Code and demonstration data for the paper, "On-the-fly closed-loop materials discovery via Bayesian active learning," Kusne, A.G., Yu, H., Wu, C. et al. Nat Commun 11, 5966 (2020). https://doi.org/10.1038/s41467-020 19597-w Code: Closed-loop autonomous materials exploration and optimization. This code is used to control an autonomous materials exploration and optimization platform. It guides subsequent experiments to learn about a material's phase map and target functional properties in a unified framework.

The first version of HVACSIM+, which stands for "HVAC SIMulation PLUS other systems", was introduced by National Institute of Standards and Technology (NIST) in 1985 as a computer simulation tool to simulate entire building systems. Since then, the HVACSIM+ computer program package and manuals have been distributed to researchers, students, and consultants in more than 40 countries around the world.

This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines. These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry.

Viscosity data for normal alkanes (methane, ethane, etc.) taken from the literature to accompany the paper "Entropy Scaling of Viscosity -- II: Predictive Scheme for Normal Alkanes". The data were obtained from an internal version of the NIST ThermoDataEngine database version 10.4.2. File contents include

## Contents ##

* MIDAS_alkanes.csv: The data file containing all the data considered in this study

* MIDAS_alkanes.bib: The bibliography associated with each datasource in BibTeX format

* allbibs.pdf: A PDF conversion of the bibliography

This dataset includes files generated from post-build X-ray computed tomography (XCT) measurements of the four parts built as part of the "Overhang Part X4" in-situ process monitoring dataset (available at https://doi.org/10.18434/M32233). The "Overhang Part X4" dataset was a three-dimensional (3D) additive manufacturing (AM) build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung and Brandon Lane on June 28, 2019.

Supplemental data for "Comparison of measured and simulated spin-wave mode spectra of magnetic nanostructures" by H. T. Nembach, R.D. McMichael, M.L. Schneider, J.M. Shaw, T.J. Silva.1) Experimental spectra of approximately elliptical, 100 nm or 200 nm elliptical magnetic structures. 2) SEM images of the magnetic structures 3) Scripts and data used in micromagnetic modeling and simulated measurements of the structures.

This is a searchable database of NVLAP-accredited laboratories. NVLAP stands for National Voluntary Laboratory Accreditation Program.

NeXLSpectrum is a collection of tools for reading, writing and processing X-ray spectra. It includes methods to read common spectrum and hyperspectrum formats, extract spectral data, plot the spectra with common spectrum markers, and perform filter-fitting. The fitting algorithm has been optimized for both speed and low memory usage (<1 ms and 160 kB per spectrum for 6 elements on a single thread).

In-situ Raman spectroscopic measurements were conducted during indentation of fused silica (FS) samples. The experiments were conducted using Raman spectroscopy-enhanced instrumented indentation testing (RS-IT). RS-IT was developed at the National Institute of Standards and Technology (NIST) and employs an in-house built instrumented indentation testing device that is coupled to a custom Raman microscope.[Rev. Sci. Instrum. 83 125106 (2012)]

Round 2 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.