Rename OpenH-RF to Open-H-RF

.claude/skills/open-h-rf-convert

@@ -0,0 +1 @@
+../../skills/open-h-rf-convert

.claude/skills/open-h-rf-submission-eval

@@ -0,0 +1 @@
+../../skills/open-h-rf-submission-eval

.github/workflows/pages.yml

@@ -5,15 +5,15 @@ on:
     branches: [main]
     paths:
       - "site/**"
-      - "plots/openh_rf_datasets.py"
+      - "plots/open_h_rf_datasets.py"
       - "scripts/authors.csv"
       - "scripts/compile_authors.py"
       - ".github/workflows/pages.yml"
   pull_request:
     branches: [main]
     paths:
       - "site/**"
-      - "plots/openh_rf_datasets.py"
+      - "plots/open_h_rf_datasets.py"
       - "scripts/authors.csv"
       - "scripts/compile_authors.py"
       - ".github/workflows/pages.yml"

.gitignore

@@ -20,7 +20,7 @@ examples/tracked-cirs-phantom/3d_views/
 
 # The Hub repo's documents, cloned by huggingface/setup.sh. Not a submodule: a checkout
 # that setup.sh does not guard would pull ~39 TB of LFS payload.
-huggingface/OpenH-RF/
+huggingface/Open-H-RF/
 
 # The website build (site/build.py); site/figures/ holds a card figure that is not on the Hub.
 site/_site/

AGENTS.md

@@ -21,14 +21,14 @@ Available skills:
 
 | Skill | Purpose |
 |---|---|
-| [`skills/openh-rf-submission-eval/`](./skills/openh-rf-submission-eval/) | Evaluate a contributor submission to the OpenH-RF initiative against the RFP and submission guide. Produces a structured acceptance report. |
-| [`skills/openh-rf-convert/`](./skills/openh-rf-convert/) | Source-agnostic dataset conversion adapter — turn an arbitrary source ultrasound dataset into the OpenH-RF (zea) file format, with a confirmed source→zea field mapping and a verified conversion. |
+| [`skills/open-h-rf-submission-eval/`](./skills/open-h-rf-submission-eval/) | Evaluate a contributor submission to the Open-H-RF initiative against the RFP and submission guide. Produces a structured acceptance report. |
+| [`skills/open-h-rf-convert/`](./skills/open-h-rf-convert/) | Source-agnostic dataset conversion adapter — turn an arbitrary source ultrasound dataset into the Open-H-RF (zea) file format, with a confirmed source→zea field mapping and a verified conversion. |
 
-[`skills/openh-rf-shared/`](./skills/openh-rf-shared/) is a shared,
+[`skills/open-h-rf-shared/`](./skills/open-h-rf-shared/) is a shared,
 non-invocable support directory (no `SKILL.md`, so no discovery symlink) holding
 files read by both skills above — the zea-format notes, the data-card template,
 the B-mode pipeline template, and the authoritative `validate_zea_spec.py`
-checker. Skills reference it by relative path (`../openh-rf-shared/…`).
+checker. Skills reference it by relative path (`../open-h-rf-shared/…`).
 
 ### Invocation per tool
 

datasets/README.md

@@ -1,6 +1,6 @@
-# OpenH-RF — reconstructions
+# Open-H-RF — reconstructions
 
-The code counterpart to [**OpenH-RF**](https://huggingface.co/datasets/nvidia/OpenH-RF) on the Hugging Face Hub. The data lives there; the reconstructions scripts are here.
+The code counterpart to [**Open-H-RF**](https://huggingface.co/datasets/nvidia/Open-H-RF) on the Hugging Face Hub. The data lives there; the reconstructions scripts are here.
 
 Each directory consists of:
 

datasets/colorado-boulder/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - 3d
 language:
   - en
@@ -20,7 +20,7 @@ size_categories:
 | 2D ATS 539 phantom | 3D phantom | In-vivo quadriceps |
 |:---:|:---:|:---:|
 | ![Tracked SSA sweep through the 2D ATS 539 phantom](assets/ssa-sweep-phantom2d.gif) | ![Tracked SSA sweep through the 3D phantom](assets/ssa-sweep-phantom3d.gif) | ![Tracked SSA sweep along in-vivo quadriceps muscle](assets/ssa-sweep-invivo.gif) |
-| [`Sub-dataset-1`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/colorado-boulder/Sub-dataset-1) | [`Sub-dataset-2`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/colorado-boulder/Sub-dataset-2) | [`Sub-dataset-3`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/colorado-boulder/Sub-dataset-3) |
+| [`Sub-dataset-1`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/colorado-boulder/Sub-dataset-1) | [`Sub-dataset-2`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/colorado-boulder/Sub-dataset-2) | [`Sub-dataset-3`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/colorado-boulder/Sub-dataset-3) |
 
 *Motion-compensated tracked SSA reconstructions, one per sub-dataset. Each frame beamforms the raw RF channel data with its own tracked probe pose; a 40 mm window of frames is then coherently summed to synthesise a larger effective aperture, and the window slides along the freehand sweep.*
 
@@ -30,7 +30,7 @@ This dataset contains tracked swept synthetic aperture (SSA) ultrasound acquisit
 
 The data were acquired using a Verasonics Vantage research ultrasound system with a P4-2 phased array transducer. The dataset includes raw RF channel data, acquisition parameters, probe geometry, transmit information, and frame-wise tracked probe pose metadata.
 
-The purpose of this dataset is to provide reproducible examples for generalized ultrasound reconstruction, with a particular focus on motion-compensated tracked SSA reconstruction using the zea/OpenH-RF data format.
+The purpose of this dataset is to provide reproducible examples for generalized ultrasound reconstruction, with a particular focus on motion-compensated tracked SSA reconstruction using the zea/Open-H-RF data format.
 
 The phantom acquisitions do not contain human subject data, animal data, or protected health information (PHI). The in-vivo acquisitions were collected from healthy volunteer participants and de-identified prior to release.
 
@@ -88,7 +88,7 @@ None (N/A).
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/colorado-boulder/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/colorado-boulder/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
 
 The script selects tracked frames at roughly 1 mm lateral spacing and writes the reconstruction to `ssa_bmode.png`. Point `ZEA_FILE` at any acquisition in the corpus to reconstruct it. [`assets/main_bmode.png`](./assets/main_bmode.png) was produced this way, compounding the full sweep into one image; the loops at the top of this card slide a shorter aperture window along the sweep instead.
 
@@ -98,7 +98,7 @@ The script selects tracked frames at roughly 1 mm lateral spacing and writes the
 
 [zea v0.1.7](https://github.com/tue-bmd/zea)
 
-The dataset is distributed in the zea/OpenH-RF HDF5 format.
+The dataset is distributed in the zea/Open-H-RF HDF5 format.
 
 Each file includes:
 

datasets/colorado-boulder/reconstruct.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
-"""Example reconstruction script for the colorado-boulder dataset of OpenH-RF.
+"""Example reconstruction script for the colorado-boulder dataset of Open-H-RF.
 
-Dataset link: https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/colorado-boulder
+Dataset link: https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/colorado-boulder
 
 B-mode reconstruction of tracked swept synthetic aperture phantom channel data.
 
@@ -51,10 +51,10 @@
 # --- Inputs -----------------------------------------------------------------
 # Defaults stream straight from the published corpus. Swap any of these for a
 # local path to run against your own copy.
-ZEA_FILE = "hf://nvidia/OpenH-RF/colorado-boulder/Sub-dataset-1/sub-phantom2d_01_tracked-ssa.hdf5"
+ZEA_FILE = "hf://nvidia/Open-H-RF/colorado-boulder/Sub-dataset-1/sub-phantom2d_01_tracked-ssa.hdf5"
 CONFIG = HERE / "pipeline.yaml"  # written by build_pipeline() below; this is what the run loads
 OUT = HERE / "ssa_bmode.png"
-HF_CONFIG = "hf://nvidia/OpenH-RF/colorado-boulder/pipeline.yaml"  # where CONFIG is published
+HF_CONFIG = "hf://nvidia/Open-H-RF/colorado-boulder/pipeline.yaml"  # where CONFIG is published
 FRAME_SPACING = 1e-3  # select tracked frames every 1 mm along the sweep
 
 

datasets/concordia/README.md

@@ -8,7 +8,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - synthetic
   - field-ii
 language:
@@ -21,11 +21,11 @@ size_categories:
 
 ![B-mode reconstructions of the five SynthUS-FSA phantom classes](assets/classes.png)
 
-*The five phantom classes (anechoic, hypoechoic, hyperechoic, diverse, point-target), one capture per column: [`image_0012`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0012.hdf5), [`image_0410`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0410.hdf5), [`image_0733`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0733.hdf5), [`image_0753`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_0753.hdf5), [`image_1808`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/concordia/data/image_1808.hdf5). Rows show the reference B-mode (`data/image`), the simulated scatterer cloud (`data/scatterers`) and the label map (`data/segmentation` for the lesion classes, `data/diverse_source_image` for diverse; point-target captures store none).*
+*The five phantom classes (anechoic, hypoechoic, hyperechoic, diverse, point-target), one capture per column: [`image_0012`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/concordia/data/image_0012.hdf5), [`image_0410`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/concordia/data/image_0410.hdf5), [`image_0733`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/concordia/data/image_0733.hdf5), [`image_0753`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/concordia/data/image_0753.hdf5), [`image_1808`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/concordia/data/image_1808.hdf5). Rows show the reference B-mode (`data/image`), the simulated scatterer cloud (`data/scatterers`) and the label map (`data/segmentation` for the lesion classes, `data/diverse_source_image` for diverse; point-target captures store none).*
 
 ## Dataset Description
 
-SynthUS-FSA is a fully synthetic corpus of pre-beamformed full-synthetic-aperture (FSA) ultrasound channel-data captures, generated with the Field II simulator to model a 128-element L11-5v linear array. Each capture fires one transmit event per element (128 transmits, receive on all 128 elements), so every element-to-element combination is retained and any receive/transmit beamforming scheme (focused B-mode, multi-angle plane-wave compounding, diverging-wave, adaptive/compressive schemes) can be retrospectively synthesized from the same channel data. The dataset is packaged in [zea](https://zea.readthedocs.io) format — the OpenH-RF reference Python library for ultrasound file I/O and beamforming — which every `.hdf5` file and `reconstruct.py` depend on. The dataset targets three OpenH-RF task categories: (1) generalized reconstruction (FSA channel data paired with a synthesized full synthetic-aperture B-mode as ground truth), (2) compressed sensing / adaptive transmit (retrospective sub-selection of the 128 transmit events — derived from the same captures, no additional files), and (3) segmentation (binary echogenicity masks on a subset of phantoms). All data is simulated — no clinical, phantom-hardware, or animal acquisition is involved.
+SynthUS-FSA is a fully synthetic corpus of pre-beamformed full-synthetic-aperture (FSA) ultrasound channel-data captures, generated with the Field II simulator to model a 128-element L11-5v linear array. Each capture fires one transmit event per element (128 transmits, receive on all 128 elements), so every element-to-element combination is retained and any receive/transmit beamforming scheme (focused B-mode, multi-angle plane-wave compounding, diverging-wave, adaptive/compressive schemes) can be retrospectively synthesized from the same channel data. The dataset is packaged in [zea](https://zea.readthedocs.io) format — the Open-H-RF reference Python library for ultrasound file I/O and beamforming — which every `.hdf5` file and `reconstruct.py` depend on. The dataset targets three Open-H-RF task categories: (1) generalized reconstruction (FSA channel data paired with a synthesized full synthetic-aperture B-mode as ground truth), (2) compressed sensing / adaptive transmit (retrospective sub-selection of the 128 transmit events — derived from the same captures, no additional files), and (3) segmentation (binary echogenicity masks on a subset of phantoms). All data is simulated — no clinical, phantom-hardware, or animal acquisition is involved.
 
 A similar data-generation approach was used in Sharifzadeh et al. (2024). Users of this dataset are kindly requested to cite that work:
 
@@ -82,13 +82,13 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/concordia/data/image_0005.hdf5 \
-  --config hf://nvidia/OpenH-RF/concordia/pipeline.yaml \
+  --dataset hf://nvidia/Open-H-RF/concordia/data/image_0005.hdf5 \
+  --config hf://nvidia/Open-H-RF/concordia/pipeline.yaml \
   --n-frames 1 \
   --save-as png
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/concordia/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF).
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/concordia/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 ## Dataset Format
 
@@ -107,7 +107,7 @@ Pre-processing applied before packaging: anti-alias FIR decimation (factor 5, 10
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 2,000 HDF5 files; 82.39 GB (82,390,286,336 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.
+**Current Open-H-RF release:** 2,000 HDF5 files; 82.39 GB (82,390,286,336 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.
 
 This dataset comprises 2,000 FSA captures across five echogenicity classes: