IOS Press Publishes Inaugural Issue of Open Access journal Data Science

Amsterdam, NL – IOS Press is proud to announce 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 exciting new journal: “In the last decades, scientific data has grown dramatically in both size and importance. Data science is therefore not a new science discipline, but rather a new pair of glasses – a new paradigm – to look at problems and questions in the existing disciplines with the new possibilities of data analytics in mind. It also stands for the development that data, when properly linked, transcend disciplines and can enable new sorts of interdisciplinary research fields and even breed entirely new areas. With this journal, called Data Science, we intend to give this type of research the focus and attention we think it deserves.”

Data Science has a number of distinctive features to maximize the transparency, speed, and quality with which results are published and made available for current and future reuse and interpretation:

  • Open access journal – Increasing the visibility and enabling 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.
  • Minimal reviewing timeData Science 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 – Increasing the visibility and recognition of reviewers and promoting accountability in the reviewing process. Moreover, submissions will be publicly available as preprints right away.
  • FAIR (findable, accessible, interoperable, and reusable) data – Requiring 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.
  • Semantic 9ublishing – 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, organization, 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 here:

Table of Contents Volume 1, Issue 1-2 (2017):
Data Science – Methods, infrastructure, and applications
Dumontier, Michel | Kuhn, Tobias
DOI: 10.3233/DS-170013

Position Papers:
Conflict forecasting and its limits
Chadefaux, Thomas
DOI: 10.3233/DS-170002

Knowledge-based biomedical Data Science
Hunter, Lawrence E.
DOI: 10.3233/DS-170001

Data Science and symbolic AI: Synergies, challenges and opportunities
Hoehndorf, Robert | Queralt-Rosinach, Núria
DOI: 10.3233/DS-170004

The knowledge graph as the default data model for learning on heterogeneous knowledge
Wilcke, Xander | Bloem, Peter | de Boer, Victor
DOI: 10.3233/DS-170007

Stream reasoning: A survey and outlook
Dell’Aglio, Daniele | Della Valle, Emanuele | van Harmelen, Frank | Bernstein, Abraham
DOI: 10.3233/DS-170006

Maintaining intellectual diversity in data science
Mann, Richard P. | Woolley-Meza, Olivia
DOI: 10.3233/DS-170003

The integration of the data scientist into the team: Implications and challenges
Desai, Manisha
DOI: 10.3233/DS-170008

Cross-disciplinary higher education of data science – beyond the computer science student
Pournaras, Evangelos
DOI: 10.3233/DS-170005

Thoughtful artificial intelligence: Forging a new partnership for data science and scientific discovery
Gil, Yolanda
DOI: 10.3233/DS-170011

Valorizing omics visualization for discovery
Chichester, Christine
DOI: 10.3233/DS-170009

Genuine semantic publishing
Kuhn, Tobias | Dumontier, Michel
DOI: 10.3233/DS-170010

Automating semantic publishing
Peroni, Silvio
DOI: 10.3233/DS-170012