How to Cite Gradiance

If you use Gradiance for analysis in published research, please cite it. The formats below cover the major reference managers and a paragraph you can adapt for your methods section.

APA

Gradiance (2026). Gradiance: Publication-ready bioassay analysis [Software]. https://gradiance.io

BibTeX

@software{gradiance2026,
  author = {{Gradiance}},
  title  = {Gradiance: Publication-ready bioassay analysis},
  year   = {2026},
  url    = {https://gradiance.io},
  note   = {Software for reproducible dose-response and bioassay curve fitting},
}

RIS (EndNote, Mendeley)

TY  - COMP
AU  - Gradiance
TI  - Gradiance: Publication-ready bioassay analysis
PY  - 2026
UR  - https://gradiance.io
N1  - Software for reproducible dose-response and bioassay curve fitting
ER  -

Suggested methods language

Adapt this paragraph for the methods section, replacing the bracketed sections with your actual model and assay:

Data were analyzed using Gradiance (2026), which performed [4PL / 5PL / linear] non-linear regression via the Levenberg-Marquardt algorithm (scipy.optimize.curve_fit). [IC₅₀ / Kd / Vmax] values are reported with 95% confidence intervals derived from the covariance matrix of the fitted parameters. Outliers were flagged by studentized residual and excluded only when explicitly noted; all exclusions are recorded in the audit trail. A reproducible Python script was exported for each analysis to ensure full provenance.

Reproducibility

Every Gradiance run exports a standalone Python script that regenerates the figure and parameters from raw input. Including the script (or its hash) in your supplementary materials is the cleanest way to make a reviewer-friendly reproducibility claim — the script depends only on Python and SciPy, and runs identically a year later.

@software{gradiance2026, author = {{Gradiance}}, title = {Gradiance: Publication-ready bioassay analysis}, year = {2026}, url = {https://gradiance.io}, note = {Software for reproducible dose-response and bioassay curve fitting}, }

Suggested methods language

Adapt this paragraph for the methods section, replacing the bracketed sections with your actual model and assay:

Data were analyzed using Gradiance (2026), which performed [4PL / 5PL / linear] non-linear regression via the Levenberg-Marquardt algorithm (scipy.optimize.curve_fit). [IC₅₀ / Kd / Vmax] values are reported with 95% confidence intervals derived from the covariance matrix of the fitted parameters. Outliers were flagged by studentized residual and excluded only when explicitly noted; all exclusions are recorded in the audit trail. A reproducible Python script was exported for each analysis to ensure full provenance.

Reproducibility