文章基本信息

标题：Formalizing an Attribution Framework for Scientific Data/Software Products and Collections
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作者：Chung-Yi Hou ; Matthew Mayernik
期刊名称：International Journal of Digital Curation
印刷版ISSN：1746-8256
出版年度：2016
卷号：11
期号：2
页码：87-103
DOI：10.2218/ijdc.v11i2.404
语种：English
出版社：University of Edinburgh
其他摘要：As scientific research and development become more collaborative, the diversity of skills and expertise involved in producing scientific data are expanding as well. Since recognition of contribution has significant academic and professional impact for participants in scientific projects, it is important to integrate attribution and acknowledgement of scientific contributions into the research and data lifecycle. However, defining and clarifying contributions and the relationship of specific individuals and organizations can be challenging, especially when balancing the needs and interests of diverse partners. Designing an implementation method for attributing scientific contributions within complex projects that can allow ease of use and integration with existing documentation formats is another crucial consideration. To provide a versatile mechanism for organizing, documenting, and storing contributions to different types of scientific projects and their related products, an attribution and acknowledgement matrix and XML schema have been created as part of the Attribution and Acknowledgement Content Framework (AACF). Leveraging the taxonomies of contribution roles and types that have been developed and published previously, the authors consolidated 16 contribution types that could be considered and used when accrediting team member’s contributions. Using these contribution types, specific information regarding the contributing organizations and individuals can be documented using the AACF. This paper provides the background and motivations for creating the current version of the AACF Matrix and Schema, followed by demonstrations of the process and the results of using the Matrix and the Schema to record the contribution information of different sample datasets. The paper concludes by highlighting the key feedback and features to be examined in order to improve the next revisions of the Matrix and the Schema.