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 embraced the mission to elucidate the evolution of Earth and life, which is preserved in the information-rich but incomplete geological record that spans more than 4.5 billion years of Earth’s history. Delving into Earth’s deep 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.
Two common approaches, deductive reasoning and inductive reasoning, have been widely used to study Earth history. 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 stewardship of scientific data. Second, the concepts and terminology used in databases are not well defined, so the same terms may have different meanings in databases. Without standardized terminology and definitions of concepts, it is difficult to achieve interoperability and reuse of data. Third, databases are very heterogeneous in terms of geographic regions, spatial and temporal resolution, coverage of geological themes, data availability limits, formats, languages ââand metadata. Due to the complex evolution of the Earth and the interactions between several spheres (e.g., lithosphere, hydrosphere, biosphere, and atmosphere) in Earth systems, it is difficult to see the full picture of the Earth evolution from separate thematic viewpoints, each with limited scope.
Big data and artificial intelligence create opportunities to solve these problems. To explore the evolution of the Earth effectively and efficiently using deep-time big data, we need FAIR, synthetic and comprehensive databases in all fields of deep-time earth sciences, coupled with methods suitable calculation methods. 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 (IUGS) and developed in cooperation with national geological surveys, professional associations, academic and scientific institutions around the world. The main objective 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 Earth data and integrating geological data that users can query by specifying time, space, and subject (i.e. a âGeological Googleâ) and to process data for knowledge discovery using a knowledge engine (Deep-time Earth Engine) which 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 working 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 includes the basic definitions and relationships between deep-time Earth concepts, which are necessary to harmonize deep-time Earth data and develop a knowledge engine to support the abductive exploration of Earth’s evolution. The first step in DDE’s research plan is to build on existing deep-time Earth knowledge systems. The second step of DDE’s research plan is to build an interoperable deep-time terrestrial data infrastructure. And the third step of DDE’s research plan is to develop an open Earth platform in the depths.
The execution of the DDE program consists of four phases. In phase 1, 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 problems.
As the integrative 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 promises to understand 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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