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IEEE Data Descriptions is a journal that publishes short articles on all aspects of data: data descriptors, data collections, and metadata. Its overarching purpose is to promote publicly available datasets (open access or subscription-based access) in support of reproducible science while at the same time bringing insights into the associated dataset, data collection methods, and data quality. The metadata collected provides enhanced dataset discoverability and creates a foundation for future data science tools such as auto-discovery and mashups. Datasets described in IEEE Data Descriptions must be findable, accessible, interoperable, and reusable. The dataset needs to be of a quality high enough that other researchers can use it for their research experimentation and have some permanence. Articles describing datasets must be comprehensive and follow the outlined sections listed in Author Information. The preference is for data to be stored within IEEE DataPort, however, IEEE Data Descriptions accepts submissions where data is stored at other persistent/permanent locations (see a comprehensive list in Author Information).

Product No: PER373