FAIR data principles
This journal supports FAIR data principles: data relevant to research published in an article should be Findable, Accessible, Interoperable, and Re-usable (see https://www.force11.org/group/fairgroup/fairprinciples).
According to FAIR principles, datasets should be Findable through a complete set of metadata, including a license for re-use and a data identifier (DOI or other). The dataset is Accessible when access is open. Interoperable means that the data can be used and combined with other datasets in a format that is sufficiently widely distributed. Re-usability is achieved when the dataset is deposited with a corresponding Creative Commons open license and is downloadable. Further, re-usability includes that parameters how this dataset has been collected and machine and experimental conditions are documented.
The FAIR data principles can be applied to research software by treating software and data as similar digital research objects, with some specific characteristics for software.
Authors are encouraged to upload supplemental datasets and code related to their research to an appropriate public data repository, under a Creative Commons (CC) license. This makes the data available for both human and machine reading in order to further aid the acceleration of scientific discovery. Data repositories generate a unique and persistent data identifier such as a digital object identifier (DOI), making the dataset citable independently of the article. This ensures that authors get credit for their data.
Data repositories allow most file formats, and large datasets. A list of available data repositories is available at https://www.re3data.org/. If you do not have a preferred repository, we recommend the generalist repositories Zenodo or Figshare.
When uploading your dataset or code to a repository, please ensure that you set it to “public” so that the data can be consulted during the peer review process and is available to all after publication. If you do not wish to make your data public during the peer review process, you may restrict access to the data and provide the link to the dataset with your submission for the attention of the reviewers. In this case please ensure that you set your data to “public” at the time your article is accepted, so that the data is available to all after publication.
If your article refers to data uploaded in a repository, please add a reference to this dataset in the reference list of your article, and include a Data Availability Statement and a Data Citation in your article, see below.
Data and code uploaded in a public repository are under the scientific responsibility of the authors.
Data Availability Statement
Please include a section called Data Availability in your article to inform readers in a structured way about the availability of data or code relevant to the research published in your article. The Data Availability Statement should be included at the end of your article, before the References. See for example.
Examples of data availability statements:
- The research data associated with this article are available in [Name of public data repository], under the reference [DOI or other data identifier]
- The research data are available on request from the authors
- The research data associated with this article are included within the article
- No new data were created or analyzed in this study
If your article refers to data or code uploaded in a public repository, please ensure the dataset or code is cited throughout the article wherever appropriate, using the DOI or persistent identifier.
Please include a reference to the dataset in the Reference list. Please follow the standard reference format for data or code, for example:
- Drewniak D, Buehler G, Karl P, et al. (2022). Factorial Survey: Decision Making for Extracorporeal life support (ECLS) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6805813
- Burn E (2020). A rapid network study on Extracorporeal membrane oxygenation (ECMO) to inform its use in the management of COVID-19 [Code]. Github. https://github.com/edward-burn/CharacterisationAndPlpECMO