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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2017
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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. |
format | Online Article Text |
id | pubmed-5605951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
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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