SRM 3669 Arsenic Species in Frozen Human Urine (Elevated Levels) - This Standard Reference Material (SRM) is intended primarily for validating analytical methods and measurements for the determination of arsenic species in human urine. A unit of SRM 3669 consists of five pouches each containing a vial of arsenic species in frozen human urine at elevated levels. Each vial contains nominally 1.5 mL of urine. This data is public in the Certificate of Analysis for this material.

Standard Reference Material 3949 Folate Vitamers in Frozen Human Serum

Standard Reference Material 909c Frozen Human Serum - This Standard Reference Material (SRM) is intended primarily for use in validating analytical methods for the determination of specified constituents in human serum. This SRM can also be used for quality assurance when assigning values to in house control materials. A unit of SRM 909c consists of three vials, each containing 2 mL of frozen human serum. This data is public in the Certificate of Analysis for this material.

Standard Reference Material (SRM) 968 and its reissues are human serum based materials used as validation standards by clinical, epidemiological, and nutrition laboratories. Historically, about 300 units of this SRM have been sold annually. Based on this annual sales projection, as of November 1, 2015 there were about 16 months of inventory of the current issue: SRM 968e Fat-Soluble Vitamins, Carotenoids, and Cholesterol in Human Serum.

SRM 972a Vitamin D Metabolites in Frozen Human Serum - This Standard Reference Material (SRM) is intended for use as an accuracy control in the critical evaluation of methods for determining the amount-of-substance concentration of vitamin D metabolites in human serum. This SRM can also be used as a quality assurance tool for assigning values to in-house control materials for these constituents. This data is public in the Certificate of Analysis for this material.

This folder contains image data sets from 14 separate serial sectioning sessions. The entire data folder consists of 1379 8 bit tif images and is 47.3 GB in size. Serial sectioning was performed using FEI Helios 660 NanoLab focused ion beam scanning electron microscope (FIB SEM) and Auto Slice and View G3 software. The sample was a heavy metal stained and resin embedded Caenorhabditis elegans (C. elegans) that were exposed to 60 nm Au nanoparticles. Detailed descriptions of the worm preparation and resin block processing are described in Johnson, M.E. et al. (ACS Nano, 2016).

Using a modified Thomas-Fermi approximation, we model a reference semiconductor system comprising a quasi-1D nanowire with a series of five depletion gates whose voltages determine the number of quantum dots (QDs), the charges on each of the QDs, as well as the conductance through the wire. The original dataset, QFlow lite, consists of 1 001 idealized simulated measurements with gate configurations sampling over different realizations of the same type of device.

There is often a large amount of maintenance data already available for use in Smart Manufacturing systems, but in a currently-unusable form: service tickets and maintenance work orders (MWOs). Nestor is a toolkit for using Natural Language Processing (NLP) with efficient user-interaction to perform structured data extraction with minimal annotation time-cost.

The Micronutrients Measurement Quality Assurance Program (MMQAP) was coordinated by the Chemical Sciences Division and supported measurement technology for selected fat- and water-soluble vitamins and carotenoids in human serum. This program was initiated in 1984 by the National Cancer Institute Division of Cancer Prevention and Control to ensure the long-term reliability of the measurements made while studying the possible cancer chemoprevention roles of these compounds.

The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization.