Open data, software, & code guidelines

Before submission, authors should deposit their data in an appropriate data repository and ensure that the dataset is published openly on the web. ORE recommends that you closely consult any Data Management Plans completed in relation to your research when choosing a data repository.

The data should be stored in an open file format. The repository chosen must supply a persistent identifier (e.g., a DOI or accession code) and apply an open license, which must be CC0, CC BY 4.0, or equivalent. Please include descriptive legends and where applicable, coding schemas alongside datasets.

All data associated with articles published in Open Research Europe must be made available to readers openly on publicly available repositories in accordance to our publishing policies. For authors unsure about which repository is best suited to your needs, we refer you to the following article, which provides an inventory of identified trusted repositories (Annex 1), their topical coverage and other assessment criteria:

Lazzeri, E. (2024). Update of the Study on the readiness of research data and literature repositories to facilitate compliance with the Open Science Horizon Europe MGA requirements (Version 1.0). Zenodo. https://doi.org/10.5281/zenodo.13919643. (Download the Annex 1 Inventory of trusted repositories.xls)

ORE can accept articles where the underlying data is hosted by a certified or trusted repository, including national repositories and institutional repositories such as the CERN Open Data Portal.

To allow the maximum possible reuse, a dataset 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 provide attribution when using the data, is acceptable. A Creative Commons Public Domain Mark (PDM) or equivalent should be applied to raw research data unless the data meet the requirements to be protected by copyright/database rights.

For software and source code, authors are strongly advised 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. The chosen data repository should provide a persistent identifier for each dataset deposit.

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

Authors must include a Data Availability Statement at the end of an 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 data must be restricted for legal, ethical, or other reasons, please see below for further information on what should be included in the data availability statement.

Examples

The dataset should be cited in the body of the article, and it should be added to the reference list as with any other bibliographic citation.

Authors may use their preferred referencing style but should include, at a minimum: dataset creator; publication year; dataset title; name of repository where the data is located; and Persistent Identifier (e.g., DOI).

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

It is an author's responsibility to share data ethically and, where relevant, protect the privacy of research participants. Authors should ensure that 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, e.g., 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, authors should state the version, details of where the software can be accessed, and any variable parameters that could impact the outcome of the results. If authors 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, authors should create a public registration for a project to obtain a DOI.

Information about software should be included in a Software Availability Statement, added to the end of an article before the references list.

When drafting the statement, the following information should be included:

• Software available from: URL for the website where software can be downloaded from, if applicable.
• Source code available from: URL for versioning control system (e.g., 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 the 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. ORE recognises that there may be cases where this may not be feasible. Please see the limited exceptions to these guidelines for more information.

For analysis code

If custom analysis code has been created, it should be archived under an open license and shared. For analysis code stored in GitHub, authors should create a ‘public registration’ for their project to obtain a DOI. ORE recommends using an OSI-approved license, but CC BY 4.0 is also acceptable.

Information about archived analysis code should be included in the Data Availability Statement, which should be added at the end of an article, before the references list.

When drafting the statement, authors should include, under the heading ‘Extended Data’:

• Analysis code available from: URL for versioning control system (e.g., 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.

Citing code and software

Code and software should be cited in the body of the article and be added to the reference list like any other bibliographic citation.

Authors may use their preferred referencing style but should include, at a minimum: creator(s); publication year; title; publication venue; publication date; Persistent Identifier (e.g., DOI); and version.

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

Open Research Europe 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.

Data standards help align with commonly used data sharing practices within a field, e.g., how data should be structured, formatted, and annotated. Please check FAIRSharing.org for details of data standards specific to the research topic.

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 datasets into a recognised data repository that assigns globally unique persistent identifiers (e.g., DOIs).
• Add as much contextual information (metadata) as possible when depositing datasets into the repository.

Accessible

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

Accessible data requires that:

• A1. (Meta)data are retrievable by their identifier using a standardised communications protocol
• A1.1 The protocol is open, free, and universally implementable
• A1.2 The protocol allows for an authentication and authorisation 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 datasets into a recognised data repository which uses standard communications protocols such as HTTP or HTTPS.
• Ensuring that the data repository chosen 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 a particular data type and using them.
• Ensuring that the data repository chosen allows links or references to other related data.
• Using open, non-proprietary file formats for 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 datasets into a repository.
• Applying an open license to data, preferably CC0 or CC BY 4.0.
• Checking FAIRsharing.org for the standards that apply to a particular data type and using them.

Some data repositories provide functionality which allows authors to add links to any published articles associated with the dataset. If possible, ORE recommends that the metadata record in the data repository is updated to include a link to the published article. Authors can link to the article using the article DOI, which will be emailed when an article is published.