COMBIgor: Data Analysis Package for Combinatorial Materials Science

Kevin R. Talley, Sage R. Bauers, Celeste L. Melamed, Meagan C. Papac, Karen N. Heinselman, Imran Khan, Dennice M. Roberts, Valerie Jacobson, Allison Mis, Geoff L. Brennecka, John D. Perkins, Andriy Zakutayev

Research output: Contribution to journalArticlepeer-review

52 Scopus Citations


Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially resolved characterization of structure and properties. Because of the maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment and designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data-analysis and -visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms. Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.

Original languageAmerican English
Pages (from-to)537-547
Number of pages11
JournalACS Combinatorial Science
Issue number7
StatePublished - 8 Jul 2019

Bibliographical note

Publisher Copyright:
© 2019 American Chemical Society.

NREL Publication Number

  • NREL/JA-5K00-73859


  • combinatorial libraries
  • data analysis
  • high-throughput experiments
  • Igor Pro
  • property mapping
  • software
  • thin films


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