工作研究

科学数据管理计划评价量表分析

  • 王丹丹
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  • 河南科技大学管理学院 洛阳 471003
王丹丹(ORCID:0000-0001-9214-4906),副教授,博士,E-mail:wangdan430806@163.com。

收稿日期: 2017-05-18

  修回日期: 2017-07-29

  网络出版日期: 2017-09-20

基金资助

2017年河南省高等学校重点科研项目资助计划"MOOC环境下的科学数据素养教育"(项目编号:17A870001)研究成果之一。

Analysis of Scientific Data Management Plan Rubrics

  • Wang Dandan
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  • Management School, Henan University of Science and Technology, Luoyang 471023

Received date: 2017-05-18

  Revised date: 2017-07-29

  Online published: 2017-09-20

摘要

[目的/意义]对目前已经出版的或者处于草案征求意见阶段的科学数据管理计划评价量表进行比较分析,使利益相关者能更加了解制定科学数据管理计划评价量表的意义及设计要点,为今后我国开发类似的评价量表提供参考。[方法/过程]收集面向美国和英国主要科研资助机构的科学数据管理计划评价量表,从评价量表的设计依据和应用目的、各项评价要素的选择以及评价等级与等级描述三个主要设计步骤入手,分析不同评价量表的共性和差异。[结果/结论]为了帮助科研人员撰写高质量的科学数据管理计划,也为图书馆员开展科学数据管理服务提供有力支持,需要设计科学数据管理计划评价量表。我国的科研资助机构有必要学习国外优秀经验,出台科学数据管理计划相关政策并设计评价量表,只有这样才能有效推动科学数据管理工作的开展。

本文引用格式

王丹丹 . 科学数据管理计划评价量表分析[J]. 图书情报工作, 2017 , 61(18) : 35 -41 . DOI: 10.13266/j.issn.0252-3116.2017.18.005

Abstract

[Purpose/significance] A comparative analysis of the scientific data management plan rubrics, which have been published or are in the draft stage of comments, will enable stakeholders to gain a better understanding of the significance and design points of the development of a scientific data management plan rubric. This paper can provide references for developing similar rubrics in the future.[Method/process] This papercollected the data management plan rubrics developed for the major scientific research institutions in the United States and the United Kingdom, and analyzed these rubrics in terms of the design basis and application purpose, the performance criteria, the performance level andits description to identify the commonness and difference of these rubrics.[Result/conclusion] In order to help researchers write high-quality scientific data management plans, and provide strong support for librarians to carry out scientific data services, designing data management plan rubric is important. China's scientific research funding agencies need to learn from foreign outstanding experience, publish related policies and rubrics, and promote the development of scientific data management effectively.

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