mahynski/pgaa-sample

Summary

This dataset was originally reported in Mahynski et al. (2023). See this publication for a full description. Briefly, rows of X are prompt-gramma ray activation analysis (PGAA) spectra for different materials. They have been normalized to sum to 1. The peaks have been binned into histograms whose centers (energy in keV) are given as the columns. From Mahynski2023:

Our dataset consists of a variety of samples of different organic and inorganic materials. [The histogram below] shows a summary of the different categories of materials used. Various SRMs and materials were selected as representative of each class, and complete descriptions of the selected materials in each category are available in the Supplemental Information (SI). For example, "steel" contains samples of various alloys, and "biomass" contains samples ranging from wood chips to plant leaves. PGAA spectra were collected as histograms. The instrument used at NIST to obtain this data collects spectra in 2^14 = 16,384 energy bins spaced evenly to cover a range of up to approximately 12 MeV. The energy value for each bin is estimated by a calibration run which produces a linear fit of bin index to energy. This means the numerical energy value of a bin can vary slightly between measurements. All spectra were aligned to 2^14 new bins evenly spaced between the global minimum and maximum energies in the dataset by linearly interpolating each spectrum at the fixed bin centers. Next, we coarsened the spectra by summing every 4 bins to produce aligned spectra with 2^12 = 4,096 total bins. Since very low energy portions of the spectra are considered unreliable, we removed the first 40 bins so that the spectra spanned from approximately 0.1 to 12 MeV using 4,056 bins. Finally, we normalized each spectrum so that the total number of counts summed to unity; while it is possible to normalize these measurements using the length of collection time, calibrated neutron flux, and the mass of the sample we found an empirical normalization to be simpler, and more consistent as it does not depend on the accurate measurement of other factors. The energy range over which spectra are collected varies. Bins beyond an individual measurement’s limit are fixed at the last measured value, creating an artifact... Detector efficiency also decreases non-linearly at higher energies, leading to lower counts. As a result, both the global mean and variance over the dataset systematically decrease in higher energy regions of the spectra. These high-energy regions contain the artifacts, which should be regarded as spurious.

The data here was obtained from github.com/mahynski/pgaa-material-authentication/data using the PyChemAuth package.

Here is a summary of the frequency of the different classes of materials in the dataset. For a more detailed description of these classes refer to the paper linked above.

Citation

@article{Mahynski2023,
   author = {Nathan A. Mahynski and Jacob I. Monroe and David A. Sheen and Rick L. Paul and H. Heather Chen-Mayer and Vincent K. Shen},
   doi = {10.1007/s10967-023-09024-x},
   issn = {0236-5731},
   journal = {Journal of Radioanalytical and Nuclear Chemistry},
   month = {7},
   title = {Classification and authentication of materials using prompt gamma ray activation analysis},
   url = {https://link.springer.com/10.1007/s10967-023-09024-x},
   year = {2023},
}

Access

See HuggingFace documentation on loading a dataset from the hub. Briefly, you can access this dataset using the huggingface api like this:

from huggingface_hub import hf_hub_download
import pandas as pd

dataset = pd.read_csv(
    hf_hub_download(
      repo_id="mahynski/pgaa-sample",
      filename="data.csv",
      repo_type="dataset",
      token="hf_*" # Enter your own token here
  ) 
)

or using datasets:

from datasets import load_dataset

ds = load_dataset(
  "mahynski/pgaa-sample",
  token="hf_*" # Enter your own token here
)
df = ds['train'].to_pandas()