These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number. The datasets are organized in the files by instrument type: PTA (particle tracking analysis), RMM (resonant mass measurement), ESZ (electrical sensing zone), and OTH (other techniques not covered in the three largest groups, including holographic particle characterization, laser diffraction, flow imaging, and flow cytometry). In the OTH group, the specific instrument type for each dataset is noted. Each instrument type (PTA, RMM, ESZ, OTH) has a dedicated file. Included in the data files for each dataset are: (1) the cumulative particle number concentration (PNC, (1/mL)); (2) the concentration distribution density (CDD, (1/mL·nm)) based upon five bins centered at each particle population peak diameter; (3) the CDD in higher resolution, varied-width bins. The lower-diameter bin edge (µm) is given for (2) and (3). Additionally, the PTA, RMM, and ESZ files each contain unweighted mean cumulative particle number concentrations and concentration distribution densities calculated from all datasets reporting values. The associated standard deviations and standard errors of the mean are also given. In the OTH file, the means and standard deviations were calculated using only data from one of the sub-groups (holographic particle characterization) that had n = 3 paired datasets. Where necessary, datasets not using the common bin resolutions are noted (PTA, OTH groups). The data contained here are presented and discussed in a manuscript to be submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.

These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number. The datasets are organized in the files by instrument type: PTA (particle tracking analysis), RMM (resonant mass measurement), ESZ (electrical sensing zone), and OTH (other techniques not covered in the three largest groups, including holographic particle characterization, laser diffraction, flow imaging, and flow cytometry). In the OTH group, the specific instrument type for each dataset is noted. Each instrument type (PTA, RMM, ESZ, OTH) has a dedicated file. Included in the data files for each dataset are: (1) the cumulative particle number concentration (PNC, (1/mL)); (2) the concentration distribution density (CDD, (1/mL·nm)) based upon five bins centered at each particle population peak diameter; (3) the CDD in higher resolution, varied-width bins. The lower-diameter bin edge (µm) is given for (2) and (3). Additionally, the PTA, RMM, and ESZ files each contain unweighted mean cumulative particle number concentrations and concentration distribution densities calculated from all datasets reporting values. The associated standard deviations and standard errors of the mean are also given. In the OTH file, the means and standard deviations were calculated using only data from one of the sub-groups (holographic particle characterization) that had n = 3 paired datasets. Where necessary, datasets not using the common bin resolutions are noted (PTA, OTH groups). The data contained here are presented and discussed in a manuscript to be submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.

This is a file containing aggregation data for two proteins that were thermomechanically aggregated. The aggregated proteins were separated by asymmetric flow field flow fractionation and separated protein fractions were detected and quantified by UV spectrophotometry and multi-angle light scattering. The UV spectrophotometry was used to quantify the amount of residual monomer, which is reported herein. The multi-angle light scattering was fitted to a relevant model to calculate the molecular weight of the aggregated protein, also reported herein. The protein aggregation was characterized as a function of time and also the azide (preservative) concentration, which is indicated as being relevant to the aggregation process. The data contained here is plotted in a manuscript submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.

This is a file containing aggregation data for two proteins that were thermomechanically aggregated. The aggregated proteins were separated by asymmetric flow field flow fractionation and separated protein fractions were detected and quantified by UV spectrophotometry and multi-angle light scattering. The UV spectrophotometry was used to quantify the amount of residual monomer, which is reported herein. The multi-angle light scattering was fitted to a relevant model to calculate the molecular weight of the aggregated protein, also reported herein. The protein aggregation was characterized as a function of time and also the azide (preservative) concentration, which is indicated as being relevant to the aggregation process. The data contained here is plotted in a manuscript submitted to the Journal of Pharmaceutical Sciences and presented as part of that scientific record.

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. Also included are the data analysis code (MATLAB 2022a*) used for non-negative matrix factorization and other processes. The code is used to learn the dataset's latent dimensionality and decompose the data into constituent phases representative of acetaminophen polymorphs. The process is also demonstrated by unmixing a multi-component particle migraine medicine sample.Associated publication: https://doi.org/10.1021/acs.analchem.2c03913*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.

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. Also included are the data analysis code (MATLAB 2022a*) used for non-negative matrix factorization and other processes. The code is used to learn the dataset's latent dimensionality and decompose the data into constituent phases representative of acetaminophen polymorphs. The process is also demonstrated by unmixing a multi-component particle migraine medicine sample.Associated publication: https://doi.org/10.1021/acs.analchem.2c03913*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.

To verify and validate the HR-VPTM technique, both synthetic images and gels with embedded particles undergoingcontrolled deformations were used to compared known and reconstructed deformations at assorted strain-ratesor frame-rates. We simulated the light-field representation of particles undergoing motion with ray tracing andinvestigated the sensitivity of the measurement technique to synthetic noise floor and various motion fields. Inexperiments, a custom-built device deformed a hydrogel specimen in nominally simple shear at applied strain ratesapproximately 2 1/s, while light-field images were collected at approximately 500 frames per second frames per second. Files and formats include .tif images (raw data, input), .mat (reconstructed images, tracking results),.txt, .csv, and .yaml (all metadata).See also the data on MINDS@UW (https://minds.wisconsin.edu/handle/1793/83031), the accompanying paper in Experimental Mechanics (https://doi.org/10.1007/s11340-022-00885-z), and the complete code package released by collaborators at UW-Madison (https://github.com/francklab/HR-VPTM).

This dataset contains 1) table of bulk Pb-XAS LCF results, 2) table of bulk As-XAS LCF results, 3) figure data of particle size distribution, and 4) figure data for the relationship of As and Pb %IVBA in the <250 µm sieved size fraction vs sieved <250 µm to >150 µm, <150 µm to >75 µm, <75 µm to >38 µm, and <38 µm; and <250 µm ground, and <150 µm sieved and ground. This dataset is associated with the following publication: Karna, R., M. Noerpel, A. Betts, and K. Scheckel. Lead and Arsenic Bioaccessibility and Speciation as a Function of Soil Particle Size. Emmanuel Doelsch JOURNAL OF ENVIRONMENTAL QUALITY. American Society of Agronomy, MADISON, WI, USA, 46(6): 1225-1235, (2017).

Single-track laser scans were produced with Yb-fiber lasers on bare plates of IN625 and IN718 using three different laser powder bed fusion machines. The laser power, scan speed, and laser spot diameter varied. Tracks were cross-sectioned and metallographically prepared. Optical micrographs were taken on etched samples. Melt pool depth and width measurements were made on optical micrographs. The dataset includes optical micrographs and melt pool width and depth measurements. These are supplemental experiments to the single-track laser scans for Additive Manufacturing Benchmark 2018 and 2022 challenges (https://www.nist.gov/ambench/am-bench-data-and-challenge-problems-0). Some of the data is associated with publications (1) https://doi.org/10.1016/j.jmapro.2021.10.053 and (2) https://doi.org/10.1007/s40192-022-00289-w.Users are strongly encouraged to first review the ?Master_TrackList_Measuremetns.xlsx? file for description of each image file.