Open Data, Software and Code Guidelines

If you cannot share your data openly, for example to protect the privacy of your research participants, you may choose to use a repository which restricts or controls who can access your data and for what purposes.

To allow the maximum possible reuse, your dataset(s) should be published with a CC0 Public Domain Dedication, which does not retain any rights to the data. Alternatively, a CC-BY 4.0 Creative Commons Attribution Only license, which requires others to attribute you when using the data, is acceptable. Your chosen repository should allow you to apply a CC0 Public Domain Dedication, CC-BY 4.0 license or equivalent to your data.

For software and source code, we strongly advise you to use an OSI-approved license.

Persistent identifiers allow datasets to be uniquely identified on the web. Commonly used persistent identifiers include DOIs and accession numbers, but other persistent identifiers such as PURLs, ARKs, Handles or URNs are also acceptable. Your chosen data repository should provide you with a persistent identifier for each dataset that you deposit.

We also recommend that you use an appropriate Research Resource Identifier (RRID) to unambiguously identify any antibodies, model organisms, cell lines, plasmids, or other tools (software, databases, services) which you used in your research. RRIDs can be found on the Resource Identification Portal and should be included in your Methods section.

You must include a data availability statement to the end of your article, before the reference list, describing each dataset and including a link to the relevant repository and the dataset’s persistent identifier.

When drafting the statement, please include:

• The name of the repository used;
• A brief description of the contents of each dataset;
• A statement that the dataset has a CC0 Public Domain Dedication or CC-BY 4.0 license applied.

If your data must be restricted for legal, ethical, or other reasons, please see below for further information on what should be included in your data availability statement.

Examples:

Your dataset should be cited in the body of your article, and you should add the dataset to your reference list as you would any other bibliographic citation.

You may use your preferred referencing style but should include, at a minimum:

Dataset creator; Publication year; Dataset title; Name of repository where the data is located; Persistent Identifier (e.g. DOI).

Please add [Dataset] to the reference to denote its type.

It is your responsibility to share data ethically and, where relevant, protect the privacy of your research participants. You should ensure that your datasets have been de-identified in accordance with the Safe Harbor method before submission.

Data sensitivity is not only connected to human research participants, so please check your datasets for other sensitive elements, for example the locations of endangered species or protected archaeological sites.

All articles should include details of any software and code that are required to view the datasets described or to replicate the analysis.

For software

For all software used, please state the version, details of where the software can be accessed, and any variable parameters that could impact the outcome of the results. If you have coded software in-house, the source code should be written in (or be compatible with) an Open Source programming language, and should be archived under an open license and shared. For code stored in GitHub, you should create a ‘public registration’ for your project to obtain a DOI.

Information about software should be included in a software availability statement, which you can add to the end of your article, before the references list.

When drafting the statement, please include:

• Software available from: URL for the website where software can be downloaded from, if applicable.
• Source code available from: URL for versioning control system (for example GitHub).
• Archived source code at time of publication: DOI and citation for project in Zenodo (please select the appropriate DOI for the version which underlies your article).
• License: Must be an open license and preferably an OSI-approved license.

Where third-party proprietary software has been used, a non-proprietary, Open Source alternative software should be suggested by the author to allow for the replication of the analysis or research by all readers. We recognize that there may be cases where this may not be feasible. Please see the limited exceptions to these guidelines for more information.

If there are ethical or privacy considerations as to why the source code may not be made available, please contact the editorial team.

For analysis code

If you have created custom analysis code, this should be archived under an open license and shared. For analysis code stored in GitHub, you should create a ‘public registration’ for your project to obtain a DOI. We recommend using an OSI-approved license, but CC-BY 4.0 is also acceptable.

Information about your archived analysis code should be included in your data availability statement, which you can add to the end of your article, before the references list.

When drafting the statement, please include, under the heading “Extended Data”:

• Analysis code available from: URL for versioning control system (for example GitHub)
• Archived analysis code as at time of publication: DOI and citation, e.g. from Zenodo (please select the appropriate DOI for the version which underlies your article).
• License: Must be an open license and preferably an OSI-approved license or CC-BY 4.0.

Code and software should be cited in the body of your article, be added to your reference list as you would any other bibliographic citation.

You may use your preferred referencing style but should include, at a minimum:

Creator(s); Publication year; Title; Publication venue; Publication date; Persistent Identifier (e.g. DOI); Version.

Please add either [Software] or [Code] as part of the reference to denote its type

The FAIR Data Principles: F1000Research endorses the FAIR Data Principles as a framework to promote the broadest reuse of research data. Datasets which are “FAIR” are Findable, Accessible, Interoperable and Reusable. More information on the FAIR Data Principles and how you can align your data sharing methods with them is available here.

Relevant data sharing standards: Data standards help you to align with commonly used data sharing practices in your field, for example how your data should be structured, formatted and annotated. Please check FAIRSharing.org for details of data standards specific to the topic of your research.

Some data repositories provide functionality which allows you to add links to any published articles associated with your dataset. If possible, we recommend that you update your metadata record in the data repository to include a link to your published article. You can link to the article using your article DOI, which will be emailed to you when your article is published.

Ethical or security considerations

If data access is restricted for ethical or security reasons, please use your data availability statement to include a description of the restrictions on the data and all necessary information required for a reader or reviewer to apply for access to the data and the conditions under which access will be granted.

Data protection and participant privacy

Where human data cannot be sufficiently de-identified to protect participant privacy, we recommend depositing the data into a controlled access repository, if your ethical approval and participant consent permits you to do so.

If you cannot share the data in a repository, please include in your data availability statement: an explanation of the data protection concern; what, if anything, the relevant Institutional Review Board (IRB) or equivalent said about data sharing; and, where applicable, all necessary information required for a reader or reviewer to apply for access to the data and the conditions under which access will be granted.

Large data

Where data is too large to be feasibly hosted by a F1000Research-approved repository, please include all necessary information required for a reader or reviewer to access the data with a description of the access process as part of your data availability statement.

Data under license or provided by a third party

In cases where data has been obtained from a third party and restrictions apply to the availability of the data, the data availability statement must include all necessary information required for a reader or reviewer to access the data by the same means as the authors; and details of any publicly available data that is representative of the analysed dataset, which can be used to apply the methodology described in the article.

Proprietary software

Where third party proprietary software has been used, an open source alternative must be provided in the article to allow for the replication of the analysis or research by all readers. Exceptions may be made if the chosen proprietary software performs specific functions and there is no open source alternative that can carry out these functions in the same manner.

If this applies to your article, your data availability statement should include a clear description of the third party proprietary software used, including the name and version number, and what it was used for in the research. The article must also include a detailed Methods section that allows for replication; for example, the mathematics underpinning any of the simulations or calculations run using the proprietary software. You must also share any output data or analysis code generated during the research, openly and ideally in an open file format, and these must also be described in the data availability statement.

If you are unable to share your data, software or code for any reason not included here, or have additional questions about data sharing, please let our editorial team know and we will be happy to advise.

F1000Research endorses the FAIR Data Principles as a framework to promote the broadest reuse of research data.

Additional, practical guidance can be found on the GoFAIR website.

For research software, please consult the FAIR4RS Principles.

Findable

Findable data should be easy for both humans and machines to find.

Findable data requires that:

• F1. (Meta)data are assigned a globally unique and persistent identifier.
• F2. Data are described with rich metadata (defined by R1 below).
• F3. Metadata clearly and explicitly include the identifier of the data they describe.
• F4. (Meta)data are registered or indexed in a searchable resource.

The best way to achieve Findable data is by:

• Depositing your dataset into a recognized data repository which assigns globally unique persistent identifiers (such as DOIs).
• Add as much contextual information (metadata) as possible when depositing your dataset into the repository.

Accessible

Accessible data refers to data that can be accessed once found; this may involve authentication of the user and authorization of access.

Accessible data requires that:

• A1. (Meta)data are retrievable by their identifier using a standardized communications protocol
  • A1.1 The protocol is open, free, and universally implementable
  • A1.2 The protocol allows for an authentication and authorization procedure, where necessary
• A2. Metadata are accessible, even when the data are no longer available

The best way to achieve Accessible data is by:

• Depositing your dataset into a recognized data repository which uses standard communications protocols like http://.
• Ensuring that the data repository you choose gives continued access to metadata even when datasets are removed.

Interoperable

Interoperable data refers to data that can be compared and combined with data from different sources, by both humans and machines.

Interoperable data requires that:

• I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
• I2. (Meta)data use vocabularies that follow FAIR principles
• I3. (Meta)data include qualified references to other (meta)data

The best way to achieve Interoperable data is by:

• Checking FAIRsharing.org for the standards that apply to your data type and using them.
• Ensuring that the data repository you choose allows you to include links or references to other related data.
• Using open, non-proprietary file formats for your data.

Reusable

Sharing data which can be reused by others is the main goal of the FAIR Principles.

Reusable data requires that:

• R1. (Meta)data are richly described with a plurality of accurate and relevant attributes
  • R1.1. (Meta)data are released with a clear and accessible data usage license
  • R1.2. (Meta)data are associated with detailed provenance
  • R1.3. (Meta)data meet domain-relevant community standards

The best way to achieve Reusable data is by:

• Adding as much contextual information (metadata) as possible when depositing your dataset into a repository.
• Applying an open license to your data, preferably CC0 or CC-BY 4.0.
• Checking FAIRsharing.org for the standards that apply to your data type and using them.

Below is a list of repositories that have already been approved for hosting data alongside an F1000Research article.

If you are an author who wishes to use a repository not already on this list, including institutional data repositories, please contact us.

If you manage a repository and would like to be included on the list, please complete our Repository Evaluation form and return it to us.

In addition to your research data, you should ensure that your research materials and supporting documents are also deposited into an appropriate repository.

Some types of data benefit from visualization within the article. F1000Research welcomes the submission of articles featuring Plot.ly interactive figures and Code Ocean compute capsules. Videos and images can be displayed through a widget provided by Figshare. If you think your dataset would benefit from visualization, please contact us.

Transcript data

Qualitative data resulting from recordings of interviews or focus group discussions should be anonymised by redaction and uploaded to a general data repository (see above). If it is not possible to anonymise the data sufficiently by redaction, a restricted route of data access should be provided by the authors and a comprehensive statement must be added to the Data Availability section of the article (see above for data that cannot be shared). If the transcript data cannot be shared under any circumstances, please contact the editorial team, who will be able to advise you.