A New Approach to Tagging Data in the Astronomical Literature

Data Tags are strings used in journals to indicate the origin of the archival data and to enable the reader to recover the data. The NASA/IPAC Infrared Science Archive (IRSA) has recently introduced a new approach to production of data tags and recovery of data from them. Many of the data access services at the IRSA return filtered data sets (such as subsets of source catalogs) and dynamically created products (such as image cutouts); these dynamically created products are not saved permanently at the archive. Rather than tag the data sets from which the query result sets are drawn, the archive tags the query that generates the results. A single tag can, then, encode a complex dynamic data set and simplifies the embedding of tags in manuscripts and journals. By logging user queries and all the parameters for those query as Data Tags, IRSA can re-create the query and rerun the IRSA service using the same search parameters used when the Data Tag was created. At the same time, the logs give a simple count of the actual numbers of queries made to the archive, a powerful metric of archive usage unobtainable from the Apache web server logs. Currently, IRSA creates tags for queries to more than 20 data sets, including the Infrared Astronomical Satellite (IRAS), Cosmic Evolution Survey (COSMOS) and Spitzer Space Telescope Legacy Data Sets. These tags are returned by the spatial query engine, Atlas. IRSA plans to create tags for queries to the rest of its services in late Spring 2007. The archive provides a simple web interface which recovers a data set that corresponds to the input data tag. Archived data sets may evolve in time due to improved calibrations or augmentations to the data set. IRSA’s query based approach guarantees that users always receive the best available data sets

Spitzer data at the NASA/IPAC Infrared Science Archive (IRSA)

The NASA/IPAC Infrared Science Archive (IRSA) curates and serves science data sets from NASA’s infrared and submillimeter projects and missions, including IRAS, 2MASS, MSX, SWAS, ISO, IRTS and from the Spitzer Space Telescope. All Spitzer data can be accessed from IRSA’s Spitzer mission page at: http://irsa.ipac.caltech.edu/Missions/spitzer.html Spitzer Legacy Enhanced Products along with ancillary data are delivered in six month intervals starting from Fall 2004, until Fall 2006. IRSA continually ingests the Spitzer data and the ancillary data, and these data are made accessible through IRSA’s query engines. Legacy products for the C2D, FEPS, GLIMPSE, GOODS, SINGS and SWIRE projects are accessible through a common interface http://irsa.ipac.caltech.edu/applications/Atlas. This engine returns the spatial footprints of observations and provides access to all flavors of released data sets, including, where appropriate, previews of image mosaics, 3-color image mosaics and spectra

Chapter 11: Web-based Tools—VO Region Inventory Service

As the size and number of datasets available through the VO grows, it becomes increasingly critical to have services that aid in locating and characterizing data pertinent to a particular scientific problem. At the same time, this same increase makes that goal more and more difficult to achieve. With a small number of datasets, it is feasible to simply retrieve the data itself (as the NVO DataScope service does). At intermediate scales, “count” DBMS searches (searches of the actual datasets which return record counts rather than full data subsets) sent to each data provider will work. However, neither of these approaches scale as the number of datasets expands into the hundreds or thousands. Dealing with the same problem internally, IRSA developed a compact and extremely fast scheme for determining source counts for positional catalogs (and in some cases image metadata) over arbitrarily large regions for multiple catalogs in a fraction of a second. To show applicability to the VO in general, this service has been extended with indices for all 4000+ catalogs in CDS Vizier (essentially all published catalogs and source tables). In this chapter, we will briefly describe the architecture of this service, and then describe how this can be used in a distributed system to retrieve rapid inventories of all VO holdings in a way that places an insignificant load on any data supplier. Further, we show and this tool can be used in conjunction with VO Registries and catalog services to zero in on those datasets that are appropriate to the user’s needs. The initial implementation of this service consolidates custom binary index file structures (external to any DBMS and therefore portable) at a single site to minimize search times and implements the search interface as a simple CGI program. However, the architecture is amenable to distribution. The next phase of development will focus on metadata harvesting from data archives through a standard program interface and distribution of the search processing across multiple service providers for redundancy and parallelization

Tica: an environment for exploring tangible vs. screen-based programming

This paper describes Tica, an environment designed to explore the differences between tangible and screen based interfaces when teaching programming to children aged 5–7 years. Tica comprises several components: a tangible programming language and a screen based equivalent, an Android app to interpret the programming languages and record data, and a physical robot that can be programmed using either the tangible or screen-based language. Tica was designed using a learner-centred approach, with a specific focus on the needs and abilities of the target user group. Once implemented, an initial pilot study was carried out with 14 adults. Although participants using the screen-based interface completed problems more quickly than those using the tangible interface, participants using the tangible interface reported a higher level of enjoyment. Next steps include a more extensive study with the target population as well as some refinements to the Tica environment