Event box

Preparing for Data Publishing: Standards and Disciplinary Repositories (Individual Case Studies)

[In-person] Research data have value beyond their original purpose and by sharing or "publishing" your data you can support open science, reproducibility, and future innovation. This workshop will explore strategies and best practices for formally publishing your data in a data repository. Topics covered will include new modes of publishing in academia, the use of data and metadata standards to support interoperability and harmonization, an overview of repository options and key features, examples of disciplinary repositories, and data publishing methods to increase the impact of research projects and support the FAIR Guiding Principles (i.e., Findable, Accessible, Interoperable, and Reusable). Participants will engage in a break-out activity where they will practice finding and assessing data repositories using their own data/research area. We are also holding an online version of this workshop on March 7 (10-12 am) where participants will engage in a similar exercise but using general case studies. This event is open to non-Duke participants.

This workshop (GS714.04) is eligible for 2 hours of Graduate School RCR Credit and 200-level faculty and staff RCR. 

This event is only be offered in-person. If you would like to attend virtually, please see the event being held on March 7

The content of the workshop may be recorded. If you are uncomfortable with a recording being published, please contact the instructor at any time prior to the conclusion of the workshop.

Data Management

Related LibGuide: Research Data Management by Jen Darragh

Date:
Tuesday, March 19, 2024
Time:
1:00pm - 3:00pm
Location:
Bostock 127 (The Edge Workshop Room)
Campus:
West Campus
Categories:
Data and Visualization   RCR Workshop  
Registration has closed.

Event Organizer

Profile photo of Jen Darragh
Jen Darragh
Profile photo of Sophia Lafferty-Hess
Sophia Lafferty-Hess
Profile photo of Center for Data and Visualization Sciences
Center for Data and Visualization Sciences