《数据智能（英文）》（Data Intelligence）作者须知
INSTRUCTIONS FOR AUTHORS
This “Instruction for Authors” describes the general information about Data Intelligence including aims and scope, main areas, and specific topics Data Intelligence shall cover, and how to prepare a manuscript for submission. We highly recommend you read this in full and have your contribution consistent with Data Intelligence publication guidelines, including format and style.
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.
The objectives of this journal are:
* 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 final goal of this journal is to build a research culture that is creating new datasets which are scientifically reward-worthy and sharing data is necessary to ensure the transparency and reproducibility in science.
Types of Articles
Data Intelligence primarily publishes the following different kinds of full-length articles:
- Data Articles
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.
Essential sections:
Introduction, Value of the data, Acquirements of the data, Application and Limitation of the Data.
Along with essential sections, articles must include general background of the data acquirements and application, a brief summary of related research (literature review), theories and methodologies that contribute to the author’s approach, specific methods on how the data was acquired, major value and significance, possible application and limitation data collection. The following types of articles fall under the “data articles” category:
A KOS paper including an ontology, a specific metadata and its standards, which describes thoroughly a KOS
A linked dataset descriptor •
A corpus descriptor
- Perspective or Commentary Articles
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.
Note: The Perspectives to be published by Data Intelligence are at the invitation of the Co-Editors-in-Chief and other Editorial staff. Unsolicited Perspectives will not be considered.
- Research Articles
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 Data application articles which report specific domain or cross domain applications based on data resources, repositories and data-enabled technologies.
Essential sections: Introduction, Methodology, Results, Discussion and Conclusion
Essential elements: context of application, system environment, key points, implementation challenges or problems during application and how to solve them, operational framework, application effects and experiences, etc.
- Letters to the Editor
Letters to the Editor (LTE) which are rapid communications to publish short articles with a high degree of novelty.
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更多详情：
http://www.data-intelligence.org/p/static/1095/
