Dear Lars,
It is my pleasure to introduce you to IEEE Data Descriptions (IEEE-DATA), a gold, fully open access periodical published on behalf of all IEEE Societies and Technical Councils. This journal specializes in peer-reviewed, concise articles detailing datasets across various domains, promoting open and reproducible scientific research.
You are invited to be among the early contributors to have your article peer-reviewed and published in the journal where your research will be exposed to nearly 8 million unique monthly users of the IEEE Xplore® Digital Library. The rapid peer-reviewed process targets a publication time frame of less than 10 weeks for most accepted papers.
The journal’s primary aim is to enhance dataset visibility and utility through comprehensive descriptions, which include data collection methods, data quality assessments, and detailed metadata. To ensure the datasets are broadly usable, they must be findable, accessible, interoperable, and reusable, with a strong preference for storage on IEEE DataPort or other permanent repositories.
There are three types of articles that can be submitted for review: descriptors, collections, and metadata.
- Descriptor articles (the main type of article published) describe the published (or soon-to-be-published) dataset. In essence, this is a detailed manual on the dataset — what it is and how to use it.
- Collections articles follow the lead and expectations of Descriptor articles and will focus on groups of datasets collected during or created from a competition, hackathon, or event.
- Meta articles depart from but follow the lead and expectations of Descriptor articles. Meta articles are articles that allow for discussion about data. Authors can express their findings about data in several areas, including, but not limited to: standardized formats for data in a given area (e.g., collecting power data from sub-stations), surveys of several datasets of a given type (e.g., GIS, time-series, etc.), data that are a library of models for a given AI or machine learning algorithm or system, data or dataset that is metadata of existing published datasets, comparing many similar datasets in an area to create meta-statistics data; or, any other data topics that may not have traditional scientific experimental results.
Visit our website for more information and instructions on how to submit your manuscript today.
Best regards,
Stephen Makonin
IEEE Data Descriptions, Editor-in-Chief
