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Enabling e-Social Science Research

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Enthusiasts demonstrators. Uninterested awareness-raising ... Geographical Urban Environments. Mike Batty, UCL. Policy Grid: Rural Policy Appraisal ... – PowerPoint PPT presentation

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Title: Enabling e-Social Science Research


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

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
  • Doesnt 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 doesnt 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 dont 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
  • Whats 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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