Workflow: The data that falls into this category are gathered from a measurement program and are published in the archival literature.

During work on the new reference equation of state for carbon dioxide [A.H. Harvey, S.A. Tashkun, R. Hellmann, and E.W. Lemmon, J. Phys. Chem. Ref. Data, in preparation], we obtained unpublished data from several sources. These represent numerical values for data only presented graphically in a publication, or in some cases data not present in the publication at all. With the permission of the authors, we document and deposit these data here so they will be available for future workers.

This is the data presented in the figures of the paper "The effects of advanced spectral line shapes on atmospheric carbon dioxide retrievals" published in J. Quant. Spectrosc. Radiat. Transfer at https://doi.org/10.1016/j.jqsrt.2022.108324

Round 10 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of object detection AIs trained on the COCO dataset. 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. This dataset consists of 144 AI models using a small set of model architectures.

We obtained stylus profilometry measurements of Parylene C devices after soaking in different fluid conditions in a microfluidic environment. The Parylene C devices consist of platinum coplanar waveguides (400 nm Pt, 50 um center conductor, 5 um gaps, 200 um ground planes) with 6.5 um of Parylene C deposited on top. A PDMS microfluidic layer was aligned on top of the 10.00 mm CPW such that the 4.00 mm channel was centered on the CPW.

OSCAL Deep Diff is a CLI application and library that can produce schema-agnostic comparisons of JSON artifacts. The purpose of this tool is to compare OSCAL artifacts.

The NISTmAb Interlaboratory NMR Study is a joint effort between 25 organizations to evaluate the performance of two-dimensional (2D) heteronuclear NMR as used to characterize the high order structure (HOS) of proteins, with the aim of harmonizing 2D-NMR methods to support industrial use of NMR as applied to mAb therapeutics. This study features nearly equal representation between laboratories from academia, government, and industry, including 4 laboratories from regulatory agencies.

The following data files include microstructure measurement results associated with the 2022 Additive Manufacturing Benchmark test series (AM-Bench 2022) AMB2022-01 benchmark on laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN718 test objects.

This data publication contains the code and demonstration data from a study using non-negative matrix factorization to learn, characterize, and chemically map crystal polymorphs at the single particle scale from high spatial resolution time-of-flight secondary ion mass spectrometry (ToF-SIMS) images. The data from this study includes the ToF-SIMS chemical imaging of three inkjet printed arrays of acetaminophen deposits, corresponding THz Raman spectra, and ToF-SIMS chemical images of a pure acetaminophen powder and a migraine medicine.

Dataset for multiple publishable figures in the paper entitled "Quasi-continuous voltage standard using sinusoidal and pulse-driven Josephson junction arrays" submitted to Meas. Sci. and Tech. journal. Figure 3. (a) Pattern of pulses used to generate a dc voltage of 0.2 V, (b) difference from the nominal of the voltage measured using the K3458A versus dc bias current for different RF power biases.Figure 4. PD JJA voltage difference from nominal, measured with a K3458A, versus dc bias current for different pulse patterns and repetition frequencies.Figure 5.