Motivation and strategies for implementing Digital Object Identifiers (DOIs) at NCAR’s Earth Observing Laboratory -- Past progress and future collaborations

Q2 Computer Science Data Science Journal Pub Date : 2017-03-22 DOI:10.5334/DSJ-2017-007
J. Aquino, J. Allison, R. Rilling, D. Stott, Kathryn Young, Michael Daniels
{"title":"Motivation and strategies for implementing Digital Object Identifiers (DOIs) at NCAR’s Earth Observing Laboratory -- Past progress and future collaborations","authors":"J. Aquino, J. Allison, R. Rilling, D. Stott, Kathryn Young, Michael Daniels","doi":"10.5334/DSJ-2017-007","DOIUrl":null,"url":null,"abstract":"In an effort to lead our community in following modern data citation practices by formally citing data used in published research and implementing standards to facilitate reproducible research results and data, while also producing meaningful metrics that help assess the impact of our services, the National Center for Atmospheric Research (NCAR) Earth Observing Laboratory (EOL) has implemented the use of Digital Object Identifiers (DOIs) (DataCite 2017) for both physical objects (e.g., research platforms and instruments) and datasets. We discuss why this work is important and timely, and review the development of guidelines for the use of DOIs at EOL by focusing on how decisions were made. We discuss progress in assigning DOIs to physical objects and datasets, summarize plans to cite software, describe a current collaboration to develop community tools to display citations on websites, and touch on future plans to cite workflows that document dataset processing and quality control. Finally, we will review the status of efforts to engage our scientific community in the process of using DOIs in their research publications.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/DSJ-2017-007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 3

Abstract

In an effort to lead our community in following modern data citation practices by formally citing data used in published research and implementing standards to facilitate reproducible research results and data, while also producing meaningful metrics that help assess the impact of our services, the National Center for Atmospheric Research (NCAR) Earth Observing Laboratory (EOL) has implemented the use of Digital Object Identifiers (DOIs) (DataCite 2017) for both physical objects (e.g., research platforms and instruments) and datasets. We discuss why this work is important and timely, and review the development of guidelines for the use of DOIs at EOL by focusing on how decisions were made. We discuss progress in assigning DOIs to physical objects and datasets, summarize plans to cite software, describe a current collaboration to develop community tools to display citations on websites, and touch on future plans to cite workflows that document dataset processing and quality control. Finally, we will review the status of efforts to engage our scientific community in the process of using DOIs in their research publications.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
NCAR地球观测实验室实施数字对象标识符(DOI)的动机和策略——过去的进展和未来的合作
为了通过正式引用已发表研究中使用的数据和实施标准来引导我们的社区遵循现代数据引用实践,以促进可重复的研究结果和数据,同时也产生有助于评估我们服务影响的有意义的指标,国家大气研究中心(NCAR)地球观测实验室(EOL)已经实施了数字对象标识符(doi) (DataCite 2017)对物理对象(例如,研究平台和仪器)和数据集。我们讨论了为什么这项工作是重要和及时的,并通过重点关注决策是如何做出的,回顾了在EOL中使用doi的指导方针的发展。我们讨论了将doi分配给物理对象和数据集的进展,总结了引用软件的计划,描述了开发社区工具以在网站上显示引用的当前合作,并触及了引用文档数据集处理和质量控制的工作流的未来计划。最后,我们将回顾我国科学界在其研究出版物中使用doi过程中的努力现状。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Data Science Journal
Data Science Journal Computer Science-Computer Science (miscellaneous)
CiteScore
5.40
自引率
0.00%
发文量
17
审稿时长
10 weeks
期刊介绍: The Data Science Journal is a peer-reviewed electronic journal publishing papers on the management of data and databases in Science and Technology. Details can be found in the prospectus. The scope of the journal includes descriptions of data systems, their publication on the internet, applications and legal issues. All of the Sciences are covered, including the Physical Sciences, Engineering, the Geosciences and the Biosciences, along with Agriculture and the Medical Science. The journal publishes papers about data and data systems; it does not publish data or data compilations. However it may publish papers about methods of data compilation or analysis.
期刊最新文献
Data on the Margins – Data from LGBTIQ+ Populations in European Social Science Data Archives Insights on Sustainability of Earth Science Data Infrastructure Projects Using OpenBIS as Virtual Research Environment: An ELN-LIMS Open-Source Database Tool as a Framework within the CRC 1411 Design of Particulate Products Umbrella Data Management Plans to Integrate FAIR Data: Lessons From the ISIDORe and BY-COVID Consortia for Pandemic Preparedness The Launch of the <em>Data Science Journal</em>&nbsp;in 2002
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1