Data Availability and Reproducibility Policy

MATHEdunesa: Jurnal Ilmiah Pendidikan Matematika is committed to transparency, verifiability, and reproducibility in mathematics education research. Making research data, instruments, and analytic procedures available allows others to check findings, build on existing work, and support the cumulative development of knowledge.

This policy sets out the journal’s expectations for data availability, data citation, and reporting that supports reproducibility. It is guided by the FAIR Data Principles (Findable, Accessible, Interoperable, Reusable), the guidance of the Committee on Publication Ethics (COPE), and the research data policies of internationally reputable, Scopus-indexed journals.

1. Scope and Definitions

Research data refers to the materials that underpin the findings reported in a manuscript. In mathematics education research, these may include:

  • quantitative data, such as test scores, questionnaire responses, and assessment results;
  • qualitative data, such as interview transcripts, field notes, observation records, and coded data;
  • student work, written solutions, worksheets, and think-aloud protocols;
  • audio or video recordings of classroom activities or interviews;
  • research instruments, such as tests, questionnaires, rubrics, interview protocols, and validation sheets;
  • learning designs and related materials, such as hypothetical learning trajectories (HLT), lesson plans, student worksheets, and applets; and
  • analysis scripts, code, and software configurations.

2. Data Availability Statement (Mandatory)

All research articles submitted to MATHEdunesa must include a Data Availability Statement, placed before the References. The statement must explain whether, where, and how the data supporting the findings can be accessed. If the data cannot be shared, it must explain why.

Authors should use whichever of the following templates fits their situation and adapt it as needed.

Data publicly available in a repository:

The data that support the findings of this study are openly available in [repository name] at [DOI/URL], reference number [number].

Data available on request:

The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to [reason, e.g., privacy of research participants].

Data restricted by ethical or legal considerations:

The data are not publicly available because they contain information that could compromise the privacy of research participants, who are minors. Anonymized excerpts are provided in the article. Requests for access may be directed to the corresponding author and are subject to approval by [institution/ethics committee].

Data included in the article:

The data supporting the findings of this study are available within the article and its supplementary materials.

No data generated:

Data sharing is not applicable to this article, as no new data were created or analyzed in this study.

The last template applies, for example, to purely theoretical or conceptual articles.

The Data Availability Statement will be published with the article. A missing statement may result in the manuscript being returned to the Authors before editorial processing continues.

3. Data Sharing

3.1 Encouraged Practice

MATHEdunesa strongly encourages Authors to deposit the data, instruments, and materials that support their findings in a recognized repository. Ideally, the repository should assign a persistent identifier such as a DOI. Suitable repositories include:

  • general repositories, such as Zenodo, Open Science Framework (OSF), Figshare, Mendeley Data, and Harvard Dataverse;
  • national repositories, such as Repositori Ilmiah Nasional (RIN), managed by BRIN; and
  • institutional repositories that provide persistent access.

Data should be shared under an open license where possible, such as CC BY 4.0 or CC0.

3.2 Minimum Expectations

Even where full datasets cannot be shared, Authors are expected to make available, in the article, in supplementary materials, or in a repository, enough material for readers to understand and evaluate the study. This includes:

  • the research instruments used, or representative items, together with evidence of their validity and reliability;
  • the main features of learning designs or interventions, such as the HLT, task sequences, and worksheets;
  • representative, anonymized examples of student work or excerpts of transcripts that support qualitative claims; and
  • coding schemes, rubrics, or analytic frameworks.

3.3 Requests After Publication

Where the Data Availability Statement says that data are “available on request”, the Corresponding Author is expected to respond to reasonable requests from researchers within a reasonable period. Failure to provide data as stated, without a valid reason, may be treated as a breach of this policy.

4. Ethics, Privacy, and Confidentiality

Research in mathematics education often involves students, including minors, as well as teachers and schools. Protecting participants takes priority over data sharing. Authors must:

  • share data only in accordance with the informed consent obtained and any approval from an ethics committee or institution;
  • anonymize or de-identify all shared data by removing names, student ID numbers, school names where identifying, faces, voices, and any other identifying information;
  • never publish identifiable photographs, video, or audio of minors without explicit written consent from parents or guardians and from the school, where applicable; and
  • respect any restrictions set by schools, government agencies, or data providers, such as national assessment data.

Where privacy considerations prevent data sharing, this must be explained in the Data Availability Statement.

5. Reproducibility and Transparent Reporting

To support verification and replication, Authors should report their methods in sufficient detail. As applicable, this includes the following.

For quantitative studies:

  • the sampling procedures, sample size, and characteristics of the participants;
  • the instruments, including their validity and reliability evidence;
  • the statistical procedures used, how assumptions were tested, effect sizes, and exact p-values; and
  • the software used and its version, such as SPSS 29 or R 4.4, together with any packages.

For qualitative studies:

  • the procedures for data collection and the researcher’s role;
  • coding procedures, including how codes and categories were developed;
  • strategies for trustworthiness, such as triangulation, member checking, peer debriefing, and inter-rater agreement; and
  • clear links between the claims made and the evidence presented.

For design research and development studies:

  • the design principles, the conjectured learning trajectory, and the cycles of design, implementation, and retrospective analysis; and
  • how the conjectures were compared with the observed learning.

For studies involving code or software:

  • analysis scripts or code should be shared in a repository or as supplementary material wherever possible, with enough documentation to rerun the analysis.

6. Data Citation

Datasets, instruments, and software used or reused in a study should be cited in the reference list, in the same way as other scholarly sources. Each citation should include the creator, year, title, repository or publisher, version (where relevant), and persistent identifier. For example:

Author, A., & Author, B. (2026). Dataset title [Data set]. Zenodo. https://doi.org/xx.xxxx/zenodo.xxxxxxx

7. Responsibilities of Reviewers and Editors

Reviewers and Editors may consult the available data, instruments, and supplementary materials when evaluating a manuscript. They may request additional information where this is needed to assess the validity of the findings. Any data provided during review are confidential and may not be used for any other purpose.

8. Handling Concerns

If there are concerns about the existence, accuracy, or integrity of the data reported in a manuscript or published article, MATHEdunesa may request access to the underlying data. Grounds for such concerns include suspected fabrication, falsification, or inconsistencies that cannot be explained. In line with COPE guidance and the journal’s Research Misconduct Policy, failure to provide data when there is a well-founded concern may lead to:

  • rejection of the manuscript;
  • a Correction or an Expression of Concern; or
  • retraction of the article, together with notification to the Authors’ institution.

9. Related Policies and Policy Review

This policy should be read together with the journal’s Publication Ethics, Research Ethics, Authorship and Contributorship, Generative AI Utilization, and Research Misconduct policies.

MATHEdunesa may revise this policy in response to developments in open science, data protection regulations, and international publishing practice. In cases not covered here, the Editor will exercise professional judgment in accordance with COPE guidance and the protection of the scholarly record. The current approved version remains in force until a revised version is formally adopted.

References:

  • Wilkinson, M. D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
  • COPE, Data and Reproducibility guidance;
  • COPE, DOAJ, OASPA, and WAME, Principles of Transparency and Best Practice in Scholarly Publishing;
  • Springer Nature, Research Data Policy Types.