Principles

Scientific Data is an open-access, online-only journal for descriptions of scientifically valuable datasets. Our articles, known as Data Descriptors, combine traditional narrative content with curated, structured descriptions (metadata) of the published data to provide a new framework for data-sharing and -reuse that we believe will ultimately 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.

Scientific Data is founded on six key principles

  Credit

 

Scientists who share their data in a FAIR manner deserve appropriate credit and recognition. Publishing at Scientific Data:

  • Provides citable, peer-reviewed credit for dataset creation.
  • Grants recognition to researchers who may not qualify for authorship on traditional articles.
  • Allows publication of valuable datasets that may not be well-suited for traditional research journals.

   Reuse

 

 

Standardized and detailed descriptions make research data easier to find and reuse. Data Descriptors:

  • Provide the information needed to interpret, reuse and reproduce data – including standardized and curated experimental metadata.
  • Ensure linking to one or more trusted data repositories where data files, code and/or workflows are stored.
  • Fulfil a significant part of funders' data-management requirements, particularly by demonstrating and promoting the reuse potential of research data.

  Quality

 

 

If released data are to be truly reusable, critical evaluation is needed to verify experimental rigour and the completeness of their description.

  • Focused peer-review evaluates the technical quality and completeness of each Data Descriptor and associated datasets.
  • Standards are upheld by an academic Editorial Board of recognized experts from a broad range of fields.
  • Editors and referees ensure alignment with community standards.

Discovery

 

 

Scientists should be able to easily find datasets that are relevant to their research. Content at Scientific Data:

  • Is uniformly searchable and discoverable.
  • Provides validated links to both related journal articles and data-repository records.
  • Is friendly to data- and text-miners, in part by providing machine-readable metadata with all Data Descriptors.
  • Accelerates integrative analyses by helping authors find relevant datasets across a wide range of different data-types.

   Open

 

 

 

We believe scientists work best when they can easily connect and collaborate with their peers, so Scientific Data aims to:

  • Offer transparency in experimental methodology, observation and collection of data.
  • Use open licences that allow for modifications and derivative works.
  • Break down barriers to interdisciplinary research — facilitating understanding, connectivity and collaboration.
  • Ensure all interested parties — scientists, policy-makers, NGOs, companies, funders and the public — can find, access, understand and reuse the data they need.

  Service

 

 

 

Scientific Data is committed to providing excellent service to both authors and readers.

  • Professional in-house curation ensures standardized and uniformly discoverable content.
  • Authors can deposit datasets in figshare or Dryad during submission, ensuring that datasets can be rapidly peer-reviewed, even when repositories do not exist for the authors’ specific data-type(s).
  • The technology and experience of the Nature Publishing Group provides powerful searching of, linking to and visualization of content.
  • Fast review turnaround and rapid publication of Data Descriptors ensures that authors are able to publish their data in a timely manner.

 

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