Documentation updates.

DEVELOP.md

@@ -1,14 +1,19 @@
 # Contributing
 
-## Create example Zarr files
-
-From the root directory, run:
+## Rust
+From the project root, run:
 
 ```bash
-uv run python scripts/generate_data.py
+cargo test
 ```
 
-This will generate `data/zarr_store.zarr` from the root directory.
+A suite of benchmarks are available (though the remote S3 benchmarks use data in a protected bucket and requires credentials). Benchmarks are in separate binaries and can be run via
+
+```bash
+cargo bench --bench datetime_local
+cargo bench --bench bbox_colunms_local
+cargo bench --bench bbox_local
+```
 
 ## Python bindings
 
@@ -20,8 +25,8 @@ uv run --no-project maturin develop --uv
 
 The `--no-project` is necessary to avoid building the Rust code (in release mode) an extra time before we even reach the `maturin develop` command.
 
-You need to add `--no-project` before any `uv run` command. For example, to run IPython:
+You need to add `--no-project` before any `uv run` command. For example
 
 ```bash
-uv run --no-project ipython
+uv run --no-project pytest
 ```

README.md

@@ -1,19 +1,21 @@
 # zarr-datafusion-search
 
-This is a prototype for being able to query _metadata_ about Zarr arrays using [DataFusion](https://datafusion.apache.org/), an extensible query engine written in Rust.
+This is a prototype for querying STAC or CMR style _metadata_ about Zarr arrays and groups using [DataFusion](https://datafusion.apache.org/), an extensible query engine written in Rust.
 
-## Zarr Schema
+This concept was conceived by the team at [Earthmover](https://www.earthmover.io/) and is outlined in their whitepaper Level 2 Data Collections in Zarr / Icechunk.
 
-In particular, we assume there is a Zarr store with multiple 1-dimensional arrays in root group named `"meta"`.
+## Schema
 
-Users can define arbitrary schemas where the 1-dimensional arrays each use a `dtype` that has an equivalent Arrow type in our supported [mappings](https://github.com/developmentseed/zarr-datafusion-search/issues/12).  A concrete example would look like
+To store this _metadata_, zarr-datafusion-search uses a convention where the Zarr store represents each metadata "field" with a 1-dimensional array in a root group named `"meta"`.
+
+Users can define arbitrary schemas where the 1-dimensional arrays each use a `dtype` that has an equivalent Arrow type in our supported [mappings](https://github.com/developmentseed/zarr-datafusion-search/issues/12).  A concrete example might look like
 
 - Inside a Zarr group named `"meta"`
     - A `datetime64[ms]` array named `"date"` with `n` timestamps named `"date"` with `n` timestamps.
     - A `VariableLengthUTF8` array named `"collection"` with `n` string values.
     - A `VariableLengthBytes` array named `"bbox"` with `n` binary values, where each value is a WKB-encoded Polygon (or MultiPolygon) with the bounding box of that Zarr record.
 
-This data schema may change over time.
+This project is under active development so these conventions may change.
 
 ## Python API