{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"SpatialData is a data framework and serialization format for uni- and multi-modal spatial omics datasets, providing schema-based storage and access across Python, R, and JavaScript implementations.","skillfed_tags":["spatial-omics","data-framework","bioinformatics"],"use_cases":["Load spatial transcriptomics or proteomics data from commercial platforms (e.g., Visium, MERFISH) into a standardized format for downstream analysis.","Store and share multi-modal spatial omics experiments (combining imaging, gene expression, and metadata) in a single interoperable file.","Build analysis pipelines that work with spatial data in Python while ensuring compatibility with R and JavaScript implementations.","Perform distributed spatial operations (clustering, segmentation, neighborhood analysis) on large datasets using dask integration.","Visualize spatial omics data interactively via the napari plugin or generate publication-ready static plots."],"what_it_does":"SpatialData is a framework and schema for storing and working with spatial omics data\u2014datasets that combine gene expression, protein abundance, or other molecular measurements with spatial coordinates. It defines a universal format built on the OME-NGFF specification, allowing researchers to represent uni- and multi-modal spatial experiments in a standardized way. The core library handles data access, manipulation, and serialization; it is part of a growing ecosystem that includes companion packages for loading data from common technologies, static plotting, and interactive exploration.\n\nThe package depends on a large stack of scientific libraries\u2014including dask for distributed computing, geopandas for spatial operations, xarray for multi-dimensional arrays, and zarr for chunked storage\u2014making it suitable for workflows that need to process, analyze, or share spatial omics datasets across teams or languages. It is actively maintained by the scverse project and has been cited in peer-reviewed literature, though the documentation notes that the library is still under community review and may see API changes.","worth_installing":"Yes, if you work with spatial omics data and need a standardized, cross-language framework. The package is actively maintained, permissively licensed, and backed by a peer-reviewed publication and institutional support. Install friction is low. However, be aware that it requires Python 3.12, carries substantial downstream dependencies, and the documentation explicitly notes the library is under review\u2014expect potential API changes as the community provides feedback. Not suitable if you need Windows-specific support guarantees or absolute API stability."},"id":"spatialdata","links":{"html":"https://skillfed.io/packages/spatialdata","md":"https://skillfed.io/packages/spatialdata.md","pypi":"https://pypi.org/project/spatialdata/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-02","license_spdx":null,"license_treatment":"permissive","name":"spatialdata","python_support":"supports_current","summary":"Spatial data format."},"popularity":{"monthly_downloads":129899,"position":11656,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.0"}