This data set accompanies the manuscript "Tailored and Externally Corrected Coupled Cluster with Quantum Inputs", which can be found on arXiv. The environment.yml file contains specifications for a conda environment that (hopefully) sets up all dependencies to run the notebooks contained herein. - matchgate_shadow_statistics contains raw data for the 8-qubit shadow recorded on a 22-layer QNP-VQE circuit of N2/cc-pVDZ. The notebook `shadow_estimation.ipynb` contains the code to produce the plots for Figures C1 and C2. - error_model contains the raw data from noisy TCCSD calculations on 13 molecules with different basis sets as a `pandas.DataFrame` in `error_model_noise_tccsd.h5`. A script `test_diagnostics.py`, including the molecular geometries, computes the CC diagonstics using `pyscf` and can be run with `pytest` using the `pytest-harvest` plugin, and writes them to `diagnostics_data_general.h5`. The notebook `error_model_resources.ipynb` includes the following tasks: 1. Read in the data and create a new `DataFrame` which contains the average TCCSD energy error (over 30 samples) 2. Power law fit of the whole data set to extract the exponents $\beta$ and $\gamma$ (eq 9, Table C3) 3. Bootstrap estimation of the exponents' standard errors 4. The per-molecule power law fit with fixed exponents to obtain the molecule-specific prefactor $a$ 5. Read-in of diagnostics data and creation of diagnostic->prefactor linear fit (Figure 4, eq C7) 6. Shot budget estimation for the N2 dissociation curve (requires `pyscf`)