Frontiers in Big Data publishes rigorously peer-reviewed research on the cutting-edge, disruptive technological revolution of big data. The journal publishes a broad range of topics in big data, data-driven sciences, and their various applications and forms. As a key interdisciplinary open-access journal, we are at the forefront of disseminating scientific knowledge and impactful discoveries to academics, policy-makers, industry, and the public worldwide.
Current Specialty Sections include:
1) Medicine and Public Health, led by Chief Editor Thomas Hartung (Johns Hopkins University)
2) Big Data Networks, led by Chief Editor Nick Duffield (Texas A&M)
3) Cybersecurity and Privacy, led by Chief Editors Elena Ferrari (University of Insubria) and Murat Kantarcioglu (UT Dallas)
4) Data Mining and Management, led by Chief Editor Huan Liu (Arizona State University)
5) Data-driven Climate Sciences, led by Chief Editor John Kimball (University of Montana)
6) Machine Learning and Artificial Intelligence, led by Chief Editor Kristian Kersting (TU Darmstadt)
Frontiers welcomes research articles on these themes and beyond. There are a number of ways you can contribute to the journal:
· Propose a Research Topic
· Submit a manuscript to the journal
· Apply to join the Editorial Board of a section as Associate or Review Editor
· Recommend a new Specialty Section for inclusion in the journal
Short Name: Front. Big Data
Electronic ISSN: 2624-909X
Indexed in: Google Scholar, CrossRef, CLOCKSS
Frontiers in Big Data is composed of the following Specialty Sections:
The specialty sections of Frontiers in Big Data welcome submission of the following article types: Book Review, Code, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Policy and Practice Reviews, Review, Specialty Grand Challenge, Technology Report, Clinical Study Protocol and Clinical Trial.
When submitting a manuscript to Frontiers in Big Data, authors must submit the material directly to one of the specialty sections. Manuscripts are peer-reviewed by the Associate and Review Editors of the respective specialty section.
Articles published in the specialty sections above will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in the Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Big Data.
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