Data and code sharing policy
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.
Many of 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. FAIR principles for Research Software have been set out, see FAIR4RS. The principles are: F: Software, and its associated metadata, is easy for both humans and machines to find. A: Software, and its metadata, is retrievable via standardized protocols. I: Software interoperates with other software by exchanging data and/or metadata, and/or through interaction via application programming interfaces (APIs), described through standards. R: Software is both usable (can be executed) and reusable (can be understood, modified, built upon, or incorporated into other software).
Authors are encouraged to upload supplemental datasets 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 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 in your article, see below.
Data uploaded in an external repository are under the scientific responsibility of the authors.
Authors are encouraged to make the code related to their research publicly available in order to make the research methodology explicit and allow for the replication of processes in subsequent exploratory research. For example GitHub is a widely used platform to host open code. For your repository to truly be open source, you will need to use an open source license (e.g. Creative Commons, GNU) so that others are free to use, change, and distribute the software.
If your article refers to code uploaded in a repository, please add a reference to this code in the reference list of your article, and include a Data Availability Statement in your article (see below). Please make sure that you set your repository to “public”.
Open source code uploaded in an external repository is under the scientific responsibility of the authors.
Data Availability Statement
If your article refers to data or code uploaded in a public repository, it is mandatory include a section titled Data Availability Statement in your article.
In other cases, we encourage you to include a Data Availability Statement in your article, to inform readers in a structured way about the availability of data relevant to the research published in your article.
The Data Availability Statement should be included at the end of your article, before the References. Examples of data availability statements can include:
- Data Availability Statement
- The research data/code associated with this article are available in [Name of public data repository], under the reference [DOI or other data identifier]
- Data/code are available on request from the authors
- The research data associated with this article are included within the article
- No new data/code were created or analyzed in this study
Format of data or code citation
If your article refers to supplementary data or code uploaded in a repository, it is mandatory to add a reference to this dataset or code in the reference list of your article. Please follow the standard reference format for data or code, for example:
M. Breden, C. Chainais-Hillairet and A. Zurek, Matlab code for “Existence of traveling wave solutions for the Diffusion Poisson Coupled Model: a computer-assisted proof” (2021). https://github.com/MaximeBreden/DPCM.