Data from controlled methane release of 10/29/2022

Data from peer-reviewed publication: A. J. Fleisher et al., Dual electro-optic frequency comb photonic thermometry, Optics Letters. We report a precision realization of photonic thermometry using dual-comb spectroscopy to interrogate a ?-phase-shifted fiber Bragg grating. We achieve read-out stability of 7.5 mK at 1 s and resolve temperature changes of similar magnitude?sufficient for most industrial applications.

This module for ns-3 implements the classes required to model a network architecture based on the O-RAN Alliance's specifications. These models include a Radio Access Network (RAN) Intelligent Controller (RIC) that is functionally equivalent to O-RAN's Near-Real Time (Near-RT) RIC, and reporting modules that attach to simulation nodes and serve as communication endpoints with the RIC in a similar fashion as the E2 Terminators in O-RAN.

This collection is comprised of multiple sets of experiments involving iPSCs with varying dosages of fluorescent light excitation. Samples were imaged every 2 minutes using phase-contrast microscopy for 20+ hours with or without fluorescence excitation. Each experiment was done on different days (i.e. exp0 vs exp1) but with triplicate wells (i.e. exp1-0, exp1-1, exp1-2). Additionally, data used to train our 2D U-Net and 3D U-Net models are included. Detailed information for each dataset can be found in Dataset_Information.xlsx.

Round 13 Train Dataset

Round 13 Test Dataset

Round 13 Holdout Dataset

The purpose of this software and data is to enable reproduction and facilitate extension of the computational results associated with the following reference

DeJaco, R. F.; Majikes, J. M.; Liddle, J. A.; Kearsley, A. J. Binding, Brightness, or Noise? Extracting Temperature-dependent Properties of Dye Bound to DNA. Biophysical Journal, 2023, https://doi.org/10.1016/j.bpj.2023.03.002.

Numerical values of all data points shown in figures for manuscript "Trap-Integrated Superconducting Nanowire Single-Photon Detectors with Improved RF Tolerance for Trapped-Ion Qubit State Readout", available on arXiv at https://arxiv.org/abs/2302.01462

Manuscript in press at Applied Physics Letters.

SDNist v2 is a Python package that provides benchmark data and evaluation metrics for deidentified data generators.

This version of SDNist supports using the NIST Diverse Communities Data Excerpts, a geographically partitioned, limited feature data set.

The deidentified data report evaluates utility and privacy of a given deidentified dataset and generates a summary quality report with performance of a deidentified dataset enumerated and illustrated for each utility and privacy metric.