Supplementary Materials, Data Deposit and Software Source Code

It is recommended that all data and code be deposited in a trusted repository to facilitate maximum reuse (refer to the Data Preservation section below). If depositing in such a repository is not feasible, authors should provide a clear rationale in the Data Availability Statement and make these materials available upon request to interested researchers. Furthermore, any research materials necessary for reproducing an experiment should be explicitly mentioned in the Materials and Methods section. Guidelines specific to each journal can be found on the journal's ‘Instructions for Authors’ page. Data sharing policies pertain to the minimal dataset required to support the primary findings of a published study, with the expectation that generated data be made publicly accessible and cited according to journal guidelines.

In cases where ethical, legal, or privacy concerns exist, data sharing should be avoided. Authors must clarify the availability status of their data at the time of submission and disclose any limitations or exceptions in the Data Availability Statement. It is imperative that shared data comply with the consent provided by participants, ensuring that confidentiality is maintained and that the anonymity of participants is not compromised, nor local data protection laws violated.

When access to data is restricted due to the need to protect confidential or proprietary information, authors are required to clearly explain these restrictions and make the data available upon request, with permission, for peer review purposes.

While some institutions and funding agencies may mandate the retention of research data for a limited period after project completion or publication, there are no such temporal restrictions within the CLS Data Availability Policy. Authors are therefore encouraged to archive their research data in suitable repositories or provide minimal datasets as Supplementary Material.

Data availability statements are mandatory for all articles published with CLS. During the peer review and editorial decision-making process, authors may be asked to share existing datasets or raw data analyzed in the manuscript and to specify whether these will be available to other researchers post-publication. Authors will also be required to provide details of any datasets that have been analyzed within the manuscript.

Data availability status may include but not limited to:

• Data available in a publicly accessible repository.
• Data available on request due to restrictions (e.g., privacy, legal or ethical reasons).
• 3rd Party Data.
• Embargo on data due to commercial restrictions.
• Restrictions apply to the datasets.
• Data derived from public domain resources.
• Data sharing is not applicable (only appropriate if no new data is generated or the article describes entirely theoretical research.
• Data is contained within the article or supplementary material.
• Dataset available on request from the authors.

Selecting an Appropriate Data Repository

The CLS strongly advocates for the submission of research data to community-recognized repositories whenever feasible. Authors are encouraged to consult resources such as re3data.org or fairsharing.org to locate certified and registered data repositories pertinent to their field. In cases where no suitable community repository exists, authors may opt for their institution's generalist data repository, provided it meets specific criteria, including the ability to generate DataCite DOIs and support open terms of data sharing (e.g., the CC0 waiver).

Criteria for Choosing a Data Repository

When selecting a data repository, the following factors should be considered to ensure the platform's suitability:

• Persistence and Preservation: The repository should guarantee the long-term persistence and preservation of datasets in their published form.
• Stable Identifiers: The platform must assign stable identifiers, typically DOIs, to the submitted datasets.
• Public Accessibility: Data should be accessible to the public without barriers such as login requirements or paywalls.
• Open Licensing: The repository should support open licenses, with CC0 and CC-BY (or their equivalents) being required in most instances.
• Confidential Review: The platform should offer confidential review of submitted datasets without requiring reviewers to disclose identifying information.

Data Citation Guidelines

Authors are encouraged to formally cite any datasets stored in external repositories that are referenced in their manuscripts. This includes both the primary datasets that are central to the submission and any additional datasets utilized in the research. For datasets that have been previously published, it is important to cite both the associated research articles and the datasets themselves. Proper data citation will be verified and enforced by the Journal Editorial staff prior to publication.