Transcript Document

Creating a Data Management
Plan for your Research
EPFL Workshop
Lausaunne, 28 Oct 2014
Robin Rice, Laine Ruus
EDINA and Data Library
Course content
• What is a Data Management Plan?
• Benefits and drivers
• What do Funders want?
• Six themes for a DMP
Exercise: Peer Review a DMP
• Support for Data Management Planning
• Top Tips for creating a DMP
What is a DMP?
DMPs are written at the start of a project to define:
• What data will be collected or created?
• How the data will be documented and described?
• Where the data will be stored?
• Who will be responsible for data security and backup?
• Which data will be shared and/or preserved?
• How the data will be shared and with whom?
Developing DMPs can help you to:
• Make informed decisions to anticipate & avoid problems.
• Avoid duplication, data loss and security breaches.
• Develop procedures early on for consistency.
• Ensure data are accurate, complete, reliable and secure.
• Save time and effort to make your lives easier.
• Plan to share data early on and increase impact.
What do RCUK Funders want?
require some form of data management or sharing plan
as part of a funding application.
• EPSRC does not ask for a DMP, but EXPECTS that one
will exist!
• The requirements are diverse, but they all have the
RCUK Common Principles as their foundation.
RCUK common principles on
data policy
Key messages:
Data are a public good and should be made openly available where
Adherence to community standards and best practice. Preserve data of
long-term value.
Metadata for discoverability and access. Link to data from publications.
Recognise constraints (legal, ethical and commercial) on what data to
Allow embargo periods delaying data release to protect the effort of
Acknowledge sources to recognise IP and abide by T&Cs.
Ensure cost-effective use of public funds for RDM.
EU Funding
• European Horizon 2020 funding programme is currently piloting
DMPs for the 2014-2015 Work Programme:
– Future and Emerging Technologies
– Research infrastructures – part e-Infrastructures
– Leadership in enabling and industrial technologies – Information and
Communication Technologies
– Societal Challenge: 'Secure, Clean and Efficient Energy'–part Smart cities and
– Societal Challenge: 'Climate Action, Environment, Resource Efficiency and Raw
– Societal Challenge: 'Europe in a changing world–inclusive, innovative and
reflective Societies'
– Science with and for Society
• Opt-out with justification
• Opt-in from other strands
EC Horizon 2020 Programme
Called a “Data Management Plan”.
No specific length restrictions.
Should cover:
– Data set reference and name
– Data set description
– Standards and metadata
– Data sharing
– Archiving and preservation
Must be delivered in first 6 months of project.
Is expected to evolve and grow throughout duration of project.
Six themes for a DMP
1. Data types, formats, standards and capture methods
2. Ethics and Intellectual Property
3. Access, data sharing and reuse
4. Short-term storage and data management
5. Deposit and long-term preservation
6. Resourcing
1. Data types, formats, standards
and capture methods
Sample Questions
What data outputs will your research generate?
outline volume, type, content, quality and format of the final dataset
Outline the metadata, documentation or other supporting material that
should accompany the data for it to be interpreted correctly.
What standards and methodologies will be utilised for data collection and
State the relationship to other data available in public repositories e.g.
existing data sources that will be used by the research project
gaps between available data and that required for the research
the added value that new data would provide in relation to existing data
Be prepared to explain and justify the choices being made.
Understand the need to create metadata and documentation to support
the re-use of research data.
2. Ethics and Intellectual Property
Sample Questions:
• Demonstrate that you have sought advice on and addressed all
copyright and rights management issues that apply to the resource.
• Make explicit mention of consent, confidentiality, anonymisation and
other ethical considerations, where appropriate.
• Are any restrictions on data sharing required – for example to
safeguard research participants or to gain appropriate intellectual
property protection?
Present a strong case for any restrictions on sharing.
Ensure all necessary ethical approvals are in place.
Clarify issues surrounding data ownership.
3. Access, data sharing and reuse
Sample Questions:
What are the further intended and/or foreseeable research uses for the
completed dataset(s)?
How will you make the resource accessible to potential audience(s):
• Where will you make the data available?
• How will other researchers be able to access the data?
• What is the timescale for public release of the data?
• How will data sharing provide opportunities for coordination or
Anticipate and plan for data re-use.
Be very clear about where, when and how data will be made available.
Use existing infrastructure wherever possible.
4. Short-term storage and data
Sample Questions:
• Describe the planned quality assurance and back-up procedures
• Specify the responsibilities for data management and curation within
research teams at all participating institutions.
• Define data management support.
Consider the practicalities.
Apply appropriate levels of data management.
5. Deposit and long-term
Sample Questions:
• Identify which of the data sets produced are considered to be of
long-term value.
• Outline the plans for preparing and documenting data for
preservation and sharing.
• Explain your archiving/preservation plan to ensure the long-term
value of key datasets.
Select data of long-term value.
Safeguard the data behind the publications.
Assure that data will remain accessible.
6. Resourcing
Sample Questions:
• What resources will you require to deliver your plan?
• Outline additional hardware, software and technical expertise,
support and training that is likely to be required and how it will be
Outline and justify costs.
Be realistic about the human time and effort required.
Show that funds will be used efficiently and effectively.
Supporting researchers with DMPs
Types of support UoE provides:
• Guidelines and templates on what to include in plans
• Example answers, guidance and links to local support
• A library of successful DMPs to reuse
• Training courses and guidance websites
• Tailored consultancy services
• Customised DMPonline:
Exercise: Peer Review a DMP
• Breaking into groups of 4-5, look over the sample data management
plans. A copy of the ESRC/ESDS data management plan guidance
for peer reviewers has been provided for reference.
• Assuming the role of ESRC bid reviewers, each group should
consider whether the sample data management plans provide
sufficient detail to enable the plans to be effectively assessed as
part of a bid. Would you need more information in some areas to be
able to determine how well the Principal Investigator will be
managing their data?
• Groups will have 20 minutes to review the sample data management
plans and related documents. Each group will be asked to report
back on areas where additional information would be needed by
reviewers to make an informed assessment.
Free and open web-based tool to
help researchers write plans:
It features:
o Templates based on
different requirements
o Tailored guidance
(disciplinary, funder etc.)
o Customised exports to a
variety of formats
o Ability to share DMPs
with others
Top tips
• Keep it simple, short and specific.
• Avoid jargon.
• Seek advice - consult and collaborate.
• Base plans on available skills and support.
• Make sure implementation is feasible.
• Justify any resources or restrictions needed.
But remember…
• Start early – don’t wait until the last minute!
• The plan will - and should - change over the life
of project. It is a living document.
• Get into the habit of updating the plan with
regular project updates.
• Use the plan as a communication tool – with
partners, funders and yourself.
Feel free to contact us:
[email protected]
[email protected]