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Enabling e-Social Science Research

Enabling e-Social Science Research. Andy Turner. Outline. Context Focus National Centre for e-Social Science NCeSS An introduction e-Infrastructure developments for e-Social Science A viewpoint Modelling and Simulation for e Social-Science MoSeS A job. Context 1/2.

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Enabling e-Social Science Research

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  1. Enabling e-Social Science Research Andy Turner

  2. Outline • Context • Focus • National Centre for e-Social Science • NCeSS • An introduction • e-Infrastructure developments for e-Social Science • A viewpoint • Modelling and Simulation for e Social-Science • MoSeS • A job

  3. Context 1/2 • Who am I and why am I here? • Andy Turner • http://www.geog.leeds.ac.uk/people/a.turner • e-Social Science in action! • Collaboration • Consortium building • I work on MoSeS • a node of NCeSS • an Other NeSC UK e-Science Centre • http://www.nesc.ac.uk/centres/ • Interdiciplinary team from • Computing • Geography • Transport • Health • Lead by Mark Birkin

  4. Context 2/2 • Who are you and why are you here? • Three types of e-Researchers • Early adopters – technical support • Enthusiasts – demonstrators • Uninterested – awareness-raising • indifferent • sceptical • antagonistic

  5. NCeSS 1/5 • http://www.ncess.ac.uk/ • National Centre for e-Social Science • Context • ESRC e-Social Science Initiative/Strategy • Aims to stimulate the uptake and use by social scientists, of new and emerging Grid-enabled computing and data infrastructure, both in quantitative and qualitative research • Four scoping studies to identify key issues • Human centred design and Grid technologies • Grid-enabling quantitative social science datasets • Qualitative research and e-SS • Social shaping perspectives on e-S and e-SS • 11 pilot demonstrator projects • Training and awareness activities (with JISC) • Fast Track • ReDReSS • Agenda setting workshops • NCeSS

  6. NCeSS 2/5 • Mission • To help social scientists make the best use of e-science technologies to address key social science research challenges. • To stimulate the uptake of Grid-enabled computing, data infrastructure and collaboration in social science research • To provide information, training, advice, support and online resources. • To advise on the future strategic direction of e-social science. • Long-term goals • Develop an e-social science culture that pervades the SS research community • Make the Grid as easy to use as the Web • Establish a leading international centre for e-social science

  7. NCeSS 3/5 • Structure and Organisation • Unified Centre with distributed structure • Co-ordinating Hub: Manchester / UKDA • Seven research Nodes: across the UK • Twelve small grant projects • Eight Access Grid Nodes • Role of NCeSS Hub • One-stop shop: • Expertise, training, technical support, data resources • website – a single ‘front door’ • Disseminate success: • Demonstrator projects • Training materials • Working papers, seminars, SIGs, conferences, summer schools, fellowships • Foster collaboration: • Between social scientists and Grid developers • Between node research teams • Common software standards

  8. NCeSS 4/5 • The 7 Current Research Nodes • Collaboration for Quantitative eSS Statistics • Rob Crouchley, Lancaster • Modelling and Simulation for eSS • Mark Birkin, Leeds • New Forms of Digital Record for eSS • Tom Rodden, Nottingham • Mixed Media Grid • Mike Fraser, Bristol • Geographical Urban Environments • Mike Batty, UCL • Policy Grid: Rural Policy Appraisal • Pete Edwards, Aberdeen • Oxford e-Social Science Project • Bill Dutton, Oxford

  9. NCeSS 5/5 • 12 Current Small Grant Projects • Headtalk • R Carter, Nottingham • Spacial Decision-Making in Distributed Envrionments • A Beradi, OU • Learning Disabilities Data Infrastructure • Simon Musgrave, Essex • Knowledge and Community Making in eSS • Ben Anderson, Essex • Use of Grid in Disclosure Risk Assessment • Mark Elliot, Manchester • Using AGNs in Field Research • Nigel Fielding, Surrey • Repository for Social Science Metadata • Karen Clarke, Manchester • Grid-enabled Occupational Data • Dr Lambert, Stirling • Data-driven Simulation for Policy Decision • G Theodoropolous, Birmingham • Semantic annotation in skills-based learning • David De Roure, Southampton • Integrating Data in Visualisation • M Chalmers, Glasgow • Grid-enabled Spatial Regression Models • R Harris, Bristol

  10. e-Infrastructure Developments for e-Social Science 1/6 • Technology • Virtualisation • Ease of use • Security • Socio-political • Communication is hard • Interoperability • Standards • Application orientated interactions • Grids become Data Grids • Interfaces • User Communities

  11. e-Infrastructure Developments for e-Social Science 2/6 • Virtualisation • Vision of the Grid: • Plug in and get the services you need • Just like electricity • Doesn’t matter what resource is supplying it, or where it is, just use the “juice” • Basic functionality now exists • Tell me what this set of resources look like • Run this job on that resource • Transfer this file • Basic functionality is not enough to fulfill the vision of the Grid • It is happening • Contribute and make it happen sooner…

  12. e-Infrastructure Developments for e-Social Science 2/6 • Ease of Use • Users will only come in droves when they have decent tools to use • Users are hampered by software that doesn’t do what they want it to • To promote this it is reckoned that closer ties between tool builders and user are need • Tool builders still creating “cool” solutions to problems that don’t exist • Users still not communicating what they need – or ignoring “not built here” solutions when available • This is being addressed • Long way to go…

  13. e-Infrastructure Developments for e-Social Science 3/6 • Security • It is needed • Major ethical and confidentiality issues with data • Needs to be easy to use and manage • Lots of work ongoing in this

  14. e-Infrastructure Developments for e-Social Science 4/6 • Socio-political • Multiple administration domains means multiple policies • Trust is needed • Communication • Language barriers • Constructive criticism, reporting of errors helps • Standards • Need for standard APIs and protocols to allow easier • Access to data sources • Registration of data • Archiving tools • For resource discovery • Semantics • Standards for communication of errors

  15. e-Infrastructure Developments for e-Social Science 5/6 • …More on Standards • What’s the real goal behind standards? • Interoperabilty! • Without standard interfaces, languages, schemas, etc we cannot have multiple implementations that work together • However, standards are hard • Agreement between many partners • This is often socio-political, not technical • Standardizing too soon versus too late • Need to be very exact in order to only have one interpretation of a standard • Need to make sure you take performance into account • Need to have take-up by the major players

  16. e-Infrastructure Developments for e-Social Science 6/6 • Application Orientated Interactions • Grids become Data Grids • In the beginning compute ruled • Then distributed and large data became the focus • Understanding data provenance became a big issue • UK funding councils are beginning to demand that projects make their data publicly available • Annotated, curated, freely available • Where are the tools to help with this? • How do you maintain Data Grids once your project ends? • Interfaces • Growing importance of Portals • User communities

  17. A proposed e-Infrastructure for e-Social Science

  18. Modelling and Simulation for e-Social Science • Covers a lot of things • Scientist from different disciplines have different needs • NCeSS organised an Agenda Setting Workshop • e-Infrastructures for Social Simulation • A coming together of experts • Details on the NCeSS wiki • URL in notes • What Architecture is needed to support this? • What Workflows and Use Cases are there?

  19. Example Simulation Workflows

  20. NCeSS MoSeS Node • Primarily tasked with a triplet of related use cases • Based on a UK human demographic simulation model • Microsimulation/Agent based • GIS and visualisation • Forecasting for Policy Analysis • Applications • Health • Business • Transport • Conceptual link to SimCityTM • The team are thinking about general work flows and use cases

  21. Summary • e-Infrastructure for e-Social Science is in its infancy • We are hoping to: • Develop and adopt standards • Collaborate • Spread the word • Develop the Semantic Web • Let engineers organise the hardware • Develop Open Source software solutions • Work with data assimilators and disseminators to encourage proliferation of meta data standards • Show case the enormous data and computational problems of e-Social Science

  22. Acknowledgements and Thanks • Peter Halfpenny • Jennifer Schopf • NCeSS • MoSeS • Mark Birkin • SIM-UK • University of Leeds • CCG • School of Geography • To you • The collaborative scientific community

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