We present the results of our first year of quasar search in the ongoing ESO public Kilo Degree Survey (KiDS) and VISTA Kilo-Degree Infrared Galaxy (VIKING) surveys. These surveys are among the deeper wide-field surveys that can be used to uncover large numbers of z ∼ 6 quasars. This allows us to probe a more common population of z ∼ 6 quasars that is fainter than the well-studied quasars from the main Sloan Digital Sky Survey. From this first set of combined survey catalogues covering ∼250 deg 2 we selected point sources down to Z AB = 22 that had a very red i − Z (i − Z > 2.2) colour. After follow-up imaging and spectroscopy, we discovered four new quasars in the redshift range 5.8 < z < 6.0. The absolute magnitudes at a rest-frame wavelength of 1450Å are between −26.6 < M 1450 < −24.4, confirming that we can find quasars fainter than M * , which at z = 6 has been estimated to be between M * = −25.1 and M * = −27.6. The discovery of four quasars in 250 deg 2 of survey data is consistent with predictions based on the z ∼ 6 quasar luminosity function. We discuss various ways to push the candidate selection to fainter magnitudes and we expect to find about 30 new quasars down to an absolute magnitude of M 1450 = −24. Studying this homogeneously selected faint quasar population will be important to gain insight into the onset of the co-evolution of the black holes and their stellar hosts.

Most workflow systems that support data provenance primarily focus on tracing lineage of data. Data provenance by data lineage provides the derivation history of data including information about services and input data that contributed to the creation of a data product. We show that tracing lineage by means of full backward chaining not only enables users to share, discover and reuse the data, but also supports scientific data processing through storage, retrieval and (re)processing of digitized scientific data. In this paper, we present Astro-WISE, a distributed system for processing, analyzing and disseminating wide field imaging astronomical data. We show how Astro-WISE traces lineage of data and how it facilitates data processing, retrieval, storage, archiving. Particularly we show how it solves issues related to the changing data items typical for the scientific environment, such as physical changes in calibrations, our insight in these changes and improved methods for deriving results.

Most workflow systems that support data provenance primarily focus on tracing lineage of data. Data provenance by data lineage provides the derivation history of data including information about services and input data that contributed to the creation of a data product. We show that tracing lineage by means of full backward chaining not only enables users to share, discover and reuse the data, but also supports scientific data processing through storage, retrieval and (re)processing of digitized scientific data. In this paper, we present Astro-WISE, a distributed system for processing, analyzing and disseminating wide field imaging astronomical data. We show how Astro-WISE traces lineage of data and how it facilitates data processing, retrieval, storage and archiving. Particularly we show how it solves issues related to the changing data items typical for the scientific environment, such as physical changes in calibrations, our insight in these changes and improved methods for deriving results.

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