中文    English

Journal of Library and Information Science in Agriculture ›› 2023, Vol. 35 ›› Issue (9): 28-42.doi: 10.13998/j.cnki.issn1002-1248.23-0699

Previous Articles     Next Articles

Influencing Factors of Scientific Data Value Increment Based on System Dynamics

SUN Lili1, WANG WeiJie2, SHENG Jiefei3   

  1. 1. Institute of Information Management and Technology, Nanjing University of Technology, Nanjing 210009;
    2. Communication University of China, Nanjing 211172;
    3. School of Economics and Management, Nanjing University of Technology, Nanjing 211816
  • Received:2023-08-07 Online:2023-09-05 Published:2024-01-12

PDF (PC)

86

Abstract: [Purpose/Significance] To explore the influencing factors of the added value of scientific data and reveal the inherent development laws of the added value of scientific data. [Method/Process] First, based on the interview data of 18 experts and the research results related to the value appreciation of scientific data in existing literature, the grounded theory method was adopted. Through open coding, main-axis coding, and selective coding, 19 categories, 6 main categories, and 3 core categories were finally obtained. From this, a theoretical model of the factors influencing the value appreciation of scientific data was obtained. On this basis, the Vensim PLE tool was used to establish a dynamic model of the value appreciation system of scientific data, and the process of value appreciation of scientific data was dynamically simulated and analyzed to reveal the relationship between various influencing factors and the value appreciation of scientific data. [Results/Conclusions] In the process of increasing the value of scientific data, the quality factor of raw data is a prerequisite, and high-quality raw scientific data are conducive to the integration and secondary development of subsequent scientific data. The data literacy of data producers represented by researchers has the most significant impact on the quality of scientific data. The level of data storage and payment has a significant impact on the added value of scientific data. When the perceived effort and perceived risk of data storage and payment by researchers decrease, accompanied by the pressure of scientific data sharing policies, researchers become more likely to increase their willingness to save and pay, thereby significantly improving the scale effect of data storage and payment. The organization and integration of scientific data is the key to the formation of value-added scientific data. Overall, the metadata quality has the most significant impact on the level of scientific data organization and integration. The quality of metadata is the foundation of scientific data processing, classification, and integration. The higher the quality of metadata, the more it helps to improve the level of scientific data organization and integration. The sharing and development of scientific data is the key to realizing the value-added of scientific data. It is the final step in realizing the value-added of scientific data. In this process, the scale of scientific data openness, the quality of scientific data sharing platforms, and development capabilities all have a positive promoting effect on the level of scientific data sharing and development.

Key words: scientific data, value increment, influencing factors, system dynamics, data elements, FAIR

CLC Number: 

  • G203
[1] HOLDREN J P.Increasing access to the results of federally funded scientific research[EB/OL]. [2023-07-06].https://www.whitehouse.gov/wp-content/uploads/2022/08/08-2022-OSTP-Public-Access-Memo.pdf.
[2] The European Parliament and the Council of the European Union. Directive(EU) 2019/1024 of the European parliament and of the council of 20 June 2019 on open data and the re-use of public sector information[EB/OL]. [2023-07-06]. https://www.legislation.gov.uk/eudr/2019/1024/introduction.
[3] Data FAlRport. Find, access, interoperate & re-use data[EB/OL].[2023-12-06]. https://www.datafairport.org/.
[4] 温亮明, 李洋, 郭蕾. 国内外开放科学的实践进展与未来探索[J]. 图书情报工作, 2021, 65(24): 109-122.
WEN L M, LI Y, GUO L.The practice progress and future explo-ration of open science at home and abroad[J]. Library and information service, 2021, 65(24): 109-122.
[5] University of Cambridge. Data champions[EB/OL].[2021-04-03]. https://www.data.cam.Ac.uk/intro-data-champions.
[6] 刘莉, 司莉. 科学数据治理实践:内容体系与发展趋势[J/OL]. 情报理论与实践, 2023: 1-9.(2023-07-24). https://kns.cnki.net/kcms/detail/11.1762.G3.20230724.1603.002.html.
LIU L, SI L. Scientific data governance practices: Content systems and development trend[J/OL]. Information studies: Theory & application, 2023: 1-9.(2023-07-24). https://kns.cnki.net/kcms/detail/11.1762.G3.20230724.1603.002.html.
[7] 董诚, 侯敏. 科技资源共享价值最大化的三层次模型(VAA)[J]. 科技管理研究, 2013, 33(11): 231-234.
DONG C, HOU M.A three-layered model that sharing maximizes the value of scientific and technical resources (VAA)[J]. Science and technology management research, 2013, 33(11): 231-234.
[8] 李海舰, 赵丽. 数据价值理论研究[J]. 财贸经济, 2023, 44(6): 5-20.
LI H J, ZHAO L.Research of data valuation[J]. Finance & trade economics, 2023, 44(6): 5-20.
[9] ALBINO J P.An approach to value creation in open data for small and medium-sized enterprises using "R" ecosystem[C]// 2017 Computing Conference. Piscataway, New Jersey: IEEE, 2018: 346-351.
[10] Improving discoverability of open government data with rich metadata descriptions using semantic government vocabulary[J]. Web semantics: Science, services and agents on the world wide web, 2019, 55(C): 1-20.
[11] 李旭晖, 凡美慧. 大数据中的知识关联[J]. 情报理论与实践, 2019, 42(2): 68-73, 107.
LI X H, FAN M H.Knowledge interaction in big data[J]. Informa-tion studies: Theory & application, 2019, 42(2): 68-73, 107.
[12] 马费成, 吴逸姝, 卢慧质. 数据要素价值实现路径研究[J]. 信息资源管理学报, 2023, 13(2): 4-11.
MA F C, WU Y S, LU H Z.Research on the path to realize the value of data elements[J]. Journal of information resources management, 2023, 13(2): 4-11.
[13] 阳巧英, 夏义堃. 我国数据要素价值形成机理、影响因素与实现路径——基于扎根理论的分析[J]. 图书与情报, 2023(2): 12-22.
YANG Q Y, XIA Y K.The formation mechanism, influencing factors and implementation path of data factor value[J]. Library & informa-tion, 2023(2): 12-22.
[14] 邓君, 贾晓青, 马晓君, 等. 科学数据价值鉴定标准研究[J]. 情报科学, 2013, 31(9): 37-41.
DENG J, JIA X Q, MA X J, et al.Study in appraisal exandard of scientific data value[J]. Information science, 2013, 31(9): 37-41.
[15] 顾立平, 陈新兰, 张潇月, 等. 开放科研数据中的数据价值提升策略[J]. 图书馆论坛, 2020, 40(9): 115-124.
GU L P, CHEN X L, ZHANG X Y, et al.A strategy for enhancing the value of open research data[J]. Library tribune, 2020, 40(9): 115-124.
[16] 孙建军, 李阳. 科学大数据: 范式重塑与价值实现[J]. 图书与情报, 2017(5): 20-26.
SUN J J, LI Y.Scientific big data: Paradigm remodeling and value realization[J]. Library & information, 2017(5): 20-26.
[17] 冯媛. 科学数据开放共享的价值共创模型及运行机制研究[J]. 图书馆, 2022(9): 29-37.
FENG Y.Value co-creation model and operation mechanisms of open sharing of scientific data[J]. Library, 2022(9): 29-37.
[18] 任颖, 李楠. 科学数据价值共创系统构建及仿真分析[J]. 数字图书馆论坛, 2023, 19(5): 42-53.
REN Y, LI N.Construction and simulation analysis of scientific data value co-creation system[J]. Digital library forum, 2023, 19(5): 42-53.
[19] 任福兵, 孙美玲. 基于价值链理论的政府开放数据价值增值过程与机理研究[J]. 情报资料工作, 2021, 42(4): 56-63.
REN F B, SUN M L.Research on the value-added process and mechanism of government open data based on value chain theory[J]. Information and documentation services, 2021, 42(4): 56-63.
[20] 宋姗姗, 白文琳. 中国大数据治理研究述评[J]. 农业图书情报学报, 2022, 34(4): 4-17.
SONG S S, BAI W L.A review of big data governance research in China[J]. Journal of library and information science in agriculture, 2022, 34(4): 4-17.
[21] GLASER B G.The grounded theory perspective: conceptualisation contrasted with description[M]. Mill Valley: Sociology Press, 2001: 145.
[22] MYERS M D, NEWMAN M.The qualitative interview in IS re-search: Examining the craft[J]. Information and organization, 2007, 17(1): 2-26.
[23] 郭安元. 基于扎根理论的心理契约违背的影响因素及其作用机制研究[D]. 武汉: 武汉大学, 2015.
GUO A Y.A study on the factors and mechanism of psychological contract violation based on the grounded theory[D]. Wuhan: Wuhan University, 2015.
[24] 柯江林, 孙健敏, 李永瑞. 心理资本:本土量表的开发及中西比较[J]. 心理学报, 2009, 41(9): 875-888.
KE J L, SUN J M, LI Y R.Psychological capital: Chinese indigenous scale's development and its validity comparison with the western scale[J]. Acta psychologica sinica, 2009, 41(9): 875-888.
[25] 蒋畅和, 刘祖德, 赵云胜. 基于系统动力学的安全经验仿真试验研究[J]. 安全与环境工程, 2012, 19(4): 97-101.
JIANG C H, LIU Z D, ZHAO Y S.Simulation study on safety experience based on system dynamics[J]. Safety and environmental engineering, 2012, 19(4): 97-101.
[26] 李旭. 社会系统动力学: 政策研究的原理、方法和应用[M]. 上海: 复旦大学出版社, 2009.
LI X.Social system dynamics: Principles, methods and applications of policy research[M]. Shanghai: Fudan Press, 2009.
[27] 高晓宁, 胡威, 臧国全. 科研数据共享效率影响因素系统动力学仿真分析[J]. 情报理论与实践, 2022, 45(8): 146-153, 103.
GAO X N, HU W, ZANG G Q.System dynamics simulation analysis on factors affecting sharing efficiency of scientific research data[J]. Information studies: Theory & application, 2022, 45(8): 146-153, 103.
[28] 袁红, 王焘. 政府开放数据生态系统可持续发展实现路径的系统动力学分析[J]. 图书情报工作, 2021, 65(17): 13-25.
YUAN H, WANG T.System dynamics analysis of the sustainable development path of the government open data ecosystem[J]. Library and information service, 2021, 65(17): 13-25.
[1] WU Yiwei, WEN Tingxiao. Effects of Public Online Health Information Search on Offline Medical Care Seeking Behavior [J]. Journal of Library and Information Science in Agriculture, 2023, 35(8): 30-42.
[2] YU Bo, CHEN Shiji, Zhao Jiayi. Influencing Factors and Indicator System Construction for the Evaluation of Talented People in Scientific and Technological Fields in Chinese Universities [J]. Journal of Library and Information Science in Agriculture, 2023, 35(7): 63-74.
[3] GAO Lan, TANG Anying, FAN Guangji, CHEN Lianfang. Influencing Factors of Public Demand for Digital Cultural Service in Fujian Province: Based on the Survey of Colleges and Universities in Fujian Province [J]. Journal of Library and Information Science in Agriculture, 2023, 35(3): 90-103.
[4] ZHAO Youlin, CAO Hongnan. Government Microblog Information Exchange Efficiency and Its Influencing Factors for Emergency Management [J]. Journal of Library and Information Science in Agriculture, 2022, 34(9): 72-85.
[5] CHEN Shuxian, LIU Guifeng, LIU Qiong. Research Progress and Implementation of FAIR Principles for Scientific Data Management [J]. Journal of Library and Information Science in Agriculture, 2022, 34(8): 30-41.
[6] XING Wenming, LIU Wo. An Investigation on University Libraries' Service in Promoting the Implementation of FAIR Data Management Principles [J]. Journal of Library and Information Science in Agriculture, 2022, 34(7): 65-75.
[7] XING Fei, LIU Caihua, CHAI Xuefei, PENG Guochao. Influencing Factors of Elderly Users' Health Information Adoption Behavior Based on Social Platforms: Taking WeChat as an Example [J]. Journal of Library and Information Science in Agriculture, 2022, 34(7): 53-64.
[8] GUO Weijia. Influencing Factors of Artificial Intelligence Readiness in Libraries [J]. Journal of Library and Information Science in Agriculture, 2022, 34(5): 47-56.
[9] CHAI Miaoling, ZOU Yixing, TAN Rongzhi, ZENG Yi, REN Yunyue. Research and Practice on Association of Scientific Data and Scientific Literature Oriented to Knowledge Service of Agricultural Industry [J]. Journal of Library and Information Science in Agriculture, 2022, 34(3): 37-50.
[10] XIONG Huan, LUO Aijing, XIE Wenzhao, HUANG Panhao. Status and Influencing Factors of Health Information Literacy of the Rural Elderly [J]. Journal of Library and Information Science in Agriculture, 2022, 34(10): 44-56.
[11] FAN Zhixuan, WANG Jian, SA Xu, ZHANG Guilan. Structure-Utility of Descriptive Information of Agricultural Scientific Data from the Perspective of Users [J]. Journal of Library and Information Science in Agriculture, 2022, 34(10): 57-69.
[12] WANG Hui. The Influence of Knowledge Service Ability on the User's Intention of Using the Knowledge Service Platform Provided by University Libraries [J]. Journal of Library and Information Science in Agriculture, 2021, 33(9): 64-71.
[13] SUN Tan, HUANG Yongwen, XIAN Guojian, CUI Yunpeng, LIU Juan. Considerations for the Development of Agricultural Informatization Driven by a New Generation of Information Technologies [J]. Journal of Library and Information Science in Agriculture, 2021, 33(3): 4-15.
[14] MENG Yintao, ZHAO Leixia, YU Qianqian. Scientific Data Evaluation Index System for Scientific Data Preservation [J]. Journal of Library and Information Science in Agriculture, 2021, 33(12): 48-59.
[15] ZHANG Xin, TIAN Xuecan, ZHANG Lu, LI Yue. User Utilization Behavior and Influencing Factors of Hospital Online Information Service: A Case Study of Hebei Province [J]. Journal of Library and Information Science in Agriculture, 2021, 33(11): 38-49.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Editorial Board of Journal of Library and Information Science in Agriculture
Tel：(010)82109667　Fax：(010)82109667　E-mail：xuekan＠caas.cn
Powered by Beijing Magtech Co., Ltd.
知识共享许可协议