Annals of Data Science
Springer Science and Business Media Deutschland GmbH Β· Germany Β· Est. 2014
Aims & Scope
Annals of Data Science is a scholarly journal focusing on Big Data analytics and applications. Publishes a broad range of research findings, experimental results, and case studies in data science. Promotes interdisciplinary techniques, including statistics, artificial intelligence, and optimization for Big Data processing and data mining. Encourages application of knowledge derived from Big Data in real-life scenarios such as finance, healthcare, climate changes, etc. Focuses on heterogeneous data analysis, data modeling, and data mining
General Information
Submission Info
Ethics & Quality
Think.Check.Submit Compliance
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Source: OpenAlex Β· Each yearβs green bar = citations earned by that yearβs papers, counted to date β so recent years look lower simply because their papers havenβt had time to be cited yet.
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category β its quartile can differ by discipline.
Subject Classification
Scopus Categories
Research Topics (OpenAlex)
Frequently asked questions about Annals of Data Science
Is Annals of Data Science a predatory journal?
PubScope has no integrity flags on record for Annals of Data Science: it is indexed in Scopus, and is not on DOAJ's withdrawn list. Its PubScope Trust Score is 78/100. Indexing is a transparency signal, not a guarantee β always confirm fit and policies before submitting.
What is the impact factor of Annals of Data Science?
Annals of Data Science is not in the Web of Science Core Collection, so it has no official Clarivate Journal Impact Factor. Its SCImago SJR score is 0.866.
Is Annals of Data Science indexed in Scopus and Web of Science?
Annals of Data Science is indexed in Scopus.
What is the aims and scope of Annals of Data Science?
Annals of Data Science is a scholarly journal focusing on Big Data analytics and applications. Publishes a broad range of research findings, experimental results, and case studies in data science. Promotes interdisciplinary techniques, including statistics, artificial intelligence, and optimization for Big Data processing and data mining. Encourages application of knowledge derived from Big Data in real-life scenarios such as finance, healthcare, climate changes, etc. Focuses on heterogeneous data analysis, data modeling, and data mining
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See all βData updated: 2026-05-22 Β· Sources: SJR, DOAJ, OpenAlex, WoS, Crossref