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A multiple species, continent-wide, million-phenotype agronomic plant dataset
A critical shortage of ‘big’ agronomic data is placing an unnecessary constraint on the conduct of public agronomic research, imparting barriers to model development and testing. Here, we address this problem by providing a large non-relational database of agronomic trials, linked to intensive manag...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065040/ https://www.ncbi.nlm.nih.gov/pubmed/33893320 http://dx.doi.org/10.1038/s41597-021-00898-8 |
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author | Newman, Saul Justin Furbank, Robert T. |
author_facet | Newman, Saul Justin Furbank, Robert T. |
author_sort | Newman, Saul Justin |
collection | PubMed |
description | A critical shortage of ‘big’ agronomic data is placing an unnecessary constraint on the conduct of public agronomic research, imparting barriers to model development and testing. Here, we address this problem by providing a large non-relational database of agronomic trials, linked to intensive management and observational data, run under a unified experimental framework. The National Variety Trials (NVTs) represent a decade-long experimental trial network, conducted across thousands of Australian field sites using highly standardised randomised controlled designs. The NVTs contain over a million machine-measured phenotypic observations, aggregated from density-controlled populations containing hundreds of millions of plants and thousands of released plant varieties. These data are linked to hundreds of thousands of metadata observations including standardised soil tests, fertiliser and pesticide input data, crop rotation data, prior farm management practices, and in-field sensors. Finally, these data are linked to a suite of ground and remote sensing observations, arranged into interpolated daily- and ten-day aggregated time series, to capture the substantial diversity in vegetation and environmental patterns across the continent-spanning NVT network. |
format | Online Article Text |
id | pubmed-8065040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80650402021-05-05 A multiple species, continent-wide, million-phenotype agronomic plant dataset Newman, Saul Justin Furbank, Robert T. Sci Data Data Descriptor A critical shortage of ‘big’ agronomic data is placing an unnecessary constraint on the conduct of public agronomic research, imparting barriers to model development and testing. Here, we address this problem by providing a large non-relational database of agronomic trials, linked to intensive management and observational data, run under a unified experimental framework. The National Variety Trials (NVTs) represent a decade-long experimental trial network, conducted across thousands of Australian field sites using highly standardised randomised controlled designs. The NVTs contain over a million machine-measured phenotypic observations, aggregated from density-controlled populations containing hundreds of millions of plants and thousands of released plant varieties. These data are linked to hundreds of thousands of metadata observations including standardised soil tests, fertiliser and pesticide input data, crop rotation data, prior farm management practices, and in-field sensors. Finally, these data are linked to a suite of ground and remote sensing observations, arranged into interpolated daily- and ten-day aggregated time series, to capture the substantial diversity in vegetation and environmental patterns across the continent-spanning NVT network. Nature Publishing Group UK 2021-04-23 /pmc/articles/PMC8065040/ /pubmed/33893320 http://dx.doi.org/10.1038/s41597-021-00898-8 Text en © The Author(s) 2021, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Newman, Saul Justin Furbank, Robert T. A multiple species, continent-wide, million-phenotype agronomic plant dataset |
title | A multiple species, continent-wide, million-phenotype agronomic plant dataset |
title_full | A multiple species, continent-wide, million-phenotype agronomic plant dataset |
title_fullStr | A multiple species, continent-wide, million-phenotype agronomic plant dataset |
title_full_unstemmed | A multiple species, continent-wide, million-phenotype agronomic plant dataset |
title_short | A multiple species, continent-wide, million-phenotype agronomic plant dataset |
title_sort | multiple species, continent-wide, million-phenotype agronomic plant dataset |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065040/ https://www.ncbi.nlm.nih.gov/pubmed/33893320 http://dx.doi.org/10.1038/s41597-021-00898-8 |
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