nv-raw2insights-us: restructure the data card to the common layout

nv-raw2insights-us/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF — NV-Raw2Insights-US (simulated FSA, sound-speed / aberration / segmentation)"
+name: nv-raw2insights-us
+pretty_name: "NV-Raw2Insights-US (simulated FSA, sound-speed / aberration / segmentation)"
 license: cc-by-4.0
 task_categories:
   - other
@@ -23,32 +24,21 @@ size_categories:
 
 ![DBUA B-mode reconstruction and estimated sound speed using NV-Raw2Insights-US data](assets/dbua-reconstruction.gif)
 
-*DBUA results using synthetic validation sample 0084 from NV-Raw2Insights-US: B-mode (left) and estimated sound speed (right). Bulk-speed calibration is followed by 400 spatial-refinement iterations, with fixed display scales. These are DBUA reconstructions, not predictions from an NV-Raw2Insights-US model.*
+*DBUA results using synthetic validation sample [`data/nv_r2i_us_validation_0084.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/nv-raw2insights-us/data/nv_r2i_us_validation_0084.hdf5): B-mode (left) and estimated sound speed (right). Bulk-speed calibration is followed by 400 spatial-refinement iterations, with fixed display scales. These are DBUA reconstructions, not predictions from an NV-Raw2Insights-US model.*
 
 <!-- assets/main.png is the unlabelled final B-mode panel from this run, for the dataset collage. -->
 
 ## Dataset Description
 
-NV-Raw2Insights-US is a **simulated full-synthetic-aperture (FSA)** ultrasound
-dataset for training and evaluating neural networks on **sound-speed
-estimation**, **phase-aberration correction**, **tissue segmentation**, and
-**learned image reconstruction** from raw pre-beamformed channel data.
+NV-Raw2Insights-US is a **simulated full-synthetic-aperture (FSA)** ultrasound dataset for training and evaluating neural networks on **sound-speed estimation**, **phase-aberration correction**, **tissue segmentation**, and **learned image reconstruction** from raw pre-beamformed channel data.
 
-Each sample is a single-frame FSA acquisition from a **180-element linear
-array** simulated with **k-Wave** over a heterogeneous tissue phantom
-containing cysts. Every acquisition provides raw baseband **IQ channel data**
-alongside co-registered ground truth: a **speed-of-sound map**, a **binary cyst
-segmentation mask**, a **DAS B-mode** and a **focused-transmit B-mode**, and a
-per-sample **phase-aberration** value. All data are **synthetic**; no human or
-animal subjects are involved.
+Each sample is a single-frame FSA acquisition from a **180-element linear array** simulated with **k-Wave** over a heterogeneous tissue phantom containing cysts. Every acquisition provides raw baseband **IQ channel data** alongside co-registered ground truth: a **speed-of-sound map**, a **binary cyst segmentation mask**, a **DAS B-mode** and a **focused-transmit B-mode**, and a per-sample **phase-aberration** value. All data are **synthetic**; no human or animal subjects are involved.
 
-This is the **zea-format (OpenH-RF) release**; the same simulations are also
-published in a Hugging Face `datasets`/Arrow build at
-[`nvidia/NV-Raw2Insights-US`](https://huggingface.co/datasets/nvidia/NV-Raw2Insights-US).
+This is the **zea-format (OpenH-RF) release**; the same simulations are also published in a Hugging Face `datasets`/Arrow build at [`nvidia/NV-Raw2Insights-US`](https://huggingface.co/datasets/nvidia/NV-Raw2Insights-US).
 
 ## Dataset Contributor(s)
 
-NVIDIA Corporation.
+- NVIDIA Corporation
 
 ## Dataset Creation Date
 
@@ -56,33 +46,27 @@ NVIDIA Corporation.
 
 ## License / Terms of Use
 
-[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en).
-Retain attribution and identify modifications when reusing the data.
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
 
 ## Intended Usage
 
-Developing and benchmarking methods that operate on raw ultrasound channel data:
-learned/adaptive beamforming, sound-speed estimation, phase-aberration
-correction, and tissue segmentation. Because the sound-speed map, segmentation
-mask, and aberration value are exact simulation ground truth, the dataset
-supports fully-supervised training of channel-data-to-insight models.
+Developing and benchmarking methods that operate on raw ultrasound channel data: learned/adaptive beamforming, sound-speed estimation, phase-aberration correction, and tissue segmentation. Because the sound-speed map, segmentation mask, and aberration value are exact simulation ground truth, the dataset supports fully-supervised training of channel-data-to-insight models.
 
 ## Dataset Characterization
 
-- **Data Collection Method:** Synthetic — k-Wave acoustic simulation of a
-  180-element linear array over heterogeneous tissue phantoms with cysts.
-- **Labeling Method:** Synthetic — ground truth taken directly from the
-  simulation parameters (sound-speed map, cyst segmentation, aberration).
-- **Acquisition model:** Full synthetic aperture (multi-static) — 180
-  single-element transmit events per frame, all 180 elements received;
-  transmit centre frequency 6.5 MHz, baseband IQ sampled at 13.3 MHz,
-  background sound speed 1540 m/s.
+- **Data Collection Method:** Synthetic — k-Wave acoustic simulation of a 180-element linear array over heterogeneous tissue phantoms with cysts.
+- **Labeling Method:** Synthetic — ground truth taken directly from the simulation parameters (sound-speed map, cyst segmentation, aberration).
+- **Acquisition model:** Full synthetic aperture (multi-static) — 180 single-element transmit events per frame, all 180 elements received; transmit centre frequency 6.5 MHz, baseband IQ sampled at 13.3 MHz, background sound speed 1540 m/s.
+
+## Processing the Dataset
+
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/nv-raw2insights-us/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), which uses the [zea library](https://github.com/tue-bmd/zea) and streams the data from the Hugging Face Hub. It reconstructs both a plain DAS B-mode (the `pipeline.yaml` in this folder) and a sound-speed-corrected B-mode using the ground-truth `sos_map`.
 
 ## Dataset Format
 
-Packaged in the **zea** file format (`zea_version` 0.1.6), **one HDF5 file per
-sample**, all under `data/`. The train/validation split is encoded in each
-filename (`nv_r2i_us_train_XXXX.hdf5`, `nv_r2i_us_validation_XXXX.hdf5`).
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+Packaged in the **zea** file format (`zea_version` 0.1.6), **one HDF5 file per sample**, all under `data/`. The train/validation split is encoded in each filename (`nv_r2i_us_train_XXXX.hdf5`, `nv_r2i_us_validation_XXXX.hdf5`).
 
 Per-file contents:
 
@@ -105,45 +89,33 @@ Per-file contents:
 
 **Current OpenH-RF release:** 923 HDF5 files; 214.13 GB (214,133,178,368 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
 
-- **923 samples (830 train / 93 validation)**, one HDF5 per sample, all in
-  `data/`; the split is carried in the filename.
+- **923 samples (830 train / 93 validation)**, one HDF5 per sample, all in `data/`; the split is carried in the filename.
 - Each sample is a single-frame FSA acquisition (frame index always 0).
 
 ## Subject Metadata
 
-- **Synthetic only** — no human or animal subjects. Each sample is a k-Wave
-  simulation over a digital tissue phantom containing cysts.
+- **Synthetic only** — no human or animal subjects. Each sample is a k-Wave simulation over a digital tissue phantom containing cysts.
 - No PHI; `subject/type` is a simulation label, not a patient identifier.
 
 ## Data Validation
 
-Each file's `tracks/track_0/data/image` is a DAS B-mode reconstructed from
-`raw_data` by the zea pipeline (see `pipeline.yaml`), so the reconstruction is
-reproducible from the raw channel data and the recorded transmit description.
+Each file's `tracks/track_0/data/image` is a DAS B-mode reconstructed from `raw_data` by the zea pipeline (see `pipeline.yaml`), so the reconstruction is reproducible from the raw channel data and the recorded transmit description.
 
 ## Known Issues
 
-- `sos_map` is a coarse `32 × 32` grid while the B-mode and segmentation are on
-  the fine `507 × 456` grid; each carries its own `coordinates` for alignment.
-- Cysts that touch the edge of the B-mode frame are not included in the
-  segmentation mask.
-- Some regions of gross reverberation artifact can be incorrectly segmented as
-  cysts.
+- `sos_map` is a coarse `32 × 32` grid while the B-mode and segmentation are on the fine `507 × 456` grid; each carries its own `coordinates` for alignment.
+- Cysts that touch the edge of the B-mode frame are not included in the segmentation mask.
+- Some regions of gross reverberation artifact can be incorrectly segmented as cysts.
 
 ## Ethical Considerations
 
-The data are entirely synthetic (k-Wave simulation of digital phantoms). There
-are no human participants, animal subjects, patient identifiers, or protected
-health information. Released under CC BY 4.0 for research and technical
-evaluation, not clinical decision-making.
+The data are entirely synthetic (k-Wave simulation of digital phantoms). There are no human participants, animal subjects, patient identifiers, or protected health information. Released under CC BY 4.0 for research and technical evaluation, not clinical decision-making.
 
 ## Source Dataset & Citation
 
-This is the **zea-format conversion** of the original **NV-Raw2Insights-US**
-dataset published by NVIDIA on Hugging Face:
+This is the **zea-format conversion** of the original **NV-Raw2Insights-US** dataset published by NVIDIA on Hugging Face:
 <https://huggingface.co/datasets/nvidia/NV-Raw2Insights-US>. The raw
-simulations, ground-truth labels, and normalization statistics originate there;
-please cite the original dataset:
+simulations, ground-truth labels, and normalization statistics originate there; please cite the original dataset:
 
 ```bibtex
 @misc{nv_raw2insights_us_2026,