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Scope
The Data Science section of Frontiers in Big Data publishes high-quality research findings in theories, methods, systems, tools, infrastructure, or industrial/domain-specific applications across the field of data science. Areas covered by this section include, but are not limited to:
• Modeling, acquisition, cleaning, integration, processing, management, and visualization of small/large data of diverse modalities including records, documents, time-series, graphs, networks, images, audios, videos, or web
• Big data and distributed algorithms and concepts
• Advanced theories and applications of data analytics and exploratory analysis
• Statistics and machine learning, inference, and optimization and related data science issues
• Trustworthiness, robustness, interpretability, causality, explainability, fairness, security, and privacy aspects of data science
• Comparison, critique, evaluation, survey, and interpretation of data science methods, systems, tools, and infrastructure
• Challenges, experiences, lessons from industrial or domain-specific applications of data science
• Modeling, detecting, and mitigating bias in data
• Ethics and reproducibility of data science research
Indexed in: CLOCKSS, CrossRef, Digital Biography & Library Project (dblp), DOAJ, Google Scholar, OpenAIRE, PubMed Central (PMC), Scopus, Web of Science Emerging Sources Citation Index (ESCI)
PMCID: NA
Data Science welcomes submissions of the following article types: Brief Research Report, Community Case Study, Conceptual Analysis, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Policy and Practice Reviews, Review, Study Protocol, Systematic Review and Technology and Code.
All manuscripts must be submitted directly to the section Data Science, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
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