File size: 3,256 Bytes
11ccf60 32fa7e5 11ccf60 | 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 | from pathlib import Path
import pandas as pd
import requests
import json
def infer_split(name):
name = name.lower()
if "train" in name:
return "train"
if "val" in name or "validation" in name:
return "validation"
if "test" in name:
return "test"
return "unspecified"
repo = Path(".").resolve()
xyz_dir = repo / "xyz"
h5_dir = repo / "h5"
rows = []
for xyz_file in sorted(xyz_dir.glob("*.xyz")):
sample_id = xyz_file.stem
h5_file = h5_dir / f"{sample_id}.h5"
rows.append({
"id": sample_id,
"xyz_path": str(xyz_file.relative_to(repo)),
"h5_path": str(h5_file.relative_to(repo)) if h5_file.exists() else None,
"split": infer_split(sample_id),
})
df = pd.DataFrame(rows)
print(df)
print(f"Rows: {len(df)}")
df.to_parquet("metadata.parquet", index=False)
# --- Fetch Croissant metadata from Hugging Face ---
url = "https://huggingface.co/api/datasets/CatalystAnonymous/catalyst_mxenes/croissant"
response = requests.get(url)
response.raise_for_status()
data = response.json()
# --- Get dataset node ---
# Croissant can either be a single Dataset node or use @graph.
if "@graph" in data:
dataset_node = data["@graph"][0]
else:
dataset_node = data
# --- Update general dataset description ---
dataset_node["description"] = (
"Dataset associated with the article: Benchmark Dataset for Catalysis on 2D MXenes. "
"The dataset contains computational catalyst data for MXene structures, including "
"first-principles simulation results intended for machine learning benchmarks."
)
# --- Inject Responsible AI metadata ---
dataset_node["rai:dataLimitations"] = (
"The dataset is limited to adsorption on MXenes with a Ti$_2$C backbone, "
"terminated by O and/or OH groups at varying surface coverages. "
"The considered chemical reactions are restricted to those involving CO$_2$, H$_2$, H$_2$O, "
"and HCOOH, including adsorbed reaction intermediates such as HCOO$^-$ and COOH$^-$. "
)
dataset_node["rai:dataBiases"] = (
"The dataset reflects the sampling strategy, structural prototypes, adsorbate choices, "
"simulation settings, and preprocessing choices used during construction. This may bias "
"models toward the represented MXene compositions, surface configurations, and "
"first-principles calculation conditions."
)
dataset_node["rai:personalSensitiveInformation"] = (
"The dataset does not contain personal or sensitive information."
)
dataset_node["rai:dataUseCases"] = (
"Intended for benchmarking and developing machine learning models for catalysis on "
"2D MXenes, "
"learning from first-principles simulation data."
)
dataset_node["rai:dataSocialImpact"] = (
"The dataset may support catalyst discovery and materials science research. Misuse "
"could include applying trained models outside the domain covered by the simulations "
"or interpreting predictions as experimentally validated results."
)
dataset_node["rai:hasSyntheticData"] = True
# --- Save updated Croissant metadata ---
with open("croissant.json", "w", encoding="utf-8") as f:
json.dump(data, f, indent=2, ensure_ascii=False)
print("Saved croissant.json with updated RAI metadata") |