Data Sharing Principles

Data archiving strengthens reproducibility and enables secondary analyses in lipid science. We encourage authors to share de identified datasets and code when possible.

When data cannot be shared due to privacy or regulatory constraints, provide a clear access statement and governance details.

Lipid datasets benefit from transparent data stewardship and clear documentation.

Provide contact points for data access questions or approval.

Provide uncertainty measures for key estimates when applicable.

Provide details on internal standards and extraction protocols.

Highlight how lipid findings inform prevention or therapeutic decisions.

Provide context for effect sizes in relation to clinical thresholds.

Specify units and conversion factors used for lipid measures.

Repository Guidance

Select repositories that provide persistent identifiers, access governance, and documentation support.

• Institutional repositories with long term access
• Discipline specific data archives
• General repositories with DOI support
• Controlled access platforms for sensitive data

Provide codebooks, provenance notes, and governance restrictions when sharing data.

Clear lipid reporting improves interpretability for clinicians and metabolic researchers.

Explain how confounders were selected in observational lipid studies.

Summarize participant demographics to contextualize lipid findings.

Discuss biological plausibility for lipid associations observed.

Clarify whether analyses were preregistered or exploratory.

Note whether lipid panels were fasting or non fasting and justify.

Access Models

Controlled access repositories may be appropriate for sensitive lipid data.

We encourage authors to document assay conditions so lipid measurements remain comparable.

Report software versions and packages to support reproducibility.

Clarify fasting status or dietary controls when relevant to lipid measures.

Describe how lipid ratios or indexes were calculated.

Summarize limitations related to lipid measurement or sample size.

Discuss generalizability to broader populations or clinical settings.

Documentation

Provide codebooks, data dictionaries, and analytic scripts to support interpretation and reuse.

Document preprocessing steps, variable definitions, and lipid transformations.

Document derived lipid ratios and compositional metrics.

Transparent reporting of sample handling helps readers interpret lipid stability.

If data access is restricted, describe approval processes and timelines.

Report variability across cohorts or sites for multicenter lipid studies.

Include quality assurance steps for instrument calibration.

Explain the choice of statistical model for lipid outcome distributions.

Provide links to protocols or supplementary methods when available.

Licensing and Citation

Choose a license that matches reuse expectations and institutional policies, and cite datasets with persistent identifiers so others can credit the source.

When datasets include derived lipid indices, explain preferred attribution and note any limits on commercial reuse if required by funders.

Version datasets when updates occur and document changes so secondary analyses remain interpretable over time.

Include readme files describing file structure and variables.

Summaries linking lipid outcomes to clinical relevance strengthen translation to care.

Describe sample storage temperatures and timing to support lipid stability assessment.

Describe statistical correction for multiple testing in lipidomics.

Report confidence intervals for key lipid effects.

Report sensitivity analyses to test robustness of lipid findings.

Summarize practical implications for lipid focused guidelines or care.

Sharing Workflow

Outline data retention timelines and stewardship responsibilities.

Define lipid classes and abbreviations clearly for multidisciplinary readers.

Include calibration standards and quality control materials for lipid assays.

Explain how missing lipid values were handled during analysis.

When using animal models, specify strain, diet, and lipid outcome timing.

Include discussion of sex or age differences in lipid outcomes when applicable.

Support

For data archiving questions, contact [email protected].

Describe anonymization or de identification steps for lipid data.

When using lipidomics, report normalization and quality control procedures.

Document batch effects and correction methods for multi run lipidomics.

Provide a rationale for chosen lipid biomarkers or panels.

Describe tissue sources and sampling methods for lipid analyses.

Describe any adjustments for medication use affecting lipid profiles.