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Our commitment to making scientific data open and accessible includes implementing accepted standards for data publication.  F1000Research is very pleased to endorse the recently finalised FORCE11 data citation principles, many of which we have already implemented:

1. Importance. F1000Research gives authors the option to publish data articles, which means that generating and sharing data can more easily feed into the current academic promotional framework.  This helps to equate data citation with other citable products of research.
2. Credit and Attribution. Data articles allow scientists to be credited appropriately for generating data, thus contributing to principle 2 by facilitating scholarly credit for data generation.  It is hoped that this will in turn support the cultural change needed to recognise data generation and sharing as a legitimate scholarly activity.
   F1000Research is working to ensure that datasets are identified in a machine-readable way, to ensure that they can be indexed and so enable appropriate credit and attribution of published datasets for their authors.
3. Evidence.  A condition of publication in F1000Research from the start has been the requirement to submit all data supporting each article regardless of article type.
4. Unique identification. F1000Research uses an appropriate unique identifier for each data type, e.g. UniProt IDs for protein sequences. Digital Object Identifiers (DOIs) are used for datasets where no standard identification system exists.  We only use unstructured research data repositories (such as figshare, Zenodo etc) that have been approved to mint DOIs.  Additionally F1000Research has been recognised by UK DataCite as meeting the requirements needed to mint our own data DOIs, as required.
5. Access. All the data associated with F1000Research publications is openly accessible.  The only exception is where there are security or privacy issues, in which case the data citation must link to a landing page providing clear conditions of access.
6. Persistence. F1000Research has chosen to use the DOI system for data citation, as this is an internationally recognised standard.  DOI persistence is assured by the International DOI Foundation.
7. Specificity and Verifiability. F1000Research is working to provide appropriately granular data citations to subsets of data within each article.  This means that each dataset within an article will be independently citable.  New versions of a dataset are assigned distinct DOIs, allowing the provenance of each dataset to be tracked.
8. Interoperability and Flexibility. F1000Research is working closely with researchers, data repositories, publishers and other relevant bodies to establish sufficiently flexible and interoperable standards for the citation of different types of research data.