File size: 4,532 Bytes
373124f 31ec806 373124f 03c6d59 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
---
language:
- en
license: mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-classification
- object-detection
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: string
- name: target
dtype: string
- name: instrument
dtype: string
- name: filter
dtype: string
- name: date_obs
dtype: string
- name: exptime
dtype: 'null'
- name: ra
dtype: 'null'
- name: dec
dtype: 'null'
- name: program
dtype: string
- name: image_path
dtype: string
- name: width
dtype: int64
- name: height
dtype: int64
- name: total_pixels
dtype: int64
- name: mean_intensity
dtype: float64
- name: std_intensity
dtype: float64
- name: min_intensity
dtype: float64
- name: max_intensity
dtype: float64
- name: median_intensity
dtype: float64
- name: skewness
dtype: float64
- name: kurtosis
dtype: float64
- name: dynamic_range
dtype: float64
- name: noise_level
dtype: float64
- name: noise_std
dtype: float64
- name: signal_to_noise
dtype: float64
- name: saturated_pixels
dtype: int64
- name: saturation_percentage
dtype: float64
- name: is_saturated
dtype: bool
- name: cosmic_rays
list:
list: int64
- name: hot_pixels
list:
list: int64
- name: bad_pixels
list: 'null'
- name: artifact_count
dtype: int64
- name: quality_score
dtype: float64
splits:
- name: train
num_bytes: 18984918
num_examples: 2709
download_size: 3821834
dataset_size: 18984918
---
# JWST Quality Analysis Dataset
## Overview
This dataset contains comprehensive quality analysis for 2,709 JWST (James Webb Space Telescope) NIRCam images from the MAST archive. Each image has been automatically analyzed for quality metrics, artifact detection, and noise characteristics.
## Dataset Information
- **Size**: 2,709 images
- **Format**: JSONL (JSON Lines)
- **Source**: JWST NIRCam observations from MAST
- **Targets**: M16, NGC 3132, NGC 3324, SMACS 0723, Stephan's Quintet
## Quality Metrics
Each image includes:
### Basic Statistics
- `mean_intensity`, `std_intensity`, `min_intensity`, `max_intensity`
- `median_intensity`, `skewness`, `kurtosis`, `dynamic_range`
### Noise Analysis
- `noise_level`, `noise_std`, `signal_to_noise`
### Saturation Analysis
- `saturated_pixels`, `saturation_percentage`, `is_saturated`
### Artifact Detection
- `cosmic_rays`: List of cosmic ray locations [x, y, area]
- `hot_pixels`: List of hot pixel locations [x, y, area]
- `bad_pixels`: List of bad pixel locations [x, y, area]
- `artifact_count`: Total number of artifacts
### Quality Assessment
- `quality_score`: Overall quality score (1-10 scale)
## Use Cases
### For Researchers
- **Quality Screening**: Filter images by quality score for analysis
- **Artifact Cataloging**: Identify and locate artifacts for cleaning
- **Statistical Analysis**: Study image quality across different targets/filters
- **Quality Benchmarking**: Compare quality across different observations
### For Machine Learning
- **Training Data**: Train quality assessment models
- **Feature Engineering**: Use quality metrics as features
- **Validation**: Quality scores for model evaluation
## Methodology
The quality analysis was performed using:
- **OpenCV** for image processing and artifact detection
- **NumPy/SciPy** for statistical analysis
- **Parallel processing** for efficient analysis of large datasets
Quality scores are calculated based on:
- Signal-to-noise ratios
- Saturation levels
- Artifact counts
- Dynamic range
## Dataset Statistics
- **Mean Quality Score**: 8.39/10
- **Quality Score Range**: 6.0 - 10.0
- **Images with Artifacts**: All images contain some artifacts (typical for astronomical data)
- **Saturated Images**: 0 (no significant saturation detected)
## Citation
If you use this dataset in your research, please cite:
```bibtex
@dataset{jwst_quality_analysis_2024,
title={JWST Quality Analysis Dataset},
author={Your Name},
year={2024},
url={https://huggingface.co/datasets/norbertm/jwst-quality-analysis-dataset}
}
```
## License
This dataset is provided for research purposes. Please refer to the original JWST data usage policies from MAST.
## Contact
For questions or feedback about this dataset, please open an issue on the Hugging Face repository. |