  Experimental materials data are heterogeneous and include a variety of
metadata for processing and characterization conditions, making the
implementation of data-driven approaches for developing novel materials
difficult. In this paper, we introduce the Thin-Film Alloy Database (TFADB), a
materials data management platform, designed for combinatorially investigated
thin-film alloys through various experimental tools. Using TFADB, researchers
can readily upload, edit, and retrieve multidimensional experimental alloy
data, such as composition, thickness, X-ray Diffraction, electrical
resistivity, nanoindentation, and image data. Furthermore,
composition-dependent properties from the database can easily be managed in a
format adequate to be preprocessed for machine learning analyses. High
flexibility of the software allows management of new types of materials data
that can be potentially acquired from new combinatorial experiments.
