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

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)

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