基于机器学习可解释拉曼签名的病毒识别数据集

数据集获取（Figshare）

• 源站标题：Dataset for the PNAS article titled ‘‘Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning’’
• DOI：10.6084/m9.figshare.19426739.v7
• 发布日期：2022-04-16
• 文件数：2 个
• 文件总体积：5.00 MB
• 源站页面：https://figshare.com/articles/dataset/pnas_dataset_csv/19426739

命令行下载

curl -L -o "virus_spectra.zip" "https://ndownloader.figshare.com/files/34826425"
curl -L -o "virus_spectra_plots.zip" "https://ndownloader.figshare.com/files/34826431"

文件清单

• virus_spectra.zip（1.72 MB）
• virus_spectra_plots.zip（3.29 MB）

文件总体积 5.00 MB（源站 API 实测）。

数据加载示例（表格/文本类）

import pandas as pd, glob, os

files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))

数据说明

About Dataset

Context

The dataset is acquired via the VIRRION platform designed for label-free capture and enrichment of viruses. It consists of Raman spectra of three groups of RNA viruses, including human respiratory viruses (influenza A H1N1 and H3N2, influenza B, rhinovirus, respiratory syncytial virus (RSV)), avian respiratory viruses (influenza A H5N2 and H7N2, infectious bronchitis virus (IBV), reovirus), and human enteroviruses (Coxsackievirus B type 1 and 3 (CVB1, CVB3), enteroviruses EV70 and EV71).

Content

The dataset contains 10 sample Raman Spectra for each virus type/subtype in “virus_spectra.zip”, and visualization of the spectra in “virus_spectra_plots.zip”.

The Raman mapping data of spectra was collected for each sample by the 20X20 grid, and the first two colomns are the corresponding X and Y coordinates respectively. In each txt file that corresponds to one Raman spectrum, the columns are:

• X coordinate

• Y coordinate

• Wavenumber

• Signal Intensity

More data and machine learning source code

More data are available upon request, for research purposes only. Please email mtterrones@gmail.com (Dr. Mauricio Terrones) with a short description about the purpose of usage along with your request for more data.

The machine learning source code for our work is available via the GitHub Repo below:

https://github.com/karenyyy/Accurate-Virus-Identification-with-Interpretable-Raman-Signatures-by-Machine-Learning-.git

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.19426739.v7（source: https://figshare.com/articles/dataset/pnas_dataset_csv/19426739）。