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Enabling e-Social Science Research Andy Turner. n Context n Focus –National Centre for e-Social Science NCeSS An introduction –e-Infrastructure developments.

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Presentation on theme: "Enabling e-Social Science Research Andy Turner. n Context n Focus –National Centre for e-Social Science NCeSS An introduction –e-Infrastructure developments."— Presentation transcript:

1 Enabling e-Social Science Research Andy Turner

2 n Context n 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 Outline

3 Context 1/2 n Who am I and why am I here? –Andy 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 n 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 n n National Centre for e-Social Science n 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 n 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. n 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 n 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 n 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 n 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 n Technology –Virtualisation –Ease of use –Security n Socio-political –Communication is hard –Interoperability –Standards n Application orientated interactions –Grids become Data Grids –Interfaces –User Communities

11 e-Infrastructure Developments for e-Social Science 2/6 n 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 n 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 n 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 n 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 n …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 n 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 n Covers a lot of things –Scientist from different disciplines have different needs n NCeSS organised an Agenda Setting Workshop –e-Infrastructures for Social Simulation A coming together of experts Details on the NCeSS wiki –URL in notes n What Architecture is needed to support this? n What Workflows and Use Cases are there?

19 Example Simulation Workflows

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

21 Summary n e-Infrastructure for e-Social Science is in its infancy n 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 n Peter Halfpenny n Jennifer Schopf n NCeSS n MoSeS n Mark Birkin n SIM-UK n University of Leeds –CCG –School of Geography n To you n The collaborative scientific community


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