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      • Record: found
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      The Deep-Time Digital Earth program: data-driven discovery in geosciences

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Abstract

          Deep-time Digital Earth is to promote data-driven discovery by harmonizing deep-time Earth data and creating ‘Geological Google’.

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          Most cited references49

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          The FAIR Guiding Principles for scientific data management and stewardship

          Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg … (2017)
          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
          Article 0 comments Cited 3511 times – based on 0 reviews     Review now
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            Deep learning and process understanding for data-driven Earth system science

            Markus Reichstein, Gustau Camps-Valls, Bjorn Stevens … (2019)
            Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.
            Article 0 comments Cited 611 times – based on 0 reviews     Review now
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              Phanerozoic trends in the global diversity of marine invertebrates.

              John Alroy, Martin Aberhan, David Bottjer … (2008)
              It has previously been thought that there was a steep Cretaceous and Cenozoic radiation of marine invertebrates. This pattern can be replicated with a new data set of fossil occurrences representing 3.5 million specimens, but only when older analytical protocols are used. Moreover, analyses that employ sampling standardization and more robust counting methods show a modest rise in diversity with no clear trend after the mid-Cretaceous. Globally, locally, and at both high and low latitudes, diversity was less than twice as high in the Neogene as in the mid-Paleozoic. The ratio of global to local richness has changed little, and a latitudinal diversity gradient was present in the early Paleozoic.
              Article 0 comments Cited 256 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Chengshan Wang
                Robert M Hazen
                Qiuming Cheng
                Michael H Stephenson
                Chenghu Zhou
                Peter Fox
                Shu-zhong Shen
                Roland Oberhänsli
                Zengqian Hou
                Xiaogang Ma:
                ORCID: https://orcid.org/0000-0002-9110-7369
                Zhiqiang Feng
                Junxuan Fan
                Chao Ma
                Xiumian Hu
                Bin Luo
                Juanle Wang
                Craig M Schiffries
                Journal
                Journal ID (nlm-ta): Natl Sci Rev
                Journal ID (iso-abbrev): Natl Sci Rev
                Journal ID (publisher-id): nsr
                Title: National Science Review
                Publisher: Oxford University Press
                ISSN (Print): 2095-5138
                ISSN (Electronic): 2053-714X
                Publication date Collection: September 2021
                Publication date (Electronic): 11 February 2021
                Publication date PMC-release: 11 February 2021
                Volume: 8
                Issue: 9
                Electronic Location Identifier: nwab027
                Affiliations
                State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences , Beijing 100083, China
                School of the Earth Science and Resources, China University of Geosciences , Beijing 100083, China
                Earth and Planets Laboratory, Carnegie Institution for Science , Washington, DC 20015, USA
                State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences , Beijing 100083, China
                British Geological Survey , Nottingham, NG12 5GG, UK
                State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources, Chinese Academy of Sciences , Beijing 100101, China
                Tetherless World Constellation, Rensselaer Polytechnic Institute , Troy, NY 12180, USA
                School of Earth Sciences and Engineering, Nanjing University , Nanjing 210023, China
                Institute of Earth and Environmental Sciences, University of Potsdam , Potsdam 14476, Germany
                Institute of Geology, Chinese Academy of Geological Sciences , Beijing 100037, China
                Department of Computer Science, University of Idaho , Moscow, ID 83844, USA
                Petroleum Exploration and Production Research Institute, SINOPEC , Beijing 100083, China
                School of Earth Sciences and Engineering, Nanjing University , Nanjing 210023, China
                Department of Computer Science, University of Idaho , Moscow, ID 83844, USA
                School of Earth Sciences and Engineering, Nanjing University , Nanjing 210023, China
                State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources, Chinese Academy of Sciences , Beijing 100101, China
                State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources, Chinese Academy of Sciences , Beijing 100101, China
                DDE Center of Excellence (Suzhou) , Kunshan 215300, China
                Author notes
                Corresponding author. E-mail: wang.chengshan@ 123456ddeworld.org
                Author information
                https://orcid.org/0000-0002-9110-7369
                Article
                Publisher ID: nwab027
                DOI: 10.1093/nsr/nwab027
                PMC ID: 8433093
                PubMed ID: 34691735
                SO-VID: 8021d05a-9662-403c-b58f-56c9139929d9
                Copyright © © The Author(s) 2021. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 10 November 2020
                Date revision received : 03 February 2021
                Date accepted : 05 February 2021
                Page count
                Pages: 11
                Funding
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 41888101
                Funded by: National Key Research and Development Program of China, DOI 10.13039/501100012166;
                Award ID: 2018YFE0204204
                Categories
                Subject: Review
                Subject: Earth Sciences
                Subject: AcademicSubjects/MED00010
                Subject: AcademicSubjects/SCI00010

                Keywords: deep-time digital earth,data-driven discovery,big data,earth evolution,cyberinfrastructure
                Data availability:
                Keywords: deep-time digital earth, data-driven discovery, big data, earth evolution, cyberinfrastructure

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