Introduction to Research Data Management: activities, roles and requirements

… because good research needs good data

Introduction to Research Data
Management: activities, roles and
requirements
Michael Day
Digital Curation Centre
UKOLN, University of Bath
m.day@ukoln.ac.uk

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 UK: Scotland
License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/2.5/scotland/ ; or,
(b) send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA. Funded by:

11th DCC Regional Roadshow, London, 22 May 2012


Outline
• The researcher perspective
• Codes of Practice
• Research funding bodies
• The institutional perspective
• Activities, roles and requirements

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The researcher perspective
• Managing and sharing data is simply part of good
research:
• Adhering to disciplinary and/or institutional codes of practice
and policies
• Has been practiced since the advent of modern science, but
not always consistently; data intensive research makes it
even more critical
• Meeting the specific requirements of funding bodies
• Reputational risks if data management is not handled
properly
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Research codes of practice (1)
• UK Research Integrity Office Code of Practice for
Research (2009)
Data management planning is an essential part of research
design
Organisations should have in place procedures, resources
(including physical space) and administrative support to
assist researchers in the accurate and efficient collection of
data and its storage in a secure and accessible form [3.12.5]

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Research codes of practice (2)
• RCUK Code of Conduct on the Governance of Good
Research Conduct (2011)
Primary data and research evidence [should be made]
accessible to others for reasonable periods after the
completion of the research: data should normally be
preserved and accessible for 10 yrs (in some cases 20 yrs or
longer)
Responsibility for proper management and preservation of
data and primary materials is shared between the researcher
and the research organisation [although deposit within
national collections is endorsed]

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Research funding bodies
• UK Research Councils
• Help fund some data archives, e.g.:
• Archaeology Data Service, European Bioinformatics
Institute, the NERC data centres, UK Data Archive
• Support for JISC (and DCC)
• RCUK Common Principles on Data Policy
• Recognises that data are a critical output of the research
process
http://www.rcuk.ac.uk/research/Pages/DataPolicy.aspx

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RCUK Principles (in a nutshell)
• Publicly funded research data should be made openly available
• Data with acknowledged long-term value should be preserved and
remain accessible and usable for future research
• Sufficient metadata should be recorded to enable other researchers to
find and understand the research to enable re-use; published results
should always include information on how to access the supporting data
• Recognition that there may be legal, ethical and commercial constraints
• Recognition that researchers may need privileged use of data for a
limited period
• All users of research data should acknowledge their sources
• Appropriate to use public funds to support MRD

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EPSRC expectations
• Roadmap approved May 2012; compliance by May
2015
Appropriate metadata (including unique IDs) to be made freely
available on the Internet within 12 months of data generation
Data not generated in digital format should be stored in a manner to
facilitate it being shared
Data should be securely preserved for a minimum of 10 years after
privileged access expires or the last date access was requested by
a third party
Adequate resources from existing funding streams
EPSRC will monitor progress and compliance, and reserves the
right to impose appropriate sanctions
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Implications for researchers
• Increasing number of research councils and funding bodies with data
management and sharing requirements
• Potential loss of research income if these mandates are not met
• Need to determine the costs associated with short and longer-term
management and curation and to request funds as part of grant
• Responsibility for infrastructure shifting more to HEIs and less to
centralised data archives, but institutional infrastructures and services
are still emerging
• Need guidance - some good external support
• But also need more local support; often fragmented (need to draw upon
existing channels within your institution wherever possible)

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Institutional drivers
• Safeguarding research integrity
• Increasing number of FOI requests for data
• Adhering to existing codes of research practice and ethics
• Developing new institution-wide strategies, policies and services
for data storage and management
• Increased institutional focus on research management (e.g., in
response to REF)
• Benchmarking – self-assessing infrastructure and planning for
improvement
• More demands but less resources to work with

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Activities, roles, requirements (1)
• Requirements gathering
• Identifying researchers’ data requirements
• Developing a shared understanding of what needs to be
done (e.g., identifying where data exist, its form and scale,
any existing retention requirements)
• Identifying good practice within the institution (and the
opposite)
• Methods: surveys, focus groups, case studies, joint R&D
projects, assessment tools (e.g. DAF)

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• Identifying motivations and benefits
• For researchers, support services, the institution
• Identifying risks
• Data loss (institution, research group, individual)
• Increased costs (lack of planning, service inefficiency, data
loss)
• Legal compliance (research funder, H&S, ethics, FoI)
• Reputation (institution, unit, individual)
• Identifying costs
• Keeping Research Data Safe (KRDS) toolkit
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• Assessing institutional preparedness
• Identifying institutional stakeholders, existing data support services,
gaps
• Benchmarking and planning for the future
• Skills audit
• CARDIO tool
• Policy development
• Policies – approval by senior management is just the start; policies
need to be embedded in research practice and responsive to
changing requirements
• Data management planning
• DMP online, DCC How-to Develop a Data Management Plan guide
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• Implementation and service development
• Integrating where possible with existing services, e.g. IR,
CRIS, VRE, HPC, cloud services, social media, etc.
• Appraisal, deciding what needs to be kept and for how long
• Storage choices – no one-size-fits-all solution, e.g. Bristol’s
BluePeta petascale storage facility, Bath’s X-Drive approach,
cloud approaches
• Data documentation and metadata – layered approaches:
top-level discovery (core metadata, collection/experiment-
level?), role of standards like DCMI, CERIF, DDI, etc.

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• Data issues:
• Appraisal: selection criteria, retention periods (who decides?)
• DCC How to appraise and select research data for
curation guide
• Documentation: metadata, schema, semantics
• Formats: proprietary formats, community standards, etc.
• Provenance and authenticity
• Citation (assignment of persistent IDs?)
• Access (embargo policies?)
• Licensing
• DCC How to license research data guide
Funded by:



Thank-you. Any questions?

Michael Day
Digital Curation Centre
UKOLN, University of Bath
m.day@ukoln.ac.uk

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 UK: Scotland
License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/2.5/scotland/ ; or,
(b) send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA. Funded by:


Introduction to Research Data Management: activities, roles and requirements

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