![]() ![]() Authors may not submit elsewhere while the manuscript is under consideration at Scientific Data. Submission to Scientific Data is taken to imply that the submitted manuscript has not been published elsewhere. An acknowledgements section is optional.Īll article types are peer-reviewed. They are required to include a competing interests statement. ![]() They may not include supplementary information, and generally do not have separate abstracts or author contributions statement. Comments should begin with a bolded “standfirst” of one or two sentences that reads smoothly with the rest of the piece. They may include one figure, table or box, and up to 25 references, however these formats are flexible. Comments are generally 1-5 pages in length. ‘ Comment’ is a flexible format used to publish short commentaries or opinions on research data policy, workflows or infrastructure that don't need to report a specific technology or finding. The Methods should be followed by References, Acknowledgements and a Competing interests statement. Guidance on writing a data availability statement can be found here. All Analysis and Article submissions should include code and data availability statements. Common topics include data policies, repositories, standards, ontologies, workflows or another other areas related to the mechanics or culture of data sharing.Īnalyses and Articles should include an Introduction followed by sections headed Results, Discussion, Methods. The ‘ Article’ format can be used to present original reports on systems and techniques related to data sharing and reproducible research. Analysis submissions should exemplify reproducible research by clearly describing all steps in the analysis, providing supporting source code, and explaining how and where others may access all data underlying the analysis. If the analysis relies on data that were not previously published, the authors may be invited to submit a Data Descriptor. Analysis submissions are not required to use data previously published at Scientific Data.
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