Vanderbilt -- reuploading files that were flagged as malware

.gitattributes

@@ -3497,15 +3497,15 @@ oslo/submission_v4/E_simulation/FieldII_P4_point_scatterers.hdf5 filter=lfs diff
 oslo/submission_v4/E_simulation/FieldII_STAI_dynamic_range.hdf5 filter=lfs diff=lfs merge=lfs -text
 oslo/submission_v4/E_simulation/FieldII_STAI_simulated_dynamic_range.hdf5 filter=lfs diff=lfs merge=lfs -text
 oslo/submission_v4/E_simulation/FieldII_STAI_uniform_fov.hdf5 filter=lfs diff=lfs merge=lfs -text
-mosaic-nuevosono/data/15_10_18_21.hdf5 filter=lfs diff=lfs merge=lfs -text
-mosaic-nuevosono/data/15_10_50_19.hdf5 filter=lfs diff=lfs merge=lfs -text
-mosaic-nuevosono/data/15_16_45_06.hdf5 filter=lfs diff=lfs merge=lfs -text
-mosaic-nuevosono/data/22_12_10_52.hdf5 filter=lfs diff=lfs merge=lfs -text
-mosaic-nuevosono/data/22_12_29_46.hdf5 filter=lfs diff=lfs merge=lfs -text
-mosaic-nuevosono/data/22_12_38_45.hdf5 filter=lfs diff=lfs merge=lfs -text
-mosaic-nuevosono/data/22_13_10_16.hdf5 filter=lfs diff=lfs merge=lfs -text
-mosaic-nuevosono/data/22_13_56_43.hdf5 filter=lfs diff=lfs merge=lfs -text
-mosaic-nuevosono/data/22_14_29_54.hdf5 filter=lfs diff=lfs merge=lfs -text
+mosaic-intelligence/data/15_10_18_21.hdf5 filter=lfs diff=lfs merge=lfs -text
+mosaic-intelligence/data/15_10_50_19.hdf5 filter=lfs diff=lfs merge=lfs -text
+mosaic-intelligence/data/15_16_45_06.hdf5 filter=lfs diff=lfs merge=lfs -text
+mosaic-intelligence/data/22_12_10_52.hdf5 filter=lfs diff=lfs merge=lfs -text
+mosaic-intelligence/data/22_12_29_46.hdf5 filter=lfs diff=lfs merge=lfs -text
+mosaic-intelligence/data/22_12_38_45.hdf5 filter=lfs diff=lfs merge=lfs -text
+mosaic-intelligence/data/22_13_10_16.hdf5 filter=lfs diff=lfs merge=lfs -text
+mosaic-intelligence/data/22_13_56_43.hdf5 filter=lfs diff=lfs merge=lfs -text
+mosaic-intelligence/data/22_14_29_54.hdf5 filter=lfs diff=lfs merge=lfs -text
 twente-vortexflow/AcqData_PVoltage120_TVoltage3.4.hdf5 filter=lfs diff=lfs merge=lfs -text
 twente-vortexflow/AcqData_PVoltage120_TVoltage7.1.hdf5 filter=lfs diff=lfs merge=lfs -text
 twente-vortexflow/AcqData_PVoltage160_TVoltage3.4.hdf5 filter=lfs diff=lfs merge=lfs -text
@@ -23487,15 +23487,3 @@ waterloo-femoralvein/data/Acq6.hdf5 filter=lfs diff=lfs merge=lfs -text
 waterloo-femoralvein/data/Acq7.hdf5 filter=lfs diff=lfs merge=lfs -text
 waterloo-femoralvein/data/Acq8.hdf5 filter=lfs diff=lfs merge=lfs -text
 waterloo-femoralvein/data/Acq9.hdf5 filter=lfs diff=lfs merge=lfs -text
-kaist-snubh-barreleye/data/S36_D1.hdf5 filter=lfs diff=lfs merge=lfs -text
-kaist-snubh-barreleye/data/S36_D2.hdf5 filter=lfs diff=lfs merge=lfs -text
-kaist-snubh-barreleye/data/S37_D1.hdf5 filter=lfs diff=lfs merge=lfs -text
-kaist-snubh-barreleye/data/S37_D2.hdf5 filter=lfs diff=lfs merge=lfs -text
-jhu-pulselab/D_alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5 filter=lfs diff=lfs merge=lfs -text
-jhu-pulselab/D_alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic.hdf5 filter=lfs diff=lfs merge=lfs -text
-jhu-pulselab/D_alpinion_phantom/JHU_Alpinion_L3-8_FI_hyperechoic_scatterers.hdf5 filter=lfs diff=lfs merge=lfs -text
-jhu-pulselab/D_alpinion_phantom/JHU_Alpinion_L3-8_FI_hypoechoic.hdf5 filter=lfs diff=lfs merge=lfs -text
-jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5 filter=lfs diff=lfs merge=lfs -text
-jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic.hdf5 filter=lfs diff=lfs merge=lfs -text
-jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_FI_hyperechoic_scatterers.hdf5 filter=lfs diff=lfs merge=lfs -text
-jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_FI_hypoechoic.hdf5 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -3,32 +3,27 @@ license: cc-by-4.0
 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;"> Open-H-RF
+<h1 align="center">
+  <img src="assets/logo-glow-transparent.svg" alt="" height="100" style="height: 1.5em; vertical-align: -0.3em;"> OpenH-RF
 </h1>
 
-<p align="center"><em>An open, large-scale, community-driven dataset of medical ultrasound raw channel data</em></p>
+<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 Open-H-RF subsets](assets/collage.webp)
+![Collage of reconstructed images and raw channel data from the OpenH-RF subsets](assets/collage.webp)
 
-<p align="center">
-  <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>
+ℹ️ This repository is currently available in early access to project contributors.
 
 ## Dataset Description
 
-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.
+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.
 
-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).
+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.
 
-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).
+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).
 
 This dataset is ready for commercial or non-commercial uses.
 
-## Maintainer / Hosting Organization:
+## Dataset Owner
 
 NVIDIA Corporation
 
@@ -38,7 +33,7 @@ NVIDIA, Stanford University, Eindhoven University of Technology, Tel Aviv Univer
 
 ## Dataset Stewardship
 
-Open-H-RF community & Steering Group
+OpenH-RF community & Steering Group
 
 ## Dataset Creation Date
 
@@ -80,15 +75,10 @@ Channel capture data and related meta data are saved in HDF5 data files using a
 
 ## Reference(s)
 
-- https://github.com/open-h/Open-H-RF
-- https://arxiv.org/abs/2610.13616
+https://github.com/open-h/OpenH-RF
 
 ## Ethical Considerations
 
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
 
 Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
-
-## 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
-  - open-h-rf
+  - openh-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/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) |
+| [`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) |
 
 *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/Open-H-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/OpenH-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/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 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 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/Open-H-RF HDF5 format.
+The dataset is distributed in the zea/OpenH-RF HDF5 format.
 
 Each file includes:
 

concordia/README.md

@@ -8,7 +8,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - open-h-rf
+  - openh-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/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).*
+*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).*
 
 ## 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 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.
+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.
 
 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/Open-H-RF/concordia/data/image_0005.hdf5 \
-  --config hf://nvidia/Open-H-RF/concordia/pipeline.yaml \
+  --dataset hf://nvidia/OpenH-RF/concordia/data/image_0005.hdf5 \
+  --config hf://nvidia/OpenH-RF/concordia/pipeline.yaml \
   --n-frames 1 \
   --save-as png
 ```
 
-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).
+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).
 
 ## Dataset Format
 
@@ -107,7 +107,7 @@ Pre-processing applied before packaging: anti-alias FIR decimation (factor 5, 10
 
 ## Dataset Quantification
 
-**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.
+**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.
 
 This dataset comprises 2,000 FSA captures across five echogenicity classes:
 

dartmouth-uct/README.md

@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - open-h-rf
+  - openh-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/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.*
+*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.*
 
 ## 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/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.
+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.
 
 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 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.
+**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.
 
 - **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**.

jhu-pulselab/README.md

@@ -1,69 +0,0 @@
----
-name: jhu-pulselab
-pretty_name: "UltraSound ToolBox (USTB) Channel Capture Data, PULSE Lab, Johns Hopkins University"
-license: cc-by-4.0
-task_categories:
-  - image-to-image
-language:
-  - en
-tags:
-  - ultrasound
-  - rf
-  - open-h-rf
-  - beamforming
-  - channel-data
-size_categories:
-  - n<1K
----
-
-# UltraSound ToolBox (USTB) Channel Capture Data, PULSE Lab, Johns Hopkins University
-
-![B-modes of all four acquisitions: plane-wave and focused imaging of a hypoechoic cyst and of hyperechoic scatterers](assets/hero.png)
-
-*First frames of all four acquisitions, reconstructed from the raw channel data with `reconstruct.py`. Top: [`alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hypoechoic.hdf5), [`alpinion_phantom/JHU_Alpinion_L3-8_FI_hypoechoic`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_FI_hypoechoic.hdf5). Bottom: [`alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers.hdf5), [`alpinion_phantom/JHU_Alpinion_L3-8_FI_hyperechoic_scatterers`](https://huggingface.co/datasets/nvidia/Open-H-RF/blob/main/jhu-pulselab/alpinion_phantom/JHU_Alpinion_L3-8_FI_hyperechoic_scatterers.hdf5). Panels are scaled to a common height per row, not to a common physical scale.*
-
-## Dataset Description
-
-Contributed by the **Photoacoustic & Ultrasonic Systems Engineering (PULSE) Lab, Johns Hopkins University (JHU)** to the [Open-H-RF](https://github.com/open-h/Open-H-RF) initiative. The data is owned by JHU. All data is pre-beamformed (raw channel-capture) ultrasound stored in the **zea** HDF5 format and released under **CC BY 4.0**.
-
-These acquisitions are part of the **UltraSound ToolBox (USTB) Channel Capture Collection**; the rest of that collection is in [`oslo/`](../oslo/README.md).
-
-4 acquisitions packaged into a single application sub-dataset. The sub-dataset folder contains its zea `.hdf5` files, a Hugging Face–style `README.md` data card, and reference B-mode images. The **`reconstruct.py`, `parameters.yaml` and `pipeline*.yaml` files live once at the root of this collection** (`jhu-pulselab/`); the sub-dataset folder also holds a `pipeline.yaml` for its example acquisition. The data card declares `license: cc-by-4.0` in its YAML frontmatter.
-
-| Folder | Sub-dataset | Tier | RFP task | Acq. |
-|---|---|---|---|---|
-| [`alpinion_phantom/`](alpinion_phantom/README.md) | Phantom (Alpinion L3-8) | phantom | 6.1 Generalized Reconstruction | 4 |
-
-Total: **4 acquisitions**, 0.47 GB of stored HDF5 data.
-
-## Dataset Contributor(s)
-
-- Muyinatu A. Lediju Bell <mledijubell@jhu.edu> (primary contact)
-- Ole Marius Hoel Rindal <omrindal@ifi.uio.no>
-- Photoacoustic & Ultrasonic Systems Engineering (PULSE) Lab, Johns Hopkins University
-
-## Dataset Creation Date
-
-06/23/2026 (packaging date; the original acquisitions were made at the PULSE Lab, Johns Hopkins University, in 2017).
-
-## License / Terms of Use
-
-[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
-
-## 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/Open-H-RF/blob/main/datasets/jhu-pulselab/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 and `REFOCUS = True` to also emit the REFoCUS variant (the script and pipelines are the same as the USTB collection's; see its card's [Reconstruction Details](../oslo/README.md#reconstruction-details)). The 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 card](alpinion_phantom/README.md)).
-
-## Dataset Format
-
-[zea v0.1.6](https://github.com/tue-bmd/zea)
-
-## Dataset Quantification
-
-**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.
-
-## Citation
-
-Please credit the PULSE Lab, Johns Hopkins University, as the owner of this data, and cite the UltraSound ToolBox (USTB) proceedings paper:
-
-A. Rodriguez-Molares, O. M. H. Rindal, O. Bernard, A. Nair, M. A. Lediju Bell, H. Liebgott, A. Austeng and L. Løvstakken, "The UltraSound ToolBox," in *2017 IEEE International Ultrasonics Symposium (IUS)*, 2017, pp. 1–4. doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)

jhu-pulselab/parameters.yaml

@@ -1,32 +0,0 @@
-# Per-acquisition reconstruction parameters, used by reconstruct.py. For each acquisition:
-#   pipeline      -- which reconstruction to use:
-#                    scanline (focused linear FI, per-beam, pipeline_scanline.yaml),
-#                    compound (plane-wave CPWC, pipeline.yaml).
-#   zlims/xlims   -- display window in mm (framing only).
-#   dynamic_range -- display dynamic range in dB (default [-60, 0]).
-#   refocus       -- if true, also emit a REFoCUS variant (<name>_zea_refocus_bmode.png,
-#                    pipeline_refocus.yaml). REFoCUS (Bottenus 2018) inverts the
-#                    transmit-encoding matrix to recover the multistatic dataset before DAS.
-
-# alpinion_phantom
-JHU_Alpinion_L3-8_CPWC_hyperechoic_scatterers:
-  pipeline: compound
-  refocus: true
-  zlims: [3, 55]
-  xlims: [-19, 19]
-JHU_Alpinion_L3-8_CPWC_hypoechoic:
-  pipeline: compound
-  refocus: true
-  zlims: [5, 50]
-  xlims: [-19, 19]
-JHU_Alpinion_L3-8_FI_hyperechoic_scatterers:
-  pipeline: scanline
-  refocus: true
-  zlims: [3, 55]
-  xlims: [-19, 19]
-JHU_Alpinion_L3-8_FI_hypoechoic:
-  pipeline: scanline
-  refocus: true
-  zlims: [0, 60]
-  xlims: [-19, 19]
-  dynamic_range: [-50, 0]

jhu-pulselab/pipeline.yaml

@@ -1,28 +0,0 @@
-# B-mode pipeline for the plane-wave compounding (CPWC) acquisitions. Plane
-# waves insonify the whole field of view, so pressure-field weighting is
-# disabled (equal coherent compounding).
-#
-# apply_window tapers/zeros the start of each RF line to suppress the transmit
-# near-field ringdown and receive sidelobes.
-
-parameters:
-  f_number: 1.75
-  grid_size_x: 400
-  grid_size_z: 600
-  dynamic_range: [-60, 0]
-  selected_transmits: all
-
-pipeline:
-  operations:
-    - name: keras.ops.cast
-      params:
-        dtype: float32
-    - name: apply_window
-    - name: demodulate
-    - name: beamform
-      params:
-        beamformer: delay_and_sum
-        enable_pfield: false
-    - name: envelope_detect
-    - name: normalize
-    - name: log_compress

jhu-pulselab/pipeline_refocus.yaml

@@ -1,42 +0,0 @@
-# B-mode pipeline with REFoCUS transmit-encoding recovery, for non-focused
-# linear-array acquisitions that have many transmit events (CPWC and focused FI).
-#
-# REFoCUS (Bottenus 2018) inverts the transmit-encoding matrix in the frequency
-# domain to recover the multistatic (per-element / full-matrix-capture) dataset
-# from the encoded transmit sequence; that synthetic aperture is then beamformed
-# with pressure-field-weighted Delay-And-Sum. Compared with directly compounding
-# the encoded transmits (pipeline.yaml) this can sharpen lateral resolution and
-# contrast. The inversion needs enough transmit events to be well posed (>= 8),
-# so this pipeline is only applied to the many-angle acquisitions flagged with
-# `refocus: true` in parameters.yaml (the standard compound reconstruction is
-# still produced for those files as well).
-#
-# jit_compile is disabled on refocus to avoid a cuBLASLt autotuning failure on
-# the complex matmul under XLA on some GPUs.
-
-parameters:
-  f_number: 1.75
-  grid_size_x: 400
-  grid_size_z: 600
-  dynamic_range: [-60, 0]
-  selected_transmits: all
-
-pipeline:
-  operations:
-    - name: keras.ops.cast
-      params:
-        dtype: float32
-    - name: apply_window
-    - name: refocus
-      params:
-        method: adjoint
-        param: null
-        jit_compile: false
-    - name: demodulate
-    - name: beamform
-      params:
-        beamformer: delay_and_sum
-        enable_pfield: true
-    - name: envelope_detect
-    - name: normalize
-    - name: log_compress

jhu-pulselab/pipeline_scanline.yaml

@@ -1,26 +0,0 @@
-# Scanline (line-by-line) B-mode for focused linear (FI) scans: each focused
-# transmit forms one image line (no cross-transmit compounding).
-
-parameters:
-  f_number: 1.75
-  dynamic_range: [-60, 0]
-  enable_scanline: true
-  grid_type: cartesian
-  selected_transmits: all
-  grid_size_z: 700
-
-pipeline:
-  operations:
-    - name: keras.ops.cast
-      params:
-        dtype: float32
-    - name: apply_window
-    - name: demodulate
-    - name: beamform
-      params:
-        beamformer: delay_and_sum
-        enable_aligned_apodization: true
-        enable_pfield: false
-    - name: envelope_detect
-    - name: normalize
-    - name: log_compress

kaist-snubh-barreleye/README.md

@@ -8,7 +8,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - open-h-rf
+  - openh-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/Open-H-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/OpenH-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/Open-H-RF/kaist-snubh-barreleye/data/S01_D1.hdf5 \
-  --config hf://nvidia/Open-H-RF/kaist-snubh-barreleye/pipeline.yaml \
+  --dataset hf://nvidia/OpenH-RF/kaist-snubh-barreleye/data/S01_D1.hdf5 \
+  --config hf://nvidia/OpenH-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/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).
+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).
 
 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 (Open-H-RF spec). One HDF5 file per acquisition; one image track per file.
+All data is delivered in the **zea HDF5** format (OpenH-RF spec). One HDF5 file per acquisition; one image track per file.
 
 ```
 hdf5/
@@ -94,14 +94,14 @@ hdf5/
 ```
 
 **`original/` — raw RF channel data.**
-- **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)`.
+- **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)`.
 - No demodulation, decimation, band-pass filtering, or value clipping — `raw_data` is bit-faithful to the scanner export.
 
 ## Dataset Quantification
 
-**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.
+**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.
 
-- **Subjects:** 35 (S01–S37 with S11 and S29 unused, all female; age groups in *Subject Metadata*).
+- **Subjects:** 35 (S01–S35, all female; age groups in *Subject Metadata*).
 - **Acquisitions:** 70 (2 repeat acquisitions per subject, named `<patient>_D1` / `<patient>_D2`).
 - **HDF5 files:** 70 (one file per acquisition in `hdf5/original/`).
 - **RF frames:** 70 (one acquired frame per acquisition).
@@ -144,13 +144,14 @@ Per-file metadata follows the **HIPAA Safe-Harbor** approach: only **de-identifi
 | Age group | Count | % |
 |---|---|---|
 | 20-29 | 2  | 5.7% |
-| 30-39 | 5  | 14.3% |
+| 30-39 | 4  | 11.4% |
 | 40-49 | 12 | 34.3% |
 | 50-59 | 8  | 22.9% |
 | 60-69 | 5  | 14.3% |
 | 70-79 | 3  | 8.6% |
+| 80-89 | 1  | 2.9% |
 
-- **Case status:** 23 benign (65.7%) / 12 malignant (34.3%)
+- **Case status:** 22 benign (62.9%) / 13 malignant (37.1%)
 - **BI-RADS distribution:**
 
 | BI-RADS | Count | % |
@@ -158,23 +159,22 @@ Per-file metadata follows the **HIPAA Safe-Harbor** approach: only **de-identifi
 | C2 | 8  | 22.9% |
 | C3 | 5  | 14.3% |
 | C4a | 11 | 31.4% |
-| C4b | 1  | 2.9% |
-| C4c | 1  | 2.9% |
+| C4b | 2  | 5.7% |
 | C5 | 2  | 5.7% |
 | C6 | 7  | 20.0% |
 
-- **Lesion size (longest axis, cm):** n=35, min 0.50, max 5.20, mean 1.36, median 1.00.
+- **Lesion size (longest axis, cm):** n=35, min 0.50, max 5.20, mean 1.40, median 1.00.
 
 | Lesion size | Count |
 |---|---|
-| < 1.0 cm | 17 |
-| 1.0 – 2.0 cm | 10 |
-| 2.0 – 3.0 cm | 6  |
-| ≥ 3.0 cm | 2  |
+| < 1.0 cm | 14 |
+| 1.0 – 2.0 cm | 13 |
+| 2.0 – 3.0 cm | 5  |
+| ≥ 3.0 cm | 3  |
 
 - **Histopathology subtypes:**
-  - *Malignant (n=12):* IDC (8), DCIS (4)
-  - *Benign (n=23):* FA (8), IDP (3), FCC (1), Usual Ductal Hyperplasia (1), ADH (1), unspecified / NA (9)
+  - *Malignant (n=13):* IDC (8), DCIS (4), ADH (1)
+  - *Benign (n=22):* FA (9), IDP (3), FCC (1), Usual Ductal Hyperplasia (1), unspecified / NA (8)
 - **Anatomical region:** Breast (left and right; mixed lesion locations).
 - **Scanner / probe model:** Clinical FDA-cleared scanner with a 192-element linear 10 MHz probe.
 
@@ -205,6 +205,6 @@ Reference output: `main.png` — `data/S01_D1.hdf5` (biopsy-proven invasive duct
 ## Ethical Considerations
 
 - **Consent status:** All subjects gave informed consent under SNUBH IRB protocol **B-2401-876-301**.
-- **De-identification:** No direct identifiers (name, full exam date, free-text clinical notes) are stored. Age is decade-binned at the dataset level (not stored per file); exact lesion size and exam dates are not stored per file; only the acquisition year (2024) is reported. Subject IDs are coded (`S01`…`S37`; `S11` and `S29` are unused).
+- **De-identification:** No direct identifiers (name, full exam date, free-text clinical notes) are stored. Age is decade-binned at the dataset level (not stored per file); exact lesion size and exam dates are not stored per file; only the acquisition year (2024) is reported. Subject IDs are coded (`S01`…`S35`).
 - **IRB approval:** SNUBH IRB **B-2401-876-301**
 - **Animal welfare (ARRIVE 2.0):** Not applicable — human-only dataset.

jhu-pulselab/alpinion_phantom/assets/hero.png → kaist-snubh-barreleye/data/S11_D1.hdf5

@@ -1,3 +1,3 @@
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-oid sha256:76981c074abbad9e4545e385790cdcd9c7fbb7b4ea060bfa56dc5f11c97c15a8
-size 824688
+oid sha256:8888931970eabd24e3adb661cd7d3af47efe9085d77fe1555a536f256c359a6c
+size 13565952

jhu-pulselab/assets/hero.png → kaist-snubh-barreleye/data/S11_D2.hdf5

@@ -1,3 +1,3 @@
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-oid sha256:76981c074abbad9e4545e385790cdcd9c7fbb7b4ea060bfa56dc5f11c97c15a8
-size 824688
+oid sha256:c2e1745a2dab465257036606e0cd448f5e9339552c7b464cd371cbbc617a76d0
+size 13434880

kaist-snubh-barreleye/data/S16_D1.hdf5

@@ -1,3 +1,3 @@
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-oid sha256:3df6c224ee0b979ca6b803f89250e6c75bab67796da0aec594b7dac82a619984
+oid sha256:7e5f32e28feacd5102ec0700ab1d1f2f39feed02ef973fef4bc7531aabb4e79d
 size 12124160

kaist-snubh-barreleye/data/S16_D2.hdf5

@@ -1,3 +1,3 @@
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 size 12124160

kaist-snubh-barreleye/data/S29_D1.hdf5

@@ -0,0 +1,3 @@
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+oid sha256:b8377bc28c3396c45b15b28a082b32e80793ccaa2b7f12b0e452730b4ec329d6
+size 10944512

kaist-snubh-barreleye/data/S29_D2.hdf5

@@ -0,0 +1,3 @@
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+oid sha256:fc9c30337bd36d78e443f99344fcb9027c1782204d987c8912cc2493bfc60886
+size 10944512

kaist-snubh-barreleye/data/S36_D1.hdf5

@@ -1,3 +0,0 @@
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kaist-snubh-barreleye/data/S36_D2.hdf5

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-size 11141120

kaist-snubh-barreleye/data/S37_D1.hdf5

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kaist-snubh-barreleye/data/S37_D2.hdf5

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-size 13172736

{mosaic-nuevosono → mosaic-intelligence}/README.md

@@ -1,5 +1,5 @@
 ---
-name: mosaic-nuevosono
+name: mosaic-intelligence
 pretty_name: "Mosaic Intelligence / NuevoSono IVUS"
 license: cc-by-4.0
 task_categories:
@@ -7,7 +7,7 @@ task_categories:
 tags:
   - ultrasound
   - rf
-  - open-h-rf
+  - openh-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/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.*
+*Pullback through [`data/22_12_10_52.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/mosaic-intelligence/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/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 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 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 Open-H-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 OpenH-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
-  - open-h-rf
+  - openh-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/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.*
+*DBUA results using synthetic validation sample [`data/nv_r2i_us_validation_0084.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/nv-raw2insights-us/data/nv_r2i_us_validation_0084.hdf5): B-mode (left) and estimated sound speed (right). Bulk-speed calibration is followed by 400 spatial-refinement iterations, with fixed display scales. These are DBUA reconstructions, not predictions from an NV-Raw2Insights-US model.*
 
 <!-- assets/main.png is the unlabelled final B-mode panel from this run, for the dataset collage. -->
 
@@ -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 (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).
+This is the **zea-format (OpenH-RF) release**; the same simulations are also published in a Hugging Face `datasets`/Arrow build at [`nvidia/NV-Raw2Insights-US`](https://huggingface.co/datasets/nvidia/NV-Raw2Insights-US).
 
 ## Dataset Contributor(s)
 
@@ -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/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`.
+The acquisitions can be processed with the `reconstruct.py` [script](https://github.com/open-h/OpenH-RF/blob/main/datasets/nv-raw2insights-us/reconstruct.py) as provided in the [OpenH-RF GitHub repository](https://github.com/open-h/OpenH-RF), which uses the [zea library](https://github.com/tue-bmd/zea) and streams the data from the Hugging Face Hub. It reconstructs both a plain DAS B-mode (the `pipeline.yaml` in this folder) and a sound-speed-corrected B-mode using the ground-truth `sos_map`.
 
 ## Dataset Format
 
@@ -87,7 +87,7 @@ Per-file contents:
 
 ## Dataset Quantification
 
-**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.
+**Current OpenH-RF release:** 923 HDF5 files; 214.13 GB (214,133,178,368 bytes) stored; root `zea_version` **0.1.6**. Sizes include all HDF5 contents and use decimal units (MB = 10^6 bytes, GB = 10^9 bytes, TB = 10^12 bytes), not decoded-array memory or original-source download sizes.
 
 - **923 samples (830 train / 93 validation)**, one HDF5 per sample, all in `data/`; the split is carried in the filename.
 - 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
-  - open-h-rf
+  - openh-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/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`.*
+*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`.*
 
 ## 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. (Open-H-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. (OpenH-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/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
+  --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
 ```
 
-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)).
+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)).
 
 ## Dataset Format
 
@@ -76,7 +76,7 @@ All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file i
 
 ## Dataset Quantification
 
-**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.
+**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.
 
 - **Number of acquisitions:** 2
 - **Total channel-capture frames:** 75
@@ -127,6 +127,4 @@ In-vivo data recorded from healthy adult volunteers at the University of Oslo wi
 
 ## Citation
 
-Please cite the UltraSound ToolBox (USTB) proceedings paper:
-
-A. Rodriguez-Molares, O. M. H. Rindal, O. Bernard, A. Nair, M. A. Lediju Bell, H. Liebgott, A. Austeng and L. Løvstakken, "The UltraSound ToolBox," in *2017 IEEE International Ultrasonics Symposium (IUS)*, 2017, pp. 1–4. doi:[10.1109/ULTSYM.2017.8092389](https://doi.org/10.1109/ULTSYM.2017.8092389)
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).