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. Technical Validation (unlimited length)Īn ‘ Analysis’ paper re-analyses or re-assesess existing open data, rather than sharing a new dataset. This format may be used to highlight examples of data reuse or present new findings and conclusions derived from published data.Background & Summary (unlimited length).Title (recommended length 110 characters or fewer).The main elements of a Data Descriptor manuscript are: Please see our submission guidelines to learn how to draft and format your Data Descriptor, and to download a manuscript template. Scientific Data publishes descriptions of datasets under its primary article-type, the Data Descriptor. Please see information on our current APC rates and licensing options, as well as our free open access funding support service. To publish in Scientific Data authors are required to pay an article-processing charge (APC). Scientific Data is an open-access publication. Please see our policies on complementary and prior publication. Data Descriptors that describe previously published datasets should provide new content sufficient to merit further publication: for example, updates to important datasets, fuller release of a dataset, or additional information that aids reuse. The actual data are stored in one or more public, community-recognized repositories, and release of the data is verified as a condition of publication.ĭata Descriptors may describe data from new or published studies, and can be published alongside traditional research works. Scientific Data uses a thorough peer-review process that evaluates the rigour and quality of the experiments used to generate the data and the completeness of the description of the data. Scientific Data also welcomes submissions describing analyses or meta-analyses of existing data, and original articles on systems, technologies and techniques that advance data sharing and reuse to support reproducible research. Data Descriptors focus on helping others reuse data, rather than testing hypotheses, or presenting new interpretations, methods or in-depth analyses. These provide detailed descriptions of research datasets, including the methods used to collect the data and technical analyses supporting the quality of the measurements. Scientific Data primarily publishes Data Descriptors. It aims to advance the sharing and reuse of scientific data, promote wider data sharing and reuse, and to credit those that share.įind out more about the key principles that drive Scientific Data 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.
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