. Authors and readers can reference elements of this bibliography and associate them with phrases in the text of the research paper through a Web interface, in a similar manner to a traditional bibliography tool.
Abstract-It has become increasingly important to capture and understand the origins and derivation of data (its provenance). A key issue in evaluating the feasibility of data provenance is its performance, overheads, and scalability. In this paper, we explore the feasibility of a general metadata storage and management layer for parallel file systems, in which metadata includes both file operations and provenance metadata. We experimentally investigate the design optimality-whether provenance metadata should be loosely-coupled or tightly integrated with a file metadata storage systems. We consider two systems that have applied similar distributed concepts to metadata management, but focusing singularly on kind of metadata: (i) FusionFS, which implements a distributed file metadata management based on distributed hash tables, and (ii) SPADE, which uses a graph database to store audited provenance data and provides distributed module for querying provenance. Our results on a 32-node cluster show that FusionFS+SPADE is a promising prototype with negligible provenance overhead and has promise to scale to petascale and beyond. Furthermore, FusionFS with its own storage layer for provenance capture is able to scale up to 1K nodes on BlueGene/P supercomputer.
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