Data Intelligence

Aims & Scope

Data Intelligence, co-sponsored by the National Science Library, Chinese Academy of Sciences and China National Publications Import & Export (Group) Corporation, is a peer-reviewed metadata centric academic journal that is targeted at data creators, data curators, data stewards, data policy makers, domain scientists and others interested in sharing data.

The point of the publication is to include, but not limited to, articles discussing methodologies and/or data resources.

The aim is to provide a vehicle to assist industry leaders, researchers and scientists in the sharing and reuse each other's data, metadata, knowledge bases, and data visualization tools.

The journal will publish not only traditional articles, but also "data articles" with the contents in the form of knowledge graphs, ontologies, linked datasets and metadata resources.

Data Intelligence aspires to introduce developing and emerging data-enabled technologies that will allow and facilitate the work of scientists to more deeply understand and extend the potential of their data.

The journal maintains an academic center, a key educational channel, to offer intelligent data services and support for both machine and human to read and reuse data.

View Aims & Scope

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Metrics & Ranking

SJR (SCImago Journal Rank)

Year	Value
2024	0.410

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	25

Journal Rank

Year	Value
2024	13552

Journal Citation Indicator

Year	Value
2024	359

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Social Sciences, designed to support cutting-edge academic discovery.

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Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

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APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Computer Science and Social Sciences, ensuring accessibility and quality in research dissemination.

This journal does not charge a mandatory Article Processing Charge (APC). However, optional open access publication may incur fees based on the publisher’s policies.

Explore journals without APCs for alternative publishing options.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Exploring the Ethical Implications of AI-Powered Personalization in Digital Marketing

  Citation: 5

  Authors:

• A Proposal for a FAIR Management of 3D Data in Cultural Heritage: The Aldrovandi Digital Twin Case

  Citation: 4

  Authors: Sebastian, Alice, Bianca, Ivan, Arcangelo, Arianna, Silvio, Giulia

• Privacy Preserving Publish/Subscribe for Geo-Textual Data Streams

  Citation: 3

  Authors: Ya, Qiuyuan, Jie

• Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution

  Citation: 3

  Authors: Absalom E., Yuh-Shan, Ojonukpe S., Olufisayo S., Annette, Apu K., Jayanta

• Large Knowledge Model: Perspectives and Challenges

  Citation: 3

  Authors: Huajun

• TibetanQA2.0: Dataset with Unanswerable Questions for Tibetan Machine Reading Comprehension

  Citation: 2

  Authors: Zhengcuo, Yuan

• Dr.ICL: Demonstration-Retrieved In-context Learning

  Citation: 2

  Authors: Man, Xin, Zhuyun, Panupong, Mehran, Chitta, Vaiva, Vincent Y

• Identifier Service in the Mindat Database: Persistent and Structured Access to Massive Records of Minerals and Other Natural Materials

  Citation: 2

  Authors: Jolyon, Pavel, Xiaogang, David, Wenjia, Jingyi, Joshua, Lucia, Anirudh, Shaunna, Xiang, Jiyin

• A Benchmark Dataset with Larger Context for Non-Factoid Question-Answering over Islamic Text

  Citation: 2

  Authors: Faiza, Seemab, Nor Shahida Mohd, Rabia

• MMAF: Masked Multi-modal Attention Fusion to Reduce Bias of Visual Features for Named Entity Recognition

  Citation: 2

  Authors: Jinhui, Xinyun, Xiaoyao, Zixuan, Taisheng

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