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 behaviour, 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
- 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
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.
Yolanda Gil, University of Southern California
Please visit http://datasciencehub.net for details.
Guidelines for Authors
Authors should closely follow the guidelines below before submitting a manuscript.
All papers have to be written in English.
The following length limits apply for the different paper types:
- Research papers: 12 000 words
- Position papers: 8 000 words
- Survey papers: 16 000 words
- Reports: 5 000 words
Note that these word counts are not targets but maximum values. Papers may be significantly shorter. Exceptions for longer papers are possible if well justified (contact the editors-in-chief before submitting papers that exceed the stated word limits).
These word counts include the abstract, tables, and figure and table captions. Author lists and references, however, are not counted. Each figure counts for an additional 300 words.
Papers in HTML
We encourage authors to submit their papers in HTML. There are various tools and templates for that, such as RASH, dokieli, and Authorea.
The Research Articles in Simplified HTML (RASH) (doc, paper) is a markup language that restricts the use of HTML elements to only 32 elements for writing academic research articles. It is possible to includes also RDFa annotations within any element of the language and other RDF statements in Turtle, JSON-LD and RDF/XML format by using the appropriate tag script. Authors can start from this generic template, which can be also found in the convenient ZIP archiveZIP archive containing the whole RASH package. Alternatively, these guidelines for OpenOffice and Word explain how to write a scholarly paper by using the basic features available in OpenOffice Writer and Microsoft Word, in a way that it can be converted into RASH by means of the RASH Online Conversion Service (ROCS) (src, paper).
As a second alternative, dokieli is a client-side editor for decentralized article publishing in HTML+RDFa, annotations and social interactions, compliant with the Linked Research initiative. There are a variety of examples in the wild, including the LNCS and ACM author guidelines as templates.
Papers in Word or LaTeX
We prefer HTML, but we also accept submissions in Word or LaTeX. In that case, please use the official templates by IOS Press.
This is optional, but we would like to encourage you to provide semantic (meta-)data with your scientific papers, but unfortunately no accepted standards, best practices, or nice tools exist for that yet. We are working to fix this. In the meantime, and if you are a bit experienced with RDF, we are very happy to receive your RDFa-enriched paper or a submission with separate RDF statements. We are also happy to help you with that, if you are not experienced with RDF.
We hope to be able to provide more general and more user-friendly guidelines for semantic publishing in the near future.
All relevant data that were used or produced for conducting the work presented in a paper must be made FAIR and compliant with the PLOS data availability guidelines prior to submission. See in particular the list of recommended data repositories. (We might provide our own data availability guidelines in the future, but we borrow the excellent PLOS guidelines for now.) In a nutshell, data have to be made openly accessible and freely reusable via established institutions and standards, unless privacy concerns forbid such a publication. In any case, metadata have to be made publicly accessible and visible.
See the reviewing guidelines below for the specific criteria according to which submitted papers are evaluated.
Guidelines for Reviewers
In order to facilitate a speedy reviewing process, reviewers are requested to submit their assessment within 10 days. Reviews consist of the parts described below.
The review of a paper should suggest one of the following overall recommendations:
- Accept. The article is accepted as is, or only minor problems must be addressed by the authors that do not require another round of reviewing but can be verified by the editorial and publication team.
- Undecided. Authors must revise their manuscript to address specific concerns before a final decision is reached. A revised manuscript will be subject to second round of peer review in which the decision will be either Accept or Reject and no further review will be performed.
- Reject. The work cannot be published based on the lack of interest, lack of novelty, insufficient conceptual advance or major technical and/or interpretational problems.
The review should evaluate the paper with respect to the following criteria.
- Does the work address an important problem within the research fields covered by the journal?
- Is the work appropriately based on and connected to the relevant related work?
- For research papers: Does the work provide new insights or new methods of a substantial kind?
- For position papers: Does the work provide a novel and potentially disruptive view on the given topic?
- For survey papers: Does the work provide an overview that is unique in its scope or structure for the given topic?
- For research papers: Are the methods adequate for the addressed problem, are they correctly and thoroughly applied, and are their results interpreted in a sound manner?
- For position papers: Is the advocated position supported by sound and thorough arguments?
- For survey papers: Is the topic covered in a comprehensive and well balanced manner, are the covered approaches accurately described and compared, and are they placed in a convincing common framework?
- Are the text, figures, and tables of the work accessible, pleasant to read, clearly structured, and free of major errors in grammar or style?
- Is the length of the manuscript appropriate for what it presents?
- Are all used and produced data are openly available in established data repositories, as mandated by FAIR and the data availability guidelines?
Summary and Comments
- Summary of paper in a few sentences
- Reasons to accept
- Reasons to reject
- Further comments (optional)