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Scientific Data Management Based on a Data Life Cycle Perspective: Using the Institutional Repositories Base of 24 Universities in the United States as an Example
Keyi XIAO, Yingying CHEN
Scientific Data Management Based on a Data Life Cycle Perspective: Using the Institutional Repositories Base of 24 Universities in the United States as an Example
[Purpose/Significance] The research paradigm is gradually shifting towards a data-intensive model, where research data has become the cornerstone in the realm of academic endeavors. Effective research data management can enhance the research efficiency of scientific researchers, reduce redundant data collection, and reduce costs. As a central repository for the storage of scholarly research outputs, it is essential that university institutional repositories fulfill their role in research data management. [Method/Process] To gain a full understanding of the evolving landscape, we embarked on a meticulous network-based research investigation. We specifically selected the institutional repositories of 24 prestigious American universities as our research subjects, with the aim of exploring the diverse range of services they provide at different stages of the research lifecycle. Our research was firmly grounded in the data lifecycle framework, which enabled us to systematically examine a wide range of research data management (RDM) services. This included critical aspects such as developing comprehensive research data management plans, establishing robust data organization services and standardized protocols, providing reliable long-term data storage solutions to ensure continued accessibility, enhancing data sharing policies to foster collaboration, strengthening research data quality control measures to maintain integrity, and developing comprehensive research data management training programs to empower researchers. Furthermore, we conducted an in-depth analysis to summarize the characteristics and valuable experiences of American universities in building and maintaining the basic infrastructure of their institutional repositories. [Results/Conclusions] Given the unique circumstances of China's modernization process, this paper distills effective insights and strategies from the institutional repositories of domestic university libraries in the field of research data management services. Our findings highlight the importance of building a localized research data management platform tailored to the specific needs and contexts of Chinese academia. Enhancing the quality of research data management is critical to building a trusted institutional knowledge base and fostering an environment of credibility and reliability. By applying the FAIR (Findable, Accessible, Interoperable, Reusable) and TRUST (Transparent, Responsible, Usable, Sustainable, and Trustworthy) principles, we can facilitate the open and seamless sharing of research data, breaking down barriers to collaboration and innovation. Finally, building a professional scientific research data management team is essential to provide the human capital necessary to navigate the complexities of data management and to promote the development and adoption of best practices in scientific research data sharing. Taken together, these findings help to improve the abiity of the scientific community to harness the full potential of research data to drive the creation and dissemination of knowledge.
institutional repository / research data management / data management plan / university libraries / open science {{custom_keyword}} /
Table 1 Statistics of IRs of university libraries in the U.S.表1 美国高校机构知识库统计 |
学校名称 | 机构知识库名称 | 网址 | 软件平台 |
---|---|---|---|
佛罗里达大西洋大学 | FAU Digital Library Digital Collections | https://library.fau.edu/digital-library/digital-collection-directory | Digitool |
俄勒冈州立大学 | ScholarsArchive@OSU | https://ir.library.oregonstate.edu/ | Hyrax |
华盛顿大学 | Research Works at the University of Washington | https://digital.lib.washington.edu/researchworks/ | Dspace |
密歇根大学 | Deep Blue Repositories | https://www.lib.umich.edu/collections/deep-blue-repositories | Samvera |
哥伦比亚大学 | Columbia Academic Commons | https://academiccommons.columbia.edu | Fedora |
加利福尼亚大学洛杉矶分校 | UCLA Dataverse | https://dataverse.ucla.edu/ | Dataverse |
罗格斯大学 | Rutgers University Community Repository(RUCore) | https://rucore.libraries.rutgers.edu/ | Fedora |
宾夕法尼亚州立大学 | Scholar Sphere | https://scholarsphere.psu.edu/ | Fedora |
北卡罗来纳大学教堂山分校 | UNC Dataverse | https://dataverse.unc.edu/ | Dataverse |
弗吉尼亚大学 | Libra Data | https://www.library.virginia.edu/libra | Dataverse |
哈佛大学 | Harvard Dataverse | https://dataverse.harvard.edu/ | Dataverse |
普林斯顿大学 | Dataspace at Princeton University | https://dataspace.princeton.edu/ | Dspace |
布朗大学 | Brown Digital Repository | https://repository.library.brown.edu/studio/ | 未知 |
卡内基梅隆大学 | Klithub | https://kilthub.figshare.com/ | Figshare |
加利福尼亚州立大学 | Scholar Works | https://scholarworks.calstate.edu/ | Samvera、Hyrax 3.6.0 |
芝加哥大学 | Knowledge@UChicago | https://knowledge.uchicago.edu/ | 未知 |
西北大学范伯格医学院 | Prism | https://prism.northwestern.edu/ | Samvera |
迈阿密大学 | Scholarly Commons | https://sc.lib.miamioh.edu | Dspace |
加州理工学院 | California Institute of Technology Research Data Repository | https://data.caltech.edu | invenio |
亚利桑那大学 | The University of Arizona Research Data Repository(ReDATA) | https://arizona.figshare.com | Figshare |
肯特州立大学 | Open Access Kent State(OAKS) | https://oaks.kent.edu/ | islandora |
杜克大学 | Duke Research Data Repository | https://repository.duke.edu/ | Samvera |
加州大学圣地亚哥分校 | UC San Diego Library Digital Collections | https://library.ucsd.edu/research-and-collections/research-data/index.html | 未知 |
普渡大学 | The Purdue University Research Repository(PURR) | https://purr.purdue.edu/ | Hubzero |
Table 2 Data storage management system and characteristics表2 数据存储管理系统及其特点 |
系统名称 | 特点 |
---|---|
Dataverse | 元数据配置个性化操作程度高,对不同版本进行分阶段存储和备份,流程比较完备 |
Figshare | 在线的数据共享云平台,接受各种研究文件类型、支持可视化 |
Fedora | ①处理分布式数据功能强大;②完善的Rest API网络服务;③技术方面:版本控制精准、缓存速度快、数据存储技术多元化;④用户界面:和科研数据对接技术好、支持可视化功能 |
Dspace | 不支持复合数据类型;可视化手段缺乏;更适合出版文献资料 |
Table 3 Research data management service projects of university libraries in the U.S.表3 美国高校机构知识库开展的科研数据管理服务项目 |
高校机构 | 数据管理计划 | 数据组织 | 数据备份与存储 | 数据获取与共享 | 教育培训 |
---|---|---|---|---|---|
佛罗里达大西洋大学 | √ | √ | √ | √ | √ |
俄勒冈州立大学 | √ | √ | √ | √ | |
华盛顿大学 | √ | √ | √ | √ | √ |
密歇根大学 | √ | √ | √ | √ | |
哥伦比亚大学 | √ | √ | √ | √ | |
加利福尼亚大学洛杉矶分校 | √ | √ | √ | √ | |
罗格斯大学 | √ | √ | √ | √ | √ |
宾夕法尼亚州立大学 | √ | √ | √ | √ | √ |
北卡罗来纳大学教堂山分校 | √ | √ | √ | √ | √ |
弗吉尼亚大学 | √ | √ | √ | √ | |
哈佛大学 | √ | √ | √ | √ | √ |
普林斯顿大学 | √ | √ | √ | ||
布朗大学 | √ | √ | √ | √ | |
卡内基梅隆大学 | √ | √ | |||
加利福尼亚州立大学 | √ | √ | √ | ||
芝加哥大学 | √ | √ | √ | ||
西北大学范伯格医学院 | √ | √ | √ | √ | √ |
迈阿密大学 | √ | √ | |||
加州理工学院 | √ | √ | √ | ||
亚利桑那大学 | √ | √ | |||
肯特州立大学 | √ | √ | √ | ||
杜克大学 | √ | √ | √ | √ | √ |
加州大学圣地亚哥分校 | √ | √ | √ | √ | √ |
普渡大学 | √ | √ | √ | √ | √ |
*注:“√”表示提供此项服务 |
Table 4 Licensing policy of university libraries in the U.S.表4 美国高校机构知识库许可证政策 |
许可证范围 | 高校机构知识库名称 |
---|---|
放弃版权 | 加利福尼亚大学洛杉矶分校、北卡罗来纳大学教堂山分校、弗吉尼亚大学、哈佛大学、杜克大学 |
受版权保护 | 华盛顿大学、罗格斯大学、普林斯顿大学、加利福尼亚州立大学 |
作者自行选择版权保护程度 | 俄勒冈州立大学、密歇根大学、哥伦比亚大学、布朗大学、卡内基梅隆大学、芝加哥大学、西北大学范伯格医学院、迈阿密大学、加州理工学院、亚利桑那大学、圣地亚哥大学、普渡大学 |
1 |
丁培. 国外大学科研数据管理政策研究[J]. 图书馆论坛, 2014, 34(5): 99-106.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
2 |
国务院. 国务院办公厅颁布《科学数据管理办法》[EB/OL]. (2018-03-17)[2024-02-10].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
3 |
中国科学院. 中国科学院科学数据管理与开放共享办法(试行)[EB/OL]. (2019-02-01)[2024-02-10].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
4 |
中华人民共和国教育部. 《高等学校数字校园建设规范(试行)》[EB/OL]. (2021-03-12)[2024-02-10].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
5 |
中央网络安全和信息化委员会办公室. 十七部门关于印发《“数据要素×”三年行动计划(2024—2026年)》的通知[EB/OL]. (2024-01-25)[2024-02-10].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
6 |
陈媛媛, 林安洁. 高校图书馆科研数据管理服务模式搭建和应用[J]. 情报理论与实践, 2023, 46(5): 99-106.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
7 |
孔晔晗, 张潇月, 李宜展. 美国高校图书馆促进数据重用的服务实践及启示[J]. 图书与情报, 2023(4): 78-89.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
8 |
胡媛, 邹小敏, 谢守美. 高校图书馆科研数据管理服务能力评价指标体系研究[J]. 图书馆理论与实践, 2024(1): 67-76.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
9 |
马海群, 李金玲, 于同同, 等. 全生命周期视阈下公共数据伦理准则框架研究[J]. 农业图书情报学报, 2023, 35(6): 29-42.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
10 |
Dataverse project[EB/OL]. [2024-02-10].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
11 |
Figshare[EB/OL]. [2024-09-02].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
12 |
The National Science Foundation. Dissemination and sharing of research results[EB/OL]. [2024-02-10].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
13 |
聂云贝, 刘桂锋, 刘琼. 数据生态链视角下科学数据生命周期运行过程分析[J]. 信息资源管理学报, 2021, 11(2): 69-77.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
14 |
University of Washington Libaries. Research data management: Implementing, organizing and format[EB/OL]. [2024-02-10].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
15 |
PennState University Libraries. Choosing a license[EB/OL]. [2024-05-14].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
16 |
中华人民共和国中央人民政府. 中共中央 国务院印发《数字中国建设整体布局规划》[EB/OL]. (2023-02-27)[2024-05-03].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
17 |
涂志芳. 科学数据出版的基础问题综述与关键问题识别[J]. 图书馆, 2018(6): 86-92, 100.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
18 |
涂志芳, 杨志萍. 我国科学数据管理与共享实践进展: 聚焦两种主要模式[J]. 图书情报知识, 2021, 38(1): 103-112.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
19 |
上海市图书馆学会. 中国高校研究数据管理推进工作组简介[EB/OL]. [2024-05-04].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
20 |
复旦大学社会科学数据平台[EB/OL]. [2024-05-04].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
21 |
北京大学开放研究数据平台[EB/OL]. [2024-05-04].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
22 |
华东师范大学人文社科大数据平台[EB/OL]. [2024-05-04].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
23 |
武汉大学图书馆. 《数据素养与数据利用》[EB/OL]. [2024-05-04].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
24 |
CityU scholars[EB/OL]. [2024-05-04].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
25 |
FORCE 11. Fair principles[EB/OL]. [2024-05-14].
{{custom_citation.content}}
{{custom_citation.annotation}}
|
26 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
27 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
28 |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
29 |
孔丽华, 习妍, 张晓林. 数据出版的趋势、机制与挑战[J]. 中国科学基金, 2019, 33(3): 237-245.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_ref.label}} |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
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