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Best practices in research data management and stewardship

The course is made up of seven sessions covering:

  • Data management planning
  • Data protection in research, requirements and responsibilities originating from GDPR
  • Practicalities of data handling
  • Exploratory analyses, reproducible manuscripts
  • FAIR data principles
  • Data publishing and archival

Teaching Objectives:

  • To remember the research data lifecycle, to reveal data management planning as a form of decision making. Listing key factors that shape data management decisions.
  • To learn about software tools that assist data management planning.
  • To learn how the GDPR affects research and to reveal researchers' responsibilities when working with human-subject data.
  • To learn about the record keeping requirements of the GDPR, and the tools that can be used for record keeping during the course of research.
  • To learn about various data transfer channels, their advantages and disadvantages.
  • To learn how to properly name files and organize research data.
  • To learn about data integrity and its role in research data management.
  • To learn how to make computational processing and analysis reproducible.
  • To learn about FAIR data principles and their rationale, to reveal key indicators for FAIR'ness for a dataset.
  • To learn how FAIR principles can be applied in data and results publishing on the example of data publishing at FAIRDOMHub.

Learning Outcomes:

  • Learners can list key decision areas that underlie data management.
  • Learners can use the Data Stewardship Wizard to record data management decisions for prospective projects.
  • Learners can list requirements for accountable use of human data in research.
  • Learners can use the Data Information System to keep record of research projects and sensitive human-subject data.
  • Learners can setup their own safe working environment.
  • Learners are able to ingest research data and perform key operations increasing the data integrity.
  • Learners can tell whether or not their current practices on data handling results in FAIR data.
  • Learners can publish their data and their results in accordance to FAIR principles.
  • Learners can use FAIRDOMHub and similar platforms for their future work.

Materials to be explored before attending the course

  • Running example for course practicals (PDF), and the paper by Gérard et.al. which is the actual study that inspired our running example

Course Programme

Session 1 Data management planning

  • Data Management planning as an intervention
  • Research data lifecycle
  • Areas of consideration in data management planning
  • Data management planning tools
  • Practical with the DMPOnline and Data Stewardship Wizard (DSW)

Session 2 Data protection in research

  • Brief overview of the GDPR
  • Impact of the GDPR on bio-medical research, ethical and legal requirements
  • Organisational and technical measures for data protection:
    • policies, training, data protection impact assessments,
    • data classification, encryption, pseudonymisation,
    • record keeping/accountability,
  • Practical with the Data Information System (DAISY)

Session 3 Practicalities of data handling

  • Research data transfer
  • Optimal file naming and organization
  • Management of data integrity
    • README files
    • Checksums
    • Encryption
    • Read-only permission
  • Data retention
  • Practical on data ingestion

Session 4 Exploratory analyses, reproducible manuscripts

  • Data and project organization for analysis
  • Dependency and workflow management tools
  • Literate programming
  • Writing manuscripts using RMarkdown

Session 5 FAIR data principles

  • Understanding FAIR principles
  • Incentives for FAIR data
  • Achieving FAIR'ness, possible paths
  • Group discussions

Session 6 Data publishing and archival

  • Recalling FAIR principles in publishing data and results
  • Introduction to FAIRDOMhub as a resource for FAIR data and results publishing
  • Practical using FAIRDOMHub for FAIR data and results publishing