Dataset_02 // Open Data

132 Calibrated MTB Suspensions

Inverse calibration by brand and model · Permanent DOI · Monte Carlo 10,000
← OPEN DATA

132 suspensions, calibrated against each brand's official table.

Each manufacturer publishes its own pressure-by-weight table, and each one uses a different curve. This dataset takes 132 forks and shocks and calibrates them by brand and model through inverse calibration against those official tables, feeding the dual-chamber polytropic model of the BikeLab SAG Calculator. The confidence of each segment is estimated with Monte Carlo of 10,000 iterations.

132
models in the dataset
10
brands covered
83–86%
confidence in the mid-high range
Module_01 // Dataset composition

Coverage by brand (the "calibrated" ones have an official table with ground truth; the rest are modeled by segment analogy):

132 models · by brand and segment
BrandModelsWith ground truthSegments
RockShox (SRAM)4512Entry → DH + Legacy
Fox Racing Shox2910Mid → DH + Legacy
SR Suntour126Entry → E-MTB Enduro
Manitou80Mid → DH
Öhlins60High (3 chambers)
Formula60Mid-High → High
Cane Creek50High
Marzocchi40Mid → Mid-High
DVO40Mid-High → Enduro/DH
X-Fusion, MRP, RST, others130Entry → High
Module_02 // Sample (confidence by segment)
Sample · Monte Carlo confidence and error by segment
SegmentRepresentative modelsMC confidenceMean error [%SAG]
Entry (Solo Air)RS Judy, SR Epixon72–76%1.3–1.5%
Mid (DebonAir, EQ Air)Fox 34 Rhythm, RS 35 Gold83%1.1%
Mid-High (DebonAir+, EVOL)RS Pike Select, Fox 36 Perf83–86%1.0%
Shocks (no ground truth)RS Super Deluxe + Megatower42%

// Sample. The complete dataset (132 models with their calibrated curve, psi/N ratio by weight and leverage) and the inverse-calibration code are delivered under agreement (see access).

Module_03 // Reserved Method

The methodology —dual-chamber polytropic model, inverse calibration, Monte Carlo— is public in the white paper with its DOI. What is not published openly is the calibrated table model by model (the exact curve of each suspension) and the fitting code. We reveal the composition and the confidence; the per-model curve is shared under agreement.

Module_04 // Record (cite this)
DATASET: 132 Calibrated MTB Suspensions — Dual-chamber model
AUTHOR: Carlos Eduardo Ravello Joo · ORCID 0009-0007-5631-7436
Permanent DOI: 10.5281/zenodo.20735088
White paper: Dual-Chamber Physical Model — MTB Suspension SAG
Indexed: Zenodo · OpenAIRE · DataCite · License CC BY 4.0
Module_05 // Limitations and Access

The shocks without an official table (ground truth) operate at lower confidence (~42%) and are modeled by analogy; the dataset declares this explicitly. The parameters allow independent validation with more field data. Provisional by design.

Need the calibrated curve of a specific model, the complete dataset or to calibrate your own line? It is delivered under agreement from the Independent Laboratory · carlos.ravello@zoovettravel.com

© 2026 BikeLab Studio. Content under CC BY 4.0: you may copy, translate and publish it, even for commercial purposes. Citation is mandatory. Without attribution, the license terminates automatically (CC BY 4.0, §6.a) and the use becomes copyright infringement: we request the removal of the content and its deindexing. · Design and ownership: Carlos Ravello Joo

BikeLab Studio · Open Data / 132 Calibrated MTB Suspensions / Carlos Eduardo Ravello Joo · ORCID 0009-0007-5631-7436 · Trujillo, Peru