Data Management Lab Pilot – Level 1 Content Outline

The final, or near final, outline for the data management lab I’m piloting in December January  is below. This is later than I had planned, but other team and Library responsibilities have taken precedence this fall, including the IU Master Digitization Plan. So far, there are 5-6 staff signed up…only 2-3 more to recruit. So here it is…with the list of activities and associated cyberinfrastructure to come after the holidays. It may look a little uneven, but the activities were developed first, with the supporting content being created to support them and provide some context. The difficult part is what I’m working on now – making sure the individual pieces are related strongly enough so they create a cohesive picture of research data management. As always, feedback and criticism is welcome!

Format details: 8-hr workshop (1-day version for staff, 2 4-hr version for graduate students)

Audience: health and social sciences graduate students, research staff, and faculty actively engaged in research

Introduction to RDM

  • Describe key challenges associated with managing digital research data
  • Identify the potential consequences for irresponsible or inattentive data management
  • Explain the life cycle approach to managing research data

Data Management Plans & Planning

  • 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
  • Identify roles and responsibilities for each member and task for group projects
  • Define expected outcomes for data
  • Develop a plan for organizing and storing research data

Ethical & legal obligations

  • Identify your legal obligations for sharing and long-term preservation
  • Identify your 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 that incorporates available cyberinfrastructure

Organizing data & files

  • Develop a file organization and naming convention scheme for all project files
  • Select appropriate non-proprietary hardware and software formats for storing data
  • Create protected copies of files at crucial points in your study
  • Use versioning software or documentation for tracking changes to files over time

Project & data documentation

  • Identify core project documents
  • Identify documentation and metadata required to describe data

Creating metadata

  • Explain the role of metadata and standards
  • Identify relevant metadata standards
  • Apply selected metadata standards to create metadata enabling discovery and reuse

Quality assurance & control

  • Describe the function of quality assurance and quality control processes
  • Use best practices for coding

Data collection

  • Describe key considerations for selecting data collection tools

Data entry

  • Use best practices for data entry

Data screening & cleaning

  • Develop a screening and cleaning protocol

Automating tasks for better provenance

  • Explain why automation provides better provenance than manual processes
  • Select effective tools for automating data processing and analysis

Data protection, rights, & access

  • Identify how ethical and legal obligations affect data protection
  • Select appropriate tools and platforms for storing, managing, and preserving data

Data sharing & re-use

  • Evaluate resources for sharing data and openly or publicly available data

Data attribution & citation

  • Identify two technologies enabling data citation
By Heather L. Coates

4 comments on “Data Management Lab Pilot – Level 1 Content Outline

  1. This looks like a great course. I’m starting to think about peer training in data management and would love to see some of your materials and hear about the success of your sessions once you’ve run them!

  2. This looks really well organized, Heather. I swear my biggest challenge in teaching and explaining data management is in breaking it down into smaller topics and issues– I’ve been looking into how others have done it so this is really great!

    • Agreed! I’m glad it’s helpful. It is challenging to figure out what different communities are doing because the terminology varies so much. I’ve found some good information from statistics, ecology, and the clinical research communities.

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