metadata

size_categories:
  - 10K<n<100K
task_categories:
  - text-classification
dataset_info:
  features:
    - name: Canonicalized SMILES
      dtype: string
    - name: Canonicalized Flavor
      dtype: string
    - name: Original Labels
      dtype: string
    - name: Source
      dtype: string
  splits:
    - name: train
      num_bytes: 1237660
      num_examples: 13634
    - name: test
      num_bytes: 263642
      num_examples: 2922
    - name: validation
      num_bytes: 266452
      num_examples: 2922
  download_size: 568637
  dataset_size: 1767754
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: validation
        path: data/validation-*
tags:
  - chemistry

FartDB

Composite dataset of 31,483 molecules and their taste (sweet, bitter, umami, sour, salty, miscellaneous).

Dataset Details

Dataset Description

Curated by: Fart Labs
License: [More Information Needed]

Dataset Sources

Gradinaru Teodora-Cristiana, Madalina Petran, Dorin Dragos, and Marilena Gilca. "PlantMolecularTasteDB: A Database of Taste Active Phytochemicals." Frontiers in Pharmacology 2022; 12:3804. doi: 10.3389/fphar.2021.751712

Neelansh Garg†, Apuroop Sethupathy†, Rudraksh Tuwani†, Rakhi NK†, Shubham Dokania†, Arvind Iyer†, Ayushi Gupta†, Shubhra Agrawal†, Navjot Singh†, Shubham Shukla†, Kriti Kathuria†, Rahul Badhwar, Rakesh Kanji, Anupam Jain, Avneet Kaur, Rashmi Nagpal, and Ganesh Bagler*, "FlavorDB: A database of flavor molecules," Nucleic Acids Research, gkx957, (2017). †Equal contribution *Corresponding Author

Rojas, C., Ballabio, D., Pacheco Sarmiento, K., Pacheco Jaramillo, E., Mendoza, M., & García, F. (2021). ChemTastesDB: A Curated Database of Molecular Tastants (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5747393

Uses

Direct Use

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Out-of-Scope Use

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Dataset Structure

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Dataset Creation

Curation Rationale

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Source Data

Data Collection and Processing

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Who are the source data producers?

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Annotations [optional]

Annotation process

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Personal and Sensitive Information

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Bias, Risks, and Limitations

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Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

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