The standards system for cultural heritage digitalization aims to build a clear and logically rigorous framework to guide the development and revision of relevant standards.This system enhances the scientific,systemat...The standards system for cultural heritage digitalization aims to build a clear and logically rigorous framework to guide the development and revision of relevant standards.This system enhances the scientific,systematic,and practical aspects of cultural heritage digitalization.This paper comprehensively analyzes the current status and needs of cultural heritage digitalization and standardization.It further examines the methods used to construct the standards system.Through comparative analysis,it establishes a lifecycle-based framework for cultural heritage.This framework accounts for the unique characteristics of cultural heritage and systematically integrates key processes such as collection,processing,storage,transmission,and utilization of data.The standards system is divided into six sections:general,data,information,knowledge,intelligence,and application.Based on the current digitalization efforts,this paper proposes key standardization directions for each section.This framework ensures the integrity and consistency of data throughout the digitalization process.It also supports the application of intelligent technologies in cultural heritage conservation,contributing to the sustainable preservation and utilization of cultural heritage data.展开更多
Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and futur...Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools＇ opportunities and suggestions in data science education. We argue that iSchools should empower their students with ＂information computing＂ disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application- based. These three loci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework （DSEF） with building blocks that include the three pillars of data science （people, technology, and data）, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.展开更多
The widespread use of machine learning techniques and artificial intelligence algorithms has highlighted the strategic role of data.To acquire data for training algorithms and eventually empowering the digital transfo...The widespread use of machine learning techniques and artificial intelligence algorithms has highlighted the strategic role of data.To acquire data for training algorithms and eventually empowering the digital transformation,data marketplaces are often required to support and coordinate cross-organizational data transactions.However,the prior industry practices have suggested that the transaction costs in the data marketplaces are severely high,and the supporting infrastructure is far from mature.This paper proposes a data attributes-affected data exchange(DADE)conceptual model to understand the challenges and directions for developing data marketplaces.Specifically,our model framework is built upon two dimensions,data lifecycle maturity and data asset specificity.Based on the DADE model,we propose four approaches for developing data marketplaces and discuss future research directions with an overview of computational methods as potential technical solutions.展开更多
基金supported by“The Palace Museum Talent Program”.The Palace Museum Talent Program is supported by The Hong Kong Jockey Club,exclusively sponsored by the Institute of Philanthropy.
文摘The standards system for cultural heritage digitalization aims to build a clear and logically rigorous framework to guide the development and revision of relevant standards.This system enhances the scientific,systematic,and practical aspects of cultural heritage digitalization.This paper comprehensively analyzes the current status and needs of cultural heritage digitalization and standardization.It further examines the methods used to construct the standards system.Through comparative analysis,it establishes a lifecycle-based framework for cultural heritage.This framework accounts for the unique characteristics of cultural heritage and systematically integrates key processes such as collection,processing,storage,transmission,and utilization of data.The standards system is divided into six sections:general,data,information,knowledge,intelligence,and application.Based on the current digitalization efforts,this paper proposes key standardization directions for each section.This framework ensures the integrity and consistency of data throughout the digitalization process.It also supports the application of intelligent technologies in cultural heritage conservation,contributing to the sustainable preservation and utilization of cultural heritage data.
文摘Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools＇ opportunities and suggestions in data science education. We argue that iSchools should empower their students with ＂information computing＂ disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application- based. These three loci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework （DSEF） with building blocks that include the three pillars of data science （people, technology, and data）, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.
基金support from the National Natural Science Foundations of China(NSFC)[Grants No.91746302 and 71822201]National Engineering Laboratory for Big Data Distribution and Exchange Technologies.
文摘The widespread use of machine learning techniques and artificial intelligence algorithms has highlighted the strategic role of data.To acquire data for training algorithms and eventually empowering the digital transformation,data marketplaces are often required to support and coordinate cross-organizational data transactions.However,the prior industry practices have suggested that the transaction costs in the data marketplaces are severely high,and the supporting infrastructure is far from mature.This paper proposes a data attributes-affected data exchange(DADE)conceptual model to understand the challenges and directions for developing data marketplaces.Specifically,our model framework is built upon two dimensions,data lifecycle maturity and data asset specificity.Based on the DADE model,we propose four approaches for developing data marketplaces and discuss future research directions with an overview of computational methods as potential technical solutions.
