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

引文是科学出版的重要组成部分，更广泛地说，是学术研究的重要组成部分。 它被用来衡量人们对已发表信息的信任程度，无论好坏，它都是判断学术声誉的一个重要因素。既然如此多的科学出版涉及数据，并且通过数据库而不是传统期刊进行，那么数据库的某些部分如何被引用？更一般地说，应该如何引用存储在具有复杂内部结构且可能发生变化的存储库中的数据？本研究的目标是开发一个数据引用框架，该框架考虑到越来越多的可能引用;需要引用是人类和机器可读的;以及需要引用符合各种规范和标准。一个基本的假设是，引文必须从数据库中动态生成。 该框架是验证的原型系统中，符合预先规定的标准的引文自动生成的数据，并在药理学和地球科学数据的业务数据库进行测试。 这项研究的更广泛影响是在有组织的数据收集或数据库中发表研究结果的科学家;发布和保存数据的数据中心;提供在线参考作品的企业和政府机构;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关联数据的问题，以便可以正确引用。

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.