This data repository contains original files (fcs) of flow cytometry experiments. The data was used to demonstrate the use of stochastic regression to quantify subpopulations of cells that have distinctly different genome copies per cell within a heterogenous population of Escherichia coli (E. coli) cells.  This new approach gives estimates of signal and noise, the former of which is used for analysis, and the latter is used to quantify uncertainty.

Data associated with figures in Optics Letters 2022 publication "A dual chirped-pulse electro-optical frequency comb method for simultaneous molecular spectroscopy and dynamics studies: Formic acid in the THz region".

This is a research data set extracted from NIST Standard Reference Database 103b and contains raw experimental data for viscosity and surface tension of binary mixtures.

SolDet is an object-oriented package for solitonic feature detection in absorption images of Bose-Einstein condensate. with wider use for cold atom image analysis. Featured with classifier, object detector, and Mexican hat metric methods. Technical details are explained in https://arxiv.org/abs/2111.04881.

A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. Jupyter Notebooks for quickly testing concepts and reproducing figures, as well as source code that computes the mean squared error as a function of dataset size for various machine learning models are included.For additional details on the data, please refer to the README.md associated with the data.

Atomic Bose-Einstein condensates (BECs) are widely investigated systems that exhibit quantum phenomena on a macroscopic scale. For example, they can be manipulated to contain solitonic excitations including conventional solitons, vortices, and many more. Broadly speaking, solitonic excitations are solitary waves that retain their size and shape and often propagate at a constant speed. They are present in many systems, at scales ranging from microscopic, to terrestrial and even astronomical.

This portal is a gateway to documented digital forensic image datasets. These datasets can assist in a variety of tasks including tool testing, developing familiarity with tool behavior for given tasks, general practitioner training and other unforeseen uses that the user of the datasets can devise. Most datasets have a description of the type and locations of significant artifacts present in the dataset. There are descriptions and finding aides to help you locate datasets by the year produced, by author, or by attributes of the dataset.

This Photopolymer AM-Bench 2022 Challenge is to accurately model the relationship between photopatterned print fidelity and cure depth to exposure time with four resins, which serve to orthogonally probe the relationship between resin reactivity and viscosity. The data sets included here are broken down into three categories and are as follows: (1) resin characterization: Fourier transform infrared spectroscopy and rheometry, (2) light engine characterization: photomask dimensions, beam profilometry and radiometry, and (3) cure depth and profile: laser scanning confocal microscopy.

These data were acquired in the process of collecting data for [1]. They comprise IQ waveform outputs of two different software-defined radios (SDRs) that were collected for various combinations of 1- and 2-tone excitation conditions. For details about how the data were collected, see that publication. [1] McNulty, M., Gu, D., Kuester, D. and Nayeri, P.

Data for all measurements published in the manuscript "Bipolar Waveform Synthesis with an Optically Driven Josephson Arbitrary Waveform Synthesizer," which will be published in the journal "IEEE Transactions on Applied Superconductivity".https://doi.org/10.1109/tasc.2022.3167660All files saved as Python pickle files.