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

该项目将数据引用方面的一组专家召集到一个研讨会，该研讨会包括数据文档计划（DDI）和元数据标准方面的研究人员和专家。DDI标准是社会科学的首要元数据标准。随着社会科学和其他学科之间的界限越来越模糊，生物医学数据也在研究范围之内。DDI的生命周期分支（DDI- l）允许记录从初始概念和研究开发到存档和重用的数据。DDI标准在过去的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，将利用这个已经被广泛采用的标准，作为开发衡量数据生产和重用的指标的基础。DDI-L已经为研究过程中的各种活动（例如，数据收集和抽样）、实体（例如，资助组织和档案）和工具（例如，软件和仪器）建立了结构。该项目的重点是确保DDI集成必要的信息元素，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- DDI4 -这是基于模型和模块化的。该研讨会将为DDI4的开发提供支持，因为健壮的引用功能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，用于引用所有级别的数据，并为所有数据集贡献者提供归属。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被采用，记录数据将越来越被视为一项带有奖励的活动，而不仅仅是一项繁重的开销。反过来，这将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.