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
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.