What is e-Research?

Rob Procter

Manchester eResearch CentreUniversity of Manchester

Research Methods Festival 2010

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

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.

e-Research: enhancing research practice

Research

Lifecycle

Publication

Literature search

Literature review

Data discovery

Data collection / re-useData

preparation

Data fusion

Data curation

Analysis

Visualisation

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.

Looking for the ‘God particle’

Large Hadron Collider

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 deluge1ZB

(2010)

161EB(2006)

Slide: Satoshi Matsuoka

1. Global Economic Performance, Policy and Management

2. Health and Wellbeing3. Understanding Individual

Behaviour4. New Technology, Innovation

and Skills5. Environment, Energy and

Resilience6. Security, Conflict and Justice7. Social Diversity and Population

Dynamics

Social Science research challengesSocial Science research challenges

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

http://www.understandingsociety.org.uk/

Ian Diamond

Open data

Easier access

The social Web

Statistical analysisStatistical analysis

Geographically Weighted Regression

Multilevel modelling through MLwiN and e-Stat

http://www.cmm.bristol.ac.uk/research/NCESS-EStat/

Social simulationSocial 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

2001 2031

2015

* Traffic Intensity=Traffic load/Road capacity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Traffic Intensity *

Transport

2031

Digital ethnographyDigital ethnography

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

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/

Text mining: sentiment analysis

Subjective SentimentAutomatic estimation of the

opinion of the writer regarding a fact or an event

Negative  opinion Neutral opinion Positive opinion

Subjective SentimentAutomatic estimation of the

opinion of the writer regarding a fact or an event

Negative  opinion Neutral opinion Positive opinion

www.nactem.ac.uk/assist/

Web mining

Using website links to map political blog community structure.

Adamic and Glance, 2004

Web mining

Using Facebook as a source of social data:

‘webnography’

http://www.thefacebookproject.com/

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.

Visualisation

Social data and google maps mash-up.

http://www.maptube.org/

VisualisationVisualisationUsing survey tools and maptube to ‘crowdsource’ opinions.

Sharing methodsSharing methods

Methodbox users include NHS Public Health analysts and Department of Health Public Health Observatory analysts, social scientists and epidemiologists

Virtual Research EnvironmentsVirtual Research Environments

A collaboration space for social scientists.A means to share scientific resources across a diverse community.

www.ourspaces.net

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.

Creating a research ‘workflow’

Automating extraction and analysis of messages in study of ‘social dynamics’ in an open source software community.

www.myexperiment.org

His friends and colleagues

Literature

ImagesLogBook

Software

Presentations

Data (files, spreadsheets)

Compute resource

Backup and Archive

New forms of scholarly communicationsNew forms of scholarly communications

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.

Where to find out more

http://www.eresearchsouth.ac.uk/uk-e-social-science

http://www.methods.manchester.ac.uk/methods/eresearch/index.shtml

Cyberinfrastructure Vision for 21st Century Discovery

http://www.nsf.gov/pubs/2007/nsf0728/index.jsp

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

Questions

rob.procter@manchester.ac.uk

http:www.merc.ac.uk