RSM 650 - Data Management in the Research Environment (2 credits)

Instructors:
Dr. Timothy Norris – Librarian Assistant Professor, Richter Library/Center for Computational Science
Angela Clark – Librarian Associate Professor, RSMAS Library

Meetings: Monday 12-1:40pm, RSMAS

Course Description:
The purpose of the course is to develop understandings of research data in broader spatial and temporal contexts--known as the research data lifecycle, to introduce several practical tools for digital scholarship, and to encourage early adoption of best practices in research data management. The course will provide students with strategies to increase productivity (efficiency), enable proper data stewardship (security), and help the student exceed data management expectations/requirements in the research environment (compliance). This is a practical course: students are required to produce a data management plan for their specific research endeavor, OR to prepare and deposit data into a discipline specific repository (other projects subject to instructor approval will be considered). The class is open to all graduate students in all disciplines.

Texts and Materials:
There are no required texts for this course. The readings will be made available through the course website.

Software:
We will use some basic open-source software packages (no license fee). You will need to install the following:

Prerequisites:
Students should have a good idea of what their MS/PhD research will be. If no project is identified at time of enrollment admission will be based upon instructor approval.

Measureable Learning Outcomes:

  1. Describe data lifecycle models and how they inform data management planning
  2. Identify file formats, data types, data levels and relevant software and understand how they inform data management and preservation.
  3. Design best practices for file system organization and file naming conventions to serve sound data storage, backup, and preservation strategies.
  4. Gain practical experience in discovering, acquiring, and cleaning data.
  5. Produce documentation and metadata for research data to facilitate discovery and re-use.
  6. Evaluate legal and ethical implications for data access and sharing strategies.
  7. Identify discipline specific or institutional data repositories and prepare data for deposit.

Course Resources: