Data Archiving & Sharing
Promoting transparency and reproducibility in toxicology research.
Open Data Policy
Experimental and Clinical Toxicology supports open science principles recognizing that data sharing accelerates scientific progress, enables verification of published findings, and maximizes investment in toxicology research through secondary analysis and meta-analysis.
All manuscripts must include a Data Availability Statement describing how underlying data supporting published findings can be accessed. Authors should specify data repository locations with accession numbers, indicate data will be available upon reasonable request, or explain restrictions preventing data sharing such as privacy protections for human subjects data or proprietary concerns.
We strongly encourage deposition of datasets in established public repositories that provide persistent identifiers and long-term preservation. Repository selection should consider data type, community standards, and funder requirements. Deposited datasets should include sufficient metadata to enable understanding and reuse by other researchers.
General Repositories
Figshare, Dryad, and Zenodo accept diverse data types with DOI assignment. These generalist repositories provide flexible options when discipline-specific alternatives are unavailable. Zenodo supports versioning and large file sizes appropriate for computational datasets.
Domain-Specific Repositories
ToxDB, CEBS, and PubChem provide specialized infrastructure for toxicology data with appropriate metadata standards. Gene expression data should be deposited in GEO or ArrayExpress. Protein structures should be submitted to PDB.
Institutional Repositories
Many institutions maintain data repositories that may satisfy data sharing requirements. Ensure institutional repositories provide persistent identifiers, appropriate access controls, and long-term preservation commitments meeting funder and journal requirements.
Authors conducting computational analyses should share analysis code and scripts to enable verification and extension of published findings. GitHub repositories with Zenodo integration provide version control and persistent DOI assignment. Include documentation describing computational environment, software dependencies, and execution instructions.
For complex computational workflows, container technologies (Docker, Singularity) can capture complete analysis environments ensuring long-term reproducibility. Authors should provide sufficient documentation for reviewers and readers to understand and potentially rerun computational analyses.
We recognize certain data may have legitimate sharing restrictions requiring controlled access or confidentiality protections. Authors should work with institutions to establish appropriate sharing mechanisms.
When full sharing is not possible, authors should share aggregated or anonymized datasets where feasible. Data Availability Statements should explain access procedures.
Funder Requirements: Many research funders mandate open data sharing as condition of funding. NIH, Wellcome Trust, and European Commission have specific data management and sharing policies. Authors should consult funder policies and include relevant compliance information in submissions.
Need Guidance?
Contact our editorial office for data sharing questions and repository recommendations.
Contact Editorial Office
