Purpose:This research endeavors to investigate the impact of open government data on corporate investment,emphasizing the exploration of underlying mechanisms,heterogeneous effects,and implications for investment effi...Purpose:This research endeavors to investigate the impact of open government data on corporate investment,emphasizing the exploration of underlying mechanisms,heterogeneous effects,and implications for investment efficiency.Utilizing the implementation of government data open platforms as a quasi-natural experiment,this study aims to elucidate how public data transparency affects firms’investment decisions and resource allocation.Design/methodology/approach:This study employs a staggered Difference-in-Differences(DID)model as its principal methodological framework.This approach facilitates causal inference by examining the differential changes in corporate investment between firms influenced by the data openness policy and those that remain unaffected over time.Findings:The findings indicate that open government data substantially enhance corporate investment levels.A mechanistic analysis identifies three principal channels through which this effect is mediated:alleviation of overall financing constraints,reduction of financing costs,and expansion of the financing scale.A heterogeneity analysis suggests that the positive impact is more pronounced in state-owned enterprises,high-tech firms,and companies experiencing elevated levels of macroeconomic uncertainty.Moreover,the transparency of government data improves the responsiveness of corporate investment to emerging opportunities,thereby augmenting the overall efficiency of corporate investment.Research limitations:This study primarily examined the influence of government data transparency on corporate investment,while not accounting for the effects of macroeconomic variability,internal corporate governance frameworks,and industry-specific regulatory policies.Practical implications:Government open data platforms can effectively boost corporate investment and resource allocation.Policymakers should focus on improving the quality and accessibility of these data,especially in areas with high economic uncertainty,to support business investments.Firms,particularly high-tech and financially constrained firms,can use open data to ease capital limitations and find investment opportunities.Regulators should promote data transparency to enhance economic vitality through efficient corporate investments.Originality/value:This study enhances the existing literature by offering causal evidence of the impact of open government data on corporate investment,a subject that has been relatively underexplored empirically.By employing a quasi-natural experiment centered on the implementation of government data platforms,this study adopts a robust methodological approach to address endogeneity issues,thereby advancing methodological rigor in investigations of public data governance and corporate behavior.展开更多
Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach...Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach: Data for openness and coverage have been collected from the Open Data Inventory 2018(ODIN), by Open Data Watch;institutional trust is built up as a formative construct based on the European Social Survey(ESS), Round 9. The relations between the open government data features and trust have been tested on the basis of structural equation modelling(SEM).Findings: The paper reveals that as European governments improve data openness, disaggregation, and time coverage, people tend to trust them more. However, the size of the effect is still small and, comparatively, data coverage effect on citizens' confidence is more than twice than the impact of openness.Research limitations: This paper analyzes the causal effect of Open Government Data(OGD) features captured in a certain moment of time. In upcoming years, as OGD is implemented and a more consistent effect on people is expected, time series analysis will provide with a deeper insight.Practical implications: Public officers should continue working in the development of a technological framework that contributes to make OGD truly open. They should improve the added value of the increasing amount of open data currently available in order to boost internal and external innovations valuable both for public agencies and citizens.Originality/value: In a field of knowledge with little quantitative empirical evidence, this paper provides updated support for the positive effect of OGD strategies and it also points out areas of improvement in terms of the value that citizens can get from OGD coverage and openness.展开更多
Data is a key factor of production in the so-called"digital economy"era.Thus,it is important to promote government data opening and sharing to advance the high-quality development of a digital economy.The ar...Data is a key factor of production in the so-called"digital economy"era.Thus,it is important to promote government data opening and sharing to advance the high-quality development of a digital economy.The article first constructs an evolutionary game model of government data opening and sharing(with local governments and enterprises as game participants)by combining realistic scenarios and evolutionary game models.Then,it discusses the evolutionary stabilization strategies under different scenarios in a categorical manner.Finally,it uses MATLAB to conduct numerical simulations to verify the accuracy of the model and analyze the key influencing factors.Several results were obtained.(1)the optimal evolutionary path to promote government data opening and sharing is for enterprises to choose to"use data"and for local governments to choose the"positive sharing"strategy,and the enterprises'decision is the internal driver.(2)The value of data assets provided by local governments when applying the"positive sharing"strategy,the cost of data used by enterprises,and the data value conversion rate of enterprises are the key factors influencing the decisions of both parties.To promote open sharing and exploitation of government data,enterprises should enhance their independent innovation capabilities,while governments should enhance the value of data assets and continuously optimize their business environments.展开更多
The management and application of government data are the bases for the construction of a digital government.Building an intelligent platform for government data by using artificial intelligence,blockchain,and other t...The management and application of government data are the bases for the construction of a digital government.Building an intelligent platform for government data by using artificial intelligence,blockchain,and other technical means to achieve open sharing,development,and utilization of data is an important link to promote continuously the construction of a digital government and improve the level of government management and service efficiency.According to the current construction status of government data platforms and the application prospect of blockchain technology,this study proposes to build a blockchain-based intelligent platform for government data.In combination with the technical advantages of blockchain,this study investigates the theory and technical logic of building a blockchain-based intelligent platform for government data and then proposes the theoretical model,architecture,operation mechanism,and core technology of platform construction.This study explores sthe implementation path of building a blockchain-based intelligent platform for government data from five aspects,i.e.,promoting technology upgrading,improving top-level design,strict supervision and regulation,strengthening collaborative research and judgment,and improving technical support.展开更多
Data factors are becoming the core driving force in the intelligent transformation of libraries.Based on a systematic review of the progress in data governance practices in libraries both domestically and internationa...Data factors are becoming the core driving force in the intelligent transformation of libraries.Based on a systematic review of the progress in data governance practices in libraries both domestically and internationally,this study delves into the mechanism by which data governance promotes data factorization and proposes implementation paths for data governance oriented toward data factorization.The aim is to facilitate the intelligent transformation and high-quality development of libraries.展开更多
With the rapid evolution of artificial intelligence(AI)technologies,the medical industry is undergoing a profound transformation driven by data intelligence.As the foundational element for intelligent diagnosis,precis...With the rapid evolution of artificial intelligence(AI)technologies,the medical industry is undergoing a profound transformation driven by data intelligence.As the foundational element for intelligent diagnosis,precision prevention,and public health governance,medical data is characterized by massive volume,complex structure,diverse sources,high dimensionality,strong privacy,and high timeliness.Traditional data analysis methods are no longer sufficient to meet the comprehensive requirements of data security,intelligent processing,and decision support.Through techniques such as machine learning,deep learning,natural language processing,and multimodal fusion,AI provides robust technical support for medical data cleaning,governance,mining,and application.At the data level,intelligent algorithms enable the standardization,structuring,and interoperability of medical data,promoting information sharing across medical systems.At the model level,AI supports auxiliary diagnosis and precision treatment through image recognition,medical record analysis,and knowledge graph construction.At the system level,intelligent decision-support platforms continuously enhance the efficiency and accuracy of healthcare services.However,the widespread adoption of AI in medicine still faces challenges such as privacy protection,data security,model interpretability,and the lack of unified industry standards.Based on a systematic review of AI’s key supporting technologies in medical data processing and application,this paper focuses on the compliance challenges and adaptation strategies during industry integration and proposes an adaptation framework centered on“technological trustworthiness,data security,and industry collaboration.”The study provides theoretical and practical insights for promoting the standardized and sustainable development of AI in the healthcare industry.展开更多
The study aimed to develop a customized Data Governance Maturity Model (DGMM) for the Ministry of Defence (MoD) in Kenya to address data governance challenges in military settings. Current frameworks lack specific req...The study aimed to develop a customized Data Governance Maturity Model (DGMM) for the Ministry of Defence (MoD) in Kenya to address data governance challenges in military settings. Current frameworks lack specific requirements for the defence industry. The model uses Key Performance Indicators (KPIs) to enhance data governance procedures. Design Science Research guided the study, using qualitative and quantitative methods to gather data from MoD personnel. Major deficiencies were found in data integration, quality control, and adherence to data security regulations. The DGMM helps the MOD improve personnel, procedures, technology, and organizational elements related to data management. The model was tested against ISO/IEC 38500 and recommended for use in other government sectors with similar data governance issues. The DGMM has the potential to enhance data management efficiency, security, and compliance in the MOD and guide further research in military data governance.展开更多
Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulati...Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulations, audit tampering, data backdating, data falsification, phishing and spoofing are no longer restricted to rogue individuals but in fact also prevalent in systematic organizations and states as well. Therefore, data security requires strong data integrity measures and associated technical controls in place. Without proper customized framework in place, organizations are prone to high risk of financial, reputational, revenue losses, bankruptcies, and legal penalties which we shall discuss further throughout this paper. We will also explore some of the improvised and innovative techniques in product development to better tackle the challenges and requirements of data security and integrity.展开更多
The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive st...The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.展开更多
In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework coveri...In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework covering the whole life cycle of data suitable for higher education is proposed, and based on this, the ideas and methods of data governance are applied to the construction of data management system for the basic development status of faculties by combining the practice of data governance of Donghua University.It forms a closed-loop management of data in all aspects, such as collection, information feedback, and statistical analysis of the basic development status data of the college. While optimizing the management business of higher education, the system provides a scientific and reliable basis for precise decision-making and strategic development of higher education.展开更多
With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaboratio...With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaborations offer numerous benefits,they also introduce a range of risks that organizations must carefully mitigate.If the obligation to meet the regulatory requirements is added to the equation,mitigating the third-party risk related to data governance,becomes one of the biggest challenges.展开更多
In the context of the digital economy,the volume of data is growing exponentially,the types of data are becoming more diverse,and its value is increasing,often providing critical support for decision-making by enterpr...In the context of the digital economy,the volume of data is growing exponentially,the types of data are becoming more diverse,and its value is increasing,often providing critical support for decision-making by enterprises and government institutions.Effective data governance is a crucial tool for maximizing data value and mitigating data risks.This article examines the application of data governance models in the digital economy,aiming to offer technical insights and guidance for data-driven enterprises and governments in China.By elevating their data governance standards in the new era,this approach will comprehensively enhance their ability to harness digital value and ensure security in the digital economy,ultimately driving the continued growth of both the digital economy and society.展开更多
Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing...Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.展开更多
at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service....at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service.This paper focuses on the analysis of the needs of marine scientific data security governance,in-depth development of marine scientific data security governance approaches and methods,and practical application in the national marine scientific data center,optimizing the existing data management model,ensuring the safety of marine scientific data,and fully releasing the data value.展开更多
基金supported by the following grants:National Natural Science Foundation of China(Grant No.72271147)Natural Science Foundation of Guangdong Province(Grant No.2022A1515010573)+1 种基金STU Scientific Research Initiation(Grant No.STF21005,STF21004)the Philosophy and Social Science Planning Project of Guangdong Province(Grant No.GD21CGL12,GD23XYJ76).
文摘Purpose:This research endeavors to investigate the impact of open government data on corporate investment,emphasizing the exploration of underlying mechanisms,heterogeneous effects,and implications for investment efficiency.Utilizing the implementation of government data open platforms as a quasi-natural experiment,this study aims to elucidate how public data transparency affects firms’investment decisions and resource allocation.Design/methodology/approach:This study employs a staggered Difference-in-Differences(DID)model as its principal methodological framework.This approach facilitates causal inference by examining the differential changes in corporate investment between firms influenced by the data openness policy and those that remain unaffected over time.Findings:The findings indicate that open government data substantially enhance corporate investment levels.A mechanistic analysis identifies three principal channels through which this effect is mediated:alleviation of overall financing constraints,reduction of financing costs,and expansion of the financing scale.A heterogeneity analysis suggests that the positive impact is more pronounced in state-owned enterprises,high-tech firms,and companies experiencing elevated levels of macroeconomic uncertainty.Moreover,the transparency of government data improves the responsiveness of corporate investment to emerging opportunities,thereby augmenting the overall efficiency of corporate investment.Research limitations:This study primarily examined the influence of government data transparency on corporate investment,while not accounting for the effects of macroeconomic variability,internal corporate governance frameworks,and industry-specific regulatory policies.Practical implications:Government open data platforms can effectively boost corporate investment and resource allocation.Policymakers should focus on improving the quality and accessibility of these data,especially in areas with high economic uncertainty,to support business investments.Firms,particularly high-tech and financially constrained firms,can use open data to ease capital limitations and find investment opportunities.Regulators should promote data transparency to enhance economic vitality through efficient corporate investments.Originality/value:This study enhances the existing literature by offering causal evidence of the impact of open government data on corporate investment,a subject that has been relatively underexplored empirically.By employing a quasi-natural experiment centered on the implementation of government data platforms,this study adopts a robust methodological approach to address endogeneity issues,thereby advancing methodological rigor in investigations of public data governance and corporate behavior.
文摘Purpose: This paper aims to assess if the extent of openness and the coverage of data sets released by European governments have a significant impact on citizen trust in public institutions.Design/methodology/approach: Data for openness and coverage have been collected from the Open Data Inventory 2018(ODIN), by Open Data Watch;institutional trust is built up as a formative construct based on the European Social Survey(ESS), Round 9. The relations between the open government data features and trust have been tested on the basis of structural equation modelling(SEM).Findings: The paper reveals that as European governments improve data openness, disaggregation, and time coverage, people tend to trust them more. However, the size of the effect is still small and, comparatively, data coverage effect on citizens' confidence is more than twice than the impact of openness.Research limitations: This paper analyzes the causal effect of Open Government Data(OGD) features captured in a certain moment of time. In upcoming years, as OGD is implemented and a more consistent effect on people is expected, time series analysis will provide with a deeper insight.Practical implications: Public officers should continue working in the development of a technological framework that contributes to make OGD truly open. They should improve the added value of the increasing amount of open data currently available in order to boost internal and external innovations valuable both for public agencies and citizens.Originality/value: In a field of knowledge with little quantitative empirical evidence, this paper provides updated support for the positive effect of OGD strategies and it also points out areas of improvement in terms of the value that citizens can get from OGD coverage and openness.
基金the Major Programs of the National Social Science Foundation of China(No.19ZDA348).
文摘Data is a key factor of production in the so-called"digital economy"era.Thus,it is important to promote government data opening and sharing to advance the high-quality development of a digital economy.The article first constructs an evolutionary game model of government data opening and sharing(with local governments and enterprises as game participants)by combining realistic scenarios and evolutionary game models.Then,it discusses the evolutionary stabilization strategies under different scenarios in a categorical manner.Finally,it uses MATLAB to conduct numerical simulations to verify the accuracy of the model and analyze the key influencing factors.Several results were obtained.(1)the optimal evolutionary path to promote government data opening and sharing is for enterprises to choose to"use data"and for local governments to choose the"positive sharing"strategy,and the enterprises'decision is the internal driver.(2)The value of data assets provided by local governments when applying the"positive sharing"strategy,the cost of data used by enterprises,and the data value conversion rate of enterprises are the key factors influencing the decisions of both parties.To promote open sharing and exploitation of government data,enterprises should enhance their independent innovation capabilities,while governments should enhance the value of data assets and continuously optimize their business environments.
文摘The management and application of government data are the bases for the construction of a digital government.Building an intelligent platform for government data by using artificial intelligence,blockchain,and other technical means to achieve open sharing,development,and utilization of data is an important link to promote continuously the construction of a digital government and improve the level of government management and service efficiency.According to the current construction status of government data platforms and the application prospect of blockchain technology,this study proposes to build a blockchain-based intelligent platform for government data.In combination with the technical advantages of blockchain,this study investigates the theory and technical logic of building a blockchain-based intelligent platform for government data and then proposes the theoretical model,architecture,operation mechanism,and core technology of platform construction.This study explores sthe implementation path of building a blockchain-based intelligent platform for government data from five aspects,i.e.,promoting technology upgrading,improving top-level design,strict supervision and regulation,strengthening collaborative research and judgment,and improving technical support.
文摘Data factors are becoming the core driving force in the intelligent transformation of libraries.Based on a systematic review of the progress in data governance practices in libraries both domestically and internationally,this study delves into the mechanism by which data governance promotes data factorization and proposes implementation paths for data governance oriented toward data factorization.The aim is to facilitate the intelligent transformation and high-quality development of libraries.
文摘With the rapid evolution of artificial intelligence(AI)technologies,the medical industry is undergoing a profound transformation driven by data intelligence.As the foundational element for intelligent diagnosis,precision prevention,and public health governance,medical data is characterized by massive volume,complex structure,diverse sources,high dimensionality,strong privacy,and high timeliness.Traditional data analysis methods are no longer sufficient to meet the comprehensive requirements of data security,intelligent processing,and decision support.Through techniques such as machine learning,deep learning,natural language processing,and multimodal fusion,AI provides robust technical support for medical data cleaning,governance,mining,and application.At the data level,intelligent algorithms enable the standardization,structuring,and interoperability of medical data,promoting information sharing across medical systems.At the model level,AI supports auxiliary diagnosis and precision treatment through image recognition,medical record analysis,and knowledge graph construction.At the system level,intelligent decision-support platforms continuously enhance the efficiency and accuracy of healthcare services.However,the widespread adoption of AI in medicine still faces challenges such as privacy protection,data security,model interpretability,and the lack of unified industry standards.Based on a systematic review of AI’s key supporting technologies in medical data processing and application,this paper focuses on the compliance challenges and adaptation strategies during industry integration and proposes an adaptation framework centered on“technological trustworthiness,data security,and industry collaboration.”The study provides theoretical and practical insights for promoting the standardized and sustainable development of AI in the healthcare industry.
文摘The study aimed to develop a customized Data Governance Maturity Model (DGMM) for the Ministry of Defence (MoD) in Kenya to address data governance challenges in military settings. Current frameworks lack specific requirements for the defence industry. The model uses Key Performance Indicators (KPIs) to enhance data governance procedures. Design Science Research guided the study, using qualitative and quantitative methods to gather data from MoD personnel. Major deficiencies were found in data integration, quality control, and adherence to data security regulations. The DGMM helps the MOD improve personnel, procedures, technology, and organizational elements related to data management. The model was tested against ISO/IEC 38500 and recommended for use in other government sectors with similar data governance issues. The DGMM has the potential to enhance data management efficiency, security, and compliance in the MOD and guide further research in military data governance.
文摘Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulations, audit tampering, data backdating, data falsification, phishing and spoofing are no longer restricted to rogue individuals but in fact also prevalent in systematic organizations and states as well. Therefore, data security requires strong data integrity measures and associated technical controls in place. Without proper customized framework in place, organizations are prone to high risk of financial, reputational, revenue losses, bankruptcies, and legal penalties which we shall discuss further throughout this paper. We will also explore some of the improvised and innovative techniques in product development to better tackle the challenges and requirements of data security and integrity.
基金supported by the EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement(Project-DEEP,Grant number:101109045)National Key R&D Program of China with Grant number 2018YFB1800804+2 种基金the National Natural Science Foundation of China(Nos.NSFC 61925105,and 62171257)Tsinghua University-China Mobile Communications Group Co.,Ltd,Joint Institutethe Fundamental Research Funds for the Central Universities,China(No.FRF-NP-20-03)。
文摘The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.
基金Special Project for Renovation and Procurement of Donghua University,Ministry of Education,China (No. CG202002845)。
文摘In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework covering the whole life cycle of data suitable for higher education is proposed, and based on this, the ideas and methods of data governance are applied to the construction of data management system for the basic development status of faculties by combining the practice of data governance of Donghua University.It forms a closed-loop management of data in all aspects, such as collection, information feedback, and statistical analysis of the basic development status data of the college. While optimizing the management business of higher education, the system provides a scientific and reliable basis for precise decision-making and strategic development of higher education.
文摘With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaborations offer numerous benefits,they also introduce a range of risks that organizations must carefully mitigate.If the obligation to meet the regulatory requirements is added to the equation,mitigating the third-party risk related to data governance,becomes one of the biggest challenges.
文摘In the context of the digital economy,the volume of data is growing exponentially,the types of data are becoming more diverse,and its value is increasing,often providing critical support for decision-making by enterprises and government institutions.Effective data governance is a crucial tool for maximizing data value and mitigating data risks.This article examines the application of data governance models in the digital economy,aiming to offer technical insights and guidance for data-driven enterprises and governments in China.By elevating their data governance standards in the new era,this approach will comprehensively enhance their ability to harness digital value and ensure security in the digital economy,ultimately driving the continued growth of both the digital economy and society.
文摘Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.
文摘at present,data security has become the most urgent and primary issue in the era of digital economy.Marine scientific data security is the most urgent core issue of marine data resource management and sharing service.This paper focuses on the analysis of the needs of marine scientific data security governance,in-depth development of marine scientific data security governance approaches and methods,and practical application in the national marine scientific data center,optimizing the existing data management model,ensuring the safety of marine scientific data,and fully releasing the data value.
