Big data is a revolutionary innovation that has allowed the development of many new methods in scientific research.This new way of thinking has encouraged the pursuit of new discoveries.Big data occupies the strategic...Big data is a revolutionary innovation that has allowed the development of many new methods in scientific research.This new way of thinking has encouraged the pursuit of new discoveries.Big data occupies the strategic high ground in the era of knowledge economies and also constitutes a new national and global strategic resource.“Big Earth data”,derived from,but not limited to,Earth observation has macro-level capabilities that enable rapid and accurate monitoring of the Earth,and is becoming a new frontier contributing to the advancement of Earth science and significant scientific discoveries.Within the context of the development of big data,this paper analyzes the characteristics of scientific big data and recognizes its great potential for development,particularly with regard to the role that big Earth data can play in promoting the development of Earth science.On this basis,the paper outlines the Big Earth Data Science Engineering Project(CASEarth)of the Chinese Academy of Sciences Strategic Priority Research Program.Big data is at the forefront of the integration of geoscience,information science,and space science and technology,and it is expected that big Earth data will provide new prospects for the development of Earth science.展开更多
Applying new approaches, methods, and technologies for the estimation of reserves can effectively improve the efficiency and accuracy of assessments of solid mineral resources. After analyzing the development of 3-D g...Applying new approaches, methods, and technologies for the estimation of reserves can effectively improve the efficiency and accuracy of assessments of solid mineral resources. After analyzing the development of 3-D geoscience modeling technology （3-D GMT）, this paper discusses the application of 3-D GMT for the estimation of solid mineral reserves, emphatically introducing its workflow and two key technologies, 3-D orebody surface modeling, and property modeling. Moreover, the paper analyzes the limitations of traditional methods, such as the section method and geological block method, and points out the advantages of 3-D GMT： building more accurate 3-D orebody models, expressing the internal inhomogeneous attributes of an orebody, reducing the potential for errors in the estimation of reserves, and implementing dynamic estimations of reserves.展开更多
Since the beginning of the 21 st century,the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the m...Since the beginning of the 21 st century,the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means.It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph.Based on adopting the graph pattern of general knowledge representation,the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge,and integrates geoscience knowledge elements,such as map,text,and number,to establish an all-domain geoscience knowledge representation model.A federated,crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here,which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists.We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis,which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph.A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis,but also advance the construction of the high-precision geological time scale driven by big data,the compilation of intelligent maps driven by rules and data,and the geoscience knowledge evolution and reasoning analysis,among others.It will further expand the new directions of geoscience research driven by both data and knowledge,break new ground where geoscience,information science,and data science converge,realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.展开更多
We examine the intersection of the FAIR principles(Findable,Accessible,Interoperable and Reusable),the challenges and opportunities presented by the aggregation of widely distributed and heterogeneous data about biolo...We examine the intersection of the FAIR principles(Findable,Accessible,Interoperable and Reusable),the challenges and opportunities presented by the aggregation of widely distributed and heterogeneous data about biological and geological specimens,and the use of the Digital Object Architecture(DOA)data model and components as an approach to solving those challenges that offers adherence to the FAIR principles as an integral characteristic.This approach will be prototyped in the Distributed System of Scientific Collections(DiSSCo)project,the pan-European Research Infrastructure which aims to unify over 110 natural science collections across 21 countries.We take each of the FAIR principles,discuss them as requirements in the creation of a seamless virtual collection of bio/geo specimen data,and map those requirements to Digital Object components and facilities such as persistent identification,extended data typing,and the use of an additional level of abstraction to normalize existing heterogeneous data structures.The FAIR principles inform and motivate the work and the DO Architecture provides the technical vision to create the seamless virtual collection vitally needed to address scientific questions of societal importance.展开更多
基金This work is supported by the Strategic Priority Research Program of Chinese Academy of Sciences,Project title:CASEarth(XDA19000000)and Digital Belt and Road(XDA19030000).
文摘Big data is a revolutionary innovation that has allowed the development of many new methods in scientific research.This new way of thinking has encouraged the pursuit of new discoveries.Big data occupies the strategic high ground in the era of knowledge economies and also constitutes a new national and global strategic resource.“Big Earth data”,derived from,but not limited to,Earth observation has macro-level capabilities that enable rapid and accurate monitoring of the Earth,and is becoming a new frontier contributing to the advancement of Earth science and significant scientific discoveries.Within the context of the development of big data,this paper analyzes the characteristics of scientific big data and recognizes its great potential for development,particularly with regard to the role that big Earth data can play in promoting the development of Earth science.On this basis,the paper outlines the Big Earth Data Science Engineering Project(CASEarth)of the Chinese Academy of Sciences Strategic Priority Research Program.Big data is at the forefront of the integration of geoscience,information science,and space science and technology,and it is expected that big Earth data will provide new prospects for the development of Earth science.
文摘Applying new approaches, methods, and technologies for the estimation of reserves can effectively improve the efficiency and accuracy of assessments of solid mineral resources. After analyzing the development of 3-D geoscience modeling technology （3-D GMT）, this paper discusses the application of 3-D GMT for the estimation of solid mineral reserves, emphatically introducing its workflow and two key technologies, 3-D orebody surface modeling, and property modeling. Moreover, the paper analyzes the limitations of traditional methods, such as the section method and geological block method, and points out the advantages of 3-D GMT： building more accurate 3-D orebody models, expressing the internal inhomogeneous attributes of an orebody, reducing the potential for errors in the estimation of reserves, and implementing dynamic estimations of reserves.
基金supported by the National Natural Science Foundation of China(Grant Nos.41421001,42050101,and 42050105)。
文摘Since the beginning of the 21 st century,the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means.It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph.Based on adopting the graph pattern of general knowledge representation,the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge,and integrates geoscience knowledge elements,such as map,text,and number,to establish an all-domain geoscience knowledge representation model.A federated,crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here,which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists.We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis,which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph.A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis,but also advance the construction of the high-precision geological time scale driven by big data,the compilation of intelligent maps driven by rules and data,and the geoscience knowledge evolution and reasoning analysis,among others.It will further expand the new directions of geoscience research driven by both data and knowledge,break new ground where geoscience,information science,and data science converge,realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.
文摘We examine the intersection of the FAIR principles(Findable,Accessible,Interoperable and Reusable),the challenges and opportunities presented by the aggregation of widely distributed and heterogeneous data about biological and geological specimens,and the use of the Digital Object Architecture(DOA)data model and components as an approach to solving those challenges that offers adherence to the FAIR principles as an integral characteristic.This approach will be prototyped in the Distributed System of Scientific Collections(DiSSCo)project,the pan-European Research Infrastructure which aims to unify over 110 natural science collections across 21 countries.We take each of the FAIR principles,discuss them as requirements in the creation of a seamless virtual collection of bio/geo specimen data,and map those requirements to Digital Object components and facilities such as persistent identification,extended data typing,and the use of an additional level of abstraction to normalize existing heterogeneous data structures.The FAIR principles inform and motivate the work and the DO Architecture provides the technical vision to create the seamless virtual collection vitally needed to address scientific questions of societal importance.
