Journal Levelize
A publishable figure is not just statistically correct — it meets every formal requirement of the target journal. Journal Levelize enforces those requirements automatically: error bar declarations, effect size mandates, causal language checks, and journal-specific formatting — all resolved before your figure reaches submission.
What is Journal Levelize?
Most figures that fail peer review fail not because the science is wrong, but because the presentation does not meet the journal's formal standards. The error bar type is unlabeled. The significance stars appear without an effect size. The figure dimensions are wrong for print. A regression line implies causation that the study design cannot support.
Journal Levelize is the system that closes the gap between "statistically valid" and "ready to submit." It operates in two layers: Statistical Disclosure, which enforces the reporting requirements that apply to every figure regardless of journal; and Journal Submission Ready, which automates the journal-specific mechanics of submission.
Journal Levelize does not replace peer review — it prevents the mechanical errors that consume reviewer time and delay publication. A reviewer should be evaluating the science. Journal Levelize ensures they are not distracted by unlabeled error bars, missing sample sizes, or causal overclaims in titles.
Statistical Disclosure
Five disclosure rules apply to every figure that carries statistical information. None can be overridden without explicit acknowledgment. Together, they ensure that every figure is self-contained: a reader can interpret it without consulting the Methods.
Journal Submission Ready
Beyond disclosure, there is a layer of mechanical submission work that currently falls to the researcher: filling in reporting checklists, resizing figures to journal specifications, verifying stylistic consistency across all figures, and drafting peer review responses. Journal Submission Ready automates all of it.