Rename OpenH-RF to Open-H-RF

README.md

@@ -4,27 +4,27 @@ viewer: false
 ---
 
 <h1 align="center" style="font-size: 2.5em; font-weight: 800; letter-spacing: -0.035em; line-height: 1.05; margin-bottom: 0.3em;">
-  <img src="assets/logo-glow-transparent.svg" alt="" height="100" style="display: inline; height: 1.5em; margin: 0; vertical-align: -0.3em;"> OpenH-RF
+  <img src="assets/logo-glow-transparent.svg" alt="" height="100" style="display: inline; height: 1.5em; margin: 0; vertical-align: -0.3em;"> Open-H-RF
 </h1>
 
 <p align="center"><em>The largest, openly licensed dataset of medical ultrasound channel capture data</em></p>
 
-![Collage of reconstructed images and raw channel data from the OpenH-RF subsets](assets/collage.webp)
+![Collage of reconstructed images and raw channel data from the Open-H-RF subsets](assets/collage.webp)
 
 <p align="center">
-  <a href="https://open-h.github.io/OpenH-RF/"><img src="assets/badge-website.svg" alt="Website" style="display: inline; margin: 0 3px;"></a>
-  <a href="https://github.com/open-h/OpenH-RF"><img src="assets/badge-github.svg" alt="GitHub" style="display: inline; margin: 0 3px;"></a>
+  <a href="https://open-h.github.io/Open-H-RF/"><img src="assets/badge-website.svg" alt="Website" style="display: inline; margin: 0 3px;"></a>
+  <a href="https://github.com/open-h/Open-H-RF"><img src="assets/badge-github.svg" alt="GitHub" style="display: inline; margin: 0 3px;"></a>
   <a href="https://arxiv.org/abs/2610.13616"><img src="assets/badge-arxiv.svg" alt="arXiv" style="display: inline; margin: 0 3px;"></a>
   <a href="https://creativecommons.org/licenses/by/4.0/"><img src="assets/badge-license.svg" alt="License: CC BY 4.0" style="display: inline; margin: 0 3px;"></a>
 </p>
 
 ## Dataset Description
 
-OpenH-RF is a community-driven dataset initiative building the open, shared foundation needed to train and evaluate AI models built on pre-beamformed (channel capture) medical ultrasound measurements.
+Open-H-RF is a community-driven dataset initiative building the open, shared foundation needed to train and evaluate AI models built on pre-beamformed (channel capture) medical ultrasound measurements.
 
-This dataset is a collection of RF samples and metadata in the [`zea` file format](https://github.com/tue-bmd/zea) from a variety of tasks and applications, including ultrasound localization microscopy, ultrasound computer tomography, b-mode, flow imaging and more. The dataset catalog and gallery is available at [open-h.github.io/OpenH-RF](https://open-h.github.io/OpenH-RF).
+This dataset is a collection of RF samples and metadata in the [`zea` file format](https://github.com/tue-bmd/zea) from a variety of tasks and applications, including ultrasound localization microscopy, ultrasound computer tomography, b-mode, flow imaging and more. The dataset catalog and gallery is available at [open-h.github.io/Open-H-RF](https://open-h.github.io/Open-H-RF).
 
-Each subdirectory here holds a data card and a processing pipeline. The matching reconstruction scripts, one runnable reference reconstruction per subset, are in the companion repository, [github.com/open-h/OpenH-RF](https://github.com/open-h/OpenH-RF).
+Each subdirectory here holds a data card and a processing pipeline. The matching reconstruction scripts, one runnable reference reconstruction per subset, are in the companion repository, [github.com/open-h/Open-H-RF](https://github.com/open-h/Open-H-RF).
 
 This dataset is ready for commercial or non-commercial uses.
 
@@ -38,7 +38,7 @@ NVIDIA, Stanford University, Eindhoven University of Technology, Tel Aviv Univer
 
 ## Dataset Stewardship
 
-OpenH-RF community & Steering Group
+Open-H-RF community & Steering Group
 
 ## Dataset Creation Date
 
@@ -80,7 +80,7 @@ Channel capture data and related meta data are saved in HDF5 data files using a
 
 ## Reference(s)
 
-- https://github.com/open-h/OpenH-RF
+- https://github.com/open-h/Open-H-RF
 - https://arxiv.org/abs/2610.13616
 
 ## Ethical Considerations
@@ -91,4 +91,4 @@ Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns [her
 
 ## Citation
 
-If you use OpenH-RF in your research, please cite the [OpenH-RF paper](https://arxiv.org/abs/2610.13616). The BibTeX entry is in the [GitHub repository](https://github.com/open-h/OpenH-RF#citation).
+If you use Open-H-RF in your research, please cite the [Open-H-RF paper](https://arxiv.org/abs/2610.13616). The BibTeX entry is in the [GitHub repository](https://github.com/open-h/Open-H-RF#citation).

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:
 

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:
 

dartmouth-uct/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - usct
   - sound-speed-estimation
   - attenuation
@@ -23,7 +23,7 @@ size_categories:
 
 ![Ring-array USCT reflectivity reconstruction of a digital breast phantom, next to its ground-truth sound-speed and attenuation maps](assets/phantom_179604449_z200.png)
 
-*A 2D-sim breast cross-section, [`data/2d/phantom_179604449_z200.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/dartmouth-uct/data/2d/phantom_179604449_z200.hdf5), reconstructed as a reflectivity image beside the ground-truth sound-speed and attenuation maps stored in the file.*
+*A 2D-sim breast cross-section, [`data/2d/phantom_179604449_z200.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/dartmouth-uct/data/2d/phantom_179604449_z200.hdf5), reconstructed as a reflectivity image beside the ground-truth sound-speed and attenuation maps stored in the file.*
 
 ## Dataset Description
 
@@ -96,7 +96,7 @@ Both collections are generated from the **same underlying digital breast phantom
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/dartmouth-uct/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/dartmouth-uct/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.
 
 Set `ZEA_FILE` at the top of the script and run `python reconstruct.py`; the figure at the top of this card is its output. `SOS_MAP = True` swaps the constant-sound-speed delays for a straight-ray integral through the ground-truth map.
 
@@ -115,7 +115,7 @@ All data are packaged in the **`zea` HDF5 file format** (one `.hdf5` file per ac
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 2,625 HDF5 files; 257.62 GB (257,619,787,776 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:** 2,625 HDF5 files; 257.62 GB (257,619,787,776 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.
 
 - **Number of acquisitions:** **2,149 (2D-sim)** (1,859 dense + 290 fatty) + **476 (3D-sim)** = **2,625 acquisitions**.
 - **Single-Tx channel-capture frames:** 2,149 × 256 (2D-sim) + 476 × 64 (3D-sim) ≈ **5.8 × 10⁵ frames**.

kaist-snubh-barreleye/README.md

@@ -8,7 +8,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - breast
   - in-vivo
   - plane-wave
@@ -23,7 +23,7 @@ size_categories:
 
 ![DAS B-mode reconstruction of a biopsy-proven invasive ductal carcinoma (S01_D1)](assets/main.png)
 
-*Delay-and-sum reconstruction of a biopsy-proven invasive ductal carcinoma, [`data/S01_D1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/kaist-snubh-barreleye/data/S01_D1.hdf5).*
+*Delay-and-sum reconstruction of a biopsy-proven invasive ductal carcinoma, [`data/S01_D1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/kaist-snubh-barreleye/data/S01_D1.hdf5).*
 
 ## Dataset Description
 
@@ -70,13 +70,13 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/kaist-snubh-barreleye/data/S01_D1.hdf5 \
-  --config hf://nvidia/OpenH-RF/kaist-snubh-barreleye/pipeline.yaml \
+  --dataset hf://nvidia/Open-H-RF/kaist-snubh-barreleye/data/S01_D1.hdf5 \
+  --config hf://nvidia/Open-H-RF/kaist-snubh-barreleye/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/kaist-snubh-barreleye/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/kaist-snubh-barreleye/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 This is a single-frame acquisition, so `zea process` outputs a `.png` rather than a `.gif` — this requires a `zea` build newer than the currently pinned 0.1.6 (single-frame PNG output landed after that release).
 
@@ -84,7 +84,7 @@ This is a single-frame acquisition, so `zea process` outputs a `.png` rather tha
 
 [zea v0.1.6](https://github.com/tue-bmd/zea)
 
-All data is delivered in the **zea HDF5** format (OpenH-RF spec). One HDF5 file per acquisition; one image track per file.
+All data is delivered in the **zea HDF5** format (Open-H-RF spec). One HDF5 file per acquisition; one image track per file.
 
 ```
 hdf5/
@@ -94,12 +94,12 @@ hdf5/
 ```
 
 **`original/` — raw RF channel data.**
-- **Channel reordering** of the scanner's raw export into the OpenH-RF convention `(n_frames=1, n_tx=9, n_ax=Ns, n_el=192, n_ch=1)`.
+- **Channel reordering** of the scanner's raw export into the Open-H-RF convention `(n_frames=1, n_tx=9, n_ax=Ns, n_el=192, n_ch=1)`.
 - No demodulation, decimation, band-pass filtering, or value clipping — `raw_data` is bit-faithful to the scanner export.
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 70 HDF5 files; 802.10 MB (802,095,104 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:** 70 HDF5 files; 802.10 MB (802,095,104 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.
 
 - **Subjects:** 35 (S01–S37 with S11 and S29 unused, all female; age groups in *Subject Metadata*).
 - **Acquisitions:** 70 (2 repeat acquisitions per subject, named `<patient>_D1` / `<patient>_D2`).

mosaic-nuevosono/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - IVUS
   - tracked-ultrasound
 language:
@@ -18,7 +18,7 @@ language:
 
 ![IVUS pullback: B-mode, segmentation overlay and pullback trajectory](assets/pullback.gif)
 
-*Pullback through [`data/22_12_10_52.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/mosaic-nuevosono/data/22_12_10_52.hdf5), reconstructed from the raw channel data. Left to right: B-mode, the same frame with the lumen and intima-media segmentation, and the linear-encoder pullback position with the current frame marked.*
+*Pullback through [`data/22_12_10_52.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/mosaic-nuevosono/data/22_12_10_52.hdf5), reconstructed from the raw channel data. Left to right: B-mode, the same frame with the lumen and intima-media segmentation, and the linear-encoder pullback position with the current frame marked.*
 
 ## Dataset Description
 
@@ -52,7 +52,7 @@ Intended for IVUS tracking and segmentation applications, including lesion detec
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/mosaic-intelligence/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/mosaic-intelligence/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 overlays the segmentation masks on frames spread evenly across the pullback:
 
@@ -139,6 +139,6 @@ A `zea.Pipeline` reconstructs the IVUS B-mode from the raw channel data (RF →
 
 ## Ethical Considerations
 
-Porcine animal study data only; contains no human subjects or PHI. Collected and released in compliance with applicable institutional animal care approvals and OpenH-RF governance and data-sharing policies.
+Porcine animal study data only; contains no human subjects or PHI. Collected and released in compliance with applicable institutional animal care approvals and Open-H-RF governance and data-sharing policies.
 
 All contributed data, labels and metadata are released by Mosaic Intelligence Labs and NuevoSono; pre-existing hardware, software, simulation and platform intellectual property remains the property of the respective owners. The license is also recorded in each file's `metadata/credit` field.

nv-raw2insights-us/README.md

@@ -8,7 +8,7 @@ tags:
   - ultrasound
   - rf
   - iq
-  - openh-rf
+  - open-h-rf
   - beamforming
   - sound-speed-estimation
   - phase-aberration
@@ -24,7 +24,7 @@ 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 [`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.*
+*DBUA results using synthetic validation sample [`data/nv_r2i_us_validation_0084.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-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. -->
 
@@ -34,7 +34,7 @@ NV-Raw2Insights-US is a **simulated full-synthetic-aperture (FSA)** ultrasound d
 
 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 (Open-H-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)
 
@@ -60,7 +60,7 @@ Developing and benchmarking methods that operate on raw ultrasound channel data:
 
 ## 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`.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/nv-raw2insights-us/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-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
 
@@ -87,7 +87,7 @@ Per-file contents:
 
 ## Dataset Quantification
 
-**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.
+**Current Open-H-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.
 - Each sample is a single-frame FSA acquisition (frame index always 0).

oslo/A_cardiac/README.md

@@ -9,7 +9,7 @@ language:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - cardiac
   - in-vivo
 size_categories:
@@ -20,7 +20,7 @@ size_categories:
 
 ![Cine loop of a parasternal long-axis view](assets/hero.gif)
 
-*All 50 frames of [`Verasonics_P2-4_parasternal_long_subject_1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
+*All 50 frames of [`Verasonics_P2-4_parasternal_long_subject_1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
 
 ## Dataset Description
 
@@ -46,7 +46,7 @@ In-vivo human cardiac channel-capture data acquired with a Verasonics Vantage 25
 
 ## Intended Usage
 
-Generalized reconstruction and adaptive beamforming of cardiac ultrasound (RFP task 6.1). Suitable for B-mode reconstruction, aperture-domain processing, and deep-learning beamforming research on in-vivo cardiac data. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
+Generalized reconstruction and adaptive beamforming of cardiac ultrasound (RFP task 6.1). Suitable for B-mode reconstruction, aperture-domain processing, and deep-learning beamforming research on in-vivo cardiac data. (Open-H-RF RFP task 6.1 Generalized Reconstruction).
 
 ## Dataset Characterization
 
@@ -62,11 +62,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5 \
-  --config hf://nvidia/OpenH-RF/oslo/A_cardiac/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5 \
+  --config hf://nvidia/Open-H-RF/oslo/A_cardiac/pipeline.yaml
 ```
 
-This `pipeline.yaml` holds the pipeline, display window and sector limits of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+This `pipeline.yaml` holds the pipeline, display window and sector limits of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
 
 ## Dataset Format
 
@@ -76,7 +76,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 2 HDF5 files; 3.53 GB (3,533,897,728 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 HDF5 files; 3.53 GB (3,533,897,728 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.
 
 - **Number of acquisitions:** 2
 - **Total channel-capture frames:** 75

oslo/B_carotid/README.md

@@ -9,7 +9,7 @@ language:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - vascular
   - carotid
   - in-vivo
@@ -21,7 +21,7 @@ size_categories:
 
 ![Focused B-mode of a carotid cross-section](assets/hero.png)
 
-*First frame of [`L7_FI_carotid_cross_1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
+*First frame of [`L7_FI_carotid_cross_1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
 
 ## Dataset Description
 
@@ -47,7 +47,7 @@ In-vivo human carotid-artery channel-capture data acquired with a Verasonics Van
 
 ## Intended Usage
 
-Generalized reconstruction and adaptive beamforming of vascular ultrasound (RFP task 6.1). (OpenH-RF RFP task 6.1 Generalized Reconstruction).
+Generalized reconstruction and adaptive beamforming of vascular ultrasound (RFP task 6.1). (Open-H-RF RFP task 6.1 Generalized Reconstruction).
 
 ## Dataset Characterization
 
@@ -63,11 +63,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5 \
-  --config hf://nvidia/OpenH-RF/oslo/B_carotid/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5 \
+  --config hf://nvidia/Open-H-RF/oslo/B_carotid/pipeline.yaml
 ```
 
-This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
 
 ## Dataset Format
 
@@ -77,7 +77,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 3 HDF5 files; 219.48 MB (219,480,064 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:** 3 HDF5 files; 219.48 MB (219,480,064 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.
 
 - **Number of acquisitions:** 3
 - **Total channel-capture frames:** 6

oslo/C_verasonics_phantom/README.md

@@ -9,7 +9,7 @@ language:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - phantom
   - cirs
 size_categories:
@@ -20,7 +20,7 @@ size_categories:
 
 ![Phased-array sector B-mode of a cyst phantom](assets/hero.png)
 
-*First frame of [`FI_P4_cysts_center.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
+*First frame of [`FI_P4_cysts_center.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
 
 ## Dataset Description
 
@@ -46,7 +46,7 @@ Tissue-mimicking and table-top phantom channel-capture data acquired on a Veraso
 
 ## Intended Usage
 
-Generalized reconstruction, image-quality assessment (resolution, contrast, dynamic range), and beamforming research (RFP task 6.1). Several acquisitions provide multiple transmit schemes on the same target for cross-method comparison. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
+Generalized reconstruction, image-quality assessment (resolution, contrast, dynamic range), and beamforming research (RFP task 6.1). Several acquisitions provide multiple transmit schemes on the same target for cross-method comparison. (Open-H-RF RFP task 6.1 Generalized Reconstruction).
 
 ## Dataset Characterization
 
@@ -62,11 +62,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5 \
-  --config hf://nvidia/OpenH-RF/oslo/C_verasonics_phantom/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5 \
+  --config hf://nvidia/Open-H-RF/oslo/C_verasonics_phantom/pipeline.yaml
 ```
 
-This `pipeline.yaml` holds the pipeline, display window and sector limits of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+This `pipeline.yaml` holds the pipeline, display window and sector limits of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
 
 ## Dataset Format
 
@@ -76,7 +76,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 15 HDF5 files; 1.06 GB (1,058,734,080 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:** 15 HDF5 files; 1.06 GB (1,058,734,080 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.
 
 - **Number of acquisitions:** 15
 - **Total channel-capture frames:** 27

oslo/D_alpinion_phantom/README.md

@@ -9,7 +9,7 @@ language:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - phantom
   - alpinion
 size_categories:
@@ -20,7 +20,7 @@ size_categories:
 
 ![Plane-wave compounded B-mode of hypoechoic cysts](assets/hero.png)
 
-*First frame of [`Alpinion_L3-8_CPWC_hypoechoic.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
+*First frame of [`Alpinion_L3-8_CPWC_hypoechoic.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
 
 ## Dataset Description
 
@@ -46,7 +46,7 @@ Phantom channel-capture data acquired on an Alpinion E-Cube 12R research scanner
 
 ## Intended Usage
 
-Generalized reconstruction and image-quality assessment on a second hardware platform (RFP task 6.1); cross-vendor robustness studies. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
+Generalized reconstruction and image-quality assessment on a second hardware platform (RFP task 6.1); cross-vendor robustness studies. (Open-H-RF RFP task 6.1 Generalized Reconstruction).
 
 ## Dataset Characterization
 
@@ -62,11 +62,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5 \
-  --config hf://nvidia/OpenH-RF/oslo/D_alpinion_phantom/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5 \
+  --config hf://nvidia/Open-H-RF/oslo/D_alpinion_phantom/pipeline.yaml
 ```
 
-This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
 
 ## Dataset Format
 
@@ -76,7 +76,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 4 HDF5 files; 466.42 MB (466,419,712 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:** 4 HDF5 files; 466.42 MB (466,419,712 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.
 
 - **Number of acquisitions:** 4
 - **Total channel-capture frames:** 4

oslo/E_simulation/README.md

@@ -9,7 +9,7 @@ language:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - simulation
   - field-ii
   - synthetic
@@ -21,7 +21,7 @@ size_categories:
 
 ![B-mode of the PICMUS numerical phantom](assets/hero.png)
 
-*First frame of [`PICMUS_numerical_calib_v2.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
+*First frame of [`PICMUS_numerical_calib_v2.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5), reconstructed from the raw channel data with `reconstruct.py`.*
 
 ## Dataset Description
 
@@ -47,7 +47,7 @@ Physics-based synthetic channel-capture data generated with the Field II ultraso
 
 ## Intended Usage
 
-Generalized reconstruction, beamformer development and validation with known ground truth (RFP task 6.1); resolution/contrast/dynamic-range characterization; training/validation of learned reconstruction methods. (OpenH-RF RFP task 6.1 Generalized Reconstruction).
+Generalized reconstruction, beamformer development and validation with known ground truth (RFP task 6.1); resolution/contrast/dynamic-range characterization; training/validation of learned reconstruction methods. (Open-H-RF RFP task 6.1 Generalized Reconstruction).
 
 ## Dataset Characterization
 
@@ -63,11 +63,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5 \
-  --config hf://nvidia/OpenH-RF/oslo/E_simulation/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5 \
+  --config hf://nvidia/Open-H-RF/oslo/E_simulation/pipeline.yaml
 ```
 
-This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)).
 
 ## Dataset Format
 
@@ -77,7 +77,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 11 HDF5 files; 2.98 GB (2,980,642,816 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:** 11 HDF5 files; 2.98 GB (2,980,642,816 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.
 
 - **Number of acquisitions:** 11
 - **Total channel-capture frames:** 17

oslo/F_motion/README.md

@@ -9,7 +9,7 @@ language:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - elastography
   - shear-wave
   - arfi
@@ -21,7 +21,7 @@ size_categories:
 
 ![Shear waves travelling outward after an acoustic push](assets/hero.gif)
 
-*First push-track sequence (frames 1-49) of [`SWE_L7_type_III.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/F_motion/SWE_L7_type_III.hdf5): B-mode (left) and axial displacement between consecutive frames (right), a lag-one autocorrelation (Kasai) estimate on the IQ beamformed with `pipeline.yaml`. The displacement estimate is not part of the reference pipeline.*
+*First push-track sequence (frames 1-49) of [`SWE_L7_type_III.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/F_motion/SWE_L7_type_III.hdf5): B-mode (left) and axial displacement between consecutive frames (right), a lag-one autocorrelation (Kasai) estimate on the IQ beamformed with `pipeline.yaml`. The displacement estimate is not part of the reference pipeline.*
 
 ## Dataset Description
 
@@ -47,7 +47,7 @@ Shear-wave elastography (SWE) and acoustic-radiation-force-impulse (ARFI) push-t
 
 ## Intended Usage
 
-Motion estimation (RFP task 6.4): shear-wave velocity estimation, ARFI displacement tracking, and high-frame-rate motion reconstruction. (OpenH-RF RFP task 6.4 Motion Estimation).
+Motion estimation (RFP task 6.4): shear-wave velocity estimation, ARFI displacement tracking, and high-frame-rate motion reconstruction. (Open-H-RF RFP task 6.4 Motion Estimation).
 
 ## Dataset Characterization
 
@@ -63,11 +63,11 @@ The example acquisition can be processed with the `pipeline.yaml` definition in
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/oslo/F_motion/SWE_L7_type_III.hdf5 \
-  --config hf://nvidia/OpenH-RF/oslo/F_motion/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/oslo/F_motion/SWE_L7_type_III.hdf5 \
+  --config hf://nvidia/Open-H-RF/oslo/F_motion/pipeline.yaml
 ```
 
-This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)). These plane-wave tracking acquisitions use the non-focused `compound` pipeline, and the reference reconstruction shows a single tracking frame.
+This `pipeline.yaml` holds the pipeline and display window of that acquisition. Alternatively, all acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py) at the root of this collection, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` definitions at the collection root and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub; `parameters.yaml` picks the pipeline, display window and dynamic range per acquisition (see the [collection card](../README.md#processing-the-dataset)). These plane-wave tracking acquisitions use the non-focused `compound` pipeline, and the reference reconstruction shows a single tracking frame.
 
 ## Dataset Format
 
@@ -77,7 +77,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 4 HDF5 files; 354.42 MB (354,418,688 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:** 4 HDF5 files; 354.42 MB (354,418,688 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.
 
 - **Number of acquisitions:** 4
 - **Total channel-capture frames:** 800

oslo/README.md

@@ -9,7 +9,7 @@ language:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - beamforming
   - channel-data
 size_categories:
@@ -20,11 +20,11 @@ size_categories:
 
 ![B-modes from five of the six sub-datasets: in-vivo cardiac, in-vivo carotid, Verasonics and Alpinion phantoms, and simulation](assets/hero.png)
 
-*First frames reconstructed from the raw channel data with `reconstruct.py`. Top: [`A_cardiac/Verasonics_P2-4_parasternal_long_subject_1`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5), [`A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1.hdf5), [`C_verasonics_phantom/FI_P4_cysts_center`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5). Bottom: [`B_carotid/L7_FI_carotid_cross_1`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5), [`C_verasonics_phantom/L7_FI_Verasonics_CIRS`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/C_verasonics_phantom/L7_FI_Verasonics_CIRS.hdf5), [`D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5), [`E_simulation/PICMUS_numerical_calib_v2`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5). Panels are scaled to a common height per row, not to a common physical scale.*
+*First frames reconstructed from the raw channel data with `reconstruct.py`. Top: [`A_cardiac/Verasonics_P2-4_parasternal_long_subject_1`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_parasternal_long_subject_1.hdf5), [`A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1.hdf5), [`C_verasonics_phantom/FI_P4_cysts_center`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/C_verasonics_phantom/FI_P4_cysts_center.hdf5). Bottom: [`B_carotid/L7_FI_carotid_cross_1`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/B_carotid/L7_FI_carotid_cross_1.hdf5), [`C_verasonics_phantom/L7_FI_Verasonics_CIRS`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/C_verasonics_phantom/L7_FI_Verasonics_CIRS.hdf5), [`D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/D_alpinion_phantom/Alpinion_L3-8_CPWC_hypoechoic.hdf5), [`E_simulation/PICMUS_numerical_calib_v2`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/oslo/E_simulation/PICMUS_numerical_calib_v2.hdf5). Panels are scaled to a common height per row, not to a common physical scale.*
 
 ## Dataset Description
 
-Contributed by the **University of Oslo (UiO), Department of Informatics** (the USTB team) to the [OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All data is pre-beamformed (raw channel-capture) ultrasound stored in the **zea** HDF5 format and released under **CC BY 4.0**.
+Contributed by the **University of Oslo (UiO), Department of Informatics** (the USTB team) to the [Open-H-RF](https://github.com/open-h/Open-H-RF) initiative. All data is pre-beamformed (raw channel-capture) ultrasound stored in the **zea** HDF5 format and released under **CC BY 4.0**.
 
 39 acquisitions packaged into six application sub-datasets. Each sub-dataset folder contains its zea `.hdf5` files, a Hugging Face–style `README.md` data card, and one reference B-mode PNG per acquisition. The **`reconstruct.py`, `pipeline.yaml`, and the CC BY 4.0 `LICENCE` live once at the submission root** — the reconstruction reconstructs every sub-dataset (the pipeline is identical across folders), and the single LICENCE covers the whole collection (each data card also declares `license: cc-by-4.0` in its YAML frontmatter).
 
@@ -57,7 +57,7 @@ Total: **39 acquisitions**, 8.61 GB of stored HDF5 data.
 
 ## Processing the Dataset
 
-Every acquisition is reconstructable from the file alone — all acquisition parameters live in the zea `/scan` and `/probe` groups. The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/oslo/reconstruct.py), as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline*.yaml` and `parameters.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub: set `PATHS` to the acquisition to reconstruct (default: the apical four-chamber cardiac scan above) and `REFOCUS = True` to also emit the REFoCUS variant. See [Reconstruction Details](#reconstruction-details) for how each acquisition's pipeline is chosen. Each sub-dataset folder also holds a `pipeline.yaml` for its example acquisition, so it can be rendered with a single `zea process` command (see the sub-dataset cards).
+Every acquisition is reconstructable from the file alone — all acquisition parameters live in the zea `/scan` and `/probe` groups. The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/oslo/reconstruct.py), as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline*.yaml` and `parameters.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub: set `PATHS` to the acquisition to reconstruct (default: the apical four-chamber cardiac scan above) and `REFOCUS = True` to also emit the REFoCUS variant. See [Reconstruction Details](#reconstruction-details) for how each acquisition's pipeline is chosen. Each sub-dataset folder also holds a `pipeline.yaml` for its example acquisition, so it can be rendered with a single `zea process` command (see the sub-dataset cards).
 
 ## Dataset Format
 
@@ -65,7 +65,7 @@ Every acquisition is reconstructable from the file alone — all acquisition par
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 39 HDF5 files; 8.61 GB (8,613,593,088 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:** 39 HDF5 files; 8.61 GB (8,613,593,088 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.
 
 ## Reconstruction Details
 

politorino/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - medical-imaging
   - beamforming
   - fascicle tracking
@@ -21,7 +21,7 @@ size_categories:
 
 ![Reconstructed cineloop from PAT02_w1.hdf5](assets/PAT02_w1.gif)
 
-*Cine loop of treadmill walking at 2 km/h, [`data/PAT02/PAT02_w1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/politorino/data/PAT02/PAT02_w1.hdf5), reconstructed from the raw channel data with the `pipeline.yaml` in this folder.*
+*Cine loop of treadmill walking at 2 km/h, [`data/PAT02/PAT02_w1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/politorino/data/PAT02/PAT02_w1.hdf5), reconstructed from the raw channel data with the `pipeline.yaml` in this folder.*
 
 ## Dataset Description
 
@@ -62,7 +62,7 @@ This data was employed for an initial study evaluating fascicle tracking algorit
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/politorino/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/politorino/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 reconstructs a B-mode from the raw channel data and overlays the stored fascicle tracking on it, writing a `.png`. The constants at the top of the script select what is drawn:
 
@@ -89,7 +89,7 @@ Per-sample contents of the HDF5:
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 30 HDF5 files; 169.95 GB (169,950,314,496 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:** 30 HDF5 files; 169.95 GB (169,950,314,496 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.
 
 - 30 files - 6 acquisitions of 9.6s at 500 fps for each of the 5 subjects
 - **Stored HDF5 size:** 169.95 GB (169,950,314,496 bytes); 5.67 GB per file on average. Individual file sizes vary.

resolvestroke/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - iq
-  - openh-rf
+  - open-h-rf
   - 3d
   - matrix-probe
   - diverging-wave
@@ -26,11 +26,11 @@ size_categories:
 
 <a href="assets/main_image.png"><img src="assets/main_image_small.png" alt="Transcranial contrast-enhanced power Doppler of a human subject (SP03-Left, bolus +10 s), x-z maximum-intensity projection" width="360"></a>
 
-*3D power Doppler of the transcranial CEUS acquisition [`clinical/SP03-Left/SP03-Left.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/resolvestroke/clinical/SP03-Left/SP03-Left.hdf5) at bolus +10 s, x-z maximum-intensity projection, reconstructed from the released channel data.*
+*3D power Doppler of the transcranial CEUS acquisition [`clinical/SP03-Left/SP03-Left.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/resolvestroke/clinical/SP03-Left/SP03-Left.hdf5) at bolus +10 s, x-z maximum-intensity projection, reconstructed from the released channel data.*
 
 ## Dataset Description
 
-[Resolve Stroke](https://www.resolvestroke.com/) develops SYLVER, a software-driven ultrasound platform that supports clinical assessment in patients suspected of, or at risk for, cerebral perfusion abnormalities by improving visualization of cerebral vasculature and providing complementary information on cerebral perfusion. SYLVER images with a 32×32 matrix array probe at 2 MHz using diverging-wave transmits. CE and FDA clearances are expected in 2026. This directory holds the data Resolve Stroke contributes to OpenH-RF: pre-beamformed RF/IQ channel data from an imaging phantom, a flow phantom, and in-vivo transcranial acquisitions, all de-identified and released under CC BY 4.0. The in-vivo data comes from the CPP-approved SCULPT clinical study. Two transmit sequences are included: a saddle sequence that images a 2D plane for real-time B-mode, and a 4 kHz single-aperture volume sequence for CEUS and blood-flow measurement.
+[Resolve Stroke](https://www.resolvestroke.com/) develops SYLVER, a software-driven ultrasound platform that supports clinical assessment in patients suspected of, or at risk for, cerebral perfusion abnormalities by improving visualization of cerebral vasculature and providing complementary information on cerebral perfusion. SYLVER images with a 32×32 matrix array probe at 2 MHz using diverging-wave transmits. CE and FDA clearances are expected in 2026. This directory holds the data Resolve Stroke contributes to Open-H-RF: pre-beamformed RF/IQ channel data from an imaging phantom, a flow phantom, and in-vivo transcranial acquisitions, all de-identified and released under CC BY 4.0. The in-vivo data comes from the CPP-approved SCULPT clinical study. Two transmit sequences are included: a saddle sequence that images a 2D plane for real-time B-mode, and a 4 kHz single-aperture volume sequence for CEUS and blood-flow measurement.
 
 Each dataset directory holds its data card (`README.md`), a `reconstruct.py` + `pipeline.yaml` beamforming recipe and, for the CEUS datasets, a power-Doppler reconstruction; the HDF5 files are on the Hub and the preview images in [`assets/`](assets/).
 
@@ -53,7 +53,7 @@ Each dataset directory holds its data card (`README.md`), a `reconstruct.py` + `
 
 ## Processing the Dataset
 
-Each sub-dataset has its own `reconstruct.py` and `pipeline.yaml`, as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF/tree/main/datasets/resolvestroke), built on the [zea library](https://github.com/tue-bmd/zea); the CEUS datasets add a power-Doppler reconstruction. See the data card of each sub-dataset for how to run them.
+Each sub-dataset has its own `reconstruct.py` and `pipeline.yaml`, as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF/tree/main/datasets/resolvestroke), built on the [zea library](https://github.com/tue-bmd/zea); the CEUS datasets add a power-Doppler reconstruction. See the data card of each sub-dataset for how to run them.
 
 The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is CC BY 4.0.
 
@@ -63,7 +63,7 @@ The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header;
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 43 HDF5 files; 110.62 GB (110,619,394,048 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:** 43 HDF5 files; 110.62 GB (110,619,394,048 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.
 
 ## Contents
 

resolvestroke/clinical/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - iq
-  - openh-rf
+  - open-h-rf
   - 3d
   - matrix-probe
   - contrast-enhanced
@@ -24,7 +24,7 @@ size_categories:
 
 <img src="../assets/clinical_mvi_montage.png" alt="Microvascular image (x-z MIP of the computed reference) for the 20 clinical acquisitions" width="100%">
 
-*Reference microvascular image (`mvi`, x-z maximum-intensity projection of the computed reference) for the 20 acquisitions in [`clinical/`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical).*
+*Reference microvascular image (`mvi`, x-z maximum-intensity projection of the computed reference) for the 20 acquisitions in [`clinical/`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical).*
 
 ## Dataset Description
 
@@ -71,12 +71,12 @@ Contrast-enhanced ultrasound flow/perfusion quantification, microbubble detectio
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/clinical/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/resolvestroke/clinical/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 reconstruction recipe is identical for all 20 acquisitions, so the scripts and pipeline YAMLs live once in this directory. Each script has a `SUBJECT` constant at the top (default `"SP02-Left-2"`); set it to any acquisition name from the inventory, e.g. `SUBJECT = "SP07-Right"`, and the `hf://` input path `hf://nvidia/OpenH-RF/resolvestroke/clinical/<SUBJECT>/<SUBJECT>.hdf5` and the output filenames follow from it. Swap `ZEA_FILE` (`INPUT` in `reconstruct_PD_3d.py`) for a local path to run against your own copy.
+The reconstruction recipe is identical for all 20 acquisitions, so the scripts and pipeline YAMLs live once in this directory. Each script has a `SUBJECT` constant at the top (default `"SP02-Left-2"`); set it to any acquisition name from the inventory, e.g. `SUBJECT = "SP07-Right"`, and the `hf://` input path `hf://nvidia/Open-H-RF/resolvestroke/clinical/<SUBJECT>/<SUBJECT>.hdf5` and the output filenames follow from it. Swap `ZEA_FILE` (`INPUT` in `reconstruct_PD_3d.py`) for a local path to run against your own copy.
 
 ```bash
-uv run --project /path/to/OpenH-RF python reconstruct.py
+uv run --project /path/to/Open-H-RF python reconstruct.py
 ```
 
 `reconstruct_PD_3d.py` (with `pipeline_PD_3d.yaml`) renders the power-Doppler reconstruction and `display_references.py` the computed reference maps; both are described below.
@@ -95,7 +95,7 @@ The files are in the zea HDF5 format, root `zea_version` 0.1.6. `data/raw_data`
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 20 HDF5 files; 105.16 GB (105,159,196,672 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:** 20 HDF5 files; 105.16 GB (105,159,196,672 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.
 
 - Acquisitions: 20 (10 subjects × 2)
 - Frames per acquisition: 20000 (5 clips × 4000 frames)
@@ -129,26 +129,26 @@ One row per acquisition. Power Doppler: x-z maximum-intensity projection of each
   <tr>
     <th>Acquisition</th><th>Subject</th><th>Side</th><th>Power Doppler, five clips (x-z MIP)</th><th>Reference maps (mvi · velocity_radial_avg, x-z and y-z)</th>
   </tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP01-Left-1"><code>SP01-Left-1</code></a></td><td>SP01</td><td>Left</td><td><a href="../assets/clinical/SP01-Left-1_pd_clips.png"><img src="../assets/clinical/SP01-Left-1_pd_clips.png" alt="SP01-Left-1 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP01-Left-1_references.png"><img src="../assets/clinical/SP01-Left-1_references.png" alt="SP01-Left-1 reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP01-Left-2"><code>SP01-Left-2</code></a></td><td>SP01</td><td>Left</td><td><a href="../assets/clinical/SP01-Left-2_pd_clips.png"><img src="../assets/clinical/SP01-Left-2_pd_clips.png" alt="SP01-Left-2 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP01-Left-2_references.png"><img src="../assets/clinical/SP01-Left-2_references.png" alt="SP01-Left-2 reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP02-Left-1"><code>SP02-Left-1</code></a></td><td>SP02</td><td>Left</td><td><a href="../assets/clinical/SP02-Left-1_pd_clips.png"><img src="../assets/clinical/SP02-Left-1_pd_clips.png" alt="SP02-Left-1 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP02-Left-1_references.png"><img src="../assets/clinical/SP02-Left-1_references.png" alt="SP02-Left-1 reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP02-Left-2"><code>SP02-Left-2</code></a></td><td>SP02</td><td>Left</td><td><a href="../assets/clinical/SP02-Left-2_pd_clips.png"><img src="../assets/clinical/SP02-Left-2_pd_clips.png" alt="SP02-Left-2 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP02-Left-2_references.png"><img src="../assets/clinical/SP02-Left-2_references.png" alt="SP02-Left-2 reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP03-Left"><code>SP03-Left</code></a></td><td>SP03</td><td>Left</td><td><a href="../assets/clinical/SP03-Left_pd_clips.png"><img src="../assets/clinical/SP03-Left_pd_clips.png" alt="SP03-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP03-Left_references.png"><img src="../assets/clinical/SP03-Left_references.png" alt="SP03-Left reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP03-Right"><code>SP03-Right</code></a></td><td>SP03</td><td>Right</td><td><a href="../assets/clinical/SP03-Right_pd_clips.png"><img src="../assets/clinical/SP03-Right_pd_clips.png" alt="SP03-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP03-Right_references.png"><img src="../assets/clinical/SP03-Right_references.png" alt="SP03-Right reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP04-Left-1"><code>SP04-Left-1</code></a></td><td>SP04</td><td>Left</td><td><a href="../assets/clinical/SP04-Left-1_pd_clips.png"><img src="../assets/clinical/SP04-Left-1_pd_clips.png" alt="SP04-Left-1 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP04-Left-1_references.png"><img src="../assets/clinical/SP04-Left-1_references.png" alt="SP04-Left-1 reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP04-Left-2"><code>SP04-Left-2</code></a></td><td>SP04</td><td>Left</td><td><a href="../assets/clinical/SP04-Left-2_pd_clips.png"><img src="../assets/clinical/SP04-Left-2_pd_clips.png" alt="SP04-Left-2 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP04-Left-2_references.png"><img src="../assets/clinical/SP04-Left-2_references.png" alt="SP04-Left-2 reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP05-Left"><code>SP05-Left</code></a></td><td>SP05</td><td>Left</td><td><a href="../assets/clinical/SP05-Left_pd_clips.png"><img src="../assets/clinical/SP05-Left_pd_clips.png" alt="SP05-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP05-Left_references.png"><img src="../assets/clinical/SP05-Left_references.png" alt="SP05-Left reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP05-Right"><code>SP05-Right</code></a></td><td>SP05</td><td>Right</td><td><a href="../assets/clinical/SP05-Right_pd_clips.png"><img src="../assets/clinical/SP05-Right_pd_clips.png" alt="SP05-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP05-Right_references.png"><img src="../assets/clinical/SP05-Right_references.png" alt="SP05-Right reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP06-Left"><code>SP06-Left</code></a></td><td>SP06</td><td>Left</td><td><a href="../assets/clinical/SP06-Left_pd_clips.png"><img src="../assets/clinical/SP06-Left_pd_clips.png" alt="SP06-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP06-Left_references.png"><img src="../assets/clinical/SP06-Left_references.png" alt="SP06-Left reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP06-Right"><code>SP06-Right</code></a></td><td>SP06</td><td>Right</td><td><a href="../assets/clinical/SP06-Right_pd_clips.png"><img src="../assets/clinical/SP06-Right_pd_clips.png" alt="SP06-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP06-Right_references.png"><img src="../assets/clinical/SP06-Right_references.png" alt="SP06-Right reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP07-Left"><code>SP07-Left</code></a></td><td>SP07</td><td>Left</td><td><a href="../assets/clinical/SP07-Left_pd_clips.png"><img src="../assets/clinical/SP07-Left_pd_clips.png" alt="SP07-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP07-Left_references.png"><img src="../assets/clinical/SP07-Left_references.png" alt="SP07-Left reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP07-Right"><code>SP07-Right</code></a></td><td>SP07</td><td>Right</td><td><a href="../assets/clinical/SP07-Right_pd_clips.png"><img src="../assets/clinical/SP07-Right_pd_clips.png" alt="SP07-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP07-Right_references.png"><img src="../assets/clinical/SP07-Right_references.png" alt="SP07-Right reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP08-Left"><code>SP08-Left</code></a></td><td>SP08</td><td>Left</td><td><a href="../assets/clinical/SP08-Left_pd_clips.png"><img src="../assets/clinical/SP08-Left_pd_clips.png" alt="SP08-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP08-Left_references.png"><img src="../assets/clinical/SP08-Left_references.png" alt="SP08-Left reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP08-Right"><code>SP08-Right</code></a></td><td>SP08</td><td>Right</td><td><a href="../assets/clinical/SP08-Right_pd_clips.png"><img src="../assets/clinical/SP08-Right_pd_clips.png" alt="SP08-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP08-Right_references.png"><img src="../assets/clinical/SP08-Right_references.png" alt="SP08-Right reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP09-Left-1"><code>SP09-Left-1</code></a></td><td>SP09</td><td>Left</td><td><a href="../assets/clinical/SP09-Left-1_pd_clips.png"><img src="../assets/clinical/SP09-Left-1_pd_clips.png" alt="SP09-Left-1 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP09-Left-1_references.png"><img src="../assets/clinical/SP09-Left-1_references.png" alt="SP09-Left-1 reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP09-Left-2"><code>SP09-Left-2</code></a></td><td>SP09</td><td>Left</td><td><a href="../assets/clinical/SP09-Left-2_pd_clips.png"><img src="../assets/clinical/SP09-Left-2_pd_clips.png" alt="SP09-Left-2 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP09-Left-2_references.png"><img src="../assets/clinical/SP09-Left-2_references.png" alt="SP09-Left-2 reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP10-Left"><code>SP10-Left</code></a></td><td>SP10</td><td>Left</td><td><a href="../assets/clinical/SP10-Left_pd_clips.png"><img src="../assets/clinical/SP10-Left_pd_clips.png" alt="SP10-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP10-Left_references.png"><img src="../assets/clinical/SP10-Left_references.png" alt="SP10-Left reference maps" width="100%"></a></td></tr>
-  <tr><td><a href="https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/clinical/SP10-Right"><code>SP10-Right</code></a></td><td>SP10</td><td>Right</td><td><a href="../assets/clinical/SP10-Right_pd_clips.png"><img src="../assets/clinical/SP10-Right_pd_clips.png" alt="SP10-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP10-Right_references.png"><img src="../assets/clinical/SP10-Right_references.png" alt="SP10-Right reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP01-Left-1"><code>SP01-Left-1</code></a></td><td>SP01</td><td>Left</td><td><a href="../assets/clinical/SP01-Left-1_pd_clips.png"><img src="../assets/clinical/SP01-Left-1_pd_clips.png" alt="SP01-Left-1 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP01-Left-1_references.png"><img src="../assets/clinical/SP01-Left-1_references.png" alt="SP01-Left-1 reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP01-Left-2"><code>SP01-Left-2</code></a></td><td>SP01</td><td>Left</td><td><a href="../assets/clinical/SP01-Left-2_pd_clips.png"><img src="../assets/clinical/SP01-Left-2_pd_clips.png" alt="SP01-Left-2 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP01-Left-2_references.png"><img src="../assets/clinical/SP01-Left-2_references.png" alt="SP01-Left-2 reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP02-Left-1"><code>SP02-Left-1</code></a></td><td>SP02</td><td>Left</td><td><a href="../assets/clinical/SP02-Left-1_pd_clips.png"><img src="../assets/clinical/SP02-Left-1_pd_clips.png" alt="SP02-Left-1 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP02-Left-1_references.png"><img src="../assets/clinical/SP02-Left-1_references.png" alt="SP02-Left-1 reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP02-Left-2"><code>SP02-Left-2</code></a></td><td>SP02</td><td>Left</td><td><a href="../assets/clinical/SP02-Left-2_pd_clips.png"><img src="../assets/clinical/SP02-Left-2_pd_clips.png" alt="SP02-Left-2 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP02-Left-2_references.png"><img src="../assets/clinical/SP02-Left-2_references.png" alt="SP02-Left-2 reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP03-Left"><code>SP03-Left</code></a></td><td>SP03</td><td>Left</td><td><a href="../assets/clinical/SP03-Left_pd_clips.png"><img src="../assets/clinical/SP03-Left_pd_clips.png" alt="SP03-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP03-Left_references.png"><img src="../assets/clinical/SP03-Left_references.png" alt="SP03-Left reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP03-Right"><code>SP03-Right</code></a></td><td>SP03</td><td>Right</td><td><a href="../assets/clinical/SP03-Right_pd_clips.png"><img src="../assets/clinical/SP03-Right_pd_clips.png" alt="SP03-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP03-Right_references.png"><img src="../assets/clinical/SP03-Right_references.png" alt="SP03-Right reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP04-Left-1"><code>SP04-Left-1</code></a></td><td>SP04</td><td>Left</td><td><a href="../assets/clinical/SP04-Left-1_pd_clips.png"><img src="../assets/clinical/SP04-Left-1_pd_clips.png" alt="SP04-Left-1 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP04-Left-1_references.png"><img src="../assets/clinical/SP04-Left-1_references.png" alt="SP04-Left-1 reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP04-Left-2"><code>SP04-Left-2</code></a></td><td>SP04</td><td>Left</td><td><a href="../assets/clinical/SP04-Left-2_pd_clips.png"><img src="../assets/clinical/SP04-Left-2_pd_clips.png" alt="SP04-Left-2 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP04-Left-2_references.png"><img src="../assets/clinical/SP04-Left-2_references.png" alt="SP04-Left-2 reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP05-Left"><code>SP05-Left</code></a></td><td>SP05</td><td>Left</td><td><a href="../assets/clinical/SP05-Left_pd_clips.png"><img src="../assets/clinical/SP05-Left_pd_clips.png" alt="SP05-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP05-Left_references.png"><img src="../assets/clinical/SP05-Left_references.png" alt="SP05-Left reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP05-Right"><code>SP05-Right</code></a></td><td>SP05</td><td>Right</td><td><a href="../assets/clinical/SP05-Right_pd_clips.png"><img src="../assets/clinical/SP05-Right_pd_clips.png" alt="SP05-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP05-Right_references.png"><img src="../assets/clinical/SP05-Right_references.png" alt="SP05-Right reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP06-Left"><code>SP06-Left</code></a></td><td>SP06</td><td>Left</td><td><a href="../assets/clinical/SP06-Left_pd_clips.png"><img src="../assets/clinical/SP06-Left_pd_clips.png" alt="SP06-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP06-Left_references.png"><img src="../assets/clinical/SP06-Left_references.png" alt="SP06-Left reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP06-Right"><code>SP06-Right</code></a></td><td>SP06</td><td>Right</td><td><a href="../assets/clinical/SP06-Right_pd_clips.png"><img src="../assets/clinical/SP06-Right_pd_clips.png" alt="SP06-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP06-Right_references.png"><img src="../assets/clinical/SP06-Right_references.png" alt="SP06-Right reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP07-Left"><code>SP07-Left</code></a></td><td>SP07</td><td>Left</td><td><a href="../assets/clinical/SP07-Left_pd_clips.png"><img src="../assets/clinical/SP07-Left_pd_clips.png" alt="SP07-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP07-Left_references.png"><img src="../assets/clinical/SP07-Left_references.png" alt="SP07-Left reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP07-Right"><code>SP07-Right</code></a></td><td>SP07</td><td>Right</td><td><a href="../assets/clinical/SP07-Right_pd_clips.png"><img src="../assets/clinical/SP07-Right_pd_clips.png" alt="SP07-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP07-Right_references.png"><img src="../assets/clinical/SP07-Right_references.png" alt="SP07-Right reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP08-Left"><code>SP08-Left</code></a></td><td>SP08</td><td>Left</td><td><a href="../assets/clinical/SP08-Left_pd_clips.png"><img src="../assets/clinical/SP08-Left_pd_clips.png" alt="SP08-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP08-Left_references.png"><img src="../assets/clinical/SP08-Left_references.png" alt="SP08-Left reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP08-Right"><code>SP08-Right</code></a></td><td>SP08</td><td>Right</td><td><a href="../assets/clinical/SP08-Right_pd_clips.png"><img src="../assets/clinical/SP08-Right_pd_clips.png" alt="SP08-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP08-Right_references.png"><img src="../assets/clinical/SP08-Right_references.png" alt="SP08-Right reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP09-Left-1"><code>SP09-Left-1</code></a></td><td>SP09</td><td>Left</td><td><a href="../assets/clinical/SP09-Left-1_pd_clips.png"><img src="../assets/clinical/SP09-Left-1_pd_clips.png" alt="SP09-Left-1 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP09-Left-1_references.png"><img src="../assets/clinical/SP09-Left-1_references.png" alt="SP09-Left-1 reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP09-Left-2"><code>SP09-Left-2</code></a></td><td>SP09</td><td>Left</td><td><a href="../assets/clinical/SP09-Left-2_pd_clips.png"><img src="../assets/clinical/SP09-Left-2_pd_clips.png" alt="SP09-Left-2 power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP09-Left-2_references.png"><img src="../assets/clinical/SP09-Left-2_references.png" alt="SP09-Left-2 reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP10-Left"><code>SP10-Left</code></a></td><td>SP10</td><td>Left</td><td><a href="../assets/clinical/SP10-Left_pd_clips.png"><img src="../assets/clinical/SP10-Left_pd_clips.png" alt="SP10-Left power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP10-Left_references.png"><img src="../assets/clinical/SP10-Left_references.png" alt="SP10-Left reference maps" width="100%"></a></td></tr>
+  <tr><td><a href="https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/clinical/SP10-Right"><code>SP10-Right</code></a></td><td>SP10</td><td>Right</td><td><a href="../assets/clinical/SP10-Right_pd_clips.png"><img src="../assets/clinical/SP10-Right_pd_clips.png" alt="SP10-Right power Doppler of the five clips" width="100%"></a></td><td><a href="../assets/clinical/SP10-Right_references.png"><img src="../assets/clinical/SP10-Right_references.png" alt="SP10-Right reference maps" width="100%"></a></td></tr>
 </table>
 
 ## Subject Metadata
@@ -177,7 +177,7 @@ A display-only linear TGC (1.5 dB/cm) adds a depth-dependent dB gain to the rend
 Run (a GPU is strongly recommended: about 14 min per acquisition with `uv sync --extra gpu`, hours with the CPU-only JAX of the plain `uv sync`):
 
 ```bash
-uv run --project /path/to/OpenH-RF python reconstruct_PD_3d.py
+uv run --project /path/to/Open-H-RF python reconstruct_PD_3d.py
 ```
 
 ## Computed References
@@ -204,7 +204,7 @@ Notes:
 ![Computed reference maps montage, SP02-Left-2](../assets/clinical_SP02-Left-2_references_montage.png)
 
 ```bash
-uv run --project /path/to/OpenH-RF python display_references.py
+uv run --project /path/to/Open-H-RF python display_references.py
 ```
 
 ## Ethical Considerations

resolvestroke/phantom_flow/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - iq
-  - openh-rf
+  - open-h-rf
   - 3d
   - matrix-probe
   - contrast-enhanced
@@ -23,7 +23,7 @@ size_categories:
 
 ![Power-Doppler 3D MIP montage with reference mvi](../assets/phantom_flow_PD_montage.png)
 
-*Power Doppler of the five clips (x-z and y-z maximum-intensity projections), with the reference `mvi` in the last column, reconstructed from [`phantom_flow.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/resolvestroke/phantom_flow/phantom_flow.hdf5).*
+*Power Doppler of the five clips (x-z and y-z maximum-intensity projections), with the reference `mvi` in the last column, reconstructed from [`phantom_flow.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/resolvestroke/phantom_flow/phantom_flow.hdf5).*
 
 ## Dataset Description
 
@@ -68,10 +68,10 @@ Contrast-enhanced ultrasound flow quantification, microbubble detection and trac
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/phantom_flow/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/resolvestroke/phantom_flow/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.
 
 ```bash
-uv run --project /path/to/OpenH-RF python reconstruct.py
+uv run --project /path/to/Open-H-RF python reconstruct.py
 ```
 
 `reconstruct_PD_3d.py` (with `pipeline_PD_3d.yaml`) renders the power-Doppler reconstruction and `display_references.py` the computed reference maps; both are described below.
@@ -90,7 +90,7 @@ The file is in the zea HDF5 format, root `zea_version` 0.1.6, validated `complia
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 1 HDF5 file; 5.25 GB (5,250,744,320 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:** 1 HDF5 file; 5.25 GB (5,250,744,320 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.
 
 - Frames: 20000 (5 clips × 4000 frames)
 - Frame rate: 4000 Hz (within each clip)
@@ -136,7 +136,7 @@ The resulting montage is shown at the top of this card.
 Run (a GPU is strongly recommended: about 14 min per acquisition with `uv sync --extra gpu`, hours with the CPU-only JAX of the plain `uv sync`):
 
 ```bash
-uv run --project /path/to/OpenH-RF python reconstruct_PD_3d.py
+uv run --project /path/to/Open-H-RF python reconstruct_PD_3d.py
 ```
 
 ## Computed References
@@ -162,7 +162,7 @@ Notes:
 
 ![Computed reference maps montage](../assets/phantom_flow_references_montage.png)
 
-Run: `uv run --project /path/to/OpenH-RF python display_references.py`
+Run: `uv run --project /path/to/Open-H-RF python display_references.py`
 
 ## Known Issues
 

resolvestroke/phantom_mp/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - iq
-  - openh-rf
+  - open-h-rf
   - 3d
   - matrix-probe
   - phantom
@@ -21,7 +21,7 @@ size_categories:
 
 ![Reference B-mode (two perpendicular sectors)](../assets/phantom_mp_bmode.png)
 
-*B-mode of one frame, x-z and y-z sectors, reconstructed from [`phantom_mp.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/resolvestroke/phantom_mp/phantom_mp.hdf5).*
+*B-mode of one frame, x-z and y-z sectors, reconstructed from [`phantom_mp.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/resolvestroke/phantom_mp/phantom_mp.hdf5).*
 
 ## Dataset Description
 
@@ -56,10 +56,10 @@ Beamforming and reconstruction research (RFP task group 6.1, Generalized Reconst
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/phantom_mp/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/resolvestroke/phantom_mp/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.
 
 ```bash
-uv run --project /path/to/OpenH-RF python reconstruct.py
+uv run --project /path/to/Open-H-RF python reconstruct.py
 ```
 
 The Python scripts carry their own `SPDX-License-Identifier: Apache-2.0` header; the dataset itself is CC BY 4.0.
@@ -72,7 +72,7 @@ Single zea HDF5 file (`phantom_mp.hdf5`), one track holding the raw channel data
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 1 HDF5 file; 33.75 MB (33,751,040 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:** 1 HDF5 file; 33.75 MB (33,751,040 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.
 
 - Frames: 1000 (single acquisition)
 - Train / val / test split: N/A

resolvestroke/saddle/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - iq
-  - openh-rf
+  - open-h-rf
   - matrix-probe
   - diverging-wave
   - b-mode
@@ -24,13 +24,13 @@ size_categories:
 
 ![Saddle-array B-modes for all 21 datasets](../assets/saddle_bmode_montage.png)
 
-*Saddle-array B-modes of all 21 files in [`saddle/data/`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/resolvestroke/saddle/data), one panel per dataset; the phantom (`PMP01`) shows a regular column of point targets.*
+*Saddle-array B-modes of all 21 files in [`saddle/data/`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/resolvestroke/saddle/data), one panel per dataset; the phantom (`PMP01`) shows a regular column of point targets.*
 
 ## Dataset Description
 
 Single-frame anatomical B-mode acquisitions from the "saddle" imaging sequence of Resolve Stroke's SYLVER ultrasound device, using a 32×32 matrix probe. Each raw acquisition contains, alongside the multi-thousand-frame contrast-enhanced ultrasound (CEUS) sequence, one wide-angle diverging-wave frame (the *saddle* sequence: `x_ang` −24°…+24° in nine steps, no elevation steering, cylindrical elevation focus) received on the full 1024-element aperture through four consecutive 256-element receive events per transmit. Beamformed, this single frame gives a sector B-mode of the imaging plane: the structural reference view acquired at the same probe placement as the contrast (CEUS) recording.
 
-This directory contains one such reference B-mode per dataset: 20 clinical transcranial acquisitions (SCULPT study) and 1 static matrix-probe imaging phantom (21 files total). It is the structural companion to the OpenH-RF Resolve Stroke clinical CEUS clip submission, and the two use the same anonymized subject codes.
+This directory contains one such reference B-mode per dataset: 20 clinical transcranial acquisitions (SCULPT study) and 1 static matrix-probe imaging phantom (21 files total). It is the structural companion to the Open-H-RF Resolve Stroke clinical CEUS clip submission, and the two use the same anonymized subject codes.
 
 ## Dataset Contributor(s)
 
@@ -51,7 +51,7 @@ This directory contains one such reference B-mode per dataset: 20 clinical trans
 
 ## Intended Usage
 
-Matrix-probe diverging-wave beamforming research, anatomical B-mode reconstruction, and structural reference for the companion transcranial CEUS flow/perfusion datasets (OpenH-RF request-for-proposals task group 6.2, Blood Flow).
+Matrix-probe diverging-wave beamforming research, anatomical B-mode reconstruction, and structural reference for the companion transcranial CEUS flow/perfusion datasets (Open-H-RF request-for-proposals task group 6.2, Blood Flow).
 
 ## Dataset Characterization
 
@@ -61,10 +61,10 @@ Matrix-probe diverging-wave beamforming research, anatomical B-mode reconstructi
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/resolvestroke/saddle/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/resolvestroke/saddle/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.
 
 ```bash
-uv run --project /path/to/OpenH-RF python reconstruct.py
+uv run --project /path/to/Open-H-RF python reconstruct.py
 ```
 
 `reconstruct.py` streams `PMP01.hdf5` from the Hub by default; set `ZEA_FILE` at the top of the script to another of the 21 files (or a local path). The B-mode PNG is written to `assets/<file>_bmode.png`.
@@ -83,7 +83,7 @@ The hardware time-gain compensation is baked into `raw_data`; `scan/tgc_gain_cur
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 21 HDF5 files; 175.70 MB (175,702,016 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:** 21 HDF5 files; 175.70 MB (175,702,016 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.
 
 - Files: 21 (20 clinical + 1 phantom)
 - Frames per file: 1

siemens-healthineers/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - iq
-  - openh-rf
+  - open-h-rf
   - medical-imaging
   - beamforming
   - in-vivo
@@ -21,7 +21,7 @@ size_categories:
 
 ![DAS B-mode reconstructions of four anatomies from the Siemens FSA channel data](assets/anatomies.png)
 
-*Four of the anatomies in this dataset, each beamformed from `data/raw_data`: [`Subject_03_acq_018`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_03_acq_018.hdf5), [`Subject_02_acq_004`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_02_acq_004.hdf5), [`Subject_03_acq_008`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_03_acq_008.hdf5), [`Subject_05_acq_031`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/siemens-healthineers/data/Subject_05_acq_031.hdf5).*
+*Four of the anatomies in this dataset, each beamformed from `data/raw_data`: [`Subject_03_acq_018`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/siemens-healthineers/data/Subject_03_acq_018.hdf5), [`Subject_02_acq_004`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/siemens-healthineers/data/Subject_02_acq_004.hdf5), [`Subject_03_acq_008`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/siemens-healthineers/data/Subject_03_acq_008.hdf5), [`Subject_05_acq_031`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/siemens-healthineers/data/Subject_05_acq_031.hdf5).*
 
 ## Dataset Description
 
@@ -61,11 +61,11 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/siemens-healthineers/data/Subject_03_acq_008.hdf5 \
-  --config hf://nvidia/OpenH-RF/siemens-healthineers/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/siemens-healthineers/data/Subject_03_acq_008.hdf5 \
+  --config hf://nvidia/Open-H-RF/siemens-healthineers/pipeline.yaml
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/siemens-healthineers/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/siemens-healthineers/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 ## Dataset Format
 
@@ -105,7 +105,7 @@ All spatial maps carry per-pixel Cartesian `coordinates` in meters, last axis `[
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 590 HDF5 files; 228.54 GB (228,538,449,920 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:** 590 HDF5 files; 228.54 GB (228,538,449,920 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.
 
 - **590 acquisitions (4,539 frames) from 15 subjects**, one continuous dataset: 51 acquisitions / 411 frames (Subjects 01–03) from the first acquisition round and 539 acquisitions / 4,128 frames (Subjects 04–15) from the second.
 - **File naming:** one HDF5 per acquisition, `Subject_XX_acq_NNN.hdf5` (acquisitions numbered per subject in acquisition order).

stanford-murine/README.md

@@ -8,7 +8,7 @@ task_categories:
 tags:
 - ultrasound
 - rf
-- openh-rf
+- open-h-rf
 - beamforming
 - sound-speed-estimation
 - aberration-correction
@@ -25,7 +25,7 @@ size_categories:
 
 ![B-mode reconstructions of a rat liver and the ATS 549 phantom](assets/hero.png)
 
-*Frame 0 reconstructed with `reconstruct.py` (L12-3v, 60 dB). Top: the exposed liver of [Rat3](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/stanford-murine/data/RatExperiments/VerasonicsAcq/Rat3/ExposedLiver/DATA_Tracks_20190319_115804.hdf5), `multifocal` track. Bottom: the ATS 549 phantom's [lesions](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/stanford-murine/data/SoSExperiments/august13_2020/L12_3v/ATS_phantom/lesion/DATA_Tracks_20200813_164505.hdf5) and [point targets](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/stanford-murine/data/SoSExperiments/august13_2020/L12_3v/ATS_phantom/point_target/DATA_Tracks_20200813_164745.hdf5), `hadamard` track. The point targets curve into arcs because the phantom is beamformed at 1540 m/s while its own sound speed is lower — the mismatch this dataset is built to estimate and correct.*
+*Frame 0 reconstructed with `reconstruct.py` (L12-3v, 60 dB). Top: the exposed liver of [Rat3](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/stanford-murine/data/RatExperiments/VerasonicsAcq/Rat3/ExposedLiver/DATA_Tracks_20190319_115804.hdf5), `multifocal` track. Bottom: the ATS 549 phantom's [lesions](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/stanford-murine/data/SoSExperiments/august13_2020/L12_3v/ATS_phantom/lesion/DATA_Tracks_20200813_164505.hdf5) and [point targets](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/stanford-murine/data/SoSExperiments/august13_2020/L12_3v/ATS_phantom/point_target/DATA_Tracks_20200813_164745.hdf5), `hadamard` track. The point targets curve into arcs because the phantom is beamformed at 1540 m/s while its own sound speed is lower — the mismatch this dataset is built to estimate and correct.*
 
 ## Dataset Description
 
@@ -38,7 +38,7 @@ The source data are available from Figshare:
 
 The original Figshare releases were published under Apache 2.0; this converted release is CC BY 4.0.
 
-The converted dataset is hosted at [nvidia/OpenH-RF/stanford-murine](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/stanford-murine).
+The converted dataset is hosted at [nvidia/Open-H-RF/stanford-murine](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/stanford-murine).
 
 ## Dataset Contributor(s)
 
@@ -109,13 +109,13 @@ The acquisitions can be processed with the `pipeline_multifocal.yaml`, `pipeline
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/stanford-murine/data/RatExperiments/VerasonicsAcq/Rat3/ExposedLiver/DATA_Tracks_20190319_115804.hdf5 \
-  --config hf://nvidia/OpenH-RF/stanford-murine/pipeline_synthetic_aperture.yaml \
+  --dataset hf://nvidia/Open-H-RF/stanford-murine/data/RatExperiments/VerasonicsAcq/Rat3/ExposedLiver/DATA_Tracks_20190319_115804.hdf5 \
+  --config hf://nvidia/Open-H-RF/stanford-murine/pipeline_synthetic_aperture.yaml \
   --track synthetic_aperture \
   --n-frames 1
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/stanford-murine/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF). It reconstructs the first frame of every track in `ZEA_FILE` with the same pipelines and writes one image per track to `assets/`. On top of the YAML, the script inserts a baseband FIR low-pass after demodulation, with its cutoff at half the probe bandwidth (4.0 MHz for L12-3v, 3.0 MHz for L12-5, 1.3 MHz for C5-2v). The cutoff depends on the probe, so it is not part of the pipeline YAMLs and `zea process` runs without it; the difference is small on the rat acquisitions and clearly visible at depth on the phantoms.
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/stanford-murine/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF). It reconstructs the first frame of every track in `ZEA_FILE` with the same pipelines and writes one image per track to `assets/`. On top of the YAML, the script inserts a baseband FIR low-pass after demodulation, with its cutoff at half the probe bandwidth (4.0 MHz for L12-3v, 3.0 MHz for L12-5, 1.3 MHz for C5-2v). The cutoff depends on the probe, so it is not part of the pipeline YAMLs and `zea process` runs without it; the difference is small on the rat acquisitions and clearly visible at depth on the phantoms.
 
 ## Dataset Format
 
@@ -141,7 +141,7 @@ Reconstructed B-mode images and segmentation masks are not stored in the convert
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 245 HDF5 files; 328.99 GB (328,985,804,800 bytes) stored; root `zea_version` **0.1.4**. 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:** 245 HDF5 files; 328.99 GB (328,985,804,800 bytes) stored; root `zea_version` **0.1.4**. 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.
 
 The original four-file demo (not the complete HF release summarized above) contains four HDF5 acquisition bundles: one Rat9 bundle and one phantom bundle for each of C5-2v, L12-3v, and L12-5. Each bundle has three sequence tracks, and each track retains every frame in its source MAT file. That demo has 12 tracks and 64 frames and occupies approximately 5.5 GiB; HDF5 writer and compression versions may change the exact byte count.
 

strasbourg-basel/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - tracked-ultrasound
   - robot-tracked
   - probe-pose
@@ -29,7 +29,7 @@ size_categories:
   <img src="assets/reference_bmode.png" alt="BoneSRF B-mode of phantom 2, whole-bone sweep" width="200">
 </p>
 
-*B-mode of a fractured-femur phantom, frame 45 of [`data/phantom2_wholebone.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5), beamformed from the recovered channel data.*
+*B-mode of a fractured-femur phantom, frame 45 of [`data/phantom2_wholebone.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5), beamformed from the recovered channel data.*
 
 ## Dataset Description
 
@@ -37,7 +37,7 @@ size_categories:
   <img src="assets/bonesrf_logo.png" alt="BoneSRF" width="200">
 </p>
 
-**BoneSRF** (Bone Surface Reflection) is an ultrasound channel-data dataset for bone-surface and fracture-reflection research. It contains three 3D-printed, fractured femur phantoms, scanned with a handheld point-of-care probe, inverted back to pre-beamformed RF and packaged in the OpenH-RF `zea` format.
+**BoneSRF** (Bone Surface Reflection) is an ultrasound channel-data dataset for bone-surface and fracture-reflection research. It contains three 3D-printed, fractured femur phantoms, scanned with a handheld point-of-care probe, inverted back to pre-beamformed RF and packaged in the Open-H-RF `zea` format.
 
 Each phantom was swept three times (`distal`, `proximal`, `wholebone`), giving nine `zea` HDF5 files. Every scan is robot-tracked: the probe was mounted on a robotic arm and its pose recorded separately. Each file also carries that phantom's CT scan and multi-label segmentation.
 
@@ -49,7 +49,7 @@ This is phantom data, not simulated, clinical or in-vivo. It is intended for ful
 
 1. **Acquisition.** Each phantom was scanned with a Clarius L20HD3 linear array, producing the probe's own beamformed RF output. This is the physically acquired data, not simulated.
 2. **Inversion.** The beamformed RF is inverted back into pre-beamformed, per-element channel data with [`das-inverse` (`clarius` branch)](https://github.com/sankethvedula/das-inverse/tree/clarius), a CGLS solver over a `zea.inverse.DASOperator` forward model of the probe's focused, walking-sub-aperture transmit sequence (`invert_clarius_beamformed.py`). The result is not sensor-captured, but numerically consistent with the real beamformed acquisition it was inverted from.
-3. **Packaging.** The inverted channel data, transmit-sequence metadata, probe geometry, per-frame probe pose, and the phantom's CT + segmentation are written out as one `zea` HDF5 file per scan, matching the OpenH-RF format spec.
+3. **Packaging.** The inverted channel data, transmit-sequence metadata, probe geometry, per-frame probe pose, and the phantom's CT + segmentation are written out as one `zea` HDF5 file per scan, matching the Open-H-RF format spec.
 
 ### Probe tracking
 
@@ -91,7 +91,7 @@ The CT and segmentation data inside the files are released under the same CC BY
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/strasbourg-basel/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/strasbourg-basel/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 loads the saved `zea.Pipeline` from `pipeline.yaml`, runs it on one frame of one scan, and writes `assets/<scan>.png`.
 
@@ -105,7 +105,7 @@ The constants at the top of the script select what is reconstructed:
 These values reconstruct `phantom2_wholebone` at its reference frame (45) on the CPU:
 
 ```python
-SCAN = "hf://nvidia/OpenH-RF/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5"
+SCAN = "hf://nvidia/Open-H-RF/strasbourg-basel/BoneSRF/data/phantom2_wholebone.hdf5"
 FRAME = None
 DEVICE = "cpu"
 ```
@@ -226,7 +226,7 @@ The CT is reference imaging of the physical phantom in scanner (LPS) space. It i
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 9 HDF5 files; 200.75 GB (200,745,025,536 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:** 9 HDF5 files; 200.75 GB (200,745,025,536 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.
 
 Nine acquisitions, one continuous sweep each; 1,427 frames in total. No train / val / test split (each file is a single reference acquisition). Every scan has one tracked probe pose per frame.
 

technion/bladder/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - iq
-  - openh-rf
+  - open-h-rf
   - beamforming
   - bladder
   - 3d
@@ -21,7 +21,7 @@ size_categories:
 
 ![Transverse suprapubic view of the bladder, one 10-frame cine loop](assets/cine.gif)
 
-*Transverse suprapubic view: one cine loop (10 frames) from [`data/a1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/technion/bladder/data/a1.hdf5).*
+*Transverse suprapubic view: one cine loop (10 frames) from [`data/a1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/technion/bladder/data/a1.hdf5).*
 
 ## Dataset Description
 
@@ -37,7 +37,7 @@ Real, **in-vivo human** pre-beamformed ultrasound **channel data** for bladder i
 
 ## Dataset Creation Date
 
-Source data 2018; converted to the OpenH-RF (zea) format 07/16/2026.
+Source data 2018; converted to the Open-H-RF (zea) format 07/16/2026.
 
 ## License / Terms of Use
 
@@ -61,12 +61,12 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/technion/bladder/data/a1.hdf5 \
-  --config hf://nvidia/OpenH-RF/technion/bladder/pipeline.yaml \
+  --dataset hf://nvidia/Open-H-RF/technion/bladder/data/a1.hdf5 \
+  --config hf://nvidia/Open-H-RF/technion/bladder/pipeline.yaml \
   --n-frames 10
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/technion/bladder/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/technion/bladder/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 ## Dataset Format
 
@@ -76,7 +76,7 @@ zea file format, one HDF5 file per sweep (`data/<subject>.hdf5`, e.g. `a1.hdf5`,
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 14 HDF5 files; 83.71 GB (83,713,785,856 bytes) stored; root `zea_version` **0.1.4**. 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:** 14 HDF5 files; 83.71 GB (83,713,785,856 bytes) stored; root `zea_version` **0.1.4**. 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.
 
 - **Frames / sweeps / subjects:** 1,508 frames · 14 sweeps · 7 subjects.
 - **Train / val / test split:** N/A (contributor to define).

technion/cardiac/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - iq
-  - openh-rf
+  - open-h-rf
   - beamforming
   - cardiac
   - 3d
@@ -21,7 +21,7 @@ size_categories:
 
 ![Apical four-chamber view, one 32-frame cardiac cine loop](assets/cine.gif)
 
-*Apical four-chamber view: one cine loop (32 frames) from [`data/c1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/technion/cardiac/data/c1.hdf5).*
+*Apical four-chamber view: one cine loop (32 frames) from [`data/c1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/technion/cardiac/data/c1.hdf5).*
 
 ## Dataset Description
 
@@ -37,7 +37,7 @@ Real, **in-vivo human** pre-beamformed ultrasound **channel data** for cardiac i
 
 ## Dataset Creation Date
 
-Acquired 2018; converted to the OpenH-RF (zea) format 07/16/2026.
+Acquired 2018; converted to the Open-H-RF (zea) format 07/16/2026.
 
 ## License / Terms of Use
 
@@ -61,12 +61,12 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/technion/cardiac/data/c1.hdf5 \
-  --config hf://nvidia/OpenH-RF/technion/cardiac/pipeline.yaml \
+  --dataset hf://nvidia/Open-H-RF/technion/cardiac/data/c1.hdf5 \
+  --config hf://nvidia/Open-H-RF/technion/cardiac/pipeline.yaml \
   --n-frames 32
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/technion/cardiac/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/technion/cardiac/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 ## Dataset Format
 
@@ -76,7 +76,7 @@ zea file format, one HDF5 file per cine loop (`data/<subject><clip>.hdf5`, e.g.
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 25 HDF5 files; 14.99 GB (14,992,998,400 bytes) stored; root `zea_version` **0.1.4**. 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:** 25 HDF5 files; 14.99 GB (14,992,998,400 bytes) stored; root `zea_version` **0.1.4**. 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.
 
 - **Frames / cines / subjects:** 777 frames · 25 cine loops · 6 subjects (a–f).
 - **Train / val / test split:** N/A (contributor to define; the `f2` patient set is a natural held-out cine).

technion/phantom/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - iq
-  - openh-rf
+  - open-h-rf
   - beamforming
   - phantom
   - 3d
@@ -21,7 +21,7 @@ size_categories:
 
 ![Tissue-mimicking phantom with point targets and an anechoic cyst](assets/bmode.png)
 
-*Frame 6 of [`data/ph.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/technion/phantom/data/ph.hdf5), reconstructed by `reconstruct.py`.*
+*Frame 6 of [`data/ph.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/technion/phantom/data/ph.hdf5), reconstructed by `reconstruct.py`.*
 
 ## Dataset Description
 
@@ -37,7 +37,7 @@ Pre-beamformed ultrasound **channel data** from a tissue-mimicking phantom, acqu
 
 ## Dataset Creation Date
 
-Source data 2018; converted to the OpenH-RF (zea) format 07/16/2026.
+Source data 2018; converted to the Open-H-RF (zea) format 07/16/2026.
 
 ## License / Terms of Use
 
@@ -61,12 +61,12 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/technion/phantom/data/ph.hdf5 \
-  --config hf://nvidia/OpenH-RF/technion/phantom/pipeline.yaml \
+  --dataset hf://nvidia/Open-H-RF/technion/phantom/data/ph.hdf5 \
+  --config hf://nvidia/Open-H-RF/technion/phantom/pipeline.yaml \
   --n-frames 1
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/technion/phantom/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/technion/phantom/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 ## Dataset Format
 
@@ -76,7 +76,7 @@ zea file format, a single HDF5 file `data/ph.hdf5`. Source complex samples repac
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 1 HDF5 file; 659.95 MB (659,947,520 bytes) stored; root `zea_version` **0.1.4**. 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:** 1 HDF5 file; 659.95 MB (659,947,520 bytes) stored; root `zea_version` **0.1.4**. 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.
 
 - **Frames / acquisitions:** 12 frames · 1 acquisition.
 - **Stored HDF5 size:** 659.95 MB (659,947,520 bytes).

tel-aviv/README.md

@@ -3,14 +3,14 @@ name: tel-aviv
 license: cc-by-4.0
 pretty_name: Ilovitsh Lab Mice Tumors
 task_categories: [image-segmentation]
-tags: [ultrasound, rf, openh-rf]
+tags: [ultrasound, rf, open-h-rf]
 ---
 
 # Ilovitsh Lab Mice Tumors & Water-Bead Phantoms
 
 ![Reconstructed cineloop from 01-scan-3.hdf5](assets/01-scan-3.gif)
 
-*Cine loop of the rotational sweep through a mouse tumor, [`mouse_tumor/seg/01-scan-3.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tel-aviv/mouse_tumor/seg/01-scan-3.hdf5), reconstructed from the raw channel data with `mouse_tumor/pipeline.yaml`.*
+*Cine loop of the rotational sweep through a mouse tumor, [`mouse_tumor/seg/01-scan-3.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tel-aviv/mouse_tumor/seg/01-scan-3.hdf5), reconstructed from the raw channel data with `mouse_tumor/pipeline.yaml`.*
 
 ## Dataset Description
 
@@ -45,7 +45,7 @@ Segmentation of tumors in mice and water-bead phantoms.
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/tel-aviv/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipeline.yaml` definitions in `mouse_tumor/` and `phantom/` and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub and overlays the stored segmentation on the reconstructed B-mode.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/tel-aviv/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipeline.yaml` definitions in `mouse_tumor/` and `phantom/` and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub and overlays the stored segmentation on the reconstructed B-mode.
 
 ## Dataset Format
 
@@ -53,7 +53,7 @@ The acquisitions can be processed with the `reconstruct.py` [script](https://git
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 22 HDF5 files; 4.36 GB (4,357,947,392 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:** 22 HDF5 files; 4.36 GB (4,357,947,392 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.
 
 - **Samples / frames:** 22 total `.hdf5` acquisitions. Each acquisition contains 144 angular frames covering a 180° rotation (1.25° increments) of 5 plane waves.
 - **Train / val / test split:** N/A

tue-aaa/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - beamforming
   - motion-estimation
   - abdominal-aortic-aneurysm
@@ -20,7 +20,7 @@ language:
 
 ![Reconstructed cineloop from AAA_subject11.hdf5](assets/AAA_subject11.gif)
 
-*Cine loop of [`data/AAA_subject11.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-aaa/data/AAA_subject11.hdf5), reconstructed from the raw channel data.*
+*Cine loop of [`data/AAA_subject11.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-aaa/data/AAA_subject11.hdf5), reconstructed from the raw channel data.*
 
 ## Dataset Description
 
@@ -58,11 +58,11 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/tue-aaa/data/AAA_subject11.hdf5 \
-  --config hf://nvidia/OpenH-RF/tue-aaa/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/tue-aaa/data/AAA_subject11.hdf5 \
+  --config hf://nvidia/Open-H-RF/tue-aaa/pipeline.yaml
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/tue-aaa/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/tue-aaa/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 ## Dataset Format
 
@@ -72,7 +72,7 @@ zea file format. Subject metadata is stored under `metadata/subject` (`age`, `se
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 15 HDF5 files; 53.08 GB (53,077,606,400 bytes) stored; root `zea_version` **0.1.4**. 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:** 15 HDF5 files; 53.08 GB (53,077,606,400 bytes) stored; root `zea_version` **0.1.4**. 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.
 
 - **Samples / frames:** 15 acquisitions (one HDF5 file each), 500 frames per acquisition — 7,500 frames total
 - **Stored HDF5 size:** 53.08 GB (53,077,606,400 bytes).

tue-cardiac/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - echocardiography
   - cardiac
   - plax
@@ -33,7 +33,7 @@ size_categories:
   </tr>
 </table>
 
-*Parasternal long-axis cine loops of four volunteers, pulse-inversion harmonic tracks: focused ([`subject-002`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-cardiac/data/subject-002.hdf5)), wide ([`subject-004`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-cardiac/data/subject-004.hdf5)), focused ([`subject-006`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-cardiac/data/subject-006.hdf5)) and wide ([`subject-009`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-cardiac/data/subject-009.hdf5)).*
+*Parasternal long-axis cine loops of four volunteers, pulse-inversion harmonic tracks: focused ([`subject-002`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-cardiac/data/subject-002.hdf5)), wide ([`subject-004`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-cardiac/data/subject-004.hdf5)), focused ([`subject-006`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-cardiac/data/subject-006.hdf5)) and wide ([`subject-009`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-cardiac/data/subject-009.hdf5)).*
 
 ## Dataset Description
 
@@ -95,7 +95,7 @@ The dataset must not be used as a clinically validated diagnostic product.
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/tue-cardiac/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the `pipelines/*.yaml` definitions 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/tue-cardiac/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the `pipelines/*.yaml` definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
 
 Each file holds the eight transmit-encoding tracks. Set `ZEA_FILE`, `TRACK` and `FRAME` at the top of the script, together with the matching pipeline under `pipelines/` as `CONFIG` (`PIPELINE_FOR_TRACK` lists which track uses which; see also [Data Validation](#data-validation)). The image is written to `assets/`.
 
@@ -178,7 +178,7 @@ The source tensor order is preserved as `(n_frames, n_tx, n_ax, n_el, n_ch) = (1
 
 Suggested citation for the dataset:
 
-> Penninga, S. W., & van Sloun, R. J. G. (2026). *TU/e Cardiac RF Multi-Transmit* [Data set]. Eindhoven University of Technology, OpenH-RF.
+> Penninga, S. W., & van Sloun, R. J. G. (2026). *TU/e Cardiac RF Multi-Transmit* [Data set]. Eindhoven University of Technology, Open-H-RF.
 
 ```bibtex
 @misc{penninga_tue_cardiac_plax_2026,
@@ -186,13 +186,13 @@ Suggested citation for the dataset:
   author       = {Penninga, Simon W. and van Sloun, Ruud J. G.},
   year         = {2026},
   publisher    = {Biomedical Diagnostics Lab, Eindhoven University of Technology},
-  howpublished = {OpenH-RF dataset},
+  howpublished = {Open-H-RF dataset},
 }
 ```
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 12 HDF5 files; 199.18 GB (199,180,025,844 bytes) stored; root `zea_version` **0.1.4**.
+**Current Open-H-RF release:** 12 HDF5 files; 199.18 GB (199,180,025,844 bytes) stored; root `zea_version` **0.1.4**.
 - **Participants/files:** 12 distinct people represented by 12 HDF5 files.
 - **Tracks/acquisitions:** 8 tracks per file; 96 acquisitions total.
 - **Frames:** 100 per track; 9,600 frames total.

tue-carotid/README.md

@@ -8,7 +8,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - carotid
   - in-vivo
 language:
@@ -20,7 +20,7 @@ language:
 
 ![Longitudinal view of a carotid bifurcation](assets/5_long_bifur_R_0000.gif)
 
-*One cardiac cycle of a longitudinal bifurcation scan, [`data/5_long_bifur_R_0000.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tue-carotid/data/5_long_bifur_R_0000.hdf5).*
+*One cardiac cycle of a longitudinal bifurcation scan, [`data/5_long_bifur_R_0000.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tue-carotid/data/5_long_bifur_R_0000.hdf5).*
 
 
 ## Dataset Description
@@ -76,11 +76,11 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/tue-carotid/data/5_long_bifur_R_0000.hdf5 \
-  --config hf://nvidia/OpenH-RF/tue-carotid/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/tue-carotid/data/5_long_bifur_R_0000.hdf5 \
+  --config hf://nvidia/Open-H-RF/tue-carotid/pipeline.yaml
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/tue-carotid/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/tue-carotid/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 ## Dataset Format
 
@@ -88,7 +88,7 @@ Alternatively, you can use the `reconstruct.py` [script](https://github.com/open
 
 ## Dataset Quantification
 
-**OpenH-RF release:** 80 HDF5 files; 435.40 GB (435,403,030,528 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.
+**Open-H-RF release:** 80 HDF5 files; 435.40 GB (435,403,030,528 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.
 
 - **Samples / frames / acquisitions:** 80 acquisitions (one HDF5 file each) from 10 subjects, 150 frames per acquisition — 12,000 frames total.
 - **Transmit events per frame:** 149 — 128 focused lines interleaved with 21 plane waves.

tumunich/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - 3d
   - robotic
   - freehand
@@ -31,7 +31,7 @@ size_categories:
   </tr>
 </table>
 
-*Acquisition setup (left), a single-frame reconstruction (middle) and a panoramic reconstruction compounded along the robot-tracked sweep (right) of the CIRS 054GS phantom, [`data/cirs_phantom/synth_apert_sweep_1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/tumunich/data/cirs_phantom/synth_apert_sweep_1.hdf5).*
+*Acquisition setup (left), a single-frame reconstruction (middle) and a panoramic reconstruction compounded along the robot-tracked sweep (right) of the CIRS 054GS phantom, [`data/cirs_phantom/synth_apert_sweep_1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/tumunich/data/cirs_phantom/synth_apert_sweep_1.hdf5).*
 
 ## Dataset Description
 
@@ -72,9 +72,9 @@ Primary task: **robotic tracked ultrasound acquisition** — compounding the per
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/tumunich/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/tumunich/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.
 
-`reconstruct.py` beamforms a single frame and compares it to the stored Verasonics B-mode; [`reconstruct_panorama.py`](https://github.com/open-h/OpenH-RF/blob/main/datasets/tumunich/reconstruct_panorama.py), with `pipeline_panorama.yaml`, compounds a tracked sweep into the panoramic reconstruction shown above, using the per-frame robot poses.
+`reconstruct.py` beamforms a single frame and compares it to the stored Verasonics B-mode; [`reconstruct_panorama.py`](https://github.com/open-h/Open-H-RF/blob/main/datasets/tumunich/reconstruct_panorama.py), with `pipeline_panorama.yaml`, compounds a tracked sweep into the panoramic reconstruction shown above, using the per-frame robot poses.
 
 ## Dataset Format
 
@@ -88,7 +88,7 @@ Zero-valued rows pad the top of each stored frame so the image starts at the tra
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 11 HDF5 files; 62.25 GB (62,245,175,296 bytes) stored; root `zea_version` **0.1.4**. 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:** 11 HDF5 files; 62.25 GB (62,245,175,296 bytes) stored; root `zea_version` **0.1.4**. 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.
 
 - **Phantoms:** 3 (vascular access arm phantom, CIRS 054GS, CIRS 074 thyroid)
 - **Files:** 11 zea HDF5 files across 3 sub-dataset folders

twente-microbubblesim/README.md

@@ -9,7 +9,7 @@ tags:
   - rf
   - microbubbles
   - synthetic-data
-  - openh-rf
+  - open-h-rf
 language:
   - en
 size_categories:
@@ -56,7 +56,7 @@ The dataset is intended for studying the effect of transmit waveforms on deep-le
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/twente-microbubblesim/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the pipeline definitions in `pipeline/` 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/twente-microbubblesim/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the pipeline definitions in `pipeline/` and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
 
 Every pulse track has its own verified pipeline file, `pipeline/pipeline_track_<index>_<label>.yaml`, all defining the same chain:
 
@@ -66,8 +66,8 @@ Each pipeline's `parameters:` block carries that pulse's beamforming peak-time r
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/twente-microbubblesim/data/Monodispers/RFDATA00002.hdf5 \
-  --config hf://nvidia/OpenH-RF/twente-microbubblesim/pipeline/pipeline_track_6_REF.yaml \
+  --dataset hf://nvidia/Open-H-RF/twente-microbubblesim/data/Monodispers/RFDATA00002.hdf5 \
+  --config hf://nvidia/Open-H-RF/twente-microbubblesim/pipeline/pipeline_track_6_REF.yaml \
   --track 6
 ```
 
@@ -110,7 +110,7 @@ The meanings and waveform values below are from Table I of Zorgdrager et al. (20
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 500 HDF5 files; 17.03 GB (17,029,267,456 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:** 500 HDF5 files; 17.03 GB (17,029,267,456 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.
 
 - **Acquisition files:** 500 files, each with 12 one-frame pulse tracks (6,000 track acquisitions total).
 - **Organization:** Two bubble populations × 250 files × 12 pulse tracks.

twente-vortexflow/README.md

@@ -8,7 +8,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - flow
   - phantom
   - blood-flow
@@ -22,7 +22,7 @@ size_categories:
 
 ![Optical camera view beside the B-mode reconstruction of a von Karman vortex street](assets/vortex_street.gif)
 
-*The Photron high-speed camera view (left) and the B-mode reconstruction (right) of [`data/AcqData_PVoltage80_TVoltage3.4.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/twente-vortexflow/data/AcqData_PVoltage80_TVoltage3.4.hdf5). Both come from `track_0`, frame for frame, so the optical and acoustic views show the same instant of the vortex street.*
+*The Photron high-speed camera view (left) and the B-mode reconstruction (right) of [`data/AcqData_PVoltage80_TVoltage3.4.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/twente-vortexflow/data/AcqData_PVoltage80_TVoltage3.4.hdf5). Both come from `track_0`, frame for frame, so the optical and acoustic views show the same instant of the vortex street.*
 
 ## Dataset Description
 
@@ -91,7 +91,7 @@ Suitable for research in:
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/twente-vortexflow/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), together with the pipeline definitions 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/twente-vortexflow/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF), together with the pipeline definitions in this folder and the [zea library](https://github.com/tue-bmd/zea). The script streams the data from the Hugging Face Hub.
 
 `ZEA_FILE` and `FRAME` at the top of the script select the acquisition and frame; each track is reconstructed with its own pipeline (`pipeline_short_imaging_pulse.yaml`, `pipeline_chirp.yaml`).
 
@@ -114,7 +114,7 @@ No pre-processing (demodulation, decimation, filtering) has been applied before
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 6 HDF5 files; 9.09 GB (9,088,991,232 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:** 6 HDF5 files; 9.09 GB (9,088,991,232 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.
 
 **Table 3. Acquisition settings.**
 

ubc/module_A/README.md

@@ -5,7 +5,7 @@ license: cc-by-4.0
 task_categories:
   - image-to-image
 tags:
-  - openh-rf
+  - open-h-rf
   - zea
   - ultrasound
   - rf
@@ -23,7 +23,7 @@ size_categories:
 
 ![Example reconstruction](./assets/module_A.png)
 
-*Scan-converted scanline B-mode of a CIRS 039 liver fibrosis phantom, reconstructed from the synthetic channel data in [`acquisitions/`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/ubc/module_A/acquisitions).*
+*Scan-converted scanline B-mode of a CIRS 039 liver fibrosis phantom, reconstructed from the synthetic channel data in [`acquisitions/`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/ubc/module_A/acquisitions).*
 
 ## Dataset Description
 
@@ -96,13 +96,13 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/ubc/module_A/acquisitions/case_1.83/ubc_swave_cirs_1.83_p10_f13.hdf5 \
-  --config hf://nvidia/OpenH-RF/ubc/module_A/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/ubc/module_A/acquisitions/case_1.83/ubc_swave_cirs_1.83_p10_f13.hdf5 \
+  --config hf://nvidia/Open-H-RF/ubc/module_A/pipeline.yaml
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/ubc/module_A/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/ubc/module_A/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
-`reconstruct.py` reconstructs one frame (default `case_1.83/ubc_swave_cirs_1.83_p10_f13.hdf5`); [`reconstruct_multiframe.py`](https://github.com/open-h/OpenH-RF/blob/main/datasets/ubc/module_A/reconstruct_multiframe.py) reconstructs the temporal sequence at one motor plane (`CASE_ID`, `PLANE`, `N_FRAMES`) into an animated GIF.
+`reconstruct.py` reconstructs one frame (default `case_1.83/ubc_swave_cirs_1.83_p10_f13.hdf5`); [`reconstruct_multiframe.py`](https://github.com/open-h/Open-H-RF/blob/main/datasets/ubc/module_A/reconstruct_multiframe.py) reconstructs the temporal sequence at one motor plane (`CASE_ID`, `PLANE`, `N_FRAMES`) into an animated GIF.
 
 ## Dataset Format
 

ubc/module_C/README.md

@@ -15,7 +15,7 @@ tags:
   - multimodal
   - timeseries
   - video
-  - openh-rf
+  - open-h-rf
 language:
   - en
 size_categories:
@@ -26,7 +26,7 @@ size_categories:
 
 ![Example reconstruction](./assets/module_C.png)
 
-*Scan-converted B-mode of the Kyoto Kagaku SPACE FAN-ST fetal phantom, reconstructed from the synthetic channel data in [`acquisitions/`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/ubc/module_C/acquisitions).*
+*Scan-converted B-mode of the Kyoto Kagaku SPACE FAN-ST fetal phantom, reconstructed from the synthetic channel data in [`acquisitions/`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/ubc/module_C/acquisitions).*
 
 ## Dataset Description
 
@@ -101,11 +101,11 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/ubc/module_C/acquisitions/session_01/session_01_f1306.hdf5 \
-  --config hf://nvidia/OpenH-RF/ubc/module_C/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/ubc/module_C/acquisitions/session_01/session_01_f1306.hdf5 \
+  --config hf://nvidia/Open-H-RF/ubc/module_C/pipeline.yaml
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/ubc/module_C/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/ubc/module_C/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 Set `ZEA_FILE` and `FRAME` at the top of the script to pick a frame (default `session_01/session_01_f1306.hdf5`).
 
@@ -232,4 +232,4 @@ Room-camera recordings originally captured people. Released RGB/IR crops retain
 
 The HDF5 `metadata/credit` field carries the following attribution:
 
-> University of British Columbia OpenH-RF contributors (2026). *UBC Module C: Synthetic Channel Proxy and Multimodal Fetal Phantom Ultrasound*. OpenH-RF. CC BY 4.0.
+> University of British Columbia Open-H-RF contributors (2026). *UBC Module C: Synthetic Channel Proxy and Multimodal Fetal Phantom Ultrasound*. Open-H-RF. CC BY 4.0.

unc-liver/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - synthetic-aperture
   - curvilinear
   - sound-speed-estimation
@@ -25,7 +25,7 @@ size_categories:
   <img src="assets/main.png" alt="Scan-converted B-mode through a simulated abdominal wall" width="480">
 </p>
 
-*Scan-converted B-mode reconstructed from the full-synthetic-aperture channel data of one simulated acquisition in [`data/`](https://huggingface.co/datasets/nvidia/OpenH-RF/tree/main/unc-liver/data): the abdominal wall layers in the near field above the liver.*
+*Scan-converted B-mode reconstructed from the full-synthetic-aperture channel data of one simulated acquisition in [`data/`](https://huggingface.co/datasets/nvidia/Open-H-RF/tree/main/unc-liver/data): the abdominal wall layers in the near field above the liver.*
 
 ## Dataset Description
 
@@ -82,7 +82,7 @@ The NLM does not endorse this dataset, and nothing here should be read as implyi
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/unc-liver/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/unc-liver/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.
 
 Set `ZEA_FILE` and `FRAME` at the top of the script to pick an acquisition. For current release files, use `zea==0.1.6` and any Keras backend (`reconstruct.py` falls back to torch if `KERAS_BACKEND` is unset, but respects whatever you have configured).
 
@@ -130,7 +130,7 @@ All maps are on the polar reconstruction grid: 640 radial samples spanning 5.0
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 1,906 HDF5 files; 202.69 GB (202,692,755,456 bytes) stored; root `zea_version` **0.1.4**. 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:** 1,906 HDF5 files; 202.69 GB (202,692,755,456 bytes) stored; root `zea_version` **0.1.4**. 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.
 
 - **1906 acquisitions**, 1 frame each, 128 transmits × 128 receives per acquisition.
 - **Stored HDF5 size:** 202.69 GB (202,692,755,456 bytes).

unc-openpros/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - prostate
   - usct
   - speed-of-sound
@@ -26,7 +26,7 @@ size_categories:
 
 <!-- ![Speed-of-sound map of a prostate slice, predicted by InversionNet](assets/main.png) -->
 
-*Speed of sound predicted from the limited-view waveform data of the first acquisition in [`data/3_01_P_prostate_00.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/unc-openpros/data/3_01_P_prostate_00.hdf5) with the pretrained OpenPros InversionNet.*
+*Speed of sound predicted from the limited-view waveform data of the first acquisition in [`data/3_01_P_prostate_00.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/unc-openpros/data/3_01_P_prostate_00.hdf5) with the pretrained OpenPros InversionNet.*
 
 ## Dataset Description
 
@@ -83,7 +83,7 @@ The original license was [Creative Commons Attribution-NonCommercial-ShareAlike
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/unc-openpros/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/unc-openpros/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 reconstruction script checks the input shapes and writes two files: `pred_sos.png`, a side-by-side comparison of the predicted and ground-truth SOS maps, and [`assets/main.png`](assets/main.png), a clean, unlabeled hero image of just the prediction.
 
@@ -106,7 +106,7 @@ The file also stores source positions, probe geometry, scan parameters, subject
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 248 HDF5 files; 6.03 TB (6,031,699,279,872 bytes) stored; root `zea_version` **0.1.5**. 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:** 248 HDF5 files; 6.03 TB (6,031,699,279,872 bytes) stored; root `zea_version` **0.1.5**. 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.
 
 The original OpenPros source documentation reports 280,000 paired samples (approximately 6.8 TB) with an official split of 224,000 training, 28,000 validation, and 28,000 test samples. It is derived from four patient-level clinical anatomies and 62 ex vivo prostate specimens. Each published HDF5 file here holds 1,140 acquisitions.
 
@@ -122,7 +122,7 @@ The converted release identifies its content as a simulation and stores composit
 
 ## Data Validation
 
-`reconstruct.py` loads [`pipeline.yaml`](pipeline.yaml) (also published on the Hub at [`hf://nvidia/OpenH-RF/unc-openpros/pipeline.yaml`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/unc-openpros/pipeline.yaml)), a `zea.Pipeline` that reproduces the OpenPros InversionNet preprocessing and postprocessing:
+`reconstruct.py` loads [`pipeline.yaml`](pipeline.yaml) (also published on the Hub at [`hf://nvidia/Open-H-RF/unc-openpros/pipeline.yaml`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/unc-openpros/pipeline.yaml)), a `zea.Pipeline` that reproduces the OpenPros InversionNet preprocessing and postprocessing:
 
 1. Restore the original acquisition-block order: body/body, body/rectum, rectum/rectum, and rectum/body.
 2. Apply the sign-preserving logarithmic transform `sign(x) * log1p(abs(1e5 * x))`.

us4us/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - zea
   - usct
   - tomography
@@ -24,7 +24,7 @@ size_categories:
 
 ![Ring-array USCT reconstruction of a forearm cross-section](assets/s1_l_reconstruct.png)
 
-*A left-forearm cross-section, slice 8 of [`data/s1_l.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/us4us/data/s1_l.hdf5), reconstructed beside the sinogram and reference image stored in the file.*
+*A left-forearm cross-section, slice 8 of [`data/s1_l.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/us4us/data/s1_l.hdf5), reconstructed beside the sinogram and reference image stored in the file.*
 
 ## Dataset Description
 Raw RF data acquired with the us4us Ltd. us4R system and a Draminski ring probe.
@@ -79,7 +79,7 @@ Ultrasound computed tomography image reconstruction.
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/us4us/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/us4us/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 rebuilds the attenuation sinogram and filtered-backprojection image from `data/raw_data` and plots them beside the versions stored in the file. Set `ZEA_FILE` and `FRAME` (the slice) at the top of the script.
 
@@ -91,7 +91,7 @@ All sub-datasets are provided in the ZEA file format. No preprocessing was perfo
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 18 HDF5 files; 32.66 GB (32,658,423,808 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:** 18 HDF5 files; 32.66 GB (32,658,423,808 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.
 
   * Each forearm HDF5 file contains 10 slices of the subject; `yezitronix-b-rg-1.2.hdf5` contains 11 and `reference_water.hdf5` a single slice.
   * Stored HDF5 file sizes vary: 1.89 GB to 1.95 GB excluding the water reference. `reference_water.hdf5` is 183,107,584 bytes (183.11 MB).

vanderbilt/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - echocardiography
   - cardiac
   - harmonic-imaging
@@ -22,7 +22,7 @@ size_categories:
 
 ![Cineloop of the left atrial appendage](assets/118420_1_Focused_Uncoded_TX.gif)
 
-*One cineloop of a fundamental focused-transmit acquisition, [`data/Fundamental/118420_1_Focused_Uncoded_TX.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/vanderbilt/data/Fundamental/118420_1_Focused_Uncoded_TX.hdf5).*
+*One cineloop of a fundamental focused-transmit acquisition, [`data/Fundamental/118420_1_Focused_Uncoded_TX.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/vanderbilt/data/Fundamental/118420_1_Focused_Uncoded_TX.hdf5).*
 
 ## Dataset Description
 
@@ -66,12 +66,12 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/vanderbilt/data/Fundamental/118420_1_Focused_Uncoded_TX.hdf5 \
-  --config hf://nvidia/OpenH-RF/vanderbilt/pipeline.yaml \
+  --dataset hf://nvidia/Open-H-RF/vanderbilt/data/Fundamental/118420_1_Focused_Uncoded_TX.hdf5 \
+  --config hf://nvidia/Open-H-RF/vanderbilt/pipeline.yaml \
   --n-frames 10
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/vanderbilt/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/vanderbilt/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 Set `ZEA_FILE` and `N_FRAMES` at the top of the script to pick a file and how many frames to beamform.
 
@@ -102,7 +102,7 @@ The current release uses the migrated schema; inspect each file's root `zea_vers
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 165 HDF5 files; 185.58 GB (185,584,517,120 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:** 165 HDF5 files; 185.58 GB (185,584,517,120 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.
 
 - **Samples:** 82 cineloops (32 frames each) from 30 patients — 2,624 fundamental frames
 - Plus a matching set of 2,624 harmonic frames (same cineloops, harmonic sequence)

waterloo-carotid/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - medical-imaging
   - in-vivo
   - carotid-artery
@@ -24,7 +24,7 @@ size_categories:
 
 ![Reconstructed cineloop from Acq1.hdf5](assets/Acq1.gif)
 
-*Cine loop of [`data/Acq1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/waterloo-carotid/data/Acq1.hdf5), rendered using provided velocity fields.*
+*Cine loop of [`data/Acq1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/waterloo-carotid/data/Acq1.hdf5), rendered using provided velocity fields.*
 
 ## Dataset Description
 
@@ -65,13 +65,13 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/waterloo-carotid/data/Acq1.hdf5 \
-  --config hf://nvidia/OpenH-RF/waterloo-carotid/pipeline.yaml \
+  --dataset hf://nvidia/Open-H-RF/waterloo-carotid/data/Acq1.hdf5 \
+  --config hf://nvidia/Open-H-RF/waterloo-carotid/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/waterloo-carotid/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/waterloo-carotid/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 Swap `--n-frames 1 --save-as png` for `--save-as gif` to get the cine loop. In the script, `ZEA_FILE`, `FRAME` and `POWER_THRESHOLD` (the power-Doppler mask threshold, in dB) at the top select what is reconstructed and overlaid.
 
@@ -108,7 +108,7 @@ All `coordinates` arrays are per-pixel Cartesian positions in meters, last axis
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 93 HDF5 files; 6.90 TB (6,902,089,770,179 bytes) stored; root `zea_version` **0.1.4**. 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:** 93 HDF5 files; 6.90 TB (6,902,089,770,179 bytes) stored; root `zea_version` **0.1.4**. 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.
 
 Data was collected from 8 participants, spanning carotid arteries (Common Carotid Artery and Internal Carotid Artery) in both longitudinal and cross-sectional views. In total, the dataset consists of 93 acquisitions, containing 30,000 or 36,000 frames of raw RF data per acquisition (47 acquisitions of 30,000 frames and 46 of 36,000 frames). 
 Each common carotid artery frame comprises 2 steered plane-wave transmits (`n_tx = 2`), 2048 axial samples, and 128 receive channels. Each internal carotid artery frame comprises 1 steered plane-wave transmits (`n_tx = 1`), 1536 axial samples, and 128 receive channels.

waterloo-femoralvein/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - medical-imaging
   - in-vivo
   - femoral-vein
@@ -25,7 +25,7 @@ size_categories:
 
 ![Vector flow cineloop from Acq0.hdf5](assets/Acq0.gif)
 
-*Cine loop of [`data/Acq0.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/waterloo-femoralvein/data/Acq0.hdf5), rendered from provided velocity fields.*
+*Cine loop of [`data/Acq0.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/waterloo-femoralvein/data/Acq0.hdf5), rendered from provided velocity fields.*
 
 ## Dataset Description
 
@@ -67,13 +67,13 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/waterloo-femoralvein/data/Acq0.hdf5 \
-  --config hf://nvidia/OpenH-RF/waterloo-femoralvein/pipeline.yaml \
+  --dataset hf://nvidia/Open-H-RF/waterloo-femoralvein/data/Acq0.hdf5 \
+  --config hf://nvidia/Open-H-RF/waterloo-femoralvein/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/waterloo-femoralvein/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/waterloo-femoralvein/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 Swap `--n-frames 1 --save-as png` for `--save-as gif` to get the cine loop. In the script, `ZEA_FILE`, `FRAME`, `POWER_THRESHOLD` (the power-Doppler mask threshold, in dB), `VMAX` (velocity colour-scale maximum) and `NO_DEALIAS` at the top select what is reconstructed and overlaid.
 
@@ -115,7 +115,7 @@ All `coordinates` arrays are per-pixel Cartesian positions in meters, last axis
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 15 HDF5 files; 941.96 GB (941,958,804,227 bytes) stored; root `zea_version` **0.1.5**. 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:** 15 HDF5 files; 941.96 GB (941,958,804,227 bytes) stored; root `zea_version` **0.1.5**. 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.
 
 Data was collected from 15 participants and consists of 15 acquisitions (one per participant), containing 12,000 frames of raw RF data per acquisition. Each participant performed isometric plantarflexion contractions at 8 Kg under head up tilt of 40 degrees. Each femoral vein acquisition comprises 2 steered plane-wave transmits (`n_tx = 2`), 2048/3072 axial samples, and 192 receive channels. 
 

waterloo-muscle/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - medical-imaging
   - in-vivo
   - muscle
@@ -23,7 +23,7 @@ size_categories:
 
 ![Reconstructed cineloop from Acq_p35_Calf_left_calf_lateral_longitudinal_relaxed_pressure.hdf5](assets/Acq_p35_Calf_left_calf_lateral_longitudinal_relaxed_pressure.gif)
 
-*Cine loop of the relaxed left calf (lateral, longitudinal), [`data/Acq_p35_Calf_left_calf_lateral_longitudinal_relaxed_pressure.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/waterloo-muscle/data/Acq_p35_Calf_left_calf_lateral_longitudinal_relaxed_pressure.hdf5), reconstructed from the raw channel data with the `pipeline.yaml` in this folder.*
+*Cine loop of the relaxed left calf (lateral, longitudinal), [`data/Acq_p35_Calf_left_calf_lateral_longitudinal_relaxed_pressure.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/waterloo-muscle/data/Acq_p35_Calf_left_calf_lateral_longitudinal_relaxed_pressure.hdf5), reconstructed from the raw channel data with the `pipeline.yaml` in this folder.*
 
 ## Dataset Description
 
@@ -73,13 +73,13 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/waterloo-muscle/data/Acq_p35_Calf_left_calf_lateral_longitudinal_relaxed_pressure.hdf5 \
-  --config hf://nvidia/OpenH-RF/waterloo-muscle/pipeline.yaml \
+  --dataset hf://nvidia/Open-H-RF/waterloo-muscle/data/Acq_p35_Calf_left_calf_lateral_longitudinal_relaxed_pressure.hdf5 \
+  --config hf://nvidia/Open-H-RF/waterloo-muscle/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/waterloo-muscle/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/waterloo-muscle/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF).
 
 Swap `--n-frames 1 --save-as png` for `--save-as gif` to get the cine loop. In the script, `ZEA_FILE` and `FRAME` at the top select what is reconstructed.
 
@@ -112,7 +112,7 @@ All `coordinates` arrays are per-pixel Cartesian positions in metres, last axis
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 1,248 HDF5 files; 576.19 GB (576,186,417,152 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:** 1,248 HDF5 files; 576.19 GB (576,186,417,152 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.
 
 Data was collected from 39 participants, each with 32 unique images spanning calf/bicep/quad, axis and muscle state. For each imaging location, 15 frames were made per limb under minimal contact and with pressure. Given that each frame consisted of 31 steered plane waves, our protocol yielded a total of 15 repeats × 31 transmits × 8 views × 2 sides × 2 pressure settings = 14,880 frame-transmits per participant, i.e. 480 stored frames per participant. In total the dataset contains 18,720 stored frames and 580,320 frame-transmits (see the table below).
 

weillcornell/README.md

@@ -8,7 +8,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - quantitative-ultrasound
   - phantom
   - raw-channel-data
@@ -22,7 +22,7 @@ size_categories:
 
 ![Weill Cornell QUS phantom dataset: B-mode, pooled Nakagami maps, and theoretical BSC curves](assets/hero.png)
 
-*B-mode (frame 0) and 20-frame pooled Nakagami maps from [`data/ac10_15m_SK.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/weillcornell/data/ac10_15m_SK.hdf5). The BSC curves are material-model references for the three phantoms; pooled Nakagami maps are statistical references.*
+*B-mode (frame 0) and 20-frame pooled Nakagami maps from [`data/ac10_15m_SK.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/weillcornell/data/ac10_15m_SK.hdf5). The BSC curves are material-model references for the three phantoms; pooled Nakagami maps are statistical references.*
 
 ## Dataset Description
 
@@ -67,11 +67,11 @@ The acquisitions can be processed with the `pipeline.yaml` definition in this fo
 
 ```bash
 zea process \
-  --dataset hf://nvidia/OpenH-RF/weillcornell/data/ac10_15m_SK.hdf5 \
-  --config hf://nvidia/OpenH-RF/weillcornell/pipeline.yaml
+  --dataset hf://nvidia/Open-H-RF/weillcornell/data/ac10_15m_SK.hdf5 \
+  --config hf://nvidia/Open-H-RF/weillcornell/pipeline.yaml
 ```
 
-Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/weillcornell/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF). Besides the B-mode, it prints the selected frame's BSC band and unit, the QUS map shapes, and the error of the direct per-frame Nakagami maps against the pooled references.
+Alternatively, you can use the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/weillcornell/reconstruct.py) as provided in the [Open-H-RF GitHub repository](https://github.com/open-h/Open-H-RF). Besides the B-mode, it prints the selected frame's BSC band and unit, the QUS map shapes, and the error of the direct per-frame Nakagami maps against the pooled references.
 
 ## Dataset Format
 
@@ -85,7 +85,7 @@ The theoretical BSC is a homogeneous phantom material property: each phantom has
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 120 HDF5 files; 1.00 GB (1,002,700,800 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:** 120 HDF5 files; 1.00 GB (1,002,700,800 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.
 
 - **Acquisitions / frames:** 120 acquisitions (3 phantoms x 2 operators x 20 probe placements, one HDF5 file each), 20 frames per acquisition — 2,400 frames total
 - **File naming:** `data/ac<n>_<phantom>_<operator>.hdf5`, with `n` from 1 to 20, e.g. `ac10_15m_SK.hdf5`

weizmann-sampl/README.md

@@ -19,7 +19,7 @@ language:
 
 ![Reconstructed cineloop from 30_1.hdf5](assets/30_1.gif)
 
-*Cine loop of a thyroid scan, [`data/30_1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/weizmann-sampl/data/30_1.hdf5), reconstructed from the raw channel data with the `pipeline.yaml` in this folder.*
+*Cine loop of a thyroid scan, [`data/30_1.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/weizmann-sampl/data/30_1.hdf5), reconstructed from the raw channel data with the `pipeline.yaml` in this folder.*
 
 ## Dataset Description
 
@@ -51,7 +51,7 @@ General-purpose ultrasound channel-data foundation model pretraining and evaluat
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/weizmann-sampl/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/weizmann-sampl/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.
 
 Set `ZEA_FILE` and `FRAME` at the top of the script to pick a scan and frame; setting `ZEA_FILE = None` switches to the grid modes that sample several scans (see the script docstring).
 
@@ -63,7 +63,7 @@ zea file format, one acquisition HDF5 file per subject. Before packaging, the fr
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 30 HDF5 files; 232.57 GB (232,573,960,192 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:** 30 HDF5 files; 232.57 GB (232,573,960,192 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.
 
 - Number of samples / acquisitions / channels: ~1600 / 128 / 128
 - Number of frames:  average 250 (dependent on subject)

wpi/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - openh-rf
+  - open-h-rf
   - 3d
   - beamforming
   - elevational-saf
@@ -21,7 +21,7 @@ size_categories:
 
 <p align="center"><img src="assets/esaf_topview_sweep.gif" width="55%" alt="Top-view MIP: the naive reconstruction (magenta) accumulating into a smear arc as the probe sweeps 180 degrees about the rotation axis, against the eSAF label (cyan)"></p>
 
-*Top view down the rotation axis of a point target at 20 mm depth, 6 mm off-axis, [`data/tall_elev_H12__point_z020_r6.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/wpi/data/tall_elev_H12__point_z020_r6.hdf5). Magenta is the naive per-angle reconstruction, filling in as the array (white line) sweeps 180° about the rotation axis (small circle); cyan is the paired eSAF label shipped in the file; white is where both agree. eSAF collapses the rotational smear arc back onto the target (arc-FWHM 1.26 → 0.39 mm).*
+*Top view down the rotation axis of a point target at 20 mm depth, 6 mm off-axis, [`data/tall_elev_H12__point_z020_r6.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/wpi/data/tall_elev_H12__point_z020_r6.hdf5). Magenta is the naive per-angle reconstruction, filling in as the array (white line) sweeps 180° about the rotation axis (small circle); cyan is the paired eSAF label shipped in the file; white is where both agree. eSAF collapses the rotational smear arc back onto the target (arc-FWHM 1.26 → 0.39 mm).*
 
 ## Dataset Description
 
@@ -56,7 +56,7 @@ Advanced beamforming and **elevational resolution recovery** for rotational 3D U
 
 ## Processing the Dataset
 
-The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/wpi/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, beamforms the rotation frame closest to ±90° (where an off-axis target lies in-plane) and plots it beside the per-frame rotation angle from `metadata/probe_pose`. Set `ZEA_FILE` and `FRAME` at the top of the script to pick another acquisition or frame. The default, frame 90 (~−90°) of [`data/experiment__acq_exp_30mm.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/wpi/data/experiment__acq_exp_30mm.hdf5), gives:
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/Open-H-RF/blob/main/datasets/wpi/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, beamforms the rotation frame closest to ±90° (where an off-axis target lies in-plane) and plots it beside the per-frame rotation angle from `metadata/probe_pose`. Set `ZEA_FILE` and `FRAME` at the top of the script to pick another acquisition or frame. The default, frame 90 (~−90°) of [`data/experiment__acq_exp_30mm.hdf5`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/wpi/data/experiment__acq_exp_30mm.hdf5), gives:
 
 <p align="center"><img src="assets/bmode.png" width="70%" alt="B-mode frame of the measured phantom at -90 deg probe rotation, with the per-frame rotation-angle trajectory alongside it"></p>
 
@@ -82,7 +82,7 @@ Pre-processing: none for the simulations beyond the forward model; for the measu
 
 ## Dataset Quantification
 
-**Current OpenH-RF release:** 195 HDF5 files; 1.87 GB (1,870,462,976 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:** 195 HDF5 files; 1.87 GB (1,870,462,976 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.
 
 - **Acquisitions:** **195** = **190 simulated** + **5 measured phantom**.
   - *Simulated (190):* **10 probe types × 19 targets** (16 single points over depth {20,45,80,130} mm × radial offset from the rotation centre {0,2,4,6} mm, plus 3 pair/oblique cases). The probe axis is listed above under Data Collection Method; each file's own `description` attribute gives its exact probe/target/r0/depth. (The earlier compatible set has 18 acquisitions.)

wpi/pipeline.yaml

@@ -1,4 +1,4 @@
-# Beamforming config for the OpenH-RF eSAF rotational 3D ultrasound sub-dataset.
+# Beamforming config for the Open-H-RF eSAF rotational 3D ultrasound sub-dataset.
 #
 # Each acquisition is raw RF channel data (n_ch = 1) from a single normal
 # plane-wave transmit per rotation frame on the JP_Linear_68 linear array