Enabling e-Social Science Research

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Enabling e-Social Science Research

• Andy Turner

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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A proposed e-Infrastructure for e-Social Science
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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?

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Example Simulation Workflows
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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

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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

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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