SciDuck

A thin DuckDB layer for managing versioned scientific data.

Each variable is stored in its own table. Variables are associated with a hierarchical dataset schema (e.g. subject -> session -> trial) and can be saved at any level of that hierarchy. Multiple versions of each variable are supported natively.

All data -- including arrays -- is stored in queryable DuckDB types (LIST, nested LIST, JSON) so the database can be inspected with DBeaver or any DuckDB-compatible viewer.

Usage

from sciduckdb import SciDuck

duck = SciDuck("data.duckdb", dataset_schema=["subject", "session"])
duck.save("MyVar", data, subject="S01", session=1)
loaded = duck.load("MyVar", subject="S01", session=1)

Features

Three save modes: DataFrame with schema columns (Mode A), single entry via kwargs (Mode B), or dict mapping tuples to values (Mode C)
Automatic type inference: Maps Python/numpy types to DuckDB types
Round-trip restoration: Metadata tracks original types for lossless load
Version management: Automatic version numbering, duplicate hash detection
Variable groups: Organize variables into named groups
Schema validation: Validates dataset schema consistency across sessions

Note: all schema key values are coerced to strings before storage.

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