Timeline Visualization Uncovers Gaps in Archived Tsunami Water Level Data Journal Article uri icon



  • We demonstrate that data abstraction via a timeline visualization is highly effective at allowing one to discover patterns in the underlying data. We describe the rapid identification of data gaps in the archival time-series records of deep-ocean pressure and coastal water level observations collected to support the NOAA Tsunami Program and successful measures taken to rescue these data. These data gaps had persisted for years prior to the development of timeline visualizations to represent when data were collected. This approach can be easily extended to all types of time-series data and the author recommends this type of temporal visualization become a routine part of data management, whether one collects data or archives data.

publication date

  • December 20, 2021

has restriction

  • gold

Date in CU Experts

  • December 21, 2021 10:38 AM

Full Author List

  • Sweeney AD

author count

  • 1

Other Profiles

Electronic International Standard Serial Number (EISSN)

  • 2624-9553

Additional Document Info


  • 3