Scientific Data is an open access journal dedicated to data, publishing descriptions of research datasets and articles on research data sharing from all areas of natural sciences, medicine, engineering and social sciences.

We believe that:  

1. Open research data sharing is crucial for facilitating scientific advancement. 
2. Articles describing how to source and use data can greatly facilitate data discovery and reuse, underpinning the aims of the FAIR principles, across any subject discipline.
3. If data are scientifically valid and of potential interest to someone it should be described and made available to the research community. Manuscripts must make an original contribution but they are not assessed based on their perceived significance, importance or impact. This is especially true for Data Descriptors (see below), which do not present hypotheses or conclusions.

 

Article types

Scientific Data publishes the following article types:

Data Descriptors: describe open research datasets in a manner that promotes reuse, without reporting whether datasets support hypotheses or conclusions. They contain details of how datasets were created (Methods), what they contain (Data Records), and how they were checked and validated (Technical Validation), alongside any code used to create them. 

Articles: cover data policy, repositories, standards, ontologies, workflows, or any topic relating to the mechanics of data sharing within public data repositories. Please note that Scientific Data does not publish traditional research articles using data to validate regular scientific hypotheses.

Articles may also present shorter commentaries or opinions on research data policy, workflows or infrastructure that can be more speculative in nature, without needing to report a specific technology or finding. These works used to be considered under a separate 'Comment' format, however we now consider these under a single 'Article' type.

All manuscripts are sent for peer review. 

Acceptance criteria

Scientific Data requires the data collection and processing methods for Data Descriptors to be correct and appropriate for the data they describe and the data to be shared openly with a data standard/format, level of rawness, and in a repository that meets expected community standards. The percieved impact of the data are not assessed as long as the data would be deemed useful to at least one other research group and all data types (subject areas) are considered. Articles may be related to previous results papers as long as the majority of the data being shared was not disclosed in the Data Availabilty statement of another work. 

Articles and should present new findings or ideas on the subject of research data, should be technically sound, but should not be assessed with respect to the impactfulness of their results.

All manuscripts that meet our technical requirements are sent for review be external peer reviewers, whom we ask to ask the technical soundness of the findings. All papers deemed technically sound are accepted. 

Scientific Data is a peer-reviewed open-access journal for descriptions of datasets and research that advances the sharing and reuse of research data. Our primary content-type, the Data Descriptor, combines traditional narrative content with structured descriptions of data to provide a framework for data-sharing to accelerate the pace of scientific discovery. These principles are designed to align with and support the FAIR Principles for scientific data management and stewardship, which declare that research data should be Findable, Accessible, Interoperable and Reusable.

Policies and guidelines

Please see our submission guidelines and policy pages for all our requirements. 

Costs

Scientific Data is an open-access publication. To publish in Scientific Data authors are required to pay an article-processing charge (APC). Please see information on our current APC rates and licensing options, as well as our free open access funding support service.

Principles

 

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