  The area-normalized change of mass ($\Delta$m/A) with time during the
oxidation of metallic alloys is commonly used to assess oxidation resistance.
Analyses of such data can also aid in evaluating underlying oxidation
mechanisms. We performed an exhaustive literature search and digitized
normalized mass change vs. time data for 407 alloys. To maximize the impact of
these and future mass uptake data, we developed and published an open, online,
computational workflow that fits the data to various models of oxidation
kinetics, uses Bayesian statistics for model selection, and makes the raw data
and model parameters available via a queryable database. The tool, Refractory
Oxidation Database (https://nanohub.org/tools/refoxdb/), uses nanoHUB's Sim2Ls
to make the workflow and data (including metadata) findable, accessible,
interoperable, and reusable (FAIR). We find that the models selected by the
original authors do not match the most likely one according to the Bayesian
information criterion (BIC) in 71% of the cases. Further, in 56% of the cases,
the published model was not even in the top 3 models according to the BIC.
These numbers were obtained assuming an experimental noise of 2.5% of the mass
gain range, a smaller noise leads to more discrepancies. The RefOxDB tool is
open access and researchers can add their own raw data (those to be included in
future publications, as well as negative results) for analysis and to share
their work with the community. Such consistent and systematic analysis of open,
community generated data can significantly accelerate the development of
machine-learning models for oxidation behavior and assist in the understanding
and improvement of oxidation resistance.
