Programme

This course is aimed at project managers, researchers and graduate students in bioinformatics and biomedical sciences who wish to improve their skills on data management. In addition to lectures, this training includes several hands-on practicals, all following one example research data management scenario.

Address

Due to COVID-19 and travel restriction, ELIXIR-FR and ELIXIR-LU have decided to deliver this course online.

The course will be held over four consecutive mornings. Course are 9:00-12:30 CET from 5th to 8th Octobre, 2020

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: DSW, DMPonline, OPIDoR
• Practical with the 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 and Reproducible computational analyses

• 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
• How to create snakemake workflow for ChIP-seq analysis

Session 4: FAIR data principles and Data publishing and archival

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

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 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