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

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

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DOE OSTI · 1839233

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

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

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Abstract

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 field, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications. To address these challenges, the open source Time-Series Data Pipelines (Tsdat) software was developed by a joint collaboration between Pacific Northwest National Laboratory, 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 software and the data standards within which the framework operates. A beta version of the framework has been released and is currently being used by several projects in marine energy, wind energy, and building energy systems.

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big data data lake data standards interoperability MATHEMATICS AND COMPUTING open-source Python

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Lansing, Carina, Levin, Maxwell, Sivaraman, Chitra, Fao, Rebecca, Driscoll, Frederick. 2022-01-04. Tsdat: An Open-Source Data Standardization Framework for Marine Energy and Beyond: Preprint. https://www.osti.gov/biblio/1839233

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

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.

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