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

University Of Kansas Center For Research Inc, Lawrence KS

Investigators

Abstract

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.