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The Deep-Time Digital Earth program: data-driven discovery in geosciences
Current barriers hindering data-driven discoveries in deep-time Earth (DE) include: substantial volumes of DE data are not digitized; many DE databases do not adhere to FAIR (findable, accessible, interoperable and reusable) principles; we lack a systematic knowledge graph for DE; existing DE databa...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433093/ https://www.ncbi.nlm.nih.gov/pubmed/34691735 http://dx.doi.org/10.1093/nsr/nwab027 |
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author | Wang, Chengshan Hazen, Robert M Cheng, Qiuming Stephenson, Michael H Zhou, Chenghu Fox, Peter Shen, Shu-zhong Oberhänsli, Roland Hou, Zengqian Ma, Xiaogang Feng, Zhiqiang Fan, Junxuan Ma, Chao Hu, Xiumian Luo, Bin Wang, Juanle Schiffries, Craig M |
author_facet | Wang, Chengshan Hazen, Robert M Cheng, Qiuming Stephenson, Michael H Zhou, Chenghu Fox, Peter Shen, Shu-zhong Oberhänsli, Roland Hou, Zengqian Ma, Xiaogang Feng, Zhiqiang Fan, Junxuan Ma, Chao Hu, Xiumian Luo, Bin Wang, Juanle Schiffries, Craig M |
author_sort | Wang, Chengshan |
collection | PubMed |
description | Current barriers hindering data-driven discoveries in deep-time Earth (DE) include: substantial volumes of DE data are not digitized; many DE databases do not adhere to FAIR (findable, accessible, interoperable and reusable) principles; we lack a systematic knowledge graph for DE; existing DE databases are geographically heterogeneous; a significant fraction of DE data is not in open-access formats; tailored tools are needed. These challenges motivate the Deep-Time Digital Earth (DDE) program initiated by the International Union of Geological Sciences and developed in cooperation with national geological surveys, professional associations, academic institutions and scientists around the world. DDE’s mission is to build on previous research to develop a systematic DE knowledge graph, a FAIR data infrastructure that links existing databases and makes dark data visible, and tailored tools for DE data, which are universally accessible. DDE aims to harmonize DE data, share global geoscience knowledge and facilitate data-driven discovery in the understanding of Earth's evolution. |
format | Online Article Text |
id | pubmed-8433093 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-84330932021-10-21 The Deep-Time Digital Earth program: data-driven discovery in geosciences Wang, Chengshan Hazen, Robert M Cheng, Qiuming Stephenson, Michael H Zhou, Chenghu Fox, Peter Shen, Shu-zhong Oberhänsli, Roland Hou, Zengqian Ma, Xiaogang Feng, Zhiqiang Fan, Junxuan Ma, Chao Hu, Xiumian Luo, Bin Wang, Juanle Schiffries, Craig M Natl Sci Rev Review Current barriers hindering data-driven discoveries in deep-time Earth (DE) include: substantial volumes of DE data are not digitized; many DE databases do not adhere to FAIR (findable, accessible, interoperable and reusable) principles; we lack a systematic knowledge graph for DE; existing DE databases are geographically heterogeneous; a significant fraction of DE data is not in open-access formats; tailored tools are needed. These challenges motivate the Deep-Time Digital Earth (DDE) program initiated by the International Union of Geological Sciences and developed in cooperation with national geological surveys, professional associations, academic institutions and scientists around the world. DDE’s mission is to build on previous research to develop a systematic DE knowledge graph, a FAIR data infrastructure that links existing databases and makes dark data visible, and tailored tools for DE data, which are universally accessible. DDE aims to harmonize DE data, share global geoscience knowledge and facilitate data-driven discovery in the understanding of Earth's evolution. Oxford University Press 2021-02-11 /pmc/articles/PMC8433093/ /pubmed/34691735 http://dx.doi.org/10.1093/nsr/nwab027 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. 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 | Review Wang, Chengshan Hazen, Robert M Cheng, Qiuming Stephenson, Michael H Zhou, Chenghu Fox, Peter Shen, Shu-zhong Oberhänsli, Roland Hou, Zengqian Ma, Xiaogang Feng, Zhiqiang Fan, Junxuan Ma, Chao Hu, Xiumian Luo, Bin Wang, Juanle Schiffries, Craig M The Deep-Time Digital Earth program: data-driven discovery in geosciences |
title | The Deep-Time Digital Earth program: data-driven discovery in geosciences |
title_full | The Deep-Time Digital Earth program: data-driven discovery in geosciences |
title_fullStr | The Deep-Time Digital Earth program: data-driven discovery in geosciences |
title_full_unstemmed | The Deep-Time Digital Earth program: data-driven discovery in geosciences |
title_short | The Deep-Time Digital Earth program: data-driven discovery in geosciences |
title_sort | deep-time digital earth program: data-driven discovery in geosciences |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433093/ https://www.ncbi.nlm.nih.gov/pubmed/34691735 http://dx.doi.org/10.1093/nsr/nwab027 |
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