Welcome to Data Intelligence!


The journal and innovation platform that publishes, connects, and analyzes data in an intelligent approach to realize their potentials, both for human and for machine! Welcome to Data Intelligence! 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.


  • Publishing papers specifically aimed at technologies and methodologies for data sharing, curation, etc.
  • Publishing papers that describe specific data- or metadata- repositories that are being maintained and shared.
  • Encouraging data sharing by systematically annotating data resources based on widely-adopted metadata standards.
  • Collecting and cataloguing various knowledge bases such as knowledge graph, ontology, linked dataset and corpus, etc. and publishing information about these.
  • Enabling automatic data annotation and semantification, and linking from newly imported data to the Data Intelligence repository.
  • Providing added value (in the form of data links, synthesized analytics) to articles and data shared in the Data Intelligence repository.
  • Promoting scientific activities that focus on creating new datasets.
  • Giving explicit credit to data creators and disseminating their contributions both in the journal and in wider social media application.
  • Facilitating connecting-dots to build and share real-time knowledge.
The journal is cataloging a number of different types of contributed content:


Data articles which describe an ontology, a knowledge graph, a vocabulary or thesaurus, a linked data set or a cluster of interoperable data sets and corresponding services, evaluation benchmarks or methods, APIs and software frameworks, workflows, crowdsourcing task designs, protocols and metrics. The contents should include the background of the work performed, the representation of standards used, information on how the datasets and services were built, descriptions of reliability, versioning, up time and sustainability and the application implications, disruptiveness and limitations as well. A full version of the data is encouraged to be stored in a sustainable, FAIR compliant repository or at minimum is to be linked to a journal or a third-party data repository to facilitate extended value through sharing, disseminating and reusing in other papers and applications as public domain resources.


Perspective or commentary articles which express new perspectives including outlook, challenge, and opportunities on a specific topic in the authors’ area of expertise of high interest to the Data Intelligence community/audience.


  • Research articles which present state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the topics on data generation, data analysis, data integration, data sharing, data management and related topics in the field of Data Intelligence.
  • Data application articles which report specific domain or cross domain applications based on data resources, repositories and data-enabled technologies.


Letters to the Editor (LTE) which are rapid communications to publish short articles with a high degree of novelty. We are eager to share our vision and open opportunities for researchers, scientists and industry leaders to publish, promote and share their work through Data Intelligence, and we hope you can join us, to help us fulfill this vision by submitting your various articles to the journal!

Please submit your papers to data@mail.las.ac.cn.