Dataset card: reader-tested rewrite (tissue table, no-code/R/Python paths, splits, access)

Revised after persona reviews (ML engineer, wet-lab PI, first-time postdoc). All numbers recomputed from the v1.0.0-20261025 catalog; code snippets tested. No data files changed.

README.md

@@ -17,15 +17,113 @@ configs:
 
 *Left to right: H&E and GFAP from the same Visium section (GSM9721856), Xenium DAPI (GSM9301054), and a CosMx composite (GSM9372712). The GFAP panel displays all 3,416 recorded spots using the existing log1p preview values.*
 
-Search and inspect the metadata for 12,163 samples, 1,265 published study folders and 635,240,170 spatial observations in the fixed `sda-unified-20261010-v3` snapshot. It includes 8,211 samples with released tissue images.
+Search 12,163 public spatial transcriptomics samples before you request any data. They come from 1,265 published
+GEO series and hold 635,240,170 spatial observations (spots, bins, cells or beads). 8,211 samples have a released
+tissue image: 5,263 H&E, 871 H&E-like (a pixel-based call that no reviewer confirmed), 1,850 fluorescence (mostly DAPI) and 227 other
+stains. Platforms include Visium, Xenium, legacy ST, CosMx, MERFISH/MERSCOPE, Slide-seq, Stereo-seq and Visium HD.
 
-This repository contains metadata only. [Full sample data](https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB) require manual access approval and are currently being uploaded. The metadata catalog's presence does not imply that the full upload is complete.
+The samples were found and standardized from GEO by the [SpatialDataAgent](https://github.com/YSTLab/spatial-data-agent)
+workflow (7,777 samples, each checked by an independent scorer) and by the earlier HESRT H&E collection (4,386
+samples, each reviewed by eye). The atlas is described in the
+[bioRxiv preprint](https://doi.org/10.64898/2026.05.27.727615).
 
-`catalog/samples.parquet` and `samples.csv` provide the original release index, including repository-relative `sample_dir` paths and source content hashes. Study metadata and source-bound biological/audit annotations are included as `atlas-study-metadata.jsonl` and `atlas-sample-metadata.jsonl`.
+This repository holds **metadata only** and needs no account. The expression matrices (H5AD), images (TIFF) and
+per-sample JSON are in the [full dataset](https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB), about
+1.8 TB in total. Every file of the release has passed content verification. To get the files:
 
-Reported vocabulary and age units are not harmonized. Missing biological metadata and review states remain explicit. Counts capability, source verification, image registration and human review describe different checks. Observations are spots, cells, bins or beads, not independent donors.
+1. Request access on that page with your Hugging Face account and an institutional e-mail; requests are reviewed
+   manually.
+2. After approval, run `hf auth login` (or set `HF_TOKEN`).
 
-Source index SHA256: `909c4ec50e71c636eadc7bf01a9479fef1b4fd480ac348dfea6b785e566c7bed`. See the main Dataset Card for data organization, quality distinctions, splits, provenance and source-specific reuse terms.
+## Is my tissue here?
+
+Samples whose GEO tissue text matches each organ. The counts are approximate, because tissue labels are free
+text; use the search for exact cohorts.
+
+| Organ | Samples | Human | Studies | With H&E |
+|---|---:|---:|---:|---:|
+| Brain | 2,562 | 999 | 241 | 1,260 |
+| Intestine / colon | 1,416 | 735 | 81 | 241 |
+| Lung | 901 | 649 | 124 | 346 |
+| Liver | 760 | 184 | 101 | 271 |
+| Spinal cord | 637 | 182 | 22 | 251 |
+| Kidney | 563 | 289 | 71 | 203 |
+| Skin | 375 | 238 | 72 | 179 |
+| Heart | 309 | 89 | 54 | 142 |
+| Pancreas | 304 | 200 | 38 | 181 |
+| Lymph node / spleen | 254 | 127 | 58 | 99 |
+| Breast | 215 | 153 | 28 | 150 |
+| Prostate | 107 | 72 | 15 | 89 |
+
+**No code needed.** Filter the `tissue`, `platform` or `organism` column in the Dataset Viewer on this page, or
+open `catalog/samples.csv` in Excel or R:
+
+```r
+samples <- read.csv("https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB-Catalog/resolve/main/catalog/samples.csv")
+kidney  <- subset(samples, grepl("kidney|renal", tissue, ignore.case = TRUE) & organism == "Homo sapiens")
+nrow(kidney)   # 289
+```
+
+The `sda-db` commands work from any terminal, also alongside an R workflow.
+
+## Search in one minute
+
+With the [spatial-data-agent-db](https://github.com/Biogod2020/spatial-data-agent-db) Python package and CLI
+(no login):
+
+```bash
+pip install "spatial-data-agent-db @ git+https://github.com/Biogod2020/spatial-data-agent-db"
+sda-db search alzheimer hippocampus --organism human --has-image    # ranked over study text and sample fields
+sda-db search --platform Visium --stain "H&E" --split train --output cohort.json
+sda-db search --gene GFAP --gene AQP4 --platform Xenium               # samples whose panel measures both genes
+```
+
+Or read the table directly:
+
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download("Biogod/SpatialDataAgent-DB-Catalog", "catalog/samples.parquet", repo_type="dataset")
+samples = pd.read_parquet(path)
+he = samples[(samples.platform == "Visium") & (samples.image_stain == "H&E")]
+print(len(he), he[["gsm", "tissue", "n_obs", "image_um_per_px", "split"]].head())
+# sample_dir is the folder in the full dataset: <sample_dir>/spatial.h5ad, sample.json, image.tif
+```
+
+After access is approved, the same package plans download sizes (`sda-db download --from cohort.json --dry-run`),
+checks every file against its SHA256, and reads samples as AnnData. Streaming-ready training shards remain a
+planned follow-up. AI agents can use the same tools through its
+MCP server (`sda-db mcp`).
+
+With approved access, load a sample and cut an image patch paired with a spot:
+
+```python
+import json
+import sda_db
+from sda_db.io import image_coordinates
+from sda_db.image import read_patch
+
+adata = sda_db.load("GSM8915497", include_image=True)    # verified download, then AnnData with release metadata
+meta = json.loads(adata.uns["sda_db"]["sample_json"])
+pixels = image_coordinates(adata, meta)                  # image pixel position of every spot
+patch, _ = read_patch(adata.uns["sda_db"]["directory"], pixels[0], 224)   # H&E patch paired with adata.X[0]
+```
+
+The H5AD files also open in R, for example with Bioconductor's `zellkonverter::readH5AD`.
+
+## Files
+
+| File | Content |
+|---|---|
+| `catalog/samples.parquet`, `catalog/samples.csv` | One row per sample: platform, organism, tissue, counts, units, image, QC, split, `sample_dir` path in the full dataset, file SHA256 |
+| `catalog/atlas-study-metadata.jsonl` | Study title, summary, design and PubMed IDs |
+| `catalog/atlas-sample-metadata.jsonl` | Reported disease, condition, age, sex, donor and publication; counts evidence and audit |
+| `catalog/feature-index.sqlite.gz` | Genes and features measured in each sample (41.9 MB; provenance in `catalog/feature-index-provenance.json`) |
+| `catalog/schema.json`, `catalog/summary.json`, `catalog/studies.csv` | Column dictionary, totals, per-study counts |
+
+Source index SHA256: `909c4ec50e71c636eadc7bf01a9479fef1b4fd480ac348dfea6b785e566c7bed` (snapshot
+`sda-unified-20261010-v3`, release tag `v1.0.0-20261025` of the full dataset).
 
 ## Collection composition
 
@@ -33,16 +131,30 @@ Source index SHA256: `909c4ec50e71c636eadc7bf01a9479fef1b4fd480ac348dfea6b785e56
 
 *Sample counts by platform, image availability, source collection and retained split (n = 12,163; source snapshot: sda-unified-20261010-v3).*
 
-## Access and reuse
+## Splits for machine learning
+
+The `split` column assigns whole GEO series to train (9,249 samples), val (1,316) or test (1,598), so samples of
+one study never cross splits. Of the H&E samples, 3,847 are in train, 358 in val and 1,058 in test. The split does not prove donor independence across studies; check
+`content_group` and `aliases` (in `samples.parquet`) and `donor_id` (in `atlas-sample-metadata.jsonl`) for
+your benchmark.
 
-Browse the public catalog, request access to the [full sample data](https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB), then retrieve H5AD, available TIFF images and JSON metadata. AnnData/Scanpy can read downloaded expression files.
+## Read the annotations correctly
 
-The [spatial-data-agent-db toolkit](https://github.com/Biogod2020/spatial-data-agent-db) is a Python package, CLI and
-MCP server for AI agents.
+- **Vocabularies.** Organism, tissue, disease and age are kept as reported in GEO, not harmonized.
+- **Missing values.** Missing values stay empty; an empty `review_decision` is not a PASS.
+- **Separate checks.** Each check has its own column. Counts capability is `counts_ready` in
+  `atlas-sample-metadata.jsonl`. The others are columns of `samples.parquet`:
+  - source verification: `verification`;
+  - image registration: `image_registration`;
+  - human review: `review_decision`.
+- **Image scale.** `image_um_per_px` gives micrometres per image pixel where known; 5,113 of the 5,263 H&E
+  images have it. `coordinate_units` and `um_per_unit` describe the spatial coordinates.
+- **Observations.** Observations are spots, cells, bins or beads, not independent donors.
 
-- Without an account, it searches this catalog by keywords and filters. It also searches by measured gene
-  through `catalog/feature-index.sqlite.gz`, the features of every sample's expression file
-  (`catalog/feature-index-provenance.json`).
-- With approved access, it plans, downloads, verifies and reads samples.
+## Terms
 
-Streaming training shards remain a planned follow-up output.
+The catalog and the data follow the terms of the original studies. There is no blanket license, and the
+catalog has no per-sample license column. See the
+[full Dataset Card](https://huggingface.co/datasets/Biogod/SpatialDataAgent-DB#data-use-and-source-terms) for
+reuse terms and citation, and cite the original studies with the data you use (`sda-db cite` writes BibTeX for
+a selection).