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