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Managing your research data webinar


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Slides for session run by Vimal Shah

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Managing your research data webinar

  1. 1. Managing your Research Data Vimal Shah Research Information Manager LSS 1
  2. 2. Introductory session • Slides available:   My Learning  Research Area  Researcher Development Programme 2
  3. 3. What type of data/material will you be working with? 3
  4. 4. Software code Audio-visual recordings Digital text / numbers Digital art Digital images Visualisations GIS datasets / maps Tabular data Simulations Models Databases Combinations 4
  5. 5. Why manage data? 5
  6. 6. Open data as public good e.g. Open Data Institute People-powered research e.g. Zooniverse 13/02/2018 6
  7. 7. 7 Visibility Discovery Impact* Reuse Collaboration Citation Sustainability Preservation Provenance Security Safety Recoverability Verifiability Scrutiny Integrity Publishers Data publication Funder expectations / Open research Institutional policies Research ethics Legislation Contracts Agreements Commercial exploitation of IP
  8. 8. “…as open as possible, as closed as necessary” European Commission (2016) Open access & Data management, Horizon 2020 8
  9. 9. Getting started: Data management planning 9
  10. 10. Data management plan templates are available on Funder-specific or general template for non-funded projects are available 10
  11. 11. Assessment of existing data • Explanation of the existing data sources that will be used by the research project, with references • Consider copyright in third party data and seek adequate permissions for use and future publication – you may need to budget for copyright clearance fees for proprietary data • To discover existing data try (Registry of data repositories);;;;;; • Some similarity with conducting a literature review • Analysis of the gaps identified between the currently available and required data for the research 11
  12. 12. Information on new data • Provide information on the data that will be produced or accessed by the research project, including the type of data. • Where possible use open file formats which are more durable for long term access. 12
  13. 13. Quality assurance of data • Describe the procedures for quality assurance that will be carried out on the data collected at the time of data collection, data entry, digitisation and data checking • How will you prevent errors from entering or remaining in a dataset? Common errors may include inaccurate data entered in a dataset as a result of mistyping or inadequate documentation of data due to human error or anomalies in the field • File naming consistency, folder organization and maintaining version control contributes to quality assurance and accuracy as well as efficiency 13
  14. 14. Backup and security of data • Describe the data backup procedures that you will adopt to ensure the data and metadata are securely stored during the lifetime of the project • Maintain regular backups of your data, test that the backups are recoverable and usable, specify locations where the data and backups will be stored • For sensitive/confidential data, encryption, password protection and physical security must be put in place • Follow the 3-2-1 principle: 3 copies of important stuff, 2 different media; 1 in a different geographical location. Some constraints apply when managing personal data (see later) 14
  15. 15. Management and curation of data • Outline your plans for preparing, organising and documenting data • Documentation provides information about how and why the data was created, what its content and structure are, and what alterations it has undergone • If data is undocumented, it may become impossible for you or someone else to understand and re-use the data later on • Metadata should be sufficient to allow others to understand what research data exists, why when and how it was generated, and how to access it. 15
  16. 16. Data sharing • How will you share your data when publishing your findings? • Via a repository? Journal supplementary material? A project website? Or upon request? • If you expect obstacles to sharing your data, explain which and the possible measures you can apply to overcome these. 16
  17. 17. Personal data and confidentiality • Make explicit mention of the planned procedures to handle consent for data sharing for data obtained from human participants, and/or how to anonymise data, to make sure that data can be made available and accessible for future scientific research • This forms part of conducting research ethically and lawfully and part of maintaining research integrity • Privacy and data protection particularly applies to human participants in your research • Additionally confidentiality of data may need to be maintained to protect individuals or under contracts/agreements and for some government or commercial research 17
  18. 18. 18 Consent Obviously you are obtaining consent from your participants but is that consent fully informed? Are you telling participants all the ways you might be using their data, where it is being stored, who is handling it and if it is being shared with any third party. Ultimately, the drive of the legislation is for organisations (known as data controllers) are as transparent with individuals (known as data subjects) as possible so they have the opportunity to exercise their individual rights properly. Data Minimisation Is the project only collecting the minimum amount of personal data needed in order to achieve its research objectives? There should be an avoidance of collecting extra data ‘just in case’ or that might be interesting but is not directly within the parameters of answering the research question. Data Storage and Security What tools/databases/systems are you actually going to use to store the data you collect? Do you have control over who can gain access to this? If the data is being stored externally or ‘in the cloud’ does the third party host have strong security controls in place? Is the data being hosted in the EU? Have you consulted any subject matter experts to determine the compliance of the tools/databases/systems you are going to use? Presentation of Data When you are presenting your findings, is the personal data that constitute them being anonymised? If not is there a justifiable reason why individuals need to be identified? Retention of Data Have you determined how long personal data needs to be kept after the final research has been published? Have you consulted any external guidance from professional bodies or other experts in the field? Do you actually have a practical way of managing retention e.g. a way to actually delete data and an individual that will be responsible for this?
  19. 19. Copyright + intellectual property • State who will own the copyright and IPR of any new data that you will generate • If unsure or you require more advice, contact the University’s Copyright Officer, Kate Vasili and the Research and Knowledge Transfer Office 19
  20. 20. Archiving data • State how your data will be archived and preserved for future reuse • There may be a suitable discipline/domain specific repository where your data can be hosted (check or deposit your data in • Plan for any future costs for long-term data storage and preservation. 20
  21. 21. Responsibilities • Outline responsibilities for data management within research teams and at all partner institutions • Keep the plan up to date 21
  22. 22. 22
  23. 23. Data citation • Citing data you use • Getting credit for your data • Cross-citation 23
  24. 24. Publications, theses, books etc. 24
  25. 25. Data and other materials 25
  26. 26. Temporary web pages:  Our Research  Research data 26