Add benchmark landing README

README.md

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+---
+pretty_name: "Lodestar — benchmark inputs"
+license: other
+---
+
+# 🧬 Lodestar — benchmark inputs
+
+**Lodestar** is a blind benchmark for transcriptomic foundation models: it measures
+how well a model's patient-level embeddings capture real clinical signal.
+
+This repo holds the **inputs you embed**. It's deliberately *blind* — datasets are
+named `d001`, `d002`, … with no disease, tissue, or target revealed. You grade the
+embedding, not task-specific tuning.
+
+## 📦 What's inside
+- **`datasets.yaml`** — the manifest: each dataset's `id`, file `path`, and shape
+  (`n_samples`, `n_genes`, `gene_id_type`).
+- **`d001/expression.h5ad`, `d002/…`** — raw counts as `AnnData`: rows = samples
+  (`sample_id`), columns = NCBI gene ids (with `gene_symbols`).
+
+## 🚀 Run the benchmark
+1. **Download** the data:
+   ```bash
+   hf download ScientaLab/lodestar --repo-type dataset --local-dir lodestar
+   ```
+2. **Embed every dataset** with your model — one vector per sample (any dimension;
+   it may differ per dataset).
+3. **Assemble one submission file** covering all datasets — `dataset_id`,
+   `sample_id`, then one column per embedding dim (`e0`, `e1`, …). Format:
+   **CSV / TSV / Parquet**, or **NPZ** with `dataset_ids` / `sample_ids` / `embeddings`.
+4. **Submit & see your rank** →
+   **[🏆 Lodestar Space](https://huggingface.co/spaces/ScientaLab/lodestar-eval)**
+
+## 📊 How it's scored
+A fixed linear probe is trained on frozen cross-validation folds over your embeddings
+and scored — **AUROC** (classification) or **Pearson r** (regression). Scores become a
+0–1 **skill** and roll up per medical specialty. **Only submissions that cover every
+dataset are ranked.** Labels stay private — grading happens server-side.
+
+➡️ **Ready?** → **https://huggingface.co/spaces/ScientaLab/lodestar-eval**
+
+*Each dataset is redistributed under its source's original open license.*