We utilize Bayesian analysis of variance to estimate the effects of calibration and disconnect levels for repeated scattering-parameter measurements made with a vector network analyzer. Here, we describe the Bayesian estimation technique, including our random effects model, prior assumptions, Markov chain Monte Carlo simulations, and convergence diagnostics. Our data is structured hierarchically, such that measurements are nested within repeats, which are nested within disconnects, which are further nested within calibrations.

Data associated with the publication: "The fundamental linewidth limit of electromagnetically induced transparency in a thermal Rydberg ladder system"

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. RNA was not included and LNPs were ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 μL of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper).

Data for a paper to be submitted to CPEM2026. Abstract:

Polymerase chain reaction (PCR) applications including sequencing rely on accurate thermostable DNA polymerases. Polymerization errors may hinder the detection of low-level DNA variants such as mutations in clinical samples or DNA from minor contributors in crime scene traces. Short Tandem Repeat (STR) markers are particularly affected by artefacts. Apart from the regular random base substitutions, the repeated structure of STRs makes them prone to formation of stutter products. However, the mechanisms leading to stutter formation have not yet been fully elucidated.

Python library for building life cycle assessment in NHERI SimCenter's Pelicun tool

This repository contains Python analysis scripts for evaluating indoor air quality during simulated wildfire smoke infiltration events in a manufactured home test structure. The research investigates the effectiveness of various mitigation strategies, including portable air cleaners, HVAC filtration systems, and compartmentalization approaches for protecting indoor environments from wildfire smoke.

The dataset is melt pool cross-sectional microscopy and measurements for 2D arrays of laser tracks on solid metal IN718 plates and single layers of IN718 powder on solid metal IN718 plates. The primary laser parameters such as laser power, speed, and spot diameter, and hatch spacing were fixed. The pad geometry, laser turnaround time (i.e., skywriting time), and powder layer thickness were the investigated variables. The experiments were part of AM Bench 2025 (AMB2025-06 and AMB2025-07 benchmarks).

Affinis is a tool for assisting in unsupervised structure learning on sparse, binary data. For large (sparse) feature matrices, especially ones with binary-valued entries, techniques to figure out the underlying structure of the feature space are widely varied, and different communities have widely different practices and assumptions for what is an appropriate approach. Affinis provides reference implementations for many of these methods, with a consistent API to enable community adoption and a shared benchmarking environment.

This data repository is the basis for the figures in the manuscript "A Fabrication-motivated Model for Improving Simulations of On-wafer Transmission Lines", F. Bergmann et al. (2026), accepted for publications in IEEE MWTL. The abstract is as follows: