Slides: Constellation Data Publication Training

Slides: Constellation Data Publication Training

Slides from this training:

 Happened: 10:00-11:30 p.m. February 24, 2020, ORNL building 5100, JICS Auditorium

Do you have an important dataset sitting on CADES or OLCF resources that you want to be able to keep track of over the course of your career? Is it work that can safely be seen by anyone who knows how to do a google search?

Do you have a dataset that you want to reference in a publication or allow others to reference?

If so, register for the Constellation Data Publication Training and we we’ll teach you how to publish your data. You will never lose track of it again!


  • CADES News
  • Constellation DOI Portal Overview
  • Constellation Challenge* (hands-on exercise)

*Users who want to take the Constellation Challenge must provide their own openly publishable dataset. During the Constellation Challenge, they will publish the dataset and obtain a DOI for it.

NOTE: All datasets published through Constellation will receive a Digital Object Identifier (DOI) that makes them google searchable. No sensitive, proprietary or business sensitive data should be published with Constellation. 


Constellation is a digital object identifier (DOI) based science network for supercomputing data.  Constellation makes it possible for ORNL researchers to obtain DOIs for large data collections by tying them together with the associated resources and processes that went into the production of the data (e.g., jobs, collaborators, projects), using a scalable database. It also allows the annotation of the scientific conduct with rich metadata and enables the cataloging and publishing of the artifacts for open access, aiding in scalable data discovery. ORNL users can use the DOI service to publish datasets even before the publication of the paper and retain key data even after project expiration. From a center standpoint, DOIs enable the stewardship of data, and better management of the scratch and archival storage.


Ross Miller, OLCF Systems Integration Programmer

Suzanne Parete-Koon, CADES Support

Hong Liu, CADES Software Team