Collaborative Research: Comprehensive Citation Across the Data Life Cycle Using DDI

该项目将一组数据引用方面的专家召集到一个有数据文件倡议和元数据标准方面的研究人员和专家参加的讲习班。DDI标准是社会科学的首要元数据标准。生物医学数据也在范围之内，因为社会科学和其他学科之间的界限正在模糊。DDI（DDI-L）的生命周期分支允许记录从初始概念和研究开发到存档和重复使用的数据。DDI标准在过去18年中不断完善。将引文信息集成到整个生命周期中唯一可识别的信息元素中，将利用这个已经广泛采用的标准作为开发衡量数据生产和重用的指标的基础。DDI-L已经有了各种活动的结构（例如，数据收集和采样），实体（例如，资助组织和档案），和工具（例如，软件和工具）在研究过程中。该项目的重点是确保DDI整合必要的信息要素，以支持收集所需的引文元数据。DDI联盟目前正在开发其下一代规范-DDI 4-这是基于模型和模块化。该研讨会将为DDI 4的开发提供信息，因为强大的引用能力是设计目标的一部分。但更重要的是，研讨会将产生一套最佳实践，用于引用各级数据并向所有数据集贡献者提供属性。这些最佳实践可以应用于其他学科的其他元数据标准。将引文信息集成到现有的生命周期元数据框架中将为数据集、工具和程序及其创建者的影响度量提供基础。它还应该作为在正常研究工作流程中收集这些信息的工具的基础。随着这些指标在晋升和任期决策中的采用，记录数据将越来越多地被视为一项有回报的活动，而不仅仅是负担沉重的开销。这反过来又会导致更好的文档和更可重用、更高质量的数据。

This project brings a group of experts in data citation into a workshop involving researchers and experts in Data Documentation Initiative (DDI) and metadata standards. The DDI standard is the premier metadata standard for the social sciences. Biomedical data are also in scope, as the lines between the social sciences and other disciplines are blurring. The lifecycle branch of DDI (DDI-L) allows documentation of data from initial concept and study development to archiving and reuse. The DDI standard has been refined over the course of the last 18 years. Integration of citation information into uniquely identifiable information elements across the whole lifecycle would leverage this already well-adopted standard as a foundation for developing metrics that measure production and reuse of data. DDI-L already has structures for various activities (e.g., data collection and sampling), entities (e.g., funding organizations and archives), and tools (e.g., software and instruments) within the research process. The focus of this project is to ensure that DDI integrates the necessary information elements to support the collection of desirable citation metadata. The DDI Alliance is currently developing its next-generation specification - DDI4 - which is model-based and modular. This workshop will feed into the DDI4 development, as robust citation capabilities are part of the design goals. But more importantly, the workshop will produce a set of best practices for citing data at all levels and providing attribution to all dataset contributors. These best practices could be applied to other metadata standards in other disciplines.Integrating citation information into an existing lifecycle metadata framework will supply the foundation for metrics of impact of datasets, instruments and procedures, as well as for their creators. It should also serve as the foundation for tools to collect this information during the normal research workflow. As these metrics become adopted in promotion and tenure decisions, documenting data will increasingly be seen as an activity carrying a reward, rather than as just burdensome overhead. This, in turn, should lead to better documentation and more reusable, better quality data.