{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"ASDF is a Python implementation of the Advanced Scientific Data Format, a file format for storing hierarchical scientific data with human-readable YAML metadata and efficient binary array storage.","skillfed_tags":["data-serialization","scientific-computing","numpy-integration"],"use_cases":["Store and share multi-dimensional numpy arrays alongside structured metadata in a single validated file.","Archive scientific simulation results with hierarchical metadata and compression for efficient storage.","Exchange complex scientific datasets between Python applications with schema validation and human-readable metadata.","Load large array data on demand via memory mapping without loading the entire file into memory.","Extend ASDF with custom tags to serialize domain-specific Python objects alongside standard scientific data."],"what_it_does":"ASDF is a file format and Python library for storing scientific data with structured, human-readable metadata in YAML and efficient binary storage for arrays. It combines hierarchical metadata validation via JSON Schema with lazy-loading and optional memory-mapping of array data, making it suitable for interchange of complex scientific datasets. The package handles native Python types transparently and supports custom extensions for domain-specific objects.\n\nTypical workflows involve creating a nested dictionary tree of data and metadata, writing it to an ASDF file with optional compression (zlib, bzp2, or lz4), and later reading it back with lazy array access. The format is designed for scientific data interchange and long-term archival, with support for flexible compression and schema-based validation to ensure data integrity.","worth_installing":"Yes. ASDF is actively maintained, has low install friction, and solves a genuine need for structured scientific data interchange. The unclear license status is a minor concern that should be resolved by checking the repository before use in proprietary contexts, but it does not block evaluation. The package is well-suited for scientific workflows involving numpy arrays and hierarchical metadata."},"id":"asdf","links":{"html":"https://skillfed.io/packages/asdf","md":"https://skillfed.io/packages/asdf.md","pypi":"https://pypi.org/project/asdf/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-08","license_spdx":null,"license_treatment":"unclear","name":"asdf","python_support":"supports_current","summary":"Python implementation of the ASDF Standard"},"popularity":{"monthly_downloads":326850,"position":7571,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"5.3.1"}