Upload embpy static embedding package (13 prefixes)

README.md

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+---
+pretty_name: Embpy Static Embeddings
+license: other
+tags:
+- embpy
+- biology
+- genomics
+- gene-embeddings
+- protein-embeddings
+- zarr
+---
+
+# Embpy Static Embeddings
+
+This dataset contains static gene and protein embeddings packaged with embpy. Embedding values are stored in Zarr arrays, while row identifiers, provenance, species metadata, and AnnData-like `.uns` metadata are stored in sidecar metadata files.
+
+## Summary
+
+- Repository: `theislab/Embpy_Data`
+- Schema version: `embpy.static_embedding_package.v1`
+- Generated at: `2026-06-01T15:07:46+00:00`
+- Number of embeddings: `11`
+- Total indexed entities: `201,717`
+- Species keys: `human_9606`
+- NCBI taxonomy IDs: `9606`
+- Default gene identifier policy: `ensembl_id`
+- Unresolved gene identifier policy: `drop`
+
+## Available Embeddings
+
+| key | entity | species | id type | rows | dims | description |
+| --- | --- | --- | --- | --- | --- | --- |
+| crispr_gene_effect | gene | human_9606 | ensembl_id | 17,087 | 1,178 | DepMap CRISPR gene effect matrix, genes as rows after transposition. |
+| crispr_gene_effect_1178 | gene | human_9606 | ensembl_id | 17,087 | 1,178 | DepMap CRISPR gene effect embedding, scaled, 1178d. |
+| crispr_gene_effect_205 | gene | human_9606 | ensembl_id | 17,916 | 205 | DepMap CRISPR gene effect embedding, scaled, 205d. |
+| gene2vec | gene | human_9606 | ensembl_id | 18,795 | 200 | Gene2Vec co-expression embedding, 200d. |
+| genept | gene | human_9606 | ensembl_id | 18,807 | 3,072 | GenePT GPT-3.5 text embedding, 3072d, Ensembl-keyed. |
+| genept_scaled | gene | human_9606 | ensembl_id | 17,728 | 3,072 | GenePT GPT-3.5 text embedding, z-scored, 3072d. |
+| omics | gene | human_9606 | ensembl_id | 19,385 | 256 | Omics 256d static gene embedding, Ensembl-keyed. |
+| pops | gene | human_9606 | ensembl_id | 18,383 | 256 | PoPS 256d gene features, Ensembl-keyed. |
+| string_functional_9606 | protein | human_9606 | string_protein_id | 19,699 | 512 | STRING/SPACE functional PPI embedding for human proteins, species 9606, 512d. |
+| string_node2vec_9606 | protein | human_9606 | string_protein_id | 19,622 | 128 | STRING node2vec PPI embedding for human proteins, species 9606, 128d. |
+| wikicrow | gene | human_9606 | ensembl_id | 17,208 | 4,096 | WikiCrow text embedding, scaled, 4096d. |
+
+## File Layout
+
+```text
+manifest.json
+embeddings/<model_key>/
+  values.zarr/
+  metadata/
+    index.parquet
+    index.csv
+    metadata.json
+    uns.json
+```
+
+The dense matrix is stored under `values.zarr`. The `metadata/index.parquet` file maps row positions to `entity_id` values and any preserved aliases such as source IDs or gene symbols.
+
+## Loading With embpy
+
+```python
+from embpy.pp import HFHandler
+
+hf = HFHandler("theislab/Embpy_Data")
+embedding = hf.download_embedding("crispr_gene_effect")
+matrix = embedding["embeddings"]
+ids = embedding["ids"]
+```
+
+For a local checkout or downloaded snapshot:
+
+```python
+from embpy import load_static_embedding_package
+
+store = load_static_embedding_package("/path/to/package", key="crispr_gene_effect")
+tp53 = store.get("ENSG00000141510")
+tp53_by_symbol = store.get("TP53", id_type="symbol")
+```
+
+Missing identifiers raise by default. Use `missing="drop"` or `missing="nan"` when a partial result is acceptable.
+
+## Metadata And Species
+
+This card reports species using package-level defaults. Regenerate the package with a current embpy build to also write per-embedding `species`, `taxonomy_id`, and `species_key` fields into `metadata/metadata.json`.
+
+## Validation
+
+The package was designed to be validated locally before upload:
+
+```bash
+python -m embpy.scripts.package_static_embeddings validate --package /path/to/package
+```
+
+This package intentionally skips 2 source collection(s) containing 2642 file(s). Those collections usually contain per-species artifacts and should be packaged only when a species/taxonomy ID is selected explicitly.
+
+## License And Attribution
+
+This repository aggregates embeddings derived from multiple upstream resources. Please check the per-embedding metadata and upstream sources for the applicable licenses and citation terms.