π rheodataο
Quality rheology data, curated for the age of simulation and AI.
rheodata is a growing collection of rheological datasets β flow curves, linear
viscoelastic spectra, and more β gathered from the literature and from the
community, cleaned and standardized so you can load a dataset with one
function call and get on with the science.
Good models are trained on good data, and good models are tested on good data
too. Machine-learning surrogates need large, tidy training sets; constitutive
models need independent curves to benchmark against; researchers need
reproducible numbers they can cite. rheodata exists to be that shared,
trusted ground: every dataset ships with its full provenance β the paper, the
figure it was digitized from, the geometry, the temperature, the protocol β
so you always know exactly what youβre looking at. π¬
π¦ Installationο
pip install rheopy-rheodata
π Quickstartο
import rheodata
rheodata.list() # what's in the registry?
df = rheodata.load("caggioni_pg_carbopol_2pct") # tidy pandas DataFrame (SI units)
rheodata.plot("caggioni_pg_carbopol_2pct") # take a look
list() returns one row per dataset:
id material_name experiment_type
0 caggioni_carbopol_glycerin_temp Carbopol in glycerin flow_curve
1 caggioni_linear_polymer_amplitude_sweep High-molecular-weight linear polymer in water amplitude_sweep
2 caggioni_linear_polymer_flow High-molecular-weight linear polymer in water flow_curve
(14 datasets in v0.1.1 β flow curves, amplitude and frequency sweeps.)
The catalog below has one page per dataset β metadata, samples, column schema, and a preview plot.
π€ Contributeο
Found a paper whose data should live here? Have data of your own to share? Read Contributing a dataset β submissions arrive as a GitHub issue with a DOI, and the bar is simple: tidy CSVs in SI units with complete metadata.
Datasets
Contribute
Reference