Data Availability Expectations

JWC encourages authors to deposit datasets in trusted repositories whenever possible. Data availability statements are required for all submissions.

If data cannot be shared due to privacy, security, or legal restrictions, explain the limitations and provide access pathways for qualified researchers.

Documentation

Provide readme files, codebooks, and variable descriptions to support reuse and replication.

Privacy and Consent

Remove identifiers and confirm consent for sharing. Use controlled access routes for sensitive data.

Versioning

If datasets are updated after publication, note version changes and provide updated identifiers.

Data Licensing

Select a data license that supports reuse while protecting attribution and ethical use.

Repository Metadata

Include keywords, geographic coverage, temporal range, and variable descriptions in repository metadata to improve discovery.

Data Quality Checks

Describe validation, cleaning, and quality control steps. Transparent QA helps reviewers assess dataset integrity.

Restricted Data

If data are restricted, provide a clear access pathway and criteria for approval. Include contact information and oversight requirements.

Code Reuse

Archive analysis scripts with a DOI when possible. Clear code reuse guidance supports replication and extension studies.

Embargoes

If an embargo is needed, state the duration and reason. Provide an expected release date for transparency.

Linking Data and Manuscript

Cite datasets in the manuscript and include links in data availability statements. Consistent linking improves reuse.

Data Citation

Include dataset citations in the reference list with DOIs or accession numbers. Data citation improves credit and transparency.

Data Updates

If access conditions change after publication, notify the editorial office so the record can be updated.

Sensitive Locations

For sensitive site locations, provide generalized coordinates or controlled access while retaining analytical integrity.

Data Sharing Benefits

Sharing data increases replication, supports new analyses, and improves the impact of climate research across disciplines.

Storage Security

Store sensitive datasets in secure environments with access logs. Controlled access protects participants and data providers.

Data Management Plans

Describe how data will be stored, preserved, and updated over time. Clear plans support funder compliance and long term access.

Machine Learning Outputs

If sharing model outputs, provide documentation of training data, feature sets, and evaluation metrics for transparency.

Data Access Requests

Include a contact for access requests and specify response timelines. Clear pathways improve trust and reuse.

Non Proprietary Formats

Use formats such as CSV, NetCDF, or GeoTIFF to ensure long term accessibility across platforms.

Climate Model Outputs

If sharing model outputs, describe variables included, temporal resolution, and file naming conventions. Clear documentation supports reuse.

Data Statement Review

Review your data statement before submission to ensure links are active and identifiers are correct. Broken links delay review.

Reuse Impact

Well documented datasets increase citations and enable secondary analyses across disciplines. Clear metadata helps other researchers interpret variables and apply results correctly. Including context notes and version details reduces misinterpretation and improves long term reuse. Add README files and cite software dependencies to support reproducibility and reviewer verification of data lineage and improve transparency for audits and compliance checks for authors.