Lectio Socialis is committed to transparency and reproducibility in research. The journal expects authors to be open about the data underpinning their findings and to make those data as accessible as ethical and legal constraints allow. This policy supports the Transparency and Openness Promotion (TOP) Guidelines, the FAIR principles, and the Joint Declaration of Data Citation Principles. It applies to all data necessary to understand, evaluate, and replicate the conclusions reported in an article.

1. Scope

In the social sciences, “data” may take many forms, including quantitative datasets, survey responses, interview and focus-group transcripts, ethnographic field notes, coding schemes, content-analysis corpora, and the analysis code or scripts used to generate results. This policy covers both original data produced by the authors and third-party or previously published data reused in the study.

2. Data availability statements are required

Every research and review article must include a data availability statement (DAS). The statement should appear in the article’s end matter, with the other end-of-article statements (after the reference list), under the heading “Data availability statement.” It must indicate what data underlie the article, where they can be accessed, and under what conditions — whether or not the data can be shared publicly, and covering both original and reused data. The DAS is published with the article and remains openly available to readers. Where data cannot be shared, the statement must say so and explain why.

3. Template statements

Authors should select and adapt one of the following statements. This list is not exhaustive; a given dataset may warrant a different statement.

–   Open in a repository: “The data that support the findings of this study are openly available in [repository name] at [DOI or URL].”

–   Available on request: “The data that support the findings of this study are available from the corresponding author upon reasonable request.”

–   Controlled or restricted access: “The data contain information that could compromise the privacy of research participants and are therefore available under controlled access; the conditions for access are described at [repository] / available from the corresponding author.”

–   Within the article: “All data generated or analyzed during this study are included in this published article and its supplementary materials.”

–   Third-party or public sources: “The data analyzed in this study were obtained from [source]; they are publicly available at [URL or DOI] / available from the third party subject to its access conditions.”

–   No new data: “No new data were created or analyzed in this study; data sharing is not applicable to this article.”

4. Data sharing and deposit

The journal strongly encourages — but does not require — authors to deposit the data supporting their findings in a trustworthy, FAIR-aligned repository, ideally before submission, so that a persistent identifier (DOI) can be cited in the manuscript. Most repositories allow data to be deposited privately and released only on acceptance or after an embargo. Recommended options include:

–   the journal’s Zenodo community, Lectio Socialis — recommended for most datasets, replication materials, and code; when uploading, select the “Lectio Socialis” community so the editorial office can review and add the deposit to the collection;

–   ICPSR for quantitative social- and political-science data;

–   Qualitative Data Repository (QDR) for qualitative and multi-method data, including sensitive data under controlled access;

–   another generalist or FAIR repository, such as Dryad or OSF, where preferred;

–   to identify a certified repository, use the DataCite Repository Finder or re3data.

Where possible, data should be released under an open license (for example, CC0 or CC BY) to maximize reuse.

5. Ethical, legal, and confidentiality exceptions

Data should be shared only where it is ethical and lawful to do so. The protection of human participants takes priority over data sharing, and authors must not disclose data that would breach confidentiality, endanger participants or third parties, reveal personally identifiable information, or violate consent agreements, contractual terms, or applicable law — considerations that are especially important in politically sensitive or conflict-affected research. In such cases authors should de-identify data where feasible, deposit them under controlled or restricted access (for example, through QDR or ICPSR), and explain the access conditions in the data availability statement.

6. Data citation

Datasets are citable products of research. Authors should cite any dataset they generate or reuse in the reference list, with its DOI, following the Joint Declaration of Data Citation Principles. Data citations help ensure that the creators of data receive appropriate credit.

7. Code and materials availability

For quantitative or computational work, authors are encouraged to share the analysis code, scripts, and other materials needed to reproduce the reported results, and to include a short “Code availability” statement alongside the data availability statement.

8. Double-anonymized peer review

Because the journal operates double-blind review, authors depositing data for a submission should remove identifying information from the data and accompanying files and use a repository’s anonymous or private view link. The data availability statement should not reveal author identity during review; full repository details can be added once the article is accepted.

9. Editorial and peer-review process

A data availability statement is required at submission. Editors and reviewers consider the statement and the author’s compliance with this policy as part of their assessment, and manuscripts may be returned for revision if the statement is missing or inadequate. On acceptance, the statement is published with the article. The journal does not itself verify, curate, or host primary data beyond its Zenodo community; responsibility for the accuracy and integrity of the data rests with the authors.

10. Funder requirements

Where a research funder or institution mandates data sharing or a data management plan, authors should follow the stricter of the funder’s and the journal’s requirements. This policy is consistent with the data-sharing expectations of the ICMJE recommendations.

11. Depositing your data

To make deposit straightforward, the journal maintains a Zenodo community, Lectio Socialis. To deposit:

1.   Create a free account at zenodo.org and link your ORCID.

2.   Prepare your files: anonymise any sensitive or personal data and add a README (and a codebook for quantitative data).

3.   Start a new upload using the community link zenodo.org/uploads/new?community=lectio, or select the “Lectio Socialis” community on upload.

4.   Choose the resource type (“Dataset”, or “Software” for code) and reserve a DOI so you can cite it in your manuscript.

5.   Complete the metadata and, under “Related works”, add your article’s DOI so the data and the article link to each other.

6.   Set the access level and license — open access with a CC BY 4.0 (or CC0) license is preferred; use restricted access for sensitive data, or an embargo to delay release until publication.

7.   Submit to the community; the editorial office will review and accept the deposit into the collection.

8.   Add the resulting DOI to your data availability statement, in the end matter after the reference list.

For questions about this policy, repository choice, or preparing a data availability statement, contact the editorial office at info@lectiosocialis.org.