Course Syllabus

Research Data Life Cycle Micro-credential

 

Ring with five sections (Planning, Collecting, Analyzing, Sharing, and Stewarding) around a center reading Foundations.

The Research Data Life Cycle Micro-credential sessions provide knowledge of the research data life cycle, with sessions covering foundational topics, as well as planning, collecting, analyzing, sharing, and stewarding research data. To earn the micro-credential, learners must complete 15 hours of approved content, but the approved sessions are open to anyone at UNL - meaning you do not have to be seeking the micro-credential to view or participate in associated sessions. See badge here.

The sessions are offered in a variety of formats - some are videos that you can access when you have time, others are webinars that require availability at a certain time, and still others will be offered in-person. The sessions are offered by providers from across campus, including the Engineering and Computing Education Core, Holland Computing Center (HCC), the Libraries, the Methodology, Analytics, and Psychometrics (MAP) Academy, and the Methodology and Evaluation Research Core Facility (MERC), among others. 

Micro-credential Requirements

To earn the micro-credential badge, learners must complete three required sessions (total time ~3 hours) and then choose from electives across three categories. The required sessions are as follows: 

  • Responsible Conduct of Research (RCR) - the campus-wide training program that covers the University of Nebraska's expectations, policies, and resources for the Responsible Conduct of Research (RCR). This is completed in Bridge, and the completion certificate uploaded here, in Canvas.
  • A Crash Course in Research Data Management - a webinar offered by the Libraries
  • ORCID 101 - a short video

The remaining 12 hours of sessions are chosen by the learner based on what is most relevant to them. At least 1 course has to be chosen from each of the three categories: 

  • Basics - this includes sessions such as Data Management and Sharing Plans, Data Quality, Developing a Strong Research Data Infrastructure, Human Data Collection, etc.
  • Software/Analyses - this includes sessions such as Introduction to Python, RAPID Qualitative Analysis, Applied Data Cleaning, etc.
  • Data Futures - this includes sessions such as Choosing a Repository, Introduction to De-identification, Telling Stories with Data, etc. 

The lengths of sessions vary, and some sessions may have a waitlist before the session is offered. Related sessions may be added when available. See the modules for more details on individual sessions, descriptions, length, format, etc.