Data Management Lab 2.0: Session 1 learning outcomes & activities

Overall, the first session went well. Turnout was low (9 of 17 registered), which is why I overbooked in the first place. I need to work on helping them build rapport with each other, but they were more responsive in the second hour. The data mapping exercise is taking more time than I expected, so I anticipate that working through an example as a group is as far as this can go in a workshop. However, I wonder if using this as a central planning exercise for a stand-alone workshop would be useful. Perhaps it will be more effective with PIs, since they have had more experience with the research process. We’ll see what the data says when the workshop series wraps up in April!

Session 1: Learning Outcomes

  • Describe key challenges associated with managing digital research data
  • Identify the potential consequences for irresponsible or inattentive data management
  • Understand the life cycle approach to managing research data
  • Summarize the basic components of US federal funding agency requirements for data management and sharing
  • Outline planned project and data documentation in a data management plan
  • Define expected outcomes for data
  • Identify legal obligations as they affect data management and protection
  • Identify ethical obligations for ensuring data confidentiality, privacy, and security
  • Describe intellectual property issues for data that result in a patentable or commercial product
  • Prepare a comprehensive storage and backup plan

 Major activities & products

  • Data management plan – begin to draft the data description, format, existing data, ethics & privacy, legal obligations, and data organization
  • Data outcomes map – identify the research questions and associated variables, analytical procedures, and data sources

Products gathered for assessment

  • Data management plan drafts
  • Minute papers

 

Materials

 

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By Heather L. Coates

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