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[Purpose/Significance] Digital technology has become an important driving force in the utilization of data production elements. To make full use of data resources, the underlying accompanying problem is rights management. In practice, the ownership and the right to use of data resource are separated in the circulation process. It has become an urgent problem to conduct data resource management activities based on reasonable and legal protection of users' rights. [Method/Process] Based on the general framework of "practical basis - abstract analysis - general concrete understanding", this paper firstly identified four practice statuses of distributed big data assets as followings: 1) the application of new technology leading to facilitating platforms; 2) attention needed to paid to intellectual properties of multiple users' groups; 3) users' needs to be satisfied on various contexts. For example, conducting resource procurement, collection, data processing and layout of resource allocation systems based on cloud service platforms; 4) maintainance and management of high quality data sets, including self-built and imported resources, the negotiation of ownership and use rights, and emphasis on the rights strategies. Next, this study conducted abstract analysis on the generalized idea of basic dimensions of data rights management from four aspects, namely, technical resource regulation, reasonable use boundary, complexity of rights, and analysis of use rights. [Results/Conclusions] Based on the abstract analysis, this paper put forward and explained four successive solutions at data resource management institute level, as described below. 1) the principle and terms of data policy, which include but not limited to general statement, policies of content use, policy statement on metadata, social media policies, and terms of service; 2) the typical contexts and content planning of rights potofolio. This paper took the resource procurement business of academic libraries as an example to illustrate and summarize the four content planning items on rights portfolio: strategic plan, operational policies, data policies, end-user policies; and 3) the allocation management of data assets, which lays the key part of data asset management. In this paper, we consider that data asset management refer to description and management of the constituent terms of data assets and the relationship between terms. The cost structure of data asset management contains tangible cost (such as consulting, software and hardware purchase fee, and digital resources purchase fee) and intangible cost (such as indirect human resource costs, risks, social credit, and loss compensation); and 4) finally, the establishment standard, working process, associated contrasts and regulations, and the evaluation measurements of data asset management businesses at the institute level. Such solutions are proposed to provide some inspirations to data resource management institutes（such as libraries） under the distributed big data circumstance.

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Data and information are both key players of the 21st century. Technology is rapidly changing, and the industrial revolution is represented in the field of agriculture as well. Precision farming helps farmers to maximise annual yields and use available data. Due to technological developments and data management, more and more information is available. Precision agriculture manages the variability in production agriculture in a more economic and environmentally efficient manner. It encompasses a suite of farm-level information technologies, monitors the major field crops and annual yields. Precision agriculture can survive only by using the data and information gained.

Due to the hierarchical management structure of electric power enterprises, data flow often needs to go through multiple layers of nodes for review, which makes data synchronization slow and distortion high and data reliability difficult to improve. The existing data asset management mode of electric power enterprises faces security problems such as node trustworthiness and data tampering in the process of identity mutual trust, transmission and storage, data collection and data usage. In view of this, this paper proposes a blockchain-based digital asset management framework using the decentralized, untrustworthy, and hard-to-tamper features of blockchain. In the proposed framework, we optimized security authentication mechanism, data recording and consensus process, and data storage and transmission strategy involved in blockchain data sharing applications. The framework can effectively ensure the security, integrity, operability and consistency of digital assets in the management process under the blockchain environment. [ABSTRACT FROM AUTHOR]

The blockchain technology can ensure the high security, high privacy and traceability of data asset management.Through research on the current blockchain-based data asset management mechanism, it is concluded that the current management mechanism is only applied in a certain layer of the blockchain framework.In order to solve this problem, a new mechanism of data asset management based on blockchain was proposed.The mechanism combined and applied all layers in the blockchain framework.In addition, this mechanism added a node authority control mechanism at the network layer, realized the consensus mechanism with customizable attributes at the consensus layer, optimized the structure and built indexes to speed up data query efficiency at the data layer, realized intelligent data management and sharing at the smart contract layer, and realized information encryption with customizable encryption algorithms at the transaction layer.Experimental results show that the new mechanism of data asset management based on blockchain improves the efficiency of data query on chain by 2.33 times compared with the traditional mechanism.

Data is considered to be the most valuable asset of various organizations,and the research on data asset has been valued by various countries,industries and organizations.From the perspective of standardization,the theoretical research and practice of data assets of relevant organizations at home and abroad was introduced,as well as the progress of standardization research on data assets.The standardization idea of data assets and the basic requirements of data assets management were put forward,which provided reference for the management and application of data assets.

In the era of the digital economy,data is increasingly becoming an important strategic asset for enterprises,but the lack of data asset management capabilities is increasingly becoming a key issue that restricts companies’ ability to add value to their data.By sorting out the evolution history of data asset management,the related concepts of data asset management was clarified,the current status of the industry of data asset management was analyzed,the design ideas and main contents of a data asset management system oriented to value realization were explained,and a complete data asset management system was shown.The important role of data operations was emphasized,a practical path for the data asset management system was established,the development trend of data asset management was summarized.

Smart grid is a strategic level construction project being promoted.However,due to the complexity of the project itself,the level of smart grid data management in various departments in China is uneven,and there are many risks in data assetization.Therefore,risk management has become an essential part of the advancement of data assetization work.The characteristics of smart grid data assets were analyzed.Combining the characteristics of power industry,big data research methods and risk management elements,risk identification and management of smart grid data assets were advised,including data governance,data security,supply chain data sharing,customer credit risk control and smart grid robustness construction.And they were supported by an actual project and implementation effect of China’s power grid.Finally,the future development trend was analyzed.