Data Science

Data Science is an interdisciplinary journal that addresses the development that data has become a crucial factor for a large number and variety of scientific fields. This journal covers aspects around scientific data over the whole range from data creation, mining, discovery, curation, modeling, processing, and management to analysis, prediction, visualization, user interaction, communication, sharing, and re-use. We are interested in general methods and concepts, as well as specific tools, infrastructures, and applications. 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.

Core Topics

This journal focuses on methods, infrastructure, and applications around the following core topics:

scientific data mining, machine learning, and Big Data analytics
scientific data management, network analysis, and knowledge discovery
scholarly communication and (semantic) publishing
research data publication, indexing, quality, and discovery
data wrangling, integration, and provenance of scientific data
trend analysis, prediction, and visualization of research topics
crowdsourcing and collaboration in science
corroboration, validation, trust, and reproducibility of scientific results
scalable computing, analysis, and learning for data science
scientific web services and executable workflows
scientific analytics, intelligence, and real time decision making
socio-technical systems
social impacts of data science
Features

Open Access
The journal is open access and articles are published under the CC-BY license.

Speedy Reviewing
Data Science is committed to avoid wasting time during the reviewing period. Authors will receive the first decision within weeks rather than months. To achieve that, the journal asks reviewers to complete their reviews within 10 days.

Open and Attributed Reviews
Reviews are non-anonymous by default (but reviewers can request to stay anonymous). All reviews are made openly available under CC-BY licenses after a decision has been made for the submission (independent of whether the decision was accept or reject). In addition to solicited reviews, everybody is welcome to submit additional reviews and comments for papers that are under review. Editors and non-anonymous reviewers will be mentioned in the published articles.

Pre-Prints
All submitted papers are made available as pre-prints before the reviewing starts, so reviewers and everybody else are free to not only read but also share submitted papers. Pre-prints will remain available after reviewing, independent of whether the paper was accepted or rejected for publication.

Data Standards
Data Science wishes to promote an environment where annotated data is produced and shared with the wider research community. The journal therefore requires authors to ensure that any data used or produced in their study are represented with community-based data formats and metadata standards. These data should furthermore be made openly available and freely reusable, unless privacy concerns apply.

Semantic Publishing
Data Science encourages authors to provide (meta)data with formal semantics, as a step towards the vision of semantic publishing to integrate, combine, organize, and reuse scientific knowledge. Data Science plans to experiment with different such approaches, and we will announce more details soon.

HTML
The journal encourages authors to submit their papers in HTML (but accepts Word and LaTeX submissions too).

ORCID
Data Science is working with ORCID to collect iDs for all authors, co-authors, editorial board members, and reviewers and connect them to the information about your research activities stored in our systems.