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