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F. Bonnarel 1 , M. Louys 1 , Igor Chilingarian 2,3 , Ivan Zolothukin 3 , Brice Gassmann 1 (1) CDS, Strasbourg, (2) LERMA , Paris, (3) SAI, Moscow. Navigation within VO collections using XML metadata descriptions
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F. Bonnarel 1, M. Louys 1, Igor Chilingarian 2,3, Ivan Zolothukin 3 ,Brice Gassmann 1 (1) CDS, Strasbourg, (2) LERMA , Paris, (3) SAI, Moscow Navigation within VO collections using XML metadata descriptions Astronomical Spectroscopy and the Virtual Observatory Workshop,ESAC, March 2007 mailto: louys@astro.u-strasbg.fr Services supporting Characterisation Data Model can build a description of the physical content of their data. They express how Observations data values are spanned along physical axes and provide for each of these axes, assessments of properties of the data like the location, the limits or bounds, the resolution, etc. These metadata allow seeking for similar or complementary data in services supporting the same description. While studying a given observation the user wants to explore other VO data collections and request for other observations with similar or complementary properties. • We offer a cross query mecanism based on the Characterisation metadata that can support various scenarii • From an image look for IFU compatible data (this scenario) • From a spectrum, search for images containing the observation position and with filters centered on some specific WL range (Ha), for instance • From a 2D image search for other 2D images on different VO servers with fine selection criteria CAMEA : Characterisation editing tool 1: Retrieve characterisation of an Observation from a VO server and visualise it using the CAMEA tool (*) Derive proper Utypes to select other observations Characterisation of a 2MASS J image 2.16e 10-6 Spatial Bounds characterizationaxis[axisframe/ucd=''pos'']/coverage/bounds/limits/stc:lolimit2vec/stc:c1/text() --> 308.512238 characterizationaxis[axisframe/ucd=''pos'']/coverage/bounds/limits/stc:lolimit2vec/stc:c2/text() --> 60.069312 characterizationaxis[axisframe/ucd=''pos'']/coverage/bounds/limits/stc:hilimit2vec/stc:c1/text() --> 308.798321 characterizationaxis[axisframe/ucd=''pos'']/coverage/bounds/limits/stc:hilimit2vec/stc:c2/text() --> 60.353806 characterizationaxis[axisframe/ucd='‘em'']/coverage/bounds/limits/stc:lolimit/text() --> 2.02 E10-6 2: Identify relevant metadata to search and filter out interesting data Define a metadata mask for query composition using Xpath syntax Spectral SELECT * FROM processed_data WHERE '//Chaxis[axisframe/ucd=''pos'']/coverage/location/coord/stc:position2d/value2/c1/text()‘ <= 308.798321 AND '//Chaxis[axisframe/ucd=''pos'']/coverage/location/coord/stc:position2d/value2/c1/text()‘ >= 308.512238 AND '//Chaxis[axisframe/ucd=''pos'']/coverage/location/coord/stc:position2d/value2/c2/text()‘ <= +60.353806 AND '//Chaxis[axisframe/ucd=''pos'']/coverage/location/coord/stc:position2d/value2/c1/text()‘ >= +60.069312 AND '//Chaxis[axisframe/ucd=''pos'']/axisframe/numbins2/i1/text()‘>1 AND '//Chaxis[axisframe/ucd=''pos'']/axisframe/numbins2/i2/text()‘>1 AND '//Chaxis[axisframe/ucd=''em'']/axisframe/numbins1/text()‘>1 AND '//Chaxis[axisframe/ucd=''em'']/coverage/location/coord/stc:spectral/stc:value/text()‘ <2.02e-06 ; 3: Build up a request on these criteria to Characterisation compliant services Find out 3D spectroscopy data falling within the bounds of our 2D image at shorter wavelength 3D-Spectroscopy Spectral Bounds 4: Get results from ASPID-SR database (#) File List FITS Headers XML Characterisation (*) Characterisation editing tool: (CAMEA: Characterisation for astronomical metadata Editing Application) : build up by CDS within the framework of VOTECH DS5) This application offers a user-friendly interface to browse, create or edit an XML metadata file compliant to the Characterisation schema. It supports all predefined axes: spatial, spectral, time, observable, but also allows for new axis definition. (#)http://alcor.sao.ru/php/search Conclusion: Indexing the content of observational data of any dimension is now feasible in the VO, using existing data models such as the Characterisation and Spectrum data model via their Utypes representations