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.

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.

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.

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomicists. There are now a wide variety of robust and inexpensive enrichment strategies to generate phosphoproteomes, while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities (ABRF) umbrella, the Proteomics Standards Research Group (sPRG) has worked to develop a multipathway phosphopeptide prototype mixture based on a pool of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This prototype mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/IGF-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. We describe a characterization of the standard spiked into a HeLa tryptic digest stimulated with both EGF and IGF1 to activate the MAPK and PI3K/AKT/mTOR pathways. We demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently by the co-authors with this prototype mixture with data independent acquisition.

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomicists. There are now a wide variety of robust and inexpensive enrichment strategies to generate phosphoproteomes, while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities (ABRF) umbrella, the Proteomics Standards Research Group (sPRG) has worked to develop a multipathway phosphopeptide prototype mixture based on a pool of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This prototype mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/IGF-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. We describe a characterization of the standard spiked into a HeLa tryptic digest stimulated with both EGF and IGF1 to activate the MAPK and PI3K/AKT/mTOR pathways. We demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently by the co-authors with this prototype mixture with data independent acquisition.

This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included. The code employs principal component analysis for dimensionality reduction, learns the resulting datasets' latent dimensionality, and completes Gaussian mixture modeling and fuzzy c-means clustering.*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.

This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included. The code employs principal component analysis for dimensionality reduction, learns the resulting datasets' latent dimensionality, and completes Gaussian mixture modeling and fuzzy c-means clustering.*Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.

Supplementary material for "Kreutz, A.; Clifton, M.S.; Henderson, W.M.; Smeltz, M.G.; Phillips, M.; Wambaugh, J.F.; Wetmore, B.A. Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry. Toxics 2023, 11, 463. https://doi.org/10.3390/toxics11050463". This dataset is associated with the following publication: Kreutz, A., M. Clifton, W. Henderson, M. Smeltz, M. Phillips, J. Wambaugh, and B. Wetmore. Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry. Toxics. MDPI, Basel, SWITZERLAND, 11(5): 463, (2023).