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    15 January 2022, Volume 8 Issue 1
    SPECIAL ISSUE: SCIENTIFIC DATA GOVERNANCE
    HEPS scientific data management policy research and applications
    Hao HU, Fazhi QI, Xiaokang SUN, Qi LUO
    2022, 8(1):  5-14.  doi:10.11959/j.issn.2096-0271.2022001
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    Combining with the problems of scientific data management and data opening and sharing at high energy photon source (HEPS), the necessity and importance of scientific data policy research for synchrotron radiation facilities which serve as public experiments platforms were illustrated at first.Secondly, the status of data policy research for domestic synchrotron radiation facilities was analyzed according to the research progress of the data policy for other countries.Thirdly, the content of HEPS data policy was expounded in focus.Fourthly, problems and some thought during the research were summarized.Finally, the research progress and the application examples of HEPS data policy were introduced, and the prospect to the data policy research for synchrotron radiation facilities in the future was proposed.

    Data management process model for the science and technology programs
    Qi XU, Ziming ZOU, Yaqin YUAN, Xiaoyan HU, Jizhou TONG, Wenzhen MA
    2022, 8(1):  15-23.  doi:10.11959/j.issn.2096-0271.2022002
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    To strengthen and standardize scientific data management for the increasing number of science and technology programs and the dramatically growing scientific data resources in the future, seven data activities to be performed in an orderly manner within the program cycle were defined, based on the program cycle as the axis and integrating the data life cycle and data curation concepts.Consequently, a data management process model for science and technology programs applicable to all types of programs was proposed.The contents, the input and output of seven data activities were described, including data output analysis and management planning, data product definition, data product generation and processing, products archiving and cataloging, data open sharing and services, appraisal and comment, maintenance and preservation, which are performed sequentially within each phase of the program.The control measures of data activities output were also given such as document signing and review in accordance with the management nodes of mission statement signing, annual/mid-term inspection, and comprehensive performance evaluation.Furthermore, the suggestions of model implementation such as adding program phases and management nodes, separating and merging data activities, and elaborating output content and management roles were given to improve the applicability to different types of science and technology programs.

    Centralization of agricultural scientific data management model based on life cycle theory
    Fei GAO, Guomin ZHOU, Rui MAN
    2022, 8(1):  24-36.  doi:10.11959/j.issn.2096-0271.2022003
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    Governments, research institutions and scientists all over the world have attached great importance to the construction, management and sharing of agricultural science data resources.Relevant international organizations and agricultural science data platforms are particularly active, providing strong support for agricultural science and technological innovation.From the current situation of science data resource construction, science data management centralization is the future development trend.The agriculture science data management centralization mode was explored, the agricultural science data life cycle management model was constructed, and the model was explained from four aspects: science data management standard, data collection and processing, long-term data preservation and data sharing service.This study tries to provide reference for science data management, sharing and service.

    Safety management and application of genomics data
    Rujiao LI, Xin ZHANG, Shuhui SONG, Yanqing WANG, Dong ZOU, Jingfa XIAO, Wenming ZHAO, Zhang ZHANG, Yiming BAO
    2022, 8(1):  37-45.  doi:10.11959/j.issn.2096-0271.2022004
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    Genomics data is essential strategic resources for population health and national security.It is of great significance to deposit, manage and utilize genomics data in a scientific manner.China is a powerhouse in generating vast quantities of biological data, while facing the situation of data loss due to the isolated data storage and the lack of systematic data monitoring and management, also with the heavy dependency on international biological data centers.Therefore, it urgently calls for China’s own life big data storage and management system at the national level.Taking the National Genomics Data Center for example, the deposition, sharing and safety management system and standards of genomics data were summarized, with data mining and application cases.Suggestions were also given on the aspects of policy-making, infrastructure, software research and development, principle building and talent development, as well as international cooperation.

    Agricultural science data sharing protocol in China
    Yifan CHEN, Shen YAN, Yachao YANG, Lin HU, Jingchao FAN, Xianghe ZHANG, Uguomin ZHO
    2022, 8(1):  46-59.  doi:10.11959/j.issn.2096-0271.2022005
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    In view of the actual situation of agricultural science data sharing in China, the characteristics of various existing licensing agreements at home and abroad and their applicability in the situation of agricultural science data sharing in China were analyzed.Combined with the actual situation of China’s current intellectual property system, legal system, scientific data management mechanism, scientific development level and so on, the scientific data sharing agreements: Weigong Village scientific data academic/general license draft were proposed, which is suitable for China’s national conditions.And some suggestions for future application were put forward.

    Classification of big data in metrology
    Feng ZHI, Feng TIAN, Ruofan ZHAO
    2022, 8(1):  60-72.  doi:10.11959/j.issn.2096-0271.2022006
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    Based on the tendency of development around data sharing in China and the relevant policies of safety management of scientific research data, the existing schemes of data classification of the metrology industry in China were studied.After investigating data classification methods of 20 national data-sharing platforms in China and relevant rules in the United States, the defects of the classification methods of metrological scientific research data in China were discussed, and the data security classification model and the data classification encoding method of metrological data were put forward.

    Status of classical marine environmental observing data products application and enlightenment to China
    Miao SUN, Zike WANG, Xin TONG, Yu FU, Yi WANG, Linchong KANG, Xiaoyi JIANG
    2022, 8(1):  73-83.  doi:10.11959/j.issn.2096-0271.2022007
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    The marine environmental observing data play an important role on the scientific research, environment safety, rights maintenance and synthetic decision support.The most widely used marine environmental observing data in the world are produced, shared and updated by the marine science and technology developing countries.However, few are produced by our country.Taking the marine environmental observing data as examples, the characteristics, quality control workflow, management, application and sharing of the representative datasets were systematically reviewed.In addition, the present status of the domestic research and the existing problems were introduced.Moreover, related suggestions were also put forward on the sharing mechanism, platform development and data producing.

    Classification of ecosystem long term observation data product
    Wen SU, Li ZHANG, Xuebing GUO, Honglin HE, Xinzhai TANG, Xiaoli REN
    2022, 8(1):  84-97.  doi:10.11959/j.issn.2096-0271.2022008
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    The long-term ecosystem observation can provide data of water, soil, meteorology and biology to support ecosystem evaluation and management.At present, there is a lack of long-term observation data product system of ecosystem in China, so no effective guidance can be provided for the production of data products.Firstly the data product system of international eco-environmental research networks was analyzed.Then a long-term observation data product system of ecosystem based on the data product classification, data product gradation two dimensions was proposed, combining the existing long-term observation protocols and data resources of ecosystem in China.It could not only express the content of long-term observation data products, but also the process of data product production and processing, and had been applied to design the long-term observation data product system of forest ecosystem of Chinese ecosystem research network (CERN).This data product system has good practical value and will play an important role in improving the value of long-term observation data.

    Resource and user access control system of scientific data center
    Qiaozhuoran CAO, Zugang CHEN, Guoqing LI, Jing LI
    2022, 8(1):  98-112.  doi:10.11959/j.issn.2096-0271.2022009
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    In the era of big data, all countries and governments generally attach importance to the opening of scientific data and actively promote data sharing.The wide opening and sharing of data poses new challenges to the whole process of data security.Based on the latest policies of international scientific data sharing and the actual situation in China, the resource and user access control system of scientific data center was put forward.Through the application of data classification, user classification and system access control strategy on the open system platform, the problem of lack of systematic scheme for resource security sharing in the data center was solved.The research results have been applied to the practical work of the National Earth Observation Scientific Data Center and achieved good results.

    Open access of scientific data in the context of open science: the practice of the National Tibetan Plateau Data Center
    Xiaoduo PAN, Xin LI, Youhua RAN, Xuejun GUO
    2022, 8(1):  113-120.  doi:10.11959/j.issn.2096-0271.2022010
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    The concept, connotation and importance of open science and open data practice to scientific research were introduced.The challenges faced by open data currently were described in detail, that included data citation, data metrics, data interoperability, and big data analysis.Taking the National Tibetan Plateau Data Center as an example, its measurements and results in data citation, data interoperability and big data analysis were expounded.Finally, the role of data center in promoting open data was prospected.

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