Home Publish with usData Availability Guide

Data Availability Guide

1. What should be shared

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Data: raw data or processed datasets used for analysis (as permitted by ethics and law).

EDITORY PRESS • Version 1.0 • Last updated: 2026-03-21

Every manuscript should include a data availability statement.

2. Code: scripts for cleaning, analysis, and figure generation (e.g., Python/R/Stata).

3. Materials: survey instruments, experimental protocols, interview guides, codebooks, and variable definitions.

4. Documentation: README describing how to reproduce results, and a data dictionary/codebook where relevant.

2. Recommended repositories (examples)

Choose a repository that provides persistent identifiers (DOIs) and stable access.

Zenodo (DOI, integrates with GitHub)

OSF (Open Science Framework)

Figshare

Institutional repositories (university/organization)

Domain repositories when available (e.g., ICPSR, GenBank, etc.)

3. When data cannot be shared openly

Sometimes data cannot be made public due to privacy, legal, or contractual restrictions. In these cases, authors should:

Explain the restriction clearly (e.g., personal data, confidentiality agreements).

Share what is possible: anonymized data, synthetic data, code, and documentation.

Provide a controlled access pathway if feasible (data use agreement, secure access, or on-request access).

4. Data Availability Statement (required)

Include a Data Availability Statement in every manuscript. Choose the option below that best applies and adapt it.

5.1. Option A — Open data

Example statement:

“The data and code supporting the findings of this study are available in Zenodo at https://doi.org/10.5281/zenodo.1234567.”

5.2. Option B — Open code, restricted data

Example statement:

“Analysis code and documentation are available at https://doi.org/10.5281/zenodo.1234567. The underlying dataset contains sensitive personal information and cannot be shared publicly. Access may be granted to qualified researchers subject to ethics approval and a data use agreement; requests should be sent to [email].”

5.3. Option C — Available on request

Example statement:

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

Note: Avoid this option if a public repository is feasible.

5.4. Option D — Restricted (legal/contractual)

Example statement:

“The data are not publicly available due to contractual restrictions with [organization]. Aggregated results and analysis code are available at https://doi.org/10.5281/zenodo.1234567.”

5.5. Option E — Not applicable

Example statement:

“No datasets were generated or analyzed during the current study.”

6. Minimum reproducibility checklist (recommended)

A README file explaining how to reproduce the main tables and figures.

Clear file structure (data/, code/, outputs/).

A codebook/data dictionary describing variables and units.

Software environment details (language + key package versions).

Random seeds set and documented where relevant.

7. How to cite data (APA 7 examples)

7.1. Dataset with DOI

Format:

Author/Organization. (Year). Title of dataset (Version) [Data set]. Repository. https://doi.org/xxxxx

Example:

Institute for Social Research. (2024). Survey dataset on labor market outcomes (Version 2.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1234567

7.2. Code repository (software) citation

Example:

Doe, J. (2025). Replication code for: Market efficiency under structural breaks [Computer software]. GitHub. https://github.com/example/repo

Cite datasets in your reference list when they are used as research outputs or evidence.

Home Publish with usData Availability Guide

Data Availability Guide

1. What should be shared

Download as PDF

Data: raw data or processed datasets used for analysis (as permitted by ethics and law).

EDITORY PRESS • Version 1.0 • Last updated: 2026-03-21

Every manuscript should include a data availability statement.

2. Code: scripts for cleaning, analysis, and figure generation (e.g., Python/R/Stata).

3. Materials: survey instruments, experimental protocols, interview guides, codebooks, and variable definitions.

4. Documentation: README describing how to reproduce results, and a data dictionary/codebook where relevant.

2. Recommended repositories (examples)

Choose a repository that provides persistent identifiers (DOIs) and stable access.

Zenodo (DOI, integrates with GitHub)

OSF (Open Science Framework)

Figshare

Institutional repositories (university/organization)

Domain repositories when available (e.g., ICPSR, GenBank, etc.)

3. When data cannot be shared openly

Sometimes data cannot be made public due to privacy, legal, or contractual restrictions. In these cases, authors should:

Explain the restriction clearly (e.g., personal data, confidentiality agreements).

Share what is possible: anonymized data, synthetic data, code, and documentation.

Provide a controlled access pathway if feasible (data use agreement, secure access, or on-request access).

4. Data Availability Statement (required)

Include a Data Availability Statement in every manuscript. Choose the option below that best applies and adapt it.

5.1. Option A — Open data

Example statement:

“The data and code supporting the findings of this study are available in Zenodo at https://doi.org/10.5281/zenodo.1234567.”

5.2. Option B — Open code, restricted data

Example statement:

5.3. Option C — Available on request

Example statement:

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

Note: Avoid this option if a public repository is feasible.

5.4. Option D — Restricted (legal/contractual)

Example statement:

“The data are not publicly available due to contractual restrictions with [organization]. Aggregated results and analysis code are available at https://doi.org/10.5281/zenodo.1234567.”

5.5. Option E — Not applicable

Example statement:

“No datasets were generated or analyzed during the current study.”

6. Minimum reproducibility checklist (recommended)

A README file explaining how to reproduce the main tables and figures.

Clear file structure (data/, code/, outputs/).

A codebook/data dictionary describing variables and units.

Software environment details (language + key package versions).

Random seeds set and documented where relevant.

7. How to cite data (APA 7 examples)

7.1. Dataset with DOI

Format:

Author/Organization. (Year). Title of dataset (Version) [Data set]. Repository. https://doi.org/xxxxx

Example:

Institute for Social Research. (2024). Survey dataset on labor market outcomes (Version 2.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1234567

7.2. Code repository (software) citation

Example:

Doe, J. (2025). Replication code for: Market efficiency under structural breaks [Computer software]. GitHub. https://github.com/example/repo

Cite datasets in your reference list when they are used as research outputs or evidence.