The journal Data Science is an interdisciplinary journal that aims to publish novel and effective methods on using scientific data in a principled, well-defined, and reproducible fashion, concrete tools that are based on these methods, and applications thereof. The ultimate goal is to unleash the power of scientific data to deepen our understanding of physical, biological, and digital systems, gain insight into human social and economic behavior, and design new solutions for the future. The rising importance of scientific data, both big and small, brings with it a wealth of challenges to combine structured, but often siloed data with messy, incomplete, and unstructured data from text, audio, visual content such as sensor and weblog data. New methods to extract, transport, pool, refine, store, analyze, and visualize data are needed to unleash their power while simultaneously making tools and workflows easier to use by the public at large. The journal invites contributions ranging from theoretical and foundational research, platforms, methods, applications, and tools in all areas. We welcome 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.
This journal focuses on methods, infrastructure, and applications around the following core topics:
- scientific data mining, machine learning, and Big Data analytics
- data management, network analysis, and scientific knowledge discovery
- scholarly communication and (semantic) publishing
- research data publication, indexing, quality, and discovery
- data wrangling, integration, provenance
- trend analysis, prediction, and visualization
- crowdsourcing and collaboration
- corroboration, validation, trust, and reproducibility
- scalable computing, analysis, and learning
- smart and semantic web services, executable workflows
- analytics, intelligence, and real time decision making
- socio-technical systems
- social impacts of data science
Semantic publishing has been defined as anything that enhances the meaning of a published journal article, facilitates its automated discovery, enables its linking to semantically related articles, provides access to data within the article in actionable form, or facilitates integration of data between papers. Towards the goal of genuine semantic publishing, where a work may be published with its content and metadata represented in a machine-interpretable semantic notation, this journal will work with a global set of partners to develop standardized methods to ensure that our publications can be seen as a machine-accessible store of knowledge.
An important goal of the journal is to promote an environment to produce and share annotated data to the wider research community. The development and use of data and metadata standards are critical for achieving this goal. Authors should ensure that any data used or produced in the study is represented with community-based data formats and metadata standards.
Rapid, Open, Transparent, and Attributed Reviews
The Data Science journal relies on an open and transparent review process. Submitted manuscripts are posted on the journal’s website and are publicly available. In addition to solicited reviews selected by members of the editorial board, public reviews and comments are welcome by any researcher and can be uploaded using the journal website. All reviews and responses from the authors are posted on the journal homepage. All involved reviewers and editors will be acknowledged in the final printed version. While we strongly encourage reviewers to participate in the open and transparent review process, it is still possible to submit anonymous reviews. Editors, non-anonymous reviewers will be included in all published articles. The journal will aim to complete reviews within 2-4 weeks of submission.
The journal will provide editor and reviewer profiles and metrics (links to ORCID, Google Scholar, etc.).
The journal will be open access.
Michel Dumontier, Maastricht University
Tobias Kuhn, VU University Amsterdam
Michael Krauthammer, Yale University
Frank van Harmelen, VU University Amsterdam
Steve Pettifer, Manchester
Mark Wilkinson, UPM Madrid
Tim Clark, Harvard
Jodi Schneider, University of Pittsburgh
Ruben Verborgh, Ghent University
Yolanda Gil, University of Southern California
Karin Verspoor, University of Melbourne
Michael Mäs, University of Groningen
Thomas Chadefaux, Trinity College Dublin
Olivia Woolley Meza, ETH Zurich
Izabela Moise, ETH Zurich
Evangelos Pournaras, ETH Zurich
Alison Callahan, Stanford
Victor de Boer, VU University Amsterdam
Christine Chichester, Nestle Institute of Health Sciences
Brian Davis, NUI Galway
Emilio Ferrara, University of Southern California
Pascale Gaudet, Swiss Institute of Bioinformatics
Rinke Hoekstra, VU University Amsterdam
Thomas Maillart, UC Berkeley
Richard Mann, Leeds University
James McCusker, RPI
Pablo Mendes, IBM
Silvio Peroni, Bologna
Núria Queralt Rosinach, Pompeu Fabra University
Olivier Gevaert, Stanford University
Oscar Corcho, Polytechnic University Madrid
Matjaz Perc, University of Maribor
Robert Hoehndorf, KAUST
Toshiaki Katayama, Database Center for Life Science
Manisha Desai, Stanford
We anticipate accepting submissions in late 2016. Please visit http://datasciencehub.net for more details.
Research (12 pages)
Reports on original research. Results previously published at conferences or workshops may be submitted as extended versions. These submissions will be reviewed along the usual dimensions for research contributions which include originality, significance of the results, and quality of writing.
Surveys (16 pages)
Solicited and unsolicited surveys of the state of the art of topics central to the journal’s scope. Survey articles should have the potential to become well-known introductory and overview texts. These submissions will be reviewed along the following dimensions: (1) Suitability as introductory text, targeted at researchers, PhD students, or practitioners, to get started on the covered topic. (2) How comprehensive and how balanced is the presentation and coverage. (3) Readability and clarity of the presentation. (4) Importance of the covered material to the broader Data Science community.
Reports (5 pages) - with the expectation of high impact
- software, web services, APIs
- datasets and databases
- visualization widgets
- meetings, hackathons, outreach activities
- book reviews