STM publisher IOS Press has announced the publication of the first issue of Data Science, a new interdisciplinary peer-reviewed Open Access journal covering a broad range of aspects around Data Science, aiming to unleash the field's full potential.
The journal's Editors-in-Chief are Michel Dumontier (Maastricht University) and Tobias Kuhn (VU Amsterdam). In the inaugural issue's introduction they explain the motivation for starting this new journal.
Data Science has a number of distinctive features to maximise the transparency, speed, and quality with which results are published and made available for current and future reuse and interpretation.
Being an Open Access journal, Data Science seeks to increase the visibility and enables simple access and use of the reported results. Article processing charges will be waived for the first year and charges thereafter will be reasonable and competitive. The journal gives reviewers only ten days to respond and aims for sending out first decisions on submissions within weeks rather than months. Open and attributable reviews increase the visibility and recognition of reviewers, thereby promoting accountability in the reviewing process. Moreover, submissions will be publicly available as preprints right away.
Additionally, FAIR (Findable, Accessible, Interoperable, and Reusable) data requires authors to represent and provide any data used or produced in their studies with community-based data formats and metadata standards. These data should furthermore be made openly available free of charge, unless privacy or other well founded concerns apply. Authors are encouraged to write their papers in HTML and to provide (meta) data with formal semantics, as a step towards the vision of semantic publishing, which will allow for, to a certain extent, automatic integration, combination, organisation, and reusable scientific knowledge.
The journal welcomes papers which add a social, geographical, and temporal dimension to Data Science research, as well as application-oriented papers that prepare and use data in discovery research. The first issue is openly accessible at https://content.iospress.com/journals/data-science/1/1-2.
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