III: Medium: Collaborative Research: Citing Structured and Evolving Data

III：媒介：协作研究：引用结构化和不断变化的数据

基本信息

批准号：
1302212
负责人：
Susan Davidson
金额：
$ 84.91万
依托单位：
University of Pennsylvania
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2013
资助国家：
美国
起止时间：
2013-08-01 至 2018-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1302212&HistoricalAwards=false
关键词：
III Medium Collaborative Research Citing

项目摘要

Citation is an essential part of scientific publishing and, more generally, of scholarship. It is used to gauge the trust placed in published information and, for better or for worse, is an important factor in judging academic reputation. Now that so much scientific publishing involves data and takes place through a database rather than conventional journals, how is some part of a database to be cited? More generally, how should data stored in a repository that has complex internal structure and that is subject to change be cited?The goal of this research is to develop a framework for data citation which takes into account the increasingly large number of possible citations; the need for citations to be both human and machine readable; and the need for citations to conform to various specifications and standards. A basic assumption is that citations must be generated, on the fly, from the database. The framework is validated by a prototype system in which citations conforming to pre-specified standards are automatically generated from the data, and tested on operational databases of pharmacological and Earth science data. The broader impact of this research is on scientists who publish their findings in organized data collections or databases; data centers that publish and preserve data; businesses and government agencies that provide on-line reference works; and on various organizations who formulate data citation principles. The research also tackles the issue of how to enrich linked data so that it can be properly cited.

引文是科学出版的重要组成部分，更普遍地是奖学金。它用于评估在已发表信息中放置的信任，无论好坏，它是判断学术声誉的重要因素。现在，如此多的科学发布涉及数据并通过数据库而不是传统期刊进行，如何引用数据库的某些部分？更一般而言，如何存储在具有复杂内部结构的存储库中，并可能引用更改？该研究的目的是为数据引用开发一个框架，该框架考虑到越来越多的可能引用的框架；需要引用是人类和机器可读的；并需要引用符合各种规格和标准。一个基本的假设是，必须从数据库中即时生成引用。该框架由原型系统验证，在该原型系统中，从数据中自动生成了符合预先指定标准的引用，并在药理学和地球科学数据的操作数据库中进行了测试。这项研究的更广泛影响是对有组织的数据收集或数据库中发表发现的科学家；发布和保留数据的数据中心；提供在线参考工作的企业和政府机构；以及制定数据引用原则的各种组织。该研究还解决了如何丰富链接数据的问题，以便可以正确引用它。