Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Creating a Data Management Plan for your Grant Application


Published on

Stuart Macdonald steps through the process of creating a robust data management plan for researchers. Presented at the European Association for Health Information and Libraries (EAHIL) 2015 workshop, Edinburgh, 11 June 2015.

Published in: Education
  • Be the first to comment

Creating a Data Management Plan for your Grant Application

  1. 1. Creating a Data Management Plan for your Grant Application Stuart Macdonald RDM Services Coordinator / Associate Data Librarian University of Edinburgh EAHIL + ICAHIS + ICLC, Edinburgh, 11 June 2015
  2. 2. Course content • Background • What is Research Data and RDM? • What is a Data Management Plan? • Benefits and drivers • What do Funders want? • Six themes for a DMP • Exercise: What makes a good DMP? • Support for DMP
  3. 3. Background • EDINA and University Data Library (EDL) together are a division within Information Services (IS) of the University of Edinburgh. • EDINA is a Jisc-designated centre for digital expertise & online service delivery • - • The Data Library assists Edinburgh University users in the discovery, access, use and management of research datasets - • Research & Learning Services – focus on developing and delivering digital library technologies (RDM Programme, OA Scholarly Communications, open scholarship, bibliometrics, resource discovery, LMS)
  4. 4. What is Research data? “Recorded, factual material commonly retained by and accepted in the [research] community as necessary to validate research findings; although the majority of such data is created in digital format, all research data is included irrespective of the format in which it is created.” UK Engineering and Physical Sciences Research Council (EPSRC) • There is no single accepted definition of research data. Several definitions exist. It may be useful to consider not just what material would be required to validate research findings but also think about what information is needed to enable re-use of the data. • Not all definitions of data will be appropriate for all disciplines. • Data can be generated for one purpose and used for a completely different one.
  5. 5. Research Data Management (RDM) • Data management is a general term covering how you organise, structure, store, and care for the data used or generated during a research project. • It includes: – How you deal with data on a day-to-day basis over the lifetime of a project. – What happens to data in the longer term – what you do with your data after the project ends. • RDM is also considered one of the areas of responsible conduct for research.
  6. 6. 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?
  7. 7. Benefits 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.
  8. 8. Drivers of RDM “Publicly funded research data are a public good, produced in the public interest, which should be made openly available with as few restrictions as possible in a timely and responsible manner that does not harm intellectual property.” RCUK Common Principles on Data Policy
  9. 9. Institutional RDM Policy 2. Responsibility for RDM lies primarily with Principal Investigators (PIs). 3. All new research proposals must include research data management plans… 4. The institution will provide training, support, advice and where appropriate guidelines and templates… 7. Research data management plans must ensure that research data are available for access and re-use where appropriate… services/about/policies-and-regulations/research-data-policy
  10. 10. What do UK Funders want?
  11. 11. What do RCUK Funders want? • AHRC, BBSRC, ESRC, MRC, NERC, and STFC all 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.
  12. 12. RCUK common principles on data policy - Key messages: 1. Data are a public good and should be made openly available where possible. 2. Adherence to community standards and best practice. Preserve data of long- term value. 3. Metadata for discoverability and access. Link to data from publications. 4. Recognise constraints (legal, ethical and commercial) on what data to release. 5. Allow embargo periods delaying data release to protect the effort of creators. 6. Acknowledge sources to recognise IP and abide by T&Cs. 7. Ensure cost-effective use of public funds for RDM.
  13. 13. MRC • Called a “Data Management Plan”. • Should be “concise” but no specific length restrictions. • Must cover: – Description of the data – Data collection / generation – Data management, documentation and curation – Data security and confidentiality of potentially disclosive personal information – Data sharing and access – Responsibilities – Other relevant policies
  14. 14. What do other funders want? • Cancer Research UK and the Wellcome Trust both require data management & sharing plans. • 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 communities – Societal Challenge: 'Climate Action, Environment, Resource Efficiency and Raw Materials' – Societal Challenge: 'Europe in a changing world–inclusive, innovative and reflective Societies' – Science with and for Society • The Pilot will give the EC a better understanding of what supporting infrastructure is needed and of what factors impact on non-sharing such as security, privacy or data protection.
  15. 15. Cancer Research UK • Called a “Data Management & Sharing Plan”. • No specific length restrictions. • Must cover: – volume, type, content and format of the final dataset – standards that will be utilised for data collection and management – metadata, documentation or other supporting material – method used to share data – timescale for public release of data – long-term preservation plan for the dataset – whether a data sharing agreement will be required – any reasons why there may be restrictions on data sharing data-sharing-and-preservation-strategy
  16. 16. Wellcome Trust • Called a “Data Management & Sharing Plan”. • No specific length restrictions. • Must cover: – what data outputs will your research generate – what data will have value to other researchers? – when will you share the data? – where will you make the data available? – how will other researchers be able to access the data? – are any limits to data sharing required? – how will you ensure that key datasets are preserved to ensure their long-term value? – what resources will you require to deliver your plan?
  17. 17. European Horizon 2020 • 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 the first 6 months of project. • Is expected to evolve and grow throughout duration of project.
  18. 18. Break Questions?
  19. 19. 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
  20. 20. 1. Data types, formats… • What data outputs will your research generate? Data type, volume, quality, formats • 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 management? • State the relationship to other data available in public repositories. • Be prepared to explain and justify the choices being made.
  21. 21. 2. Ethics and IP • Make explicit mention of consent, confidentiality, anonymisation and other ethical considerations, where appropriate, and strategies taken to not preclude further re-use of data • Demonstrate that you have sought advice on and addressed all copyright and rights management issues that apply to the resource • Are any restrictions on data sharing required, for example to safeguard research participants or to gain appropriate intellectual property protection? • Get advice on IPR from your research office at an early stage – before signing over ANY rights to collaborators or commercial partners!
  22. 22. 3. Short-term storage & data management • Describe the planned quality assurance and back-up procedures [security/storage]. • Use managed networked services for storing your data. • Specify the responsibilities for data management and curation within research teams and at all participating institutions. • Keep track of all data generated.
  23. 23. 4. Access, data sharing & reuse • Anticipate and plan for data reuse. • Clarify issues surrounding data ownership. • Present a strong case for any restrictions on sharing. • Ensure all necessary ethical approvals are in place. • Be very clear about where, when and how data will be made available. • Get advice on ethics from Ethics Committee and advice on DPA from Records Management before sharing, or refusing to share, any data.
  24. 24. Data sharing • You should consider making your data available for others to re-use where possible under appropriate safeguards. • Repositories: ways of sharing data – – Figshare (interdisciplinary): – Institutional (data) repository – Domain or national data archive • Registries: – – – • Advantages - permanent / stable, findable, citable, safe and controlled environment Have announced their plan to merge their two projects into one service that will be managed under the auspices of DataCite by the end of 2015.
  25. 25. 5. Deposit & long-term preservation • Explain your archiving/preservation plan to ensure the long-term value of key datasets. • Select data of long-term value: identify which of the data sets produced are considered to be of long-term value. • Deposit all data with a responsible data repository within specific period after the end of the grant (as determined by funder) • Assure that data will remain accessible. Use existing infrastructure e.g. data archive or the institutional repository, • Some funders regards non-deposit of research data as an exception and reserves the right to request deposit when there is insufficient evidence to prevent data sharing.
  26. 26. External repositories When choosing an external repository you should consider: • Does your funder require data to be offered to a specific repository? • Is the repository sustainable? • What will be done with your data if the repository closes down? • How much will it cost? Are costs upfront or annual? • Will data be easily accessible to them and to third parties? • How does the repository promote discoverability? • Does the repository record when data is accessed, downloaded, or cited so they will get recognition for their work?
  27. 27. 6. Resourcing • Outline and justify costs: what resources will you require to deliver your plan, e.g. data management and data preparation for sharing. • Be realistic about the human time and effort required. • Show that funds will be used efficiently and effectively.
  28. 28. Supporting researchers with DMPs Various types of support can be provided: • 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 • Online tools (e.g. customised DMPonline)
  29. 29. DMPonline Free and open web-based tool to help researchers write plans: • Templates based on different requirements • Tailored guidance (disciplinary, funder etc.) • Customised exports to a variety of formats • Ability to share DMPs with others Edinburgh has started the process of customising DMPonline for researchers. DMPonline screencast:
  30. 30. Software Management Plans • A prototype Software Management Plan Service has been developed by the Software Sustainability Institute to help researchers write software management plans: • They are relatively new for research software proposals, though many of the elements discussed in these plans would be expected in standard proposals. • The EPSRC Software for the Future call explicitly requires software management plans as part of the Pathways to Impact. NSF SI2 funding requires software to be addressed as part of the mandatory data management plan. • A guide is on writing & using a software management plan is available:
  31. 31. Software Management Plans A software management plan can help researchers to: • formalise a set of structures and goals that ensure research software is accessible and reusable in the short, medium and long term • consider whether third-party software to be used within a research project will be available, and supported, for the lifetime of the project • give funders confidence that software they have funded survives beyond the funding period, that there is something to show for their investment
  32. 32. Exercise: What makes a good DMP? In groups of 3 or 4: • Read the materials that have been handed round. • Thinking as a reviewer compare the 2 plans against the ESDS guidance you have been given. • Identify the strengths and weaknesses of each. • Which one of these plans would you approve? • What changes would you want made to the other plan before it would gain approval?
  33. 33. Tips to share • Keep DMPs simple, short and specific, avoid jargon. • Seek advice - consult and collaborate. • 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 so need updating regularly. • Always contact your funder when you need clarification or further information. • Include all expected costs in your data management costing, esp. extra storage space for active data, data deposit / long-term storage etc. • Agree in your Team on using an open /standard /common format for long-term preservation, and be consistent with it. • Also see:
  34. 34. A final recommendation… • MANTRA is an internationally recognized self-paced online training course developed by EDINA & Data Library for PGR’s and early career researchers in data management issues. • 8 self-paced learning modules which map onto the research data lifecycle • Data handling exercises with open datasets in 4 analytical packages: R, SPSS, NVivo, ArcGIS.
  35. 35. Links • MRC Good research practice: Principles and guidelines guidelines/ • Checklist for a data management plan. DCC (Digital Curation Centre) f • UK Data Archive: Data management costing tool • UK Data Archive: Anonymisation • UK Data Archive: Ethical/Legal • Record Management: Taking sensitive information and personal data outside the University’s computing environment
  36. 36. Thank You Questions?