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NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format

The Neurodata Without Borders (abbreviation NWB) format is a current technology for storing neurophysiology data along with the associated metadata. Data stored in the format is organized into separate HDF5 files, each file usually storing the data associated with a single recording session. While t...

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Autores principales: Ježek, Petr, Teeters, Jeffery L., Sommer, Friedrich T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526650/
https://www.ncbi.nlm.nih.gov/pubmed/33041776
http://dx.doi.org/10.3389/fninf.2020.00027
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author Ježek, Petr
Teeters, Jeffery L.
Sommer, Friedrich T.
author_facet Ježek, Petr
Teeters, Jeffery L.
Sommer, Friedrich T.
author_sort Ježek, Petr
collection PubMed
description The Neurodata Without Borders (abbreviation NWB) format is a current technology for storing neurophysiology data along with the associated metadata. Data stored in the format is organized into separate HDF5 files, each file usually storing the data associated with a single recording session. While the NWB format provides a structured method for storing data, so far there have not been tools which enable searching a collection of NWB files in order to find data of interest for a particular purpose. We describe here three tools to enable searching NWB files. The tools have different features making each of them most useful for a particular task. The first tool, called the NWB Query Engine, is written in Java. It allows searching the complete content of NWB files. It was designed for the first version of NWB (NWB 1) and supports most (but not all) features of the most recent version (NWB 2). For some searches, it is the fastest tool. The second tool, called “search_nwb” is written in Python and also allow searching the complete contents of NWB files. It works with both NWB 1 and NWB 2, as does the third tool. The third tool, called “nwbindexer” enables searching a collection of NWB files using a two-step process. In the first step, a utility is run which creates an SQLite database containing the metadata in a collection of NWB files. This database is then searched in the second step, using another utility. Once the index is built, this two-step processes allows faster searches than are done by the other tools, but does not enable as complete of searches. All three tools use a simple query language which was developed for this project. Software integrating the three tools into a web-interface is provided which enables searching NWB files by submitting a web form.
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spelling pubmed-75266502020-10-09 NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format Ježek, Petr Teeters, Jeffery L. Sommer, Friedrich T. Front Neuroinform Neuroscience The Neurodata Without Borders (abbreviation NWB) format is a current technology for storing neurophysiology data along with the associated metadata. Data stored in the format is organized into separate HDF5 files, each file usually storing the data associated with a single recording session. While the NWB format provides a structured method for storing data, so far there have not been tools which enable searching a collection of NWB files in order to find data of interest for a particular purpose. We describe here three tools to enable searching NWB files. The tools have different features making each of them most useful for a particular task. The first tool, called the NWB Query Engine, is written in Java. It allows searching the complete content of NWB files. It was designed for the first version of NWB (NWB 1) and supports most (but not all) features of the most recent version (NWB 2). For some searches, it is the fastest tool. The second tool, called “search_nwb” is written in Python and also allow searching the complete contents of NWB files. It works with both NWB 1 and NWB 2, as does the third tool. The third tool, called “nwbindexer” enables searching a collection of NWB files using a two-step process. In the first step, a utility is run which creates an SQLite database containing the metadata in a collection of NWB files. This database is then searched in the second step, using another utility. Once the index is built, this two-step processes allows faster searches than are done by the other tools, but does not enable as complete of searches. All three tools use a simple query language which was developed for this project. Software integrating the three tools into a web-interface is provided which enables searching NWB files by submitting a web form. Frontiers Media S.A. 2020-09-11 /pmc/articles/PMC7526650/ /pubmed/33041776 http://dx.doi.org/10.3389/fninf.2020.00027 Text en Copyright © 2020 Ježek, Teeters and Sommer. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Ježek, Petr
Teeters, Jeffery L.
Sommer, Friedrich T.
NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format
title NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format
title_full NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format
title_fullStr NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format
title_full_unstemmed NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format
title_short NWB Query Engines: Tools to Search Data Stored in Neurodata Without Borders Format
title_sort nwb query engines: tools to search data stored in neurodata without borders format
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526650/
https://www.ncbi.nlm.nih.gov/pubmed/33041776
http://dx.doi.org/10.3389/fninf.2020.00027
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