How BioRender supports FAIR data principles (Findable, Accessible, Interoperable, and Reusable)

The FAIR principles: Findable, Accessible, Interoperable, and Reusable, provide a framework for scientific data management that enables reproducibility, collaboration, and compliance with funder requirements (including NIH open data mandates). BioRender Graphing is built with transparency and reproducibility in mind, and supports key FAIR principles today, with a roadmap to deepen this alignment over time.

F - Findable

Data and analyses are easy to locate and reference

• Direct shareable links: Every graph and analysis in BioRender Graphing can be shared via a persistent URL, making specific outputs easy to reference in lab notebooks or cross-team handoffs.
• Org-level sharing: Files can be shared with individuals, groups, or an entire organization, supporting discoverability within labs and institutions.
• DOI support (potential future roadmap): BioRender is evaluating DOI assignment for published figures and datasets, which would enable indexed, citable research outputs aligned with NIH and funder open data requirements.

A - Accessible

Data can be retrieved by those with appropriate permissions

• Granular access controls: BioRender uses a permissions model where files can be set to view-only or edit access, with controls at the individual, group, or organization level.
• Account-based access: Viewing shared content currently requires a BioRender account. 

I - Interoperable

Data works across tools, platforms, and systems

• Open export formats: Graphs can be exported as SVG, PNG, or JPEG: open, widely-supported formats compatible with any publication workflow, ELN, or downstream tool.
• Machine-readable data exports: Underlying data and analysis reports export as CSV, a universal format compatible with R, Python, Excel, and virtually any statistical or repository platform.
• Statistical output portability: Analysis outputs include parameters and test settings, making it straightforward to reproduce or import results into other environments.
• Documented methodology: BioRender Graphing's statistical analyses are powered by open-source R packages. Full documentation on analysis methodologies and R packages is available here: Statistical Tests in BioRender Graphing: Methods, Assumption Checks, and R Packages.

R - Reusable

Data is well-described and can be used again in future work

• Data stored with visualization: Source data is stored within the same BioRender Graphing file as the graph and analyses. The data is always accessible in addition to the final output.
• Provenance metadata captured: Each analysis records a timestamp, the R version used, and statistical parameters and settings - providing the context needed to understand, reproduce, or build on the analysis.
• Storage-ready exports: Analysis results can be exported as CSVs, fitting into existing open data workflows.
• Versioned: all changes in BioRender Graphing are versioned, and earlier versions can be restored as needed by the end-user.

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