Presentation at Dementia Conference (Evington Initiative) held at Wellcome Trust, 22-23 October 2012. Acknowledgements to McKinsey & Company, also Tim Clark (MGH) and Iain Buchan (University of Manchester), for input to slides.

1. Introduction to Big Data and its
Potential for Dementia Research
David De Roure

2. Overview
• What do we mean by Big Data?
• Role in medical research
• Impact on future research
• Application to dementia research
• Challenges and issues

3. ...the imminent flood of
scientific data expected
from the next generation of
experiments, simulations,
sensors and satellites
Source: CERN, CERN-EX-0712023, http://cdsweb.cern.ch/record/1203203

5. http://www.slideshare.net/RockHealth/rock-report-big-data

6. Linked Data Investment is worthwhile
when the data is:
• Discoverable
1. Use URIs as names for things • Reusable
2. Use HTTP URIs so that • Linkable
people can look up those
names
3. When someone looks up
a URI, provide useful
information, using the
standards
4. Include links to other URIs
so that they can discover
more things

8. http://www.ajtmh.org/content/86/1/39.full

9. http://stanmed.stanford.edu/2012summer/

10. http://www.slideshare.net/RockHealth/rock-report-big-data

11. BioEssays,, 26(1):99–105, January 2004
http://research.microsoft.com/en-us/collaboration/fourthparadigm/

12. A Big Picture
e-infrastructure
More machines
Big Data The Future!
Big Compute
Conventional Social
online
Computation Networking R&D
More people

13. method
data

14. Some Social Machines
Nigel Shadbolt

15. Notifications of new data and results,
automatic re-runs of analysis pipelines
New research?
Autonomic
Curation
Self-repair
• Automation assists the scientist
• Use the computational capability
• Scale the capability to the problem,
not the problem to the desktop
Machines are users too

16. Massachusetts General Hospital (MGH) will receive $5.4
million from the nonprofit Cure Alzheimer’s Fund, in what the
fund said was the largest single private scientific grant ever
invested in Alzheimer’s whole-genome sequencing focused
on families with the disease.
Over the next 12 to 18 months, the Alzheimer’s Genome
Project will obtain complete genomic sequences of more than
1,500 patients in families that have Alzheimer’s, and will
include over 100 brain samples. The genomes of family
members with Alzheimer’s will be compared to those
members who have been spared the disease to identify sites
in the genome that influence risk for Alzheimer’s.
Tim Clark
http://www.genengnews.com/gen-news-highlights/mgh-wins-5-4m-grant-toward-sequencing-for-alzheimer-s-risk/81247502/

17. Significant added value through
appropriate additional data collection

18. Big Data
methodology
www.methodbox.org

20. Troublesome Threes
• 3 Ingredients
– Data; Models; Expertise Challenge
conventional
assumptions
• 3 Myths
– Big data warehouses are the solution
– Science provides the models to utilise the data
– Clinicians will continue to be the main source of data
• 3 Pipelines
– R&D; Quality Improvement; Payor & Public Health
Iain Buchan

21. Big Data in Context
or
Datasets
(+ models) Data Models Expertise
(searched by experts) “sense-making network”
Iain Buchan

22. Closing thoughts
1. Big Data is not just a quantitative change, it’s a
methodological change – using digital methods
• Use what we already have (in silos)
2. Tremendous opportunity to collect additional data
with significant impact on dementia research
• Surveys and social machines
• Data from instrumenting care process today
3. Think sociotechnical – community matters
• Method sharing, and usage adds value
• Machines are users too – assistance vs automation

23. david.deroure@oerc.ox.ac.uk
www.oerc.ox.ac.uk/people/dder
www.scilogs.com/eresearch
@dder
Personal slide credits: Nigel Shadbolt, Tim Clark, Iain Buchan