Collaborative systems for enhancing the analysis of social surveys: the grid enabled specialist data environments

This paper describes a group of online services which are designed to support social survey research and the production of statistical results. The 'Grid Enabled Specialist Data Environment' (GESDE) services constitute three related systems which offer facilities to search for, extract and exploit supplementary data and metadata concerned with the measurement and operationalisation of survey variables. The services also offer users the opportunity to deposit and distribute their own supplementary data resources for the benefit of dissemination and replication of the details of their own analysis. The GESDE services focus upon three application areas: specialist data relating to the measurement of occupations; educational qualifications; and ethnicity (including nationality, language, religion, national identity). They identify information resources related to the operationalisation of variables which seek to measure each of these concepts - examples include coding frames, crosswalk and translation files, and standardisation and harmonisation recommendations. These resources constitute important supplementary data which can be usefully exploited in the analysis of survey data. The GESDE services work by collecting together as much of this supplementary data as possible, and making it searchable and retrievable to others. This paper discusses the current features of the GESDE services (which have been designed as part of a wider programme of ‘e-Science’ research in the UK), and considers ongoing challenges in providing effective support for variable-oriented statistical analysis in the social sciences

Metadata Creation, Transformation and Discovery for Social Science Data Management: The DAMES Project Infrastructure

This paper discusses the use of metadata, underpinned by DDI (Data Documentation Initiative), to support social science data management. Social science data management refers broadly to the discovery, preparation, and manipulation of social science data for the purposes of research and analysis. Typical tasks include recoding variables within a dataset, and linking data from different sources. A description is given of the DAMES project (Data Management through e-Social Science), a UK project which is building resources and services to support quantitative social science data management activities. DAMES provides generic facilities for performing (and recording) operations on data. Specific resources include support for analysis through micro-simulation, and support for access to specialist data on occupations, educational qualifications, measures of ethnicity and immigration, social care, and mental health. The DAMES project tools and services can generate, use, transform, and search metadata that describe social science datasets (including microdata from social survey datasets and aggregate-level macrodata). On DAMES, these metadata are described by various standards including DDI Version 2, DDI Version 3, JSDL (Job Submission Definition Language), and the purpose-designed JFDL (Job Flow Definition Language). The paper describes how DAMES uses metadata with a range of resources that are integrated with a job execution infrastructure, a Web portal, and a tool for data fusion

Enabling quantitative data analysis through e-infrastructures

This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can benefit from e-infrastructure support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences

Educational and vocational goal disruption in adolescent and young adult cancer survivors

ObjectiveCancer in adolescents and young adults (AYAs) can interrupt important developmental milestones. Absence from school and time lost from work, together with the physical impacts of treatment on energy and cognition, can disrupt educational and vocational goals. The purpose of this paper is to report on AYA cancer survivors’ experiences of reintegration into school and/or work and to describe perceived changes in their educational and vocational goals.MethodsAdolescents and young adults recruited from 7 hospitals in Australia, aged 15 to 26 years and â ¤24 months posttreatment, were interviewed using the psychosocial adjustment to illness scale. Responses were analysed to determine the extent of, and explanations for, cancer’s effect on school/work.ResultsFortyâ two AYA cancer survivors (50% female) participated. Compared with their previous vocational functioning, 12 (28.6%) were scored as experiencing mild impairment, 14 (33.3%) moderate impairment, and 3 (7.1%) marked impairment. Adolescents and young adults described difficulties reintegrating to school/work as a result of cognitive impacts such as concentration problems and physical impacts of their treatment, including fatigue. Despite these reported difficulties, the majority indicated that their vocation goals were of equal or greater importance than before diagnosis (26/42; 62%), and most AYAs did not see their performance as compromised (23/42; 55%). Many survivors described a positive shift in life goals and priorities. The theme of goal conflict emerged where AYAs reported compromised abilities to achieve their goals.ConclusionsThe physical and cognitive impacts of treatment can make returning to school/work challenging for AYA cancer survivors. Adolescents and young adults experiencing difficulties may benefit from additional supports to facilitate meaningful engagement with their chosen educational/vocational goals.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142495/1/pon4525_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142495/2/pon4525.pd

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie