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DroNER: Dataset for drone named entity recognition

The dataset is constructed from the drone flight log messages extracted from publicly available drone image datasets provided by VTO Labs under the Drone Forensic Program. The entire process of building this dataset includes extraction, decryption, parsing, cleansing, unique filtering, annotation, s...

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Detalles Bibliográficos
Autores principales: Silalahi, Swardiantara, Ahmad, Tohari, Studiawan, Hudan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10293979/
https://www.ncbi.nlm.nih.gov/pubmed/37383784
http://dx.doi.org/10.1016/j.dib.2023.109179
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author Silalahi, Swardiantara
Ahmad, Tohari
Studiawan, Hudan
author_facet Silalahi, Swardiantara
Ahmad, Tohari
Studiawan, Hudan
author_sort Silalahi, Swardiantara
collection PubMed
description The dataset is constructed from the drone flight log messages extracted from publicly available drone image datasets provided by VTO Labs under the Drone Forensic Program. The entire process of building this dataset includes extraction, decryption, parsing, cleansing, unique filtering, annotation, splitting, and analysis. The resulting dataset is in CoNLL format, annotated using the IOB2 scheme with six entity types. The total number of log messages acquired from 12 DJI drone models is 1850. The data are split based on the drone models, resulting in 1412 messages for training and 438 messages for testing. The average length of log messages is 6.5 globally, 6.6 and 8.8 for the train and the test sets, respectively.
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spelling pubmed-102939792023-06-28 DroNER: Dataset for drone named entity recognition Silalahi, Swardiantara Ahmad, Tohari Studiawan, Hudan Data Brief Data Article The dataset is constructed from the drone flight log messages extracted from publicly available drone image datasets provided by VTO Labs under the Drone Forensic Program. The entire process of building this dataset includes extraction, decryption, parsing, cleansing, unique filtering, annotation, splitting, and analysis. The resulting dataset is in CoNLL format, annotated using the IOB2 scheme with six entity types. The total number of log messages acquired from 12 DJI drone models is 1850. The data are split based on the drone models, resulting in 1412 messages for training and 438 messages for testing. The average length of log messages is 6.5 globally, 6.6 and 8.8 for the train and the test sets, respectively. Elsevier 2023-04-25 /pmc/articles/PMC10293979/ /pubmed/37383784 http://dx.doi.org/10.1016/j.dib.2023.109179 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Silalahi, Swardiantara
Ahmad, Tohari
Studiawan, Hudan
DroNER: Dataset for drone named entity recognition
title DroNER: Dataset for drone named entity recognition
title_full DroNER: Dataset for drone named entity recognition
title_fullStr DroNER: Dataset for drone named entity recognition
title_full_unstemmed DroNER: Dataset for drone named entity recognition
title_short DroNER: Dataset for drone named entity recognition
title_sort droner: dataset for drone named entity recognition
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10293979/
https://www.ncbi.nlm.nih.gov/pubmed/37383784
http://dx.doi.org/10.1016/j.dib.2023.109179
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