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About

Focus and Scope

The Data Science Journal (DSJ) was founded in 2002 to help build knowledge and understanding of how data practices can advance research and human knowledge.

Recent decades have seen an unprecedented explosion in the human capacity to acquire, store, and manipulate data and information. We are experiencing an historic revolution in knowledge creation, communication, and utilisation. DSJ explores what that means for the conduct of research and data management. We regard data science as the ‘science of data’ — the evidence-based study of the socio-technical developments and transformations that affect science policy; the conduct and methods of research; and the data systems, standards, and infrastructure that are integral to research. 

A central goal of DSJ is to ensure that data are used in the most effective, efficient, and ethical way in promoting knowledge and learning. It is a peer-reviewed, open-access, electronic journal that is relevant to all sciences as well as the empirical humanities. The scope of the journal includes descriptions of data systems; their implementations, applications, infrastructures, software, legal, reproducibility, and transparency issues; the availability and usability of complex datasets; and with a particular focus on the principles, policies, and practices for data preservation and reuse.

Our focus is on data, notably research data. DSJ articles should faithfully represent data as an important topic in the research, discussion, or analysis — how data is part of a problem, solution or both. Related, cross-cutting topics may also be relevant. Machine learning, cybersecurity, differential privacy, and analytics-based optimization are within scope of DSJ, provided that the focus is on how data act as the input fuel for the algorithms, the test sets for the algorithms, the validation sets for the algorithms (performance metrics), and the real-world outputs (classifications, diagnoses, decisions, predictions, optimizations) from the algorithms. Similarly, the DSJ deems data-intensive and/or data-related scientific computing, which of course is a natural component of simulation, modeling, and visualization, as being in scope, if the focus is on data aspects of the issue.

Also in scope is Data Science Education content (e.g., research and insights into Data Dexterity and Analytics Training in different disciplines).

For all of these topics, an attempt to generalise their significance should be included in the submission.  They should not just present a solution or an enquiry into a unitary problem but make an effort to demonstrate wider significance and application and say something more about the ‘science of data’ more generally.

All data are in scope, whether born digital or converted from other sources, and all research disciplines are covered. The science of data is a cross-domain, cross-discipline topic, with common issues, regardless of the domain the data themselves serve.

The Data Science Journal publishes a variety of article types (research articles, practice papers, review articles, and essays).  The Data Science Journal also publishes data articles, describing datasets or data compilations, data and metadata specifications and ontologies, if the potential for reuse of the data, or the specification or ontology, is significant or if considerable efforts were required in compilation. Similarly, the Data Science Journal also publishes descriptions of online simulations, databases, and experiments, partnering with digital repositories on ‘meta articles’ that link to and allow visualisation of the data, thereby adding an entirely new dimension to the communication and exchange of data research results and educational materials.

Types of article:

Research articles must describe the outcomes and application of unpublished original research. These should make a substantial contribution to knowledge and understanding in the subject matter and should be supported by relevant figures and tabulated data. Research articles should be no more than 8,000 words in length. Data and software supporting the research should be formally cited and available through a trusted repository.

Practice papers should report upon or critique a specific "happening" such as a release of a major study or other notable occurrence related to the journal focus. Authors interested in submitting a practice paper should discuss the content with the editor before submitting a manuscript. Practice papers should describe the finished outputs of a project, or the procedures in operation in an established data system. Practice papers should be no longer than 3,000 words in length.

Data articles should present data of interest to the community, giving details of the how, when and why the dataset was collected in a structured and standardized way (taking into account discipline-based standards and processes). Authors should provide a detailed summary of the protocol followed to collect the data, along with any processing required to establish replicability. The data itself should not be part of the article; instead persistent, unique links should link the data (which is held in a trusted repository) to the article. Data articles should be no longer than 3,000 words in length.

Review articles can cover topics such as current controversies, the current “state of the art” or the historical development of studies as well as issues of regional or temporal focus. Papers should critically engage with the relevant body of extant literature. Review articles should be no longer than 3,000 words in length.

Essays cover topics and controversies of interest to the community and aim to stimulate discussion and debate. Essays may be provocative and less focused on reporting original research work but should still consist of original thoughts and ideas. Essays should be no longer than 3,000 words in length.


Publication Frequency

The journal is published online as a continuous volume and issue throughout the year. Articles are made available as soon as they are ready to ensure that there are no unnecessary delays in getting content  publically available.

Special collections of articles are welcomed and will be published as part of the normal issue, but also within a separate collection page.


Open Access Policy

This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. There is no embargo on the journal’s publications. Submission and acceptance dates, along with publication dates, are made available on the PDF format for each paper.

Authors of articles published remain the copyright holders and grant third parties the right to use, reproduce, and share the article according to the Creative Commons license agreement.

Authors are encouraged to publish their data in recommended repositories. For a list of generic and subject specific repositories that meet our peer review criteria, see here.


Archiving Policy

The journal’s publisher, Ubiquity Press, focuses on making content discoverable and accessible through indexing services. Content is also archived around the world to ensure long-term availability.

Ubiquity Press journals are indexed by the following services:

CrossRef, JISC KB+, SHERPA RoMEO, Directory of Open Access Journals (DOAJ), EBSCOHost, Scopus and Google Scholar. In addition, all journals are available for harvesting via OAI-PMH.

To ensure permanency of all publications, this journal also utilises CLOCKSS, and LOCKSS archiving systems to create permanent archives for the purposes of preservation and restoration.

If the journal is not indexed by your preferred service, please let us know by emailing support@ubiquitypress.com or alternatively by making an indexing request directly with the service.


Publication Ethics

Data Science Journal welcomes suggestions for special issues or conference proceedings focusing on specific topics within the journal's scope. For more information about publishing a special issue with us, please contact the Editor-in-Chief, Mark Parsons. 

Ubiquity Press, the journal’s publisher, is a member of the Committee on Publication Ethics  (COPE), the Open Access Scholarly Publishers Association (OASPA), and the Association of Learned and Professional Society Publishers (ALPSP). The Press recognises its responsibility as a guardian of the scholarly record and takes an active role in establishing standards and policies in publication ethics.

The Editors have committed to maintaining high editorial standards through rigorous peer review and strict ethical policies. The Editors follow the COPE code of conduct and refer to COPE for guidance as appropriate. The journal and the publisher ensure that advertising and commercial interests do not impact or influence editorial decisions.


Special Issues

Data Science Journal welcomes suggestions for special issues or conference proceedings focusing on specific topics within the journal's scope. For more information about publishing a special issue with us, please contact the Editor-in-Chief, Mark Parsons.

 

Annotation and post-publication comment

The journal platform permits readers to leave comments on the publication page, via the Disqus service. Readers will need a Disqus account to leave comments. Comments may be moderated by the journal, however, if they are non-offensive and relevant to the publication subject, comments will remain online without edit.

The journal platform also includes in-browser annotation and text highlighting options on full text formats via hypothes.is. Readers will require a hypothes.is account to create annotations, and will have the option to make these publicly available, available to a group, or private.


Advertisement Policy

The journal only displays advertisements that are of relevance to its scope and will be of interest to the readership (e.g. upcoming conferences). All advertising space is provided free of charge and the editor and publisher have the right to decline or withdraw adverts at any point. Adverts will include a text heading to make it clear that they are adverts not related to the journal.

If you wish to propose a potential advert then please contact the editorial team. All advert images will have to be provided to the publisher.


Sponsors

History

Data Science Journal was transferred to Ubiquity Press in 2015.

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