datascribe_api: Enabling Data-Driven Materials Discovery with DataScribe

Abstract

datascribe_api is a Python library and command-line interface (CLI) that gives researchers programmatic access to DataScribe Cloud, an AI-native platform for materials design and discovery. DataScribe integrates two data services: user-managed scientific data tables, where research groups store experimental measurements, simulation outputs, and derived features; and curated materials science databases from the Materials Project, AFLOW, and OQMD. datascribe_api exposes both through a single, typed Python interface, so researchers can retrieve, filter, and analyze data without writing custom HTTP code. The library handles authentication, connection management, and transient network errors automatically. It exposes nine query endpoints as ordinary Python method calls. Filter conditions use standard Python comparison syntax and are passed as a list, keeping query logic readable without format-specific boilerplate. Pydantic models validate all API responses at parse time, so schema mismatches surface as exceptions rather than silent wrong values. Each result collection converts to a pandas DataFrame in a single call, so retrieved data is directly available for machine learning and statistical analysis. Every materials search result carries a provenance field identifying its source database, satisfying FAIR traceability requirements. The library includes a CLI that exposes all nine endpoints as subcommands, using Typer for argument parsing and Rich for terminal output. Every subcommand supports machine-readable JSON output for shell pipelines and LLM agents. Pre-built wheels for Python 3.11 and later are published on PyPI under the GPLv3 license for Linux, macOS, and Windows.