The Deep-time Digital Earth program: data-dri

image: DDE aims to harmonize Earth’s deep-time data based on a knowledge system to study the evolution of Earth, including life, Earth materials, geography and climate. Integrated methods include artificial intelligence (AI), high performance computing (HPC), cloud computing, semantic web, natural language processing, and other methods.
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Credit: @Science China Press

Humans have long explored three major scientific questions: the evolution of the universe, the evolution of the Earth and the evolution of life. Geoscientists have made it their mission to elucidate the evolution of Earth and life, which is preserved in the information-rich but incomplete geological records that cover more than 4.5 billion years of Earth’s history. . Delving into Earth’s distant history helps geoscientists decipher the mechanisms and rates of Earth’s evolution, unravel the rates and mechanisms of climate change, locate natural resources, and envision the future of Earth. Earth.

Two common approaches, deductive reasoning and inductive reasoning, have been widely used to study the history of Earth. Unlike deduction and induction, abduction is derived from the accumulation and analysis of large amounts of reliable data, regardless of a premise or generalization. Kidnapping therefore has the potential to generate transformative discoveries in science. With the accumulation of huge volumes of deep-time terrestrial data, we are poised to transform deep-time earth science research through data-driven abductive discovery.

However, three issues need to be addressed to facilitate abductive discovery using deep-time databases. First, many relevant geodata resources do not conform to the FAIR (Findable, Accessible, Interoperable and Reusable) principles for the management and management of scientific data. Second, the concepts and terminologies used in databases are not well defined, so the same terms can have different meanings from one database to another. Without standardized terminology and concept definitions, it is difficult to ensure data interoperability and reuse. Third, databases are very heterogeneous in terms of geographic regions, spatial and temporal resolution, geological theme coverage, limitations in data availability, formats, languages ​​and metadata. Due to the complex evolution of the Earth and the interactions between multiple spheres (e.g., lithosphere, hydrosphere, biosphere, and atmosphere) in Earth systems, it is difficult to see the whole of Earth evolution from separate thematic views, each with limited scope.

Big data and artificial intelligence create opportunities to solve these problems. To explore Earth’s evolution effectively and efficiently using deep-time big data, we need FAIR, synthetic and comprehensive databases in all areas of deep-time earth sciences, coupled with methods of custom calculation. This objective motivates the Deep-time Digital Earth (DDE) program, which is the first “major scientific program” initiated by the International Union of Geological Sciences (UISG) and developed in cooperation with national geological commissions, professional associations, academic and scientific institutions around the world. The main goal of DDE is to facilitate deep-time, data-driven discoveries through international and interdisciplinary collaborations. DDE aims to provide an open platform for linking existing deep-time land data and integrating geological data that users can query by specifying time, space, and subject (i.e. a ‘Google geological ‘) and to process data for knowledge discovery using a (Deep-time Earth Engine) that provides computing power, models, methods and algorithms (Figure 1).

To achieve its mission and vision, the DDE program has three main components: program management committees, centers of excellence and working, platform and work groups. And DDE will build on existing deep-time Earth knowledge systems and develop an open platform (Figure 2). A deep-time Earth knowledge system consists of the basic definitions and relationships between deep-time Earth concepts, which are needed to harmonize deep-time Earth data and develop a knowledge engine to support the abductive exploration of the evolution of the Earth. The first step in the DDE research plan is to build on existing deep-time Earth knowledge systems. The second step of the DDE research plan is to build an interoperable deep-time terrestrial data infrastructure. And the third step in DDE’s research plan is to develop an open deep-time Earth platform.

The execution of the DDE program consists of four phases. In phase 1, the DDE establishes an organizational structure with international policy and management standards. In Phase 2, DDE trains the initial teams and builds on existing deep-time Earth knowledge systems and data standards by collaborating with existing ontology researchers in the geosciences, while working to link and harmonize the Earth’s databases in deep time. In phase 3, DDE develops custom algorithms and techniques for cloud computing and supercomputing environments. In Phase 4, Earth scientists and data scientists seamlessly collaborate on compelling and integrative science issues.

As the integrating and international ambitions of the DDE program, several challenges were anticipated. However, by creating an open-access data resource that for the first time incorporates all aspects of Earth’s narrated past, DDE delivers on the promise of understanding the past, present and future of our planet in new and new detail. striking.


See the article:

The Deep-time Digital Earth program: discovery guided by geoscience data. Wang et al., National scientific journal,

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About Lucille Thompson

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