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MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope
Modern automated telescopes allow to capture astronomical images in a reproducible way. During the MILAN research project (MachIne Learning for AstroNomy), we have observed deep sky with a Stellina observation station for twelve months from the Luxembourg Greater Region. Thus, we have captured raw i...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10293918/ https://www.ncbi.nlm.nih.gov/pubmed/37383774 http://dx.doi.org/10.1016/j.dib.2023.109133 |
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author | Parisot, Olivier Hitzelberger, Patrik Bruneau, Pierrick Krebs, Gilles Destruel, Christophe Vandame, Benoît |
author_facet | Parisot, Olivier Hitzelberger, Patrik Bruneau, Pierrick Krebs, Gilles Destruel, Christophe Vandame, Benoît |
author_sort | Parisot, Olivier |
collection | PubMed |
description | Modern automated telescopes allow to capture astronomical images in a reproducible way. During the MILAN research project (MachIne Learning for AstroNomy), we have observed deep sky with a Stellina observation station for twelve months from the Luxembourg Greater Region. Thus, we have captured raw images of more than 188 deep sky objects visible from the Northern Hemisphere (galaxies, stars clusters, nebulae, etc.), We have compiled and published this data as the MILAN Sky Survey dataset, allowing interested researchers, industry practitioners and citizens to reuse it. |
format | Online Article Text |
id | pubmed-10293918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-102939182023-06-28 MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope Parisot, Olivier Hitzelberger, Patrik Bruneau, Pierrick Krebs, Gilles Destruel, Christophe Vandame, Benoît Data Brief Data Article Modern automated telescopes allow to capture astronomical images in a reproducible way. During the MILAN research project (MachIne Learning for AstroNomy), we have observed deep sky with a Stellina observation station for twelve months from the Luxembourg Greater Region. Thus, we have captured raw images of more than 188 deep sky objects visible from the Northern Hemisphere (galaxies, stars clusters, nebulae, etc.), We have compiled and published this data as the MILAN Sky Survey dataset, allowing interested researchers, industry practitioners and citizens to reuse it. Elsevier 2023-04-11 /pmc/articles/PMC10293918/ /pubmed/37383774 http://dx.doi.org/10.1016/j.dib.2023.109133 Text en © 2023 The Authors. Published by Elsevier Inc. 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 Parisot, Olivier Hitzelberger, Patrik Bruneau, Pierrick Krebs, Gilles Destruel, Christophe Vandame, Benoît MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope |
title | MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope |
title_full | MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope |
title_fullStr | MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope |
title_full_unstemmed | MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope |
title_short | MILAN Sky Survey, a dataset of raw deep sky images captured during one year with a Stellina automated telescope |
title_sort | milan sky survey, a dataset of raw deep sky images captured during one year with a stellina automated telescope |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10293918/ https://www.ncbi.nlm.nih.gov/pubmed/37383774 http://dx.doi.org/10.1016/j.dib.2023.109133 |
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