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ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION

Imaging optimisation can benefit from combining structured data with qualitative data in the form of audio and video recordings. Since video is complex to work with, there is a need to find a workable solution that minimises the additional time investment. The purpose of the paper is to outline a ge...

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Detalles Bibliográficos
Autores principales: Ivarsson, Jonas, Almén, Anja, Falkenberg, Mårten, Lundh, Charlotta, Båth, Magnus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507456/
https://www.ncbi.nlm.nih.gov/pubmed/34037218
http://dx.doi.org/10.1093/rpd/ncab071
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author Ivarsson, Jonas
Almén, Anja
Falkenberg, Mårten
Lundh, Charlotta
Båth, Magnus
author_facet Ivarsson, Jonas
Almén, Anja
Falkenberg, Mårten
Lundh, Charlotta
Båth, Magnus
author_sort Ivarsson, Jonas
collection PubMed
description Imaging optimisation can benefit from combining structured data with qualitative data in the form of audio and video recordings. Since video is complex to work with, there is a need to find a workable solution that minimises the additional time investment. The purpose of the paper is to outline a general workflow that can begin to address this issue. What is described is a data management process comprising the three steps of collection, mining and contextualisation. This process offers a way to work systematically and at a large scale without succumbing to the context loss of statistical methods. The proposed workflow effectively combines the video and structured data to enable a new level of insights in the optimisation process.
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spelling pubmed-85074562021-10-13 ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION Ivarsson, Jonas Almén, Anja Falkenberg, Mårten Lundh, Charlotta Båth, Magnus Radiat Prot Dosimetry Paper Imaging optimisation can benefit from combining structured data with qualitative data in the form of audio and video recordings. Since video is complex to work with, there is a need to find a workable solution that minimises the additional time investment. The purpose of the paper is to outline a general workflow that can begin to address this issue. What is described is a data management process comprising the three steps of collection, mining and contextualisation. This process offers a way to work systematically and at a large scale without succumbing to the context loss of statistical methods. The proposed workflow effectively combines the video and structured data to enable a new level of insights in the optimisation process. Oxford University Press 2021-05-25 /pmc/articles/PMC8507456/ /pubmed/34037218 http://dx.doi.org/10.1093/rpd/ncab071 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Paper
Ivarsson, Jonas
Almén, Anja
Falkenberg, Mårten
Lundh, Charlotta
Båth, Magnus
ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION
title ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION
title_full ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION
title_fullStr ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION
title_full_unstemmed ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION
title_short ALIGNING VIDEO-AND STRUCTURED DATA FOR IMAGING OPTIMISATION
title_sort aligning video-and structured data for imaging optimisation
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507456/
https://www.ncbi.nlm.nih.gov/pubmed/34037218
http://dx.doi.org/10.1093/rpd/ncab071
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