From a paper to a model

The package distinguishes a source, a dataset, and a model. That distinction lets you inspect a result without confusing what a paper reported with how optmat uses it. The Quick start shows how to retrieve a dataset and its citation in Python.

Sources and datasets

A source record describes one paper, compilation, calculation, or database. It holds the citation, DOI where available, technique, and notes about the source’s conditions and conventions. A dataset records one quantity for one material from that source, with its units, reference state where relevant, uncertainty, and location in the source.

Source-specific extraction scripts preserve the strings printed in tables. Values published only in the text are recorded with their page. Corrections are separate errata: each keeps the printed value, the correction, the reason, and the evidence. The loader applies the recorded units and corrections when the data are read.

The records distinguish original data, reproduced values, checks, and exclusions. This matters when several papers quote the same measurement: they are not independent evidence. Some records contain formulas or notes without extracted datasets. Likewise, the presence of data for a material does not mean that optmat provides a recommended model for every property of that material.

Browse Source catalogue for the recorded citations and contributions. The curated data describes the record format and conversions; Quantity vocabulary defines the vocabulary. optmat.datasets is the Python interface to the records.

Models and their support

A model may implement a published formula, interpolate a curated table, or fit selected datasets. A fit records its data selection and uncertainty treatment; measurements held out of the fit can test its predictions. The model’s description explains the physical assumptions and which data support it.

Use the functionality overview to find the appropriate model family. The User guide explains its conventions, supported ranges, and validation. Model identity, status, and validity make these choices inspectable from a calculation.

Statistical uncertainty, sensitivity to individual datasets, and agreement with independent measurements answer different questions. Read the relevant accuracy chapter before treating a numerical estimate as the uncertainty of your application: Gas dispersion: accuracy and limitations for gas dispersion or Third-order accuracy for the third-order response, for example.

The review register

The review register explains what was checked, corrections made, decisions about fits, and questions still open. References such as TC-92 point to individual entries. The source record describes the data; the register preserves the evidence and reasoning behind their treatment.

You can examine the gallery to see data, fits, and validation together. To contribute a source or reproduce a fit, follow the Developers guide; to use the existing models, continue with the User guide.