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arXiv 1706.07976cs.DL
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数据引用的理论与实践

Theory and Practice of Data Citation

Gianmaria Silvello

摘要:

引文是知识传播的基石,也是评估研究质量和指导科学投资的主要手段。科学越来越成为“数据密集型”,收集和分析大量数据,通过模拟和实验发现复杂的模式,大多数科学参考作品已被在线策划的数据集所取代。然而,给定一个数据集,没有定量的、一致的和既定的方法来了解它随着时间的推移是如何被使用的,谁对它的管理做出了贡献,产生了什么结果,或者它有什么价值。数据引用的理论和实践的发展是将数据视为与传统科学产品具有相同相关性和中心性的一流研究对象的基础。近年来,许多著作从不同的角度讨论了数据引用:说明为什么需要数据引用,定义数据引用系统的原则和概述建议,并提供解决数据引用特定问题的计算方法。目前的全景是多方面的,仍然缺乏一个将这一专题的各个方面结合在一起的总体看法。因此,本文旨在从理论(为什么和什么)和实践(如何)的角度描述数据引用的土地。

英文摘要:

Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive", where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated datasets. Yet, given a dataset, there is no quantitative, consistent and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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