A dataset of 2400 quantum cascade structures at 15 electric field iterations, for a total of 36000 unique designs. The structures are generated by randomly altering a starting 10-layer design of alternating Al0.48In0.52As barrier material and In0.53Ga0.47As well material, with layer thickness sequence of 9/57/11/54/12/45/25/34/14/33 Angstroms (starting with well material). The random tolerance range is from -5 to +20 Angstroms in 5 Angstrom increments. The laser transition Figure of Merit, among other quantities of interest, is identified for each design using a method found in:
A. C. Hernandez, M. Lyu and C. F. Gmachl, "Generating Quantum Cascade Laser Datasets for Applications in Machine Learning," 2022 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 2022, pp. 1-2, doi: 10.1109/SUM53465.2022.9858281
The dielectric function for "Astrodust" grain material is provided for different assumed values of the dust grain shape (spheroid axis ratio) and porosity (vacuum fraction), and fraction of the interstellar iron present as metallic inclusions. For each case, the dielectric function is obtained by requiring that the grains reproduce the observed infrared opacity, and match to a physically reasonable dielectric function at 1 micron, and extending to X-ray energies. The derived dielectric functions satisfy the Kramers-Kronig relations. Dielectric functions are provided from 1 Angstrom to 5 cm (12.4 keV to 2.59e-5 eV).
For each dielectric function, we also calculate absorption and scattering corss sections for spheroidal grains, for three orientations of the grain relative to incident linearly-polarized light, for wavelengths from the Lyman limit (0.0912 micron) to the microwave (4 cm), and grain "effective radii" a_eff from 3.162A to 5.012 micron.
These data include 39 structured interview transcripts. Each case is someone who worked at the time for Uber, UberEats, Lyft, and/or Amazon Flex (Amazon’s contractor delivery service). These data were collected between July and September 2019. All but one of the interviews occurred over the phone. My questions are focused on the structure of their gig work jobs and the technology they used at work or expected to use at work in the future. I included a description of the data, the recruitment methods, and the discussion guide in this ReadMe file.
