[1]刘西涛 王浩宇.政府数据开放存在的问题与对策探析[J].现代农业研究,2018,(09):86-88.
　Liu Xitao Wang Haoyu.Analysis on the problems and countermeasures of government data opening[J].Modern Agricultural Research,2018,(09):86-88.
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政府数据开放存在的问题与对策探析
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《现代农业研究》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2018年09期
页码:
86-88
栏目:
推广与实践
出版日期:
2018-09-07

文章信息/Info

Title:
Analysis on the problems and countermeasures of government data opening
作者:
刘西涛 王浩宇
哈尔滨商业大学财政与公共管理学院
Author(s):
Liu Xitao Wang Haoyu
School of finance and public administration, Harbin business university
关键词:
大数据; 政府数据开放; 服务意识; 信息公开
Keywords:
big data;  open government data;  service awareness;  information disclosure
摘要:
与传统行政方式的不同，政府数据开放不仅是时代发展的必然，同时也是政府向透明、 阳光政府的转变过程。当前，政府数据开放呈现出数据孤岛化、民众普遍参与度不高、政府部门 服务意识不强、网络安全缺乏法律法规作为保障、缺乏数据更新的时效性等问题。因此，政府数 据开放应从整合片面数据入手、加强硬件与软环境之间的相适应的协调、扩大民众的参与度、加 强服务意识的转变、完善网络安全的法律法规、注重数据时效更新等方面加以强化。
Abstract:
Different from the traditional administrative mode, the opening of government data is not on? ly the inevitable development of The Times, but also the transformation process of the government to transparent and sunshine government. At present, government data opening presents problems such as data island, low public participation, weak service awareness of government departments, lack of laws and regulations for network security, and lack of timeliness of data update. Therefore, government data opening should be strengthened by integrating one-sided data, strengthening the coordination between hardware and soft environment, expanding the participation of the public, strengthening the transforma? tion of service awareness, improving the laws and regulations of network security, and paying attention to the update of data timeliness.

参考文献/References:

[1] 刘坤.电子政务环境下我国政府数据公开问题研究[D].黑 龙江:黑龙江大学，2016. [2] 郑磊;高丰.中国开放政府数据平台研究:框架、现状与建 议[J].电子政务,2015,(7). [3] 王斯妤.中国政府数据开放:现状问题与策略选择[D].吉林: 吉林大学,2016. [4] 孙艳艳;吕志坚.中国开放政府数据发展策略浅析[J].电子 政务,2015,(5). [5] 张晓娟;王文强;唐长乐.中美政府数据开放和个人隐私保 护的政策法规研究[J].情报理论与实践,2016,(1). [6] 付熙雯;郑磊.政府数据开放国内研究综述[J].电子政务, 2013,(6). [7] 周大铭.我国政府数据开放现状和保障机制[J].大数据, 2015,(2).

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更新日期/Last Update: 2018-09-07