期刊档案
Annals of Data Science
— · ISSN 2198-5804 · Decision Sciences-Statistics, Probability and Uncertainty
数据可追溯 · letpub-v6 · 更新于 2026-08-26期刊简介
研究范围与定位
Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
结构化分区
学科分区明细
不同版本、大类与小类分别展示，不将不同评价口径合并为一个分区值。
《新锐期刊分区表》（ 2026年3月发布）
2026-03 · 未被该版本收录
该期刊未被此版本分区表收录。
期刊分区表（ 2025年3月升级版）
2025-03 · 未被该版本收录
该期刊未被此版本分区表收录。
期刊分区表（ 2023年12月旧的升级版）
2023-12 · 未被该版本收录
该期刊未被此版本分区表收录。
期刊档案
出版与身份
- 期刊ISSN
- 2198-5804
- E-ISSN
- 2198-5812
- 是否OA开放访问
- No
- 出版商
- Springer Nature
- 出版周期
- 6 issues per year
- 出版年份
- 0
期刊档案
研究范围
- 期刊简介
- Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
- 涉及的研究方向
- Decision Sciences-Statistics, Probability and Uncertainty
期刊档案
指标与活跃度
- 2025-2026最新IF（数据来源于网友提供）
- 注册或登录后，查看IF
- 2025-2026自引率
- N.A.点击查看自引率趋势图
- 五年IF（数据来源于网友提供）
- 0
- h-index
- 暂无h-index数据
- CiteScore （ 2026年6月最新版）
- CiteScoreSJRSNIPCiteScore排名11.400.8662.041学科分区排名百分位大类：Decision Sciences小类：Statistics, Probability and UncertaintyQ17 / 178 96% 大类：Decision Sciences小类：Business, Management and Accounting (miscellaneous)Q113 / 235 94% 大类：Decision Sciences小类：Computer Science ApplicationsQ1115 / 1022 88% 大类：Decision Sciences小类：Artificial IntelligenceQ177 / 570 86%
- 年文章数
- 0点击查看年文章数趋势图
- Gold OA文章占比
- 0.00%
- 研究类文章占比：文章 ÷（文章 + 综述）
- 0.00%
期刊档案
分区、收录与风险
- WOS期刊JCR分区 （ 2025-2026年最新版）
- 注册或登录后，查看WOS分区等级
- 期刊分区表预警名单
- 2026年03月发布的新锐学术版：不在预警名单中2025年03月发布的2025版：不在预警名单中2024年02月发布的2024版：不在预警名单中2023年01月发布的2023版：不在预警名单中2021年12月发布的2021版：不在预警名单中2020年12月发布的2020版：不在预警名单中
- SCI期刊收录coverage
- Scopus (CiteScore)
- PubMed Central (PMC)链接
- 访问官方页面 ↗
期刊档案
投稿与评审
来源与统计口径
来源：LetPub 原始详情页 ↗。投稿经验仅展示聚合统计，不公开第三方正文或用户标识。当前聚合样本：暂无。