China Scientific Data
Jointly sponsored by the Computer Network Information Center of the Chinese Academy of Sciences, and the Committee on Data of the International Science Council, advised by National Science & Technology Infrastructure Center, and Office of CAS Leading Group for Cybersecurity and Informatization, China Scientific Data (CN11-6035/N,ISSN 2096-2223) is a bilingual open-access quarterly publishing data papers of multidisciplinary fields. It is indexed by Chinese Science Citation Database.
The journal is dedicated to promoting the sharing and citation of scientific data, to making them findable, accessible, interoperable and reusable..
Data papers describing (but not limited to) the following:
(1) Datasets or data products generated from major scientific activities;
(2) Refined datasets or data products processed from raw data;
(3) Datasets linked to already published articles;
China Scientific Data does not publish pure research articles, or articles that describe data-related techniques, methods and cases.
Why Publish in China Scientific Data
Open access
Articles and datesets published by China Scientific Data shall be reused under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Users have the right to read, download, copy, distribute, print, search, or link to any full texts or contents on this website.
Professional publishing
Both articles and datasets published by China Scientific Data shall undergo a rigorous review process to ensure data quality and reusability.
Efficient processing, high exposure, rapid dissemination and intelligent services
The publishing process shall be carried out transparently online through this platform. Datasets shall be stored in an accredited data repository (e.g., for open services. Datasets and their descriptors are linked by DOI and semantic web to provide further information about the usage of the data published.
Online phased peer review, driving high transparency
Sumissions are subject to peer review and crowd rating to drive a high transparency of the datasets under consideration, making them more accessible, intelligible and reusable.