e-social science and the doctorate peter halfpenny esrc national centre for e-social science new...
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e-Social Science and the doctorate
Peter HalfpennyESRC National Centre for e-Social Science
New Forms of Doctorate
London Knowledge Lab
10 November 2008
Outline
What is e-social science? Three key features of e-social science From the Grid to Web 2.0 e-social science and the research cycle Implications for the doctorate What is NCeSS? What can NCeSS do for you?
What is e-social science?
Harnessing innovations in digital technologies Integrating them into an e-infrastructure
• networked – across the Internet
• interoperable – seamless, single sign-on
• scalable – to any magnitude
Automating the tedious, time-consuming, error-prone bits – into workflows
enabling social science• new methods of research
• overcoming past limitations
Research infrastructure today
Lots of computer-based support
Database
HPC
Audio data
Analysis
Computing
Social Scientist
Computing
HPC
AnalysisData
Archive
Video data
Experiment
Many separate accesses, multiple interfaces
Future research e-Infrastructure
Seamless integration of data, analytic tools and compute resources
Social scientist
Social scientist
Social scientist
Grid
Middle-ware
Simple interface
Single sign on
Data
Data
Storage
Storage
ComputingAnalysis
Analysis
Experiment
HPC
HPC
e-Infrastructure
Future research e-Infrastructure
Seamless integration of data, analytic tools and compute resources
Social scientist
Social scientist
Social scientist
Grid
Middle-ware
Simple interface
Single sign on
Data
Data
Storage
Storage
ComputingAnalysis
Analysis
Experiment
HPC
HPC
e-Infrastructure
Key features of e-social science
1. Benefit from the digital data deluge• data born digital
- every interaction with a computer leaves a trace
- computers are everywhere
• digitisation projects- books, pictures, archives, newspapers, sounds
• discoverable- search engines – Google
- semantic grid – machine-processable descriptions
Key features of e-social science
2. Computer power on tap• High Performance Computers
- available to all UK academics
- accessible via the National Grid Service
• Clusters of ordinary computers- harness wasted power from idle desktop PCs
• No computational task too big- weather prediction, earthquake modelling
- population modelling
Key features of e-social science
3. Collaboration• Asynchronous
- Portals – iGoogle; Facebook
- Virtual Research Environments
NCeSS Portal
ourSpaces
• My tools• My collaborators• Our activities• My tags• New resources• Search• Upload• Explore• Messages
Key features of e-social science
3. Collaboration• Asynchronous
- Portals – iGoogle; Facebook
- Virtual Research Environments
• Synchronous- Voice over Internet – Skype
- High bandwidth teleconferencing
- Access Grid
Typical Views of Access GridETF Management Meeting
Lecture
SeminarSC Global Workshop Performance ArtSeminar
Key features of e-social science
3. Collaboration• Asynchronous
- Portals – iGoogle; Facebook
- Virtual Research Environments
• Synchronous- Voice over Internet – Skype
- High bandwidth teleconferencing
- Access Grid
• Support collaboratories- distributed, virtual research centres
From the Grid to Web 2.0
Early e-Science emphasised HPC• delivered over the Grid
- like electricity, gas, etc
- the ‘plumbing’
• heavyweight middleware- needed programmers
- out of reach of most social scientists
The first Grid book
• Ian Foster• Carl Kesselman• 1998• 700 pages• £46
The second edition
• Ian Foster• Carl Kesselman• 2004• 750 pages• and a website• £42
From the Grid to Web 2.0
Originated in 2004• name of a commercial conference
Users become producers• Blogs, Wikis, social networking
• sharing photos, videos
• tagging
• mashups
Exponential growth of Web 2.0
From the Grid to Web 2.0
From the Grid to Web 2.0
e-social science & research cycle
Seminar series focuses on the thesis Consider this in the context of the full
research life-cycle From initial idea to final output Socio-technology approach
• technology alone does not provide solutions
• technology embedded in social practices
e-social science & research cycle
literature search• boundless
• machine translation
• personal and shared bibliographic databases
literature review• text mining
e-social science & research cycle
data discovery• boundless
• fully documented – provenance and use
• multi-modal
data access• authorisation via ‘role’
data integration• matching, imputation, statistical methods
e-social science & research cycle
data security• virtual safe settings
analysis• boundless
• data mining
• pattern matching for visual data
• mixed methods / multi-modal
Digital Replay System
system log
video
transcript
code tree
Collaborative video analysis
e-social science & research cycle
presentation of results• multi-modal
• dynamic
• non-linear hyperlinking
• visualisation
• mapping
Grid-Enabled Micro-Econometric Data Analysis
London Profiler
Higher Education
Higher Education
Higher Education
e-social science & research cycle
simulation• micro-simulation
• agent-based modelling
real-time data collection and analysis• sensor networks
• GPRS / GPS
practical knowledge / skill• ‘how to’ videos
implications for the doctorate
location• student and supervisor(s) not co-located
topic choice• multidisciplinarity
fieldwork• digital technology enabled
data• re-use
implications for the doctorate
loneliness• networking
supervision• channels of communication
thesis• digital, multi-modal, hyperlinked
examination• originality
implications for the doctorate
collaboration• is there a role for the lone scholar?
• technology developer as partner?
the original e-science vision:“e-Science is about global collaboration in key areas
of science and the next generation of infrastructure that will enable it.”
John Taylor, former DG of Research Councils,
UK Office of Science and Technology (as was)
A word from the top
“We are now living in an increasingly complex, dynamic and diverse society. This means that there is a pressing need to create better resources to answer some of the more complex research and policy questions this poses. Developments in technology, particularly e-social science, are creating path-breaking new opportunities to link, model and mine large datasets.”
Ian Diamond, Chief Executive, ESRC Preface to the
National Strategy for Data Resources for Research in the Social Sciences
What is e-social science?
using the e-Infrastructure to: • locate, access, share, integrate, analyse and
visualise digitised data seamlessly across the Internet on a hitherto unrealisable scale
• facilitate collaboration across distributed teams
• enable advances in social research that would not otherwise have been possible.
What is NCeSS?
major ESRC investment co-ordinating Hub at Manchester 8 major research Nodes across the UK
100+ investigators developing the e-Infrastructure advanced digital tools and services for
(collaborative) social research
What can NCeSS do for you?
ICTs and social research• three variants
ICTs and social research
1. social research on technologies• studies of innovation
• uses
• markets
• digital divides
NCeSS ‘social shaping’ research• barriers to uptake, facilitators
ICTs and social research
2. social research using existing ICTs• computer assisted interviewing
• statistical analysis
• qualitative data ‘analysis’
• web-based surveys
NCeSS develop refinements• e.g. data-mining, text-mining
ICTs and social research
3. social research enabled by e-infrastructure• data discovery• data manipulation• data integration• data analysis• collaboration• modelling• simulation • visualisation
NCeSS ‘applications’ research
Where to find out more
From our website: www.ncess.ac.uk
Thank you
Questions?
Pardon?