Aims and scope
Big Data Analytics is a multi-disciplinary open access, peer-reviewed journal, which welcomes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of big data science analytics. Spanning the life sciences, social sciences, engineering, physical and mathematical sciences, Big Data Analytics aims to provide a platform for the dissemination of research, current practices, and future trends in the emerging discipline of big data analytics.
Big Data Analytics invites high-quality original research articles and timely reviews on current developments in the field, covering all aspects of big data analytics, including, but not limited to the following topics:
- algorithmic, theoretical and computational approaches such as deep learning networks, nature-inspired and brain-inspired cognitive computation, statistical and mathematical analytics, visualization and informatics.
- implementations and platforms such as neuromorphic, GPUs, clusters and clouds, and open-source software.
- applications in domains as diverse as genomics, medicine, healthcare, clinical, biological and neuro-informatics, natural robotics, language processing, meteorology, geoscience, multimedia and business intelligence, social media and network analytics, trend discovery, opinion mining, smart cities, surveillance, transportation, power, energy and economic management, internet search, biological, chemical, physical, environmental, oceanic and planetary sciences, and e-Science in general.
All articles published by Big Data Analytics 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 Big Data Analytics 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 BioMed Central license agreement.
For those of you who are US government employees or are prevented from being copyright holders for similar reasons, BioMed Central can accommodate non-standard copyright lines. Please contact us if further information is needed.
Open access publishing is not without costs. Big Data Analytics therefore levies an article-processing charge of £1370.00/$2145.00/€1745.00 for each article accepted for publication, plus VAT or local taxes where applicable.
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.
BMC 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.
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 BioMed Central on SpringerLink.
We are working closely with relevant indexing services including PubMed Central and Web of Science (Clarivate Analytics) to ensure that articles published in Big Data Analytics will be available in their databases when appropriate.
Big Data Analytics 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.
The benefit of single-blind peer review is that it is the traditional model of peer review that many reviewers are comfortable with, and it facilitates a dispassionate critique of a manuscript.
The Editor-in-Chief will manage the editorial and refereeing procedure in which at least two reviewers will assess the manuscripts and suggest decisions. The Editor-in-Chief will be responsible for the final decision about acceptance or rejection of the manuscript.
Big Data Analytics considers the following types of article: Research, Commentary, Meeting report, Methodology, Review, and Software. For more specific information, please take a look at our Submission Guidelines.
All manuscripts submitted to Big Data Analytics should adhere to BioMed Central's editorial policies.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Citing articles in Big Data Analytics
Articles in Big Data Analytics 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. Big Data Anal [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. Big Data Anal 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. For complaints that cannot be resolved with the Editor, the authors should contact the Publisher.
Why publish your article in Big Data Analytics
Big Data Analytics'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
Big Data Analytics 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.
Online publication in Big Data Analytics 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 Big Data Analytics are included in article alerts and regular email updates. Some may be highlighted on Big Data Analytics’s pages and on the BioMed Central homepage.
In addition, articles published in Big Data Analytics 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 Big Data Analytics. A list of articles recently press-released by journals published by BioMed Central is available here.
As an author of an article published in Big Data Analytics you retain the copyright of your article and you are free to reproduce and disseminate your work (for further details, see the BioMed Central license agreement).
For further information about the advantages of publishing in a journal from BioMed Central, please click here.
2017 Journal Metrics
52 days from submission to first decision
128 days from submission to acceptance
45 days from acceptance to publication
17 Altmetric mentions