This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.

Sim-PROCESD is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems.

Lipid nanoparticles (LNPs) were prepared as described (https://doi.org/10.1038/s42003-021-02441-2) using the lipids DLin-KC2-DMA, DSPC, cholesterol, and PEG-DMG2000 at mol ratios of 50:10:38.5:1.5. Four sample types were prepared: LNPs in the presence and absence of RNA, and with LNPs ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 ?L of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper).

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.

The dataset can be used to develop and test algorithms for communication and sensing in the 60GHz band. The dataset consists of synthetically generated indoor mm-wave channels between a MIMO transmitter and a MIMO receivers. Multiple targets are moving in the room. Number of targets, velocity of each target and trajectory are randomized across the dataset. The dataset contains also noisy received IEEE 802.11ay channel estimation fields. The dataset is suitable for development and testing of machine/deep learning algorithms.

SRM 2969 Electronic files containing certified values and their uncertainties, and the data used to assign those values

Electronic files containing certified values and their uncertainties, and the data used to assign those values.

This software and data have been superseded. Please visit https://doi.org/10.18434/mds2-2532

The National Institute of Standards and Technology, Intelligent Systems Division has collected data measuring human subjects, while performing common, simulated industrial manufacturing tasks with and without wearing an exoskeleton. Five tests were completed as part of a research study to develop measurement science towards standard test methods. For simulated industrial manufacturing tasks were performed using a novel, now standardized apparatus, called the Position and Load Test Apparatus for Exoskeletons (PoLoTAE).

Here we provide curated analytical chemistry data for eggs collected from 1999 to 2010 on a subset of species and analytes that were measured regularly and reasonably systematically. Included in this publication are 487 samples analyzed for 174 ubiquitous environmental contaminants such as (poly)brominated diphenyl ethers (BDEs), mercury, organochlorine pesticides (OCPs), and polychlorinated biphenyls (PCBs). Data were collated to form a dataset useful in chemometric and related analyses of the marine ecosystem in the north Pacific Ocean.