Research Data Policy

Research Data Policy

Purpose

The journal is committed to supporting transparency, reproducibility, and verification of published research, consistent with its emphasis on applied and validated findings.

Data Availability Statement

All submissions must include a Data Availability Statement, indicating where the underlying research data can be accessed, or explaining why data cannot be shared.

Deposit of Data

Authors are strongly encouraged to deposit datasets underlying their submitted manuscript in a recognized public repository (such as Zenodo, Figshare, or the Open Science Framework) prior to submission, and to include the repository link and, where available, its DOI in the manuscript.

Deposit of Code

For manuscripts involving the development or application of machine learning or artificial intelligence systems, authors are encouraged to make source code available through a public repository (such as GitHub, with an archival DOI assigned via Zenodo or Figshare), under a permissive open-source license.

Restrictions on Data Sharing

Where data cannot be made publicly available due to legitimate reasons, such as participant confidentiality, proprietary organizational data, or legal restrictions, authors must clearly state this in the Data Availability Statement and describe, where possible, the conditions under which the data may be accessed (e.g., upon reasonable request, subject to a data-sharing agreement).

Data for Applied Research and Case Studies

Where a submission reports outcomes from implementation within a real organization, authors should provide sufficient detail regarding the measurement methodology and outcome data to allow reviewers and readers to assess the credibility of the reported results, even where full underlying data cannot be publicly released due to organizational confidentiality.

Data Integrity

Authors are responsible for ensuring that all data reported in the manuscript is accurate and has not been manipulated, selectively reported, or misrepresented. Any evidence of data fabrication or falsification will be handled in accordance with the journal's Publication Ethics and Corrections and Retraction policies.

Licensing of Shared Data

Where possible, authors are encouraged to release datasets under an open license, such as CC0 or CC BY 4.0, to maximize the reusability of research data by others.