Institutional Data Management Blueprint Kenji Takeda (Engineering Sciences), Mark Brown (University...

Institutional Data Management Blueprint

Kenji Takeda (Engineering Sciences), Mark Brown (University Librarian), Simon Coles (Chemistry), Les Carr (ECS, EPrints), Jeremy Frey (Chemistry), Graeme Earl (Archaeology) Peter Hancock (iSolutions), Wendy White (Library)

Introduction• Why data management?

• IDMB project

• Key findings

• Recommendations

• Business plan

• Conclusions

www.southamptondata.org 2

Data Management @ Southampton• What do we mean?

– Everything• Why do we care?

– Foundation for all of our research• How should it be managed?

– We want to find out from users• How can the University help?

– What do researchers need?• Key outcomes

– Impact & profile

3

cop

yri

ght

© 2

01

0 S

ean D

reili

ng

er.

R

ep

rod

uce

d u

nd

er

Cre

ati

ve C

om

mons

license

IDMB Project Overview• Produce framework for managing research data for

an HEI

• Scope and evaluate a pilot implementation plan for an institution-wide data model

4

Review of Data Management

Questionnaire and interviews

Figure 12. Where do you store your current data? - please tick all that apply

6

Key Findings• Schools research practice is embedded and unified

• Schools data management capabilities vary widely

• Data management is carried out on an ad-hoc basis in many cases

• Researchers demand for storage is significant

• Researchers resort to their own best efforts in many cases, where central support does not meet their needs

• Users want more support for backup, particularly for large quantities of data

7

Key Findings• Researchers want to

keep their data for a long time

• There is a need from researchers to share data, both locally and globally

• Data curation and preservation support needs to be improved

8

• Large data stores are unstructured

• Large data stores not shared

• Structured data stores are difficult to adopt

• Structured data stores tend to be task-specific

• Coherent approach to data storage & management needed

Data Management Infrastructure

9

accessibility

manageabili

ty

Gap Analysis• Policy and governance is robust, but is not

communicated to researchers in the most accessible way

• Services and infrastructure are in place, but lack capacity and coherence

• There is a lack of training and guidance on data management

• Lack of coherence and sustainable business model

10

AIDA benchmarking, crowdsourcing and launch workshop useful additional

tools

AIDA benchmarking, crowdsourcing and launch workshop useful additional

tools

Recommendations

Recommendations• Short-term (1 year)

– Develop an institutional data repository

– Develop a scalable business model

– One-stop shop for data management advice and guidance

• Medium-term (1-3 years)

– Comprehensive and affordable backup service for all

– Open research data mandate, and supporting infrastructure

– Research data lifecycle management

– Embedding data management training and support

12

Long-term recommendations• Provide coherent data

management support across all disciplines

• Embed exemplary data management practice across the institution

• Agile business plan for continual improvement

13

Pilot Projects

Metadata Framework

15

Archaeology Data Management• Archaeology is all about

data and metadata

• Spectrum of data is huge

– Laser scans– Photography– Geophysics– CAD– CGI

• Context is everything

http://www.portusproject.org/

SharePoint 2010 Data Management

http://dl.dropbox.com/u/3404785/microsoft/Pivot%20in%20SP%20-%20HD.mp417

Federated Data Repositories• Repositories

– Institutional

– Discipline

• Link publications to data in different repositories

• Materials Data Centre

– EP2DC

18

www.materialsdatacentre.com

Demo

19

Business planning

Business Plan• Strategy

• Principles

• Policy

• Infrastructure and services

• Business model

Evolving data partnership approach

21

Final Deliverables• Data management framework

• Business Plan

• One-stop shop for data management

• Data management pilots

– Archaeology, nano-fabrication, meta-search

• Training courses and material

• Final report – blueprint document

22

Conclusions

• Good data management is vital for better research

• Two-pronged approach

– Bottom-up to augment researcher’s world

– Top-down to provide support and guidance

• Providing a roadmap for the future

23

• www.southamptondata.org

• ktakeda@soton.ac.uk

Expertise

• Fiona Nichols, Pam Wake, Michael Whitton (Library)

• Steve Patterson, Mark Scott (iSolutions)

• Hembo Pagi (Archaeology)

• Richard Boardman, Steven Johnston, Simon Cox, Philippa Reed, Ian Sinclair, Tim Austin, Khalid Abdulbagi (Engineering Sciences)

• Mark Schueler, Niruwan Turnbull (Electronics and Computer Science

24