| """Minimal, runnable example: load and analyse the dataset straight from HF. |
| |
| Requires: pandas, pyarrow |
| pip install pandas pyarrow |
| |
| Run: |
| python examples/quickstart.py |
| """ |
| import pandas as pd |
|
|
| BASE = "hf://datasets/skyleee/diversa-biomedical-v1" |
|
|
|
|
| def main(): |
| works = pd.read_parquet( |
| f"{BASE}/data/works.parquet", |
| columns=["work_id", "publication_year", "field_name", "cited_by_count", |
| "is_oa", "international_collaboration"], |
| ) |
| print(f"works: {len(works):,}") |
| print(f"years: {works.publication_year.min()}-{works.publication_year.max()}") |
| print(f"open access share: {works.is_oa.mean():.1%}") |
| print(f"international collaboration rate: {works.international_collaboration.mean():.1%}") |
|
|
| print("\nworks per field:") |
| print(works.field_name.value_counts().head(10).to_string()) |
|
|
| print("\nmost-cited topics (dimension table):") |
| topics = pd.read_parquet(f"{BASE}/data/topics.parquet", |
| columns=["topic_name", "works_count", "total_citations"]) |
| print(topics.sort_values("total_citations", ascending=False).head(10).to_string(index=False)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
