README.md

---
license: other
task_categories:
- image-to-text
language:
- en
tags:
- spectroscopy
- materials-science
- multimodal
- benchmark
- raman
- xrd
- ftir
- mass-spectrometry
- scientific-reasoning
- foundation-models
pretty_name: SpectraNet
size_categories:
- 10K<n<100K
---

# SpectraNet

SpectraNet is a multimodal spectroscopy benchmark for evaluating scientific reasoning in foundation models.

The benchmark contains curated experimental spectra across Raman, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and mass spectrometry (MS), together with metadata, spectrum images, processed spectral arrays, peak annotations, and evaluation-related outputs.

SpectraNet is designed to evaluate whether foundation models can perceive plotted experimental spectra, extract characteristic peaks, and support downstream scientific reasoning over spectroscopic evidence.

## Dataset Structure

The dataset is released in sharded form because the full image directory contains a large number of files.

```text
spectranet/
├── public_shards/
│   ├── public_part_0000.zip
│   ├── public_part_0001.zip
│   └── public_part_0002.zip
├── splits/
│   └── data.csv
└── manifest.json
```

After extracting all ZIP files in `public_shards/`, the original public dataset structure will be restored as:

```text
public/
├── data/
│   └── shards/
│       ├── 00.parquet
│       ├── 01.parquet
│       └── ...
└── images/
    ├── *.png
    └── ...
```

## Files

### `public_shards/`

The full `public/` folder is provided as multiple ZIP shards. Each ZIP file preserves the original folder structure, including:

```text
public/data/
public/images/
```

To reconstruct the full dataset, download all ZIP files under `public_shards/` and extract them into the same directory.

### `public/data/`

This folder contains Parquet shards storing the processed spectral arrays and compact metadata.

Each Parquet record includes fields such as:

```text
sample_id
mid
modality
subkey
x
y
x_unit
y_unit
x_min
x_max
y_min
y_max
n_points
```

The `x` and `y` fields store the processed spectral coordinates and intensities.

### `public/images/`

This folder contains PNG spectrum previews generated from the processed spectral arrays.

The image filenames correspond to the `sample_id` field used in the metadata table.

### `splits/data.csv`

`data.csv` is the main post-processed metadata table. It contains one row per processed sample and includes both metadata fields and file pointers.

Important columns include:

```text
sample_id
mid
modality
subkey
image_path
data_path
x_min
x_max
y_min
y_max
n_points
x_unit
y_unit
method
measurement_condition
crystal_system
material_name
phase_label
classification
preferred_chemical_formula
source_format
file_path
```

The `image_path` column points to the corresponding PNG image under `public/images/`.

The `data_path` column points to the corresponding Parquet shard under `public/data/shards/`.

## Reconstructing the Dataset

Download all ZIP files in `public_shards/`, then extract them into the same output directory:

```python
from pathlib import Path
import zipfile

root = Path("spectranet")
shard_dir = root / "public_shards"
out_dir = root / "extracted"

out_dir.mkdir(parents=True, exist_ok=True)

for zip_path in sorted(shard_dir.glob("public_part_*.zip")):
    print("Extracting", zip_path.name)
    with zipfile.ZipFile(zip_path, "r") as zf:
        zf.extractall(out_dir)

print("Done.")
```

After extraction, the reconstructed dataset should contain:

```text
extracted/public/data/
extracted/public/images/
```

## Loading Metadata

```python
import pandas as pd

df = pd.read_csv("splits/data.csv")
print(df.head())
```

## Loading a Parquet Shard

```python
import pandas as pd

parquet_path = "public/data/shards/00.parquet"
data = pd.read_parquet(parquet_path)
print(data.head())
```

## Linking Images and Data

Each row in `splits/data.csv` contains an `image_path` and a `data_path`.

```python
import pandas as pd
from pathlib import Path

df = pd.read_csv("splits/data.csv")

row = df.iloc[0]
image_path = Path(row["image_path"])
data_path = Path(row["data_path"])

print("Image:", image_path)
print("Parquet shard:", data_path)
```

## License

This dataset is released for research use.

Please refer to the original data sources for source-specific licensing terms.

## Citation

If you use SpectraNet, please cite the associated paper:

```bibtex
@article{spectranet2026,
  title={SpectraNet: A Multimodal Spectroscopy Benchmark for Evaluating Scientific Reasoning in Foundation Models},
  author={Gu, Yijun and Yang, Jingyun and Liu, Yongtao and Wang, Haozhe},
  year={2026}
}
```

## Notes

This repository is currently under active release preparation.

Dataset files are uploaded in segmented batches because of repository file-count and upload-rate limitations.