Data Sharing and Reproducibility Policy

Data Sharing and Reproducibility Policy

Neutrosophic Sets and Systems

Neutrosophic Sets and Systems (NSS) is dedicated to fostering transparency, reproducibility, and openness in scientific research. We encourage authors to share the data, materials, software, and methods underlying their published findings, whenever possible and appropriate.

This policy aligns with global best practices from COPE, ICMJE, and the ALPSP-STM Statement on Data and Databases.

  1. Importance of Data Transparency

Data sharing enables:

  • Independent verification of results
  • Reproducibility of experiments and analyses
  • Reuse of data in future research
  • Greater trust and credibility in published work

We support a scientific culture where access to underlying research data is the norm rather than the exception.

  1. Authors' Responsibilities

Authors submitting to NSS are expected to:

  • Clearly describe how and where supporting data can be accessed (if applicable)
  • Include a Data Availability Statement within their manuscript
  • Ensure that data is archived in a trusted repository and is properly cited (using a DOI or persistent identifier)

If data is not publicly available, the reason must be justified (e.g., ethical, legal, or confidentiality restrictions).

  1. Data Availability Statement (Required)

All manuscripts must contain a Data Availability Statement outlining one of the following:

  • Open Access Data:

“The data supporting the findings of this study are openly available in [repository name] at [DOI or URL].”

  • Data Available Upon Request:

“The data are available from the corresponding author upon reasonable request.”

  • Restricted Access:

“The data are not publicly available due to [reason].”

  • No Data Generated:

“No new data were created or analyzed in this study.”

This statement appears before the references or in a dedicated section.

  1. Accepted Data Repositories

We encourage authors to use discipline-specific, institutional, or general repositories that support persistent identifiers. Recommended options include:

For mathematical code or computational scripts, repositories such as GitHub (with a DOI via Zenodo) are also accepted.

  1. Citing Data

When datasets are used or created, they must be formally cited in the reference list using the appropriate citation format (including authors, title, repository, year, and DOI).

Example:

Smith J. (2024). Dataset on fuzzy algebraic topologies. Zenodo. https://doi.org/10.5281/zenodo.1234567

  1. Reproducibility

Authors must provide sufficient methodological detail to allow replication of the results. This includes:

  • Complete algorithm or model descriptions
  • Parameters and assumptions used
  • Access to software, code, or supplementary materials

If a method has been described elsewhere, a proper citation is required, along with a summary in the submitted manuscript.

  1. Peer Review and Data

During peer review, authors may be asked to:

  • Provide access to raw data
  • Clarify data processing steps
  • Submit supplementary files

All shared data will remain confidential during peer review and used solely for assessment purposes.

  1. Long-Term Access and Preservation

Authors are responsible for ensuring that deposited datasets remain accessible for a minimum of 5 years after publication. The journal collaborates with digital preservation platforms to ensure the long-term accessibility of published articles and supplementary content.

  1. Ethical Considerations

Data containing personally identifiable information, sensitive details, or third-party content must comply with both ethical standards and applicable legal requirements (e.g., the GDPR). Anonymization and appropriate consent must be documented.