1. How Can e-Social Science Promote the Re-Use of Data?
Rob Procter
National Centre for e-Social Science
The Grid and e-Social Science
NCeSS:
Background
Research agenda
Some examples:
ConvertGrid
How can e-SS benefit data providers and users
Some issues
Over to you …
UPTAP Workshop 2007

2. The 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, Research Councils)
That infrastructure is the Grid:
“ … a software infrastructure that enables flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions and resources” (Foster, Kesselman and Tuecke)
The Grid is not just an enabler of visionary research, however, but can help researchers in more mundane ways.
But, to be successful, the development of the Grid must be driven by researchers’ needs.
I want to use the opportunity provided by this workshop to gather ideas from you about what those needs are with a specific focus on the (re-)use of data.
If that sounds too abstract or technical, lets make it more concrete with some examples.
UPTAP Workshop 2007

3. NCeSS Overview
Launched in May 2004 to develop and promote UK e-Social Science.
Unified Centre with distributed structure:
Co-ordinating Hub: Manchester & UKDA
Seven research Nodes located across UK
Twelve small projects
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4. NCeSS Overview Applications of e-Social Science: Social shaping:
Harnessing new kinds of research infrastructure and tools to tackle substantive problems and promote innovation in research methods
Social shaping:
Usability of new infrastructure and tools
Socio-technical factors in their design, uptake and use
Research and policy drivers, impacts
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5. NCeSS 2006 Social Shaping CQeSS MoSeS PolicyGrid MiMeG Analysis OeSS
Disclosure Risk Assessment
CeSDeMIDE
GeSRM
Intelligent Simulation
MiMeG
HeadTalk
Analysis
OeSS
DReSS
AGN enabled interviews
Learning Disabilities
Entangled Data
Data chronicles
Replayer
Grid-enabled data collection
Data
GeoVUE
GeODE
Tools
Infrastructure and services
Hub
Conceptual map. Emphasises the importance of NCeSS achieving synergies between the research projects.
Research methods
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6. Today’s Research Infrastructure
Heterogeneous resources with poor inter-operability and complex administrative arrangements.
Data archive
HPC
Study
Analysis
HPC
Researcher
Experiment
Computing
Data archive
Analysis
HPC
Doesn’t scale well and makes re-use and sharing of data and other research resources difficult.
UPTAP Workshop 2007

7. Grid-Enabled Research Infrastructure
Grid middleware manages the interactions between users, and heterogeneous and distributed resources, providing seamless integration of data, analytic tools and compute resources.
Data archive
Study
Social scientist
Grid
Middle-
ware
HPC
Storage
Analysis
The grid is a new kind of research infrastructure based on distributed, interoperable and composable resources such as data and computational services.
Social scientist
Data archive
HPC
Computing
Social scientist
Analysis
Experiment
Storage
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8. The Grid Dissected
Tools to support collaboration between distributed researchers.
Computational Grids for scalable, high-performance computation.
Data Grids for accessing and integrating heterogeneous datasets.
Sensor Grids for collecting real-time data.
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9. Research and Policy Drivers
Census and population surveys
Administrative data
Longitudinal surveys
Socio-medical data
Business and economic data
International macro/micro data
Ageing population
Migration
Globalisation
Childhood development
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It will also involve linking multiple datasets.
Provision of datasets through services such as UKDA is well established
But re-use of datasets and especially their linking is inhibited by problems of heterogeneity
Data linking and data fusion (where there are no common elements in the sets?)
Multi-disciplinary, multi-scale problems:
Ageing pop: social scientists of different kinds, healthcare researchers
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10. Research and Policy Drivers
The range of research resources on offer to the social science community has never been greater.
These include not only traditional research datasets, but new kinds of social data.
However, the often highly distributed and heterogeneous character of these datasets makes it difficult to exploit them to their full potential.
UPTAP Workshop 2007

11. Research and Policy Drivers
The data deluge in social sciences:
WWW archive currently contains 55 billion Web pages or 2 petabytes (2x1015) of data and is growing at the rate of 20 terabytes (20x1012) per month
Administrative and transactional data is generated on increasing scale as by product of our everyday activities:
This data is complex and multi-dimensional
Exploiting these new data sources to their full potential requires more sophisticated techniques for multi-media data discovery, linking and management, and more powerful services – such as text mining and social network analysis – for data extraction, annotation and analysis.
These services are potentially computationally intensive and emphasise how social sciences can benefit from compute grids.
But it is data grids that I want now to focus on.
UPTAP Workshop 2007

12. Data Grids for Social Science
Data Grids are designed to provide unimpeded and integrated use of distributed, heterogeneous, autonomous data resources.
Grid enabling a dataset creates new opportunities for (re-)use:
enables users to integrate it with other datasets
makes it possible to analyse the dataset using techniques that require the kind of computational power that is only feasible using the Grid (e.g., more complex models, more data points)
standardisation of procedures and mechanisms used to access and update the dataset increase its shareability
Automated analyses (i.e., analyses can be re-run automatically when databases are updated)
Social data is currently under-analysed – Ian Diamond.
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13. An Example Data Linkage Problem
Many research questions require combination of data from multiple geo-referenced datasets:
E.g., Linking post coded data to census geography
Conversion of data relating to different geographies to a common target geography is
A complex time consuming task
Requires a range of data handling/processing skills
A major barrier to use!
The data conversion process requires users to perform the following generic tasks:
Extract and download data in different formats from a number of databases using different interfaces
Convert each dataset to the desired target geography using geographical conversion tables
Combine the converted sets into a single dataset for analysis
These generic tasks can be automated.
Is automation relevant where human judgment is critical? Automate the routine parts of the process
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14. A Solution: ConvertGrid
ConvertGrid provides access to 225 UK-wide geography conversion tables between census, electoral, administrative, postal, health and statistical geographies derived from the AFPD.
Facility to convert a researcher’s data from one set of geographical units to another (e.g., from postcode geography to heath geography).
Extensible system - further conversion tables from any source can be incorporated.
UPTAP Workshop 2007

15. ConvertGrid – Data Visualisation Interface
High average house price sales but low participation rates
ConvertGrid is an example of a grid service – it runs on a server (in this case located at MIMAS) and not on the user’s desktop. However, this would normally be transparent to the user.
Different services can be composed together to carry out more complex tasks. This slide Illustrates how ConvertGrid results can be fed into a visualisation service.
Low average house price sales but high participation rates
Ten minutes from start to finish
Relationship between average house price sales (Experian) and percentage of year olds entering university (Neighbourhood Statistics & Census aggregate statistics).
Contact Keith Cole for more information.
UPTAP Workshop 2007

16. Supporting the Research Lifecycle
Share results and conclusions and discuss with collaborators
Explore datasets and determine suitability
Analyse results and compare with hypothesis
Review literature and generate hypothesis
Write papers
Build models and execute them
Publish papers
Find datasets related to proposed area of work
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17. Increasing (Re-)Use of Social Data
Removing barriers to more effective use of existing social data collections:
Data providers (e.g., ONS, data archives)
Data users
Many researchers are both generators and users of data:
Preparation of data for submission to data archives is not well rewarded so re-use suffers
Removing barriers to use of new kinds of social data:
Privacy and confidentiality of personal data
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18. The Data Provider Perspective
Preparation procedures:
Cleaning the data
Generating derived variables
Re-weighting
Adding metadata
Writing user documentation
Maintenance:
Managing changes in sampling frames, definitions, variables and questionnaire over time
User support:
Handling queries from users about concepts, meaning and linking waves
Definitions: ILO definition of unemployment adopted in 1984, replacing LF definition.
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19. The Data User Perspective
Discovering appropriate data:
Determining what can be done with the data and how.
Accessing the data:
Are existing provisions, such as VMDLs, for access to confidential data adequate?
Understanding how the data has been used to generate answers to other research questions:
Provenance of results, links to publications
Re-running statistical models, comparing results
Ease and of use and quality of documentation:
User manuals
This meeting is intended to improve understanding between users and providers. Can we use technology to facilitate this further? E.g., Annual Population Survey?
An Amazon for datasets?
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20. The Data User Perspective
Data preparation:
Selecting variables
Linking waves
Linking data sets
Performing and possibly repeating analysis with different data.
Interpreting and visualising results.
Supporting the research lifecycle.
Collaboration with other users and with data providers.
This meeting is intended to improve understanding between users and providers. Can we use the Grid to facilitate this further? E.g., Annual Population Survey?
An Amazon for datasets?
UPTAP Workshop 2007

21. Contacting NCeSS and Getting Involved
Join our list:
Participate in events:
Agenda setting workshop on combining and sharing data, January 22nd-23rd, Manchester
Annual conference
UPTAP Workshop 2007