Preparing a Research Data 
Management response: policy, 
infrastructure and practice 
April 2015 
Overview 
•Developing a RDM response  
•Policy principles 
•Building infrastructure 
•Frameworks and practice 
•Challenges and risks 
 
Developing a RDM response  
Action Timeframe  
Raise awareness June 2012 - Dec 2013 
Identify best practice and understand capacity of infrastructure in 
the UK 
Jan - June 2013 
Audit the data management landscape June 2013 
Analyse costs and benefits of data management infrastructure March 2013 - Dec 
2013 
Develop best practice policies and guidelines  Sept - Dec 2013 
Establish governance structures Jan - Feb 2014 
Disseminate best practice and importance of RDM Jan - Dec 2014  
Establish infrastructure to enable the preservation, security and 
public access of research data 
March - Dec 2014 
Appoint staff April 2014 - May 2014 
Provide skills training in data management May 2014 - May 2015 
Embed RDM training and good practice guidelines Jan 2015 - May 2015 
Offer support help desk on data planning and management Sept 2014 - May 2015 
Key challenges 
• Complex and varying funder requirements  
• No-one model of best practice 
• No common processes, systems or data capture for 
RDM across the University  
• Balancing (often) competing objectives – e.g. 
sharing vs confidentiality   
• Concern / ‘fear’ over infrastructural solution and 
cost  
• Policy and best practice vs communication and 
raising awareness 
• Different University priorities, not least REF 2014  
 
Understanding the research data 
management landscape  
• Pockets of good practice but generally, lack of  
• Awareness 
• Data sharing 
• Planning for financial sustainability  
• Range of datasets contained in a variety of holdings: 
• School servers (with back up) 
• External hard drives 
• PCs 
• Lab note books  
• Laboratories 
• Government offices 
• Cloud services  
• Subject repositories 
Understanding the research data 
management landscape  
• Difficult to audit the extent of data holdings 
• MBs - 10s of TB 
• ‘Don’t know’ 
• ‘A lot’  
• ‘Many files’ 
• ‘What a silly question’  
• Clear signage for needs: 
• Advice and training (funder requirements and legal issues) 
• Research data cataloguer per project / research group 
• Storage /space with back up 
Networking and sharing experience 
• Learning by doing………….…by others 
• Digital Curation Centre 
• RDM networks and seminars  
• Pure User Group  
• Individual universities  
Policy principles  
• Restating expected data management practices  
• Institutional  
• Researcher (PI) level 
 
• Sharing data  
• compliance with funder requirements or 
• the University….. ‘encourages all researchers to make 
their publicly funded research accessible and freely 
available’ 
Building RDM infrastructure  
• Utilising Pure developments 
• Future storage requirements unclear 
• External storage solutions impressive but costly  
• Short / medium term internal 2PB storage solution: 
• Tier 1: active project data 
• Tier 2: data to be made publically available  
• Tier 3: archive data (longer recall period and offsite 
copy to mitigate against disk failure) 
• Researcher access to 2TB (> PIs of larger data driven 
subjects) 
RDM framework 
PI T1 
T2 
Pure / 
Metadata 
Research 
Portal 
T3 
Basic metadata 
Basic metadata 
Subject 
repository 
Metadata 
Repository 
website 
Pure research dataset  
QUB Research Portal  
QUB Research Portal  
Challenges and risks 
• Not a perfect, comprehensive & everlasting 
solution 
• Commercial products vs in-house solution  
• An incremental approach - evidence gathering 
on future data requirements 
• Extensive storage / infrastructure 
• FTE  
• A broader organisational approach  
Organisational collaboration 
Future RDM framework 
Apps / 
awards 
DMP online 
T1  
Active data 
T2  
Open data 
repository 
T3  
Data archive 
Research 
Portal 
Pure