README.md

---
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: amusing
    dtype: float64
  - name: angry
    dtype: float64
  - name: annoying
    dtype: float64
  - name: anxious/tense
    dtype: float64
  - name: awe-inspiring/amazing
    dtype: float64
  - name: beautiful
    dtype: float64
  - name: bittersweet
    dtype: float64
  - name: calm/relaxing/serene
    dtype: float64
  - name: compassionate/sympathetic
    dtype: float64
  - name: dreamy
    dtype: float64
  - name: eerie/mysterious
    dtype: float64
  - name: energizing/pump-up
    dtype: float64
  - name: entrancing
    dtype: float64
  - name: erotic/desirous
    dtype: float64
  - name: euphoric/ecstatic
    dtype: float64
  - name: exciting
    dtype: float64
  - name: goose bumps
    dtype: float64
  - name: indignant/defiant
    dtype: float64
  - name: joyful/cheerful
    dtype: float64
  - name: nauseating/revolting
    dtype: float64
  - name: painful
    dtype: float64
  - name: proud/strong
    dtype: float64
  - name: romantic/loving
    dtype: float64
  - name: sad/depressing
    dtype: float64
  - name: scary/fearful
    dtype: float64
  - name: tender/longing
    dtype: float64
  - name: transcendent/mystical
    dtype: float64
  - name: triumphant/heroic
    dtype: float64
  splits:
  - name: train
    num_bytes: 166110026.787
    num_examples: 1841
  download_size: 159674012
  dataset_size: 166110026.787
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- feature-extraction
- audio-classification
tags:
- Music
- Emotion
- Recognition
- MERT
- Dataset
- Audio
pretty_name: 13 Dimension Emotions Dataset
size_categories:
- 1K<n<10K
---
# Dataset Card for **Music Emotion Ratings Across Cultures**

This dataset captures the mean emotional category ratings for 1,841 music samples based on subjective experiences reported by participants from the United States and China. The ratings were collected as part of the study to uncover the **universal and nuanced emotions** evoked by instrumental music.

---

## Dataset Details

### Dataset Sources

- **Paper**: [What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures](https://www.pnas.org/cgi/doi/10.1073/pnas.1910704117)  
- **Demo (Interactive Map)**: [Music Emotion Map](https://www.ocf.berkeley.edu/~acowen/music.html)  

---

## Uses

### Direct Use

This dataset is designed for:  
- **Music Emotion Classification**: Training multi-label classifiers for identifying emotions in music based on 13 universal categories.  
- **Cross-Cultural Emotion Analysis**: Analyzing similarities and differences in emotional responses to music across cultures.  
- **Emotion Visualization**: Creating high-dimensional visualizations of emotional distributions in music.  

### Out-of-Scope Use

The dataset is **not suitable** for:  
- Identifying lyrics-related emotions (as the music is instrumental).  
- Cultural or genre-specific emotional predictions outside the U.S. and China.  
- Misuse for building biased systems that assume emotional responses are fixed across all populations.  

---

## Dataset Structure

### Data Fields

- **Sample ID**: Unique identifier for each of the 2,168 music clips.  
- **Category Ratings**: Mean ratings for each of the 13 universal emotional categories:  
  - Joyful/Cheerful  
  - Calm/Relaxing  
  - Sad/Depressing  
  - Scary/Fearful  
  - Triumphant/Heroic  
  - Energizing/Pump-up  
  - Dreamy  
  - Romantic/Loving  
  - Amusing  
  - Exciting  
  - Compassionate/Sympathetic  
  - Awe-Inspiring  
  - Eerie/Mysterious  
- **Valence**: Mean ratings for pleasantness (positive or negative feelings).  
- **Arousal**: Mean ratings for energy levels (calm or excited feelings).  

### Splits

The dataset does not use predefined splits but can be segmented based on:  
- **Cultural groups**: U.S. vs. China.  
- **Emotional dimensions**: Individual emotional categories or broad features like valence/arousal.  

---

## Dataset Creation

### Curation Rationale

The dataset was created to:  
- **Map Universal Emotions in Music**: Investigate whether emotional experiences evoked by music are universal across cultures.  
- **Broaden Emotional Taxonomies**: Move beyond traditional models that use only 6 emotions or simple valence/arousal dimensions.  
- **Enable Nuanced Emotional Understanding**: Provide a high-dimensional framework for understanding and classifying emotional responses to music.  

---

### Source Data

- **Original Sources**: Instrumental music samples (5 seconds each) were contributed by participants to represent specific emotional categories.  
- **Annotations**: Ratings collected through large-scale crowdsourcing from 1,591 U.S. and 1,258 Chinese participants.  

---

## License

[More Information Needed]  

---

## Citation

If you use this dataset, please cite the following paper:
Cowen, A. S., Fang, X., Sauter, D., & Keltner, D. (2020). What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures. PNAS, 117(4), 1924-1934. https://doi.org/10.1073/pnas.1910704117

### Source Data

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#### Data Collection and Processing

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[More Information Needed]

#### Who are the source data producers?

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### Annotations [optional]

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#### Annotation process

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#### Who are the annotators?

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#### Personal and Sensitive Information

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## Bias, Risks, and Limitations

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### Recommendations

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

## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Dataset Card Authors [optional]

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## Dataset Card Contact

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