About

Aims and scope

The 21st century is currently witnessing the establishment of data-driven science as a complementary approach to the traditional hypothesis-driven method. This (r)evolution accompanying the paradigm shift from reductionism to complex systems sciences has already largely transformed the natural sciences and is about to bring the same changes to the techno-socio-economic sciences, viewed broadly.

EPJ Data Science offers a publication platform to address this evolution by bringing together all academic disciplines concerned with the same challenges:

    • how to extract meaningful data from systems with ever increasing complexity
    • how to analyse them in a way that allows new insights
    • how to generate data that is needed but not yet available
    • how to find new empirical laws, or more fundamental theories, concerning how any natural or artificial (complex) systems work

This is accomplished through experiments and simulations, by data mining or by enriching data in a novel way. The focus of this journal is on conceptually new scientific methods for analyzing and synthesizing massive data sets, and on fresh ideas to link these insights to theory building and corresponding computer simulations. As such, articles mainly applying classical statistics tools to data sets or with a focus on programming and related software issues are outside the scope of this journal.

EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.

Why publish your article in EPJ Data Science?

High visibility

EPJ Data Science's open access policy allows maximum visibility of articles published in the journal as they are available to a wide, global audience. 

Speed of publication

EPJ Data Science offers a fast publication schedule whilst maintaining rigorous peer review; all articles must be submitted online, and peer review is managed fully electronically (articles are distributed in PDF form, which is automatically generated from the submitted files). Articles will be published with their final citation after acceptance, in both fully browsable web form, and as a formatted PDF; the article will then be available through EPJ Data Science and SpringerOpen.

Flexibility

Online publication in EPJ Data Science gives you the opportunity to publish large datasets, large numbers of color illustrations and moving pictures, to display data in a form that can be read directly by other software packages so as to allow readers to manipulate the data for themselves, and to create all relevant links (for example, to PubMed, to sequence and other databases, and to other articles).

Promotion and press coverage

Articles published in EPJ Data Science are included in article alerts and regular email updates. 
In addition, articles published in EPJ Data Science may be promoted by press releases to the general or scientific press. These activities increase the exposure and number of accesses for articles published in EPJ Data Science

Copyright

Authors of articles published in EPJ Data Science retain the copyright of their articles and are free to reproduce and disseminate their work (for further details, see the copyright and license agreement).

For further information about the advantages of publishing in a journal from SpringerOpen, please click here.

Open access

All articles published by EPJ Data Science are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. Further information about open access can be found here.

As authors of articles published in EPJ Data Science you are the copyright holders of your article and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate your article, according to the SpringerOpen copyright and license agreement.

For those of you who are US government employees or are prevented from being copyright holders for similar reasons, SpringerOpen can accommodate non-standard copyright lines. Please contact us if further information is needed.

Article-processing charges

Open access publishing is not without costs. EPJ Data Science therefore levies an article-processing charge of £785.00/$1230.00/€1000.00 for each article accepted for publication.

If the corresponding author's institution participates in our open access membership program, some or all of the publication cost may be covered (more details available on the membership page). We routinely waive charges for authors from low-income countries. For other countries, article-processing charge waivers or discounts are granted on a case-by-case basis to authors with insufficient funds. Authors can request a waiver or discount during the submission process. For further details, see our article-processing charge page.

SpringerOpen provides a free open access funding support service to help authors discover and apply for article processing charge funding. Visit our OA funding and policy support page to view our list of research funders and institutions that provide funding for APCs, and to learn more about our email support service. 

Indexing services

The full text of all articles is deposited in digital archives around the world to guarantee long-term digital preservation. You can also access all articles published by SpringerOpen on SpringerLink

EPJ Data Science is indexed in the following services:

  • Science Citation Index Expanded (SciSearch)
  • Journal Citation Reports/Science Edition
  • Social Science Citation Index
  • Journal Citation Reports/Social Sciences Edition
  • Google Scholar
  • Current Contents/Engineering, Computing and Technology
  • DOAJ
  • OCLC
  • Summon by ProQuest

Peer-review policy

Peer-review is the system used to assess the quality of a manuscript before it is published. Independent researchers in the relevant research area assess submitted manuscripts for originality, validity and significance to help editors determine whether the manuscript should be published in their journal. You can read more about the peer-review process here.

EPJ Data Science operates a single-blind peer-review system, where the reviewers are aware of the names and affiliations of the authors, but the reviewer reports provided to authors are anonymous Publication of research articles by EPJ Data Science is dependent primarily on their scientific validity and coherence as judged by our external expert editors and/or peer reviewers, who will also assess whether the writing is comprehensible and whether the work represents a useful contribution to the field.

Submitted manuscripts will generally be reviewed by two to three experts who will be asked to evaluate whether the manuscript is scientifically sound and coherent, whether it duplicates already published work, and whether or not the manuscript is sufficiently clear for publication. Reviewers will also be asked to indicate how interesting and significant the research is. The Editors will reach a decision based on these reports and, where necessary, they will consult with members of the Editorial Board.

Editorial policies

All manuscripts submitted to EPJ Data Science should adhere to SpringerOpen's editorial policies.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Citing articles in EPJ Data Science

Articles in EPJ Data Science  should be cited in the same way as articles in a traditional journal. Because articles are not printed, they do not have page numbers; instead, they are given a unique article number.

Article citations follow this format:

Authors: Title. EPJ Data Sci [year], [volume number]:[article number].

e.g. Roberts LD, Hassall DG, Winegar DA, Haselden JN, Nicholls AW, Griffin JL: Increased hepatic oxidative metabolism distinguishes the action of Peroxisome Proliferator-Activated Receptor delta from Peroxisome Proliferator-Activated Receptor gamma in the Ob/Ob mouse. EPJ Data Sci 2009, 1:115.

refers to article 115 from Volume 1 of the journal.

Appeals and complaints

If you wish to appeal a rejection or make a complaint you should, in the first instance, contact the Editor who will provide details of the journal's complaints procedure.