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Synthetic ALSPAC longitudinal datasets for the Big Data VR project

Three synthetic datasets - of observation size 15,000, 155,000 and 1,555,000 participants, respectively - were created by simulating eleven cardiac and anthropometric variables from nine collection ages of the ALSAPC birth cohort study. The synthetic datasets retain similar data properties to the AL...

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Detalles Bibliográficos
Autores principales: Avraam, Demetris, Wilson, Rebecca C., Burton, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5605951/
https://www.ncbi.nlm.nih.gov/pubmed/28989981
http://dx.doi.org/10.12688/wellcomeopenres.12441.1
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author Avraam, Demetris
Wilson, Rebecca C.
Burton, Paul
author_facet Avraam, Demetris
Wilson, Rebecca C.
Burton, Paul
author_sort Avraam, Demetris
collection PubMed
description Three synthetic datasets - of observation size 15,000, 155,000 and 1,555,000 participants, respectively - were created by simulating eleven cardiac and anthropometric variables from nine collection ages of the ALSAPC birth cohort study. The synthetic datasets retain similar data properties to the ALSPAC study data they are simulated from (co-variance matrices, as well as the mean and variance values of the variables) without including the original data itself or disclosing participant information.  In this instance, the three synthetic datasets have been utilised in an academia-industry collaboration to build a prototype virtual reality data analysis software, but they could have a broader use in method and software development projects where sensitive data cannot be freely shared.
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spelling pubmed-56059512017-10-06 Synthetic ALSPAC longitudinal datasets for the Big Data VR project Avraam, Demetris Wilson, Rebecca C. Burton, Paul Wellcome Open Res Data Note Three synthetic datasets - of observation size 15,000, 155,000 and 1,555,000 participants, respectively - were created by simulating eleven cardiac and anthropometric variables from nine collection ages of the ALSAPC birth cohort study. The synthetic datasets retain similar data properties to the ALSPAC study data they are simulated from (co-variance matrices, as well as the mean and variance values of the variables) without including the original data itself or disclosing participant information.  In this instance, the three synthetic datasets have been utilised in an academia-industry collaboration to build a prototype virtual reality data analysis software, but they could have a broader use in method and software development projects where sensitive data cannot be freely shared. F1000 Research Limited 2017-08-30 /pmc/articles/PMC5605951/ /pubmed/28989981 http://dx.doi.org/10.12688/wellcomeopenres.12441.1 Text en Copyright: © 2017 Avraam D et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Data Note
Avraam, Demetris
Wilson, Rebecca C.
Burton, Paul
Synthetic ALSPAC longitudinal datasets for the Big Data VR project
title Synthetic ALSPAC longitudinal datasets for the Big Data VR project
title_full Synthetic ALSPAC longitudinal datasets for the Big Data VR project
title_fullStr Synthetic ALSPAC longitudinal datasets for the Big Data VR project
title_full_unstemmed Synthetic ALSPAC longitudinal datasets for the Big Data VR project
title_short Synthetic ALSPAC longitudinal datasets for the Big Data VR project
title_sort synthetic alspac longitudinal datasets for the big data vr project
topic Data Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5605951/
https://www.ncbi.nlm.nih.gov/pubmed/28989981
http://dx.doi.org/10.12688/wellcomeopenres.12441.1
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