Tsdat: An Open-Source Data Standardization Framework for Marine Energy and Beyond

Carina Lansing, Maxwell Levin, Chitra Sivaraman, Rebecca Fao, Frederick Driscoll

Research output: Contribution to conferencePaperpeer-review

1 Scopus Citations


Many organizations are tasked with the collection and processing of large quantities of data from various measurement devices. Data reported from these sources are often not interoperable with datasets and software used by analysts and other organizations in the same domain, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications, which rely on large quantities of data from multiple sources. To address these challenges, the open-source Time-Series Data Pipelines (Tsdat) Python framework was developed by Pacific Northwest National Laboratory, with strategic guidance and direction provided by the National Renewable Energy Laboratory and Sandia National Laboratories to facilitate collaboration and accelerate advancements in the marine energy domain through the development of an open-source ecosystem of tools. This paper will describe the Tsdat framework and the data standards within which it operates. A beta version of Tsdat has been released and is being used by several projects in marine energy, wind energy, and building energy systems.

Original languageAmerican English
StatePublished - 2022
EventOCEANS 2021: San Diego - Porto - San Diego, United States
Duration: 20 Sep 202123 Sep 2021


ConferenceOCEANS 2021: San Diego - Porto
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

See NREL/CP-5700-80561 for preprint

NREL Publication Number

  • NREL/CP-5700-82499


  • Big data
  • Data lake
  • Data standards
  • Interoperability
  • Open-source
  • Python


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