This web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes. The motivation behind this web validation system is to prepare a large number of pixel-level multi-class annotated microscopy images for training artificial intelligence (AI) based segmentation models (U-Net and SegNet models). While the purpose of the AI models is to predict accurately four damage labels, such as, paste damage, aggregate damage, air voids, and no-damage, our goal is to assert trust in such predictions (a) by using contextual labels and (b) by enabling visual validations of predicted damage labels.

Automated particle analysis (SEM/EDS) data from samples known to have been exposed to gunshot residue and from samples occasionally mistaken for gunshot residue - like brake dust and fireworks. The dataset consists of analyses of 30 discrete samples: 12 from sampling automobiles ("brake dust"), 10 from sampling fireworks ("sparklers" and "spinners" and "roman candles"), 8 from shooter's left or right hands. The analysis configuration meta-data for each analysis are contained in the "configuration.txt" and "script.py" files. The raw data from each analysis is in the file pair "data.pxz" and "data.hdz". The HDZ-file details the contents of the PXZ-file. In addition, the "mag0" directory contains TIFF images with embedded X-ray spectra for each particle in the dataset. Additional HDZ/PXZ files contain the results of reprocessing the "data.hdz/.pxz" in light of the "mag0" spectra and the standard spectra in "25 keV.zip" The samples came from Amy Reynolds (amy.reynolds@pd.boston.gov) at the Boston Police Department. The "Shooter" samples were taken from a volunteer who fired a gun at a local firing range and was then sampled immediately after. They are part of a time series that was used to study GSR retention. The TIFF Image/Spectrum files can be read using NIST DTSA-II (https://www.nist.gov/services-resources/software/nist-dtsa-ii) or NeXLSpectrum.jl (https://doi.org/10.18434/M32286). The HDZ/PXZ files can be read using NIST Graf (available on request) or NeXLParticle.jl (https://github.com/usnistgov/NeXLParticle.jl).

Automated particle analysis (SEM/EDS) data from samples known to have been exposed to gunshot residue and from samples occasionally mistaken for gunshot residue - like brake dust and fireworks. The dataset consists of analyses of 30 discrete samples: 12 from sampling automobiles ("brake dust"), 10 from sampling fireworks ("sparklers" and "spinners" and "roman candles"), 8 from shooter's left or right hands. The analysis configuration meta-data for each analysis are contained in the "configuration.txt" and "script.py" files. The raw data from each analysis is in the file pair "data.pxz" and "data.hdz". The HDZ-file details the contents of the PXZ-file. In addition, the "mag0" directory contains TIFF images with embedded X-ray spectra for each particle in the dataset. Additional HDZ/PXZ files contain the results of reprocessing the "data.hdz/.pxz" in light of the "mag0" spectra and the standard spectra in "25 keV.zip" The samples came from Amy Reynolds (amy.reynolds@pd.boston.gov) at the Boston Police Department. The "Shooter" samples were taken from a volunteer who fired a gun at a local firing range and was then sampled immediately after. They are part of a time series that was used to study GSR retention. The TIFF Image/Spectrum files can be read using NIST DTSA-II (https://www.nist.gov/services-resources/software/nist-dtsa-ii) or NeXLSpectrum.jl (https://doi.org/10.18434/M32286). The HDZ/PXZ files can be read using NIST Graf (available on request) or NeXLParticle.jl (https://github.com/usnistgov/NeXLParticle.jl).

Making Earth System Data Records for Use in Research Environments (MEaSUREs) empowers the research community to participate in developing and generating data products that complement and augment NASA produced and distributed Earth science data products. NASA’s Enhanced Solid Earth Science Earth Science Data Record (ESDR) System (ESESES) continues and extends mature geodetic data product generation and archival as part of the MEaSUREs SESES project providing new, multi-decade, calibrated and validated geodetic-derived ESDRs obtained by the Scripps Institution of Oceanography (SIO) and NASA's Jet Propulsion Laboratory (JPL). These data-derived products include continuous multi-year high-rate GNSS, seismogeodetic, and meteorological time series, a catalog of transient deformation in tectonically active areas known for aseismic motion such as ETS with focus in Cascadia, and continuous estimation and cataloging of total near-surface water content derived from continuous GNSS time series over the continental U.S. These data products are high-rate seismogeodetic displacement and velocity records for historical earthquakes (M>6); broadband displacement and seismic velocity time series combining 1 Hz GPS displacements and 100 Hz accelerometer data for select large earthquakes and collocated CGPS and seismic instruments from regional networks.

The dataset consists of synthetic seismograms recorded using the SPECFEM3D wave propagation software. Data from 8 isotropic earthquake sources were recorded at 630 stations while data from 8 realistic earthquakes were recorded at 520.

Measurements of Selenium collected at Sacramento River Below Shasta Dam. Currently collected twice a year, previously collected quarterly. Access further information for this data set by contacting Bureau of Reclamation, California-Great Basin Region, Environmental Affairs Division (CGB-157). See ResultAttributes for STAFF_GAUGE, SMPL_DEPTH, SMPL_CATEGORY_NAME, METHOD_CODE, RESULT_RL, RESULT_RL-UNIT_STD_NAME, RESULT_MDL, RESULT_MDL-UNIT_STD_NAME, USBR_QA_SUBTYPE_NAME, USBR_QULFR_DESCRIPTION. STAFF_GAUGE is the water height in decimal feet measured by gauge (e.g., 15.2). SMPL_DEPTH is the vertical depth at which sample is collected (e.g., 0 - 15 cm). For water samples: depth below water/air interface. For sediment and soil samples: depth below water/solid or air/solid interface. SMPL_CATEGORY_NAME is the category type of sample (e.g., Composite). METHOD_CODE is the name of method used to obtain result (e.g., EPA 200.8). RESULT_RL is the result reporting limit (accounting for dilution) (e.g., 0.02). RESULT_RL-UNIT_STD_NAME is the unit associated with RESULT_RL (e.g., mg/L). RESULT_MDL is the result method detection limit (e.g., 0.007). RESULT_MDL-UNIT_STD_NAME is the unit associated with RESULT_MDL (e.g., mg/L). USBR_QA_SUBTYPE_NAME is the quality control type of the sample (e.g., USBR_BLANK_SPIKE). USBR_QULFR_DESCRIPTION is the quality assurance description (if any) (e.g., Result may have a high bias.).