1. What is e-Research? Rob Procter Manchester eResearch Centre University of Manchester Research Methods Festival 2010

2. Outline ■Overview of e-Research  What is e-Research?  e-Research drivers ■e-Research in the social sciences  Data collection  Analysis  Visualisation  Collaboration ■What might e-Research mean for you? ■Where to find out more ■Questions

3. What is e-Research? ■Application of advanced digital methods and tools in all parts of research lifecycle:  Locate and access research resources.  Discover, access, integrate and analyse digital data on a hitherto unrealisable scale.  Facilitate sharing and collaboration.

4. e-Research: enhancing research practice Research Lifecycle Publication Literature search Literature review Data discovery Data collection / re-use Data preparation Data fusion Data curation Analysis Visualisation

5. e-Research drivers ■Research challenges become more complex:  Larger in scale, multi-disciplinary ■The ‘data deluge’:  Volume of digital research data is increasing at an exponential rate.

6. Looking for the ‘God particle’ Large Hadron Collider

7. セキュリティ GRID/ ペタコン ユビキタス ITS ではない 情報系アンブレラ The data deluge 1ZB (2010) 161EB (2006) Slide: Satoshi Matsuoka

8. 1.Global Economic Performance, Policy and Management 2.Health and Wellbeing 3.Understanding Individual Behaviour 4.New Technology, Innovation and Skills 5.Environment, Energy and Resilience 6.Security, Conflict and Justice 7.Social Diversity and Population Dynamics Social Science research challenges

9. The data deluge in social sciences ■‘Born digital’ data is generated on increasing scale as by product of everyday activities:  Patterns of consumption: -Public and private goods and services  Patterns of communication: -Email, bulletin boards, weblogs, chat rooms, news feeds, mobile phones, SMS  Patterns of movement of people and goods: -CCTV, speed cameras, traffic monitoring, GPRS, embedded devices ■Move from survey-based methods to using administrative data

10. http://www.understandingsociety.org.uk/ Ian Diamond

11. Open data

12. Easier access

13. The social Web

14. Statistical analysis Geographically Weighted Regression Multilevel modelling through MLwiN and e-Stat http://www.cmm.bristol.ac.uk/research/NCESS-EStat/

15. Social simulation National e-Infrastructure for Social Simulation: –Introduce social scientists to new ways of thinking about social problems –Enable researchers to to run simulations, visualise and analyse results, publish for future discovery, sharing –Facilitate development and sharing of social simulation resources http://www.geog.leeds.ac.uk/projects/neiss/about.php

16. 2001 2031 2015 * Traffic Intensity=Traffic load/Road capacity Traffic Intensity * Transport 2031

17. Digital ethnography http://www.mrl.nott.ac.uk/research/projects/dress/

18. Text mining: document analysis Identification of conceptually similar documents using most commonly occurring terms and words in the source document Highlighting selected semantic information within the document Selecting terms according to importance and using them to browse documents Identification of conceptually similar documents using most commonly occurring terms and words in the source document Highlighting selected semantic information within the document Selecting terms according to importance and using them to browse documents www.nactem.ac.uk/assist/

19. Text mining: sentiment analysis Subjective Sentiment Automatic estimation of the opinion of the writer regarding a fact or an event Negative opinion Neutral opinion Positive opinion Subjective Sentiment Automatic estimation of the opinion of the writer regarding a fact or an event Negative opinion Neutral opinion Positive opinion www.nactem.ac.uk/assist/

20. Web mining Using website links to map political blog community structure. Adamic and Glance, 2004

21. Web mining Using Facebook as a source of social data: ‘webnography’ http://www.thefacebookproject.com/

22. Web mining in real time http://www.casa.ucl.ac.uk/tom/ ‘Tweet-o-Meter’ – an example of how we can capture, visualise and extract patterns from mobile communications.

23. Visualisation Social data and google maps mash-up. http://www.maptube.org/

24. Visualisation Using survey tools and maptube to ‘crowdsource’ opinions.

25. Sharing methods Methodbox users include NHS Public Health analysts and Department of Health Public Health Observatory analysts, social scientists and epidemiologists

26. Virtual Research Environments A collaboration space for social scientists. A means to share scientific resources across a diverse community. www.ourspaces.net

27. What e-Research means for you ■Easy-to-use, ‘shrink-wrapped’ tools and services:  DRS, NeISS, etc. ■Build your own:  Create new datasets by mashing up existing data.  Create ‘workflows’ to discover, extract and analyse data. ■Engage in new forms of scholarly communications:  Make data and methods freely available so that others can re-use them.

28. Creating a research ‘workflow’ Automating extraction and analysis of messages in study of ‘social dynamics’ in an open source software community. www.myexperiment.org

29. His friends and colleagues Literature Images LogBook Software Presentations Data (files, spreadsheets) Compute resource Backup and Archive New forms of scholarly communications

30. Summary of e-Research ■Application of advanced digital methods and tools in all parts of the research lifecycle:  Locate and access research resources.  Discover, access, integrate and analyse digital data on a hitherto unrealisable scale.  Facilitate sharing and collaboration. ■Enhanced research practice:  Reduce ‘time to discovery’, improve robustness, enable research advances that would not otherwise be possible.

31. Where to find out more http://www.eresearchsouth.ac.uk/uk-e- social-science http://www.methods.manchester.ac.uk/meth ods/eresearch/index.shtml Cyberinfrastructure Vision for 21st Century Discovery http://www.nsf.gov/pubs/2007/nsf0728/ind ex.jsp

32. Thanks to ■Peter Halfpenny ■Dave De Roure ■Marina Jirotka ■Anne Trefethen ■Carole Goble ■Mark Birkin ■Andy Crabtree ■Sophia Ananiadou ■Andy Hudson-Smith ■Richard Milton ■Meik Poschen ■Alex Voss

33. Questions rob.procter@manchester.ac.uk http:www.merc.ac.uk