technion: restructure the data cards to the common layout

technion/bladder/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF — Technion Bladder Pre-Beamformed Channel Data"
+name: technion-bladder
+pretty_name: "Technion Bladder Pre-Beamformed Channel Data"
 license: cc-by-4.0
 task_categories:
   - image-to-image
@@ -16,37 +17,23 @@ size_categories:
   - 1K<n<10K
 ---
 
-# OpenH-RF — Bladder pre-beamformed RF channel data
+# Technion In-vivo Bladder Pre-beamformed RF Channel Data
 
 ![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).
-
-`zea` renders it straight from the Hub with the
-`pipeline.yaml` in this folder. Try it out with the following command:
-
-```bash
-zea process \
-  --dataset hf://nvidia/OpenH-RF/technion/bladder/data/a1.hdf5 \
-  --config hf://nvidia/OpenH-RF/technion/bladder/pipeline.yaml \
-  --n-frames 10
-```
+*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).*
 
 ## Dataset Description
 
-Real, **in-vivo human** pre-beamformed ultrasound **channel data** for bladder
-imaging: per-element I/Q recorded before receive beamforming on a 64-element
-phased array — a sector scan of 180 transmit beams steered over ±45.13° (≈90°),
-one image line per transmit (steering angles in `scan.polar_angles`). 1,508
-frames across 14 sweeps from seven subjects. Acquired on a GE research system in
-tissue-harmonic mode; the harmonic echo is demodulated to I/Q at 3.44 MHz and
-band-pass filtered. No paired image is supplied — the B-mode is reproduced from
-the channel data by the released beamformer.
+Real, **in-vivo human** pre-beamformed ultrasound **channel data** for bladder imaging: per-element I/Q recorded before receive beamforming on a 64-element phased array — a sector scan of 180 transmit beams steered over ±45.13° (≈90°), one image line per transmit (steering angles in `scan.polar_angles`). 1,508 frames across 14 sweeps from seven subjects. Acquired on a GE research system in tissue-harmonic mode; the harmonic echo is demodulated to I/Q at 3.44 MHz and band-pass filtered. No paired image is supplied — the B-mode is reproduced from the channel data by the released beamformer.
 
 ## Dataset Contributor(s)
 
-Sanketh Vedula, Ortal Senouf, Dean Zadok, Alex M. Bronstein (PI) —
-Technion – Israel Institute of Technology. Primary contact: sanketh@campus.technion.ac.il.
+- Sanketh Vedula <sanketh@campus.technion.ac.il> (primary contact)
+- Ortal Senouf
+- Dean Zadok
+- Alex M. Bronstein (PI)
+- Technion – Israel Institute of Technology
 
 ## Dataset Creation Date
 
@@ -54,40 +41,38 @@ Source data 2018; converted to the OpenH-RF (zea) format 07/16/2026.
 
 ## License / Terms of Use
 
-CC BY 4.0. The contributors confirm intent to release under CC BY 4.0 with no
-third-party IP encumbrances (proposal §8).
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
 
 ## Intended Usage
 
-Primary: **generalized reconstruction** (§6.1) — learned receive beamforming and
-image reconstruction from raw channel data. The quasi-static bladder is also
-suited to multi-line-transmission (MLT) emulation and high-frame-rate research,
-and to anatomy/cohort interpretation (§6.5).
+Primary: **generalized reconstruction** (§6.1) — learned receive beamforming and image reconstruction from raw channel data. The quasi-static bladder is also suited to multi-line-transmission (MLT) emulation and high-frame-rate research, and to anatomy/cohort interpretation (§6.5).
 
 ## Dataset Characterization
 
-- **Data Collection Method:** in-vivo human (research platform) — GE Vivid S70
-  scanner with raw per-element channel access, tissue-harmonic mode.
-- **Labeling Method:** N/A — no per-frame image label; the `zea.Pipeline` in
-  `pipeline.yaml` reconstructs a B-mode from the channel data for validation.
-- **Acquisition system:** GE Vivid S70 scanner; GE 3Sc-RS 64-element phased-array
-  probe, 0.30 mm pitch; sector scan, 180 transmit beams steered over ±45.13°
-  (≈90.25° FOV), one image line per transmit.
-  Per proposal: 2.56-cycle 1.6 MHz transmit, no transmit apodization,
-  tissue-harmonic mode, harmonic echo demodulated to I/Q at 3.44 MHz and filtered,
-  ~18 fps; transversal plane with slow longitudinal probe sweep to decorrelate
-  frames.
+- **Data Collection Method:** in-vivo human (research platform) — GE Vivid S70 scanner with raw per-element channel access, tissue-harmonic mode.
+- **Labeling Method:** N/A — no per-frame image label; the `zea.Pipeline` in `pipeline.yaml` reconstructs a B-mode from the channel data for validation.
+- **Acquisition system:** GE Vivid S70 scanner; GE 3Sc-RS 64-element phased-array probe, 0.30 mm pitch; sector scan, 180 transmit beams steered over ±45.13° (≈90.25° FOV), one image line per transmit. Per proposal: 2.56-cycle 1.6 MHz transmit, no transmit apodization, tissue-harmonic mode, harmonic echo demodulated to I/Q at 3.44 MHz and filtered, ~18 fps; transversal plane with slow longitudinal probe sweep to decorrelate frames.
+
+## Processing the Dataset
+
+The acquisitions can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/technion/bladder/data/a1.hdf5 \
+  --config hf://nvidia/OpenH-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).
 
 ## Dataset Format
 
-zea file format, one HDF5 file per sweep (`data/<subject>.hdf5`, e.g. `a1.hdf5`,
-`ak.hdf5`, `s2.hdf5`). The source complex `double` samples were repackaged to
-`float32` I/Q with I and Q on the final channel axis (`n_ch = 2`); values are
-otherwise verbatim (band-pass filtered baseband IQ, as archived). Each file
-carries `metadata/subject/{id,type=human}`, `metadata/credit`, and
-`metadata/annotations/{anatomy=bladder, label=in vivo, view=transverse suprapubic
-pelvic ultrasound}`. Probe model (`probe.name = GE 3Sc-RS`) and scanner
-(`us_machine = GE Vivid S70`) are stored too.
+[zea v0.1.4](https://github.com/tue-bmd/zea)
+
+zea file format, one HDF5 file per sweep (`data/<subject>.hdf5`, e.g. `a1.hdf5`, `ak.hdf5`, `s2.hdf5`). The source complex `double` samples were repackaged to `float32` I/Q with I and Q on the final channel axis (`n_ch = 2`); values are otherwise verbatim (band-pass filtered baseband IQ, as archived). Each file carries `metadata/subject/{id,type=human}`, `metadata/credit`, and `metadata/annotations/{anatomy=bladder, label=in vivo, view=transverse suprapubic pelvic ultrasound}`. Probe model (`probe.name = GE 3Sc-RS`) and scanner (`us_machine = GE Vivid S70`) are stored too.
 
 ## Dataset Quantification
 
@@ -108,12 +93,7 @@ pelvic ultrasound}`. Probe model (`probe.name = GE 3Sc-RS`) and scanner
 
 ## Subject Metadata
 
-**Seven in-vivo human volunteers**, 14 sweeps, 1,508 frames. (The proposal's
-"six" was an undercount; verified from the acquisitions to be seven distinct
-volunteers.) No phantom is included in this collection — the calibration phantom
-is a separate submission (`../phantom/`). No PHI stored: only anonymized
-`subject.id`, `subject.type = human`, and `annotations.anatomy = bladder`.
-Age and sex were not recorded for these acquisitions.
+**Seven in-vivo human volunteers**, 14 sweeps, 1,508 frames. (The proposal's "six" was an undercount; verified from the acquisitions to be seven distinct volunteers.) No phantom is included in this collection — the calibration phantom is a separate submission (`../phantom/`). No PHI stored: only anonymized `subject.id`, `subject.type = human`, and `annotations.anatomy = bladder`. Age and sex were not recorded for these acquisitions.
 
 | Subject | Sweeps (files) | Frames |
 |---|---|---|
@@ -127,38 +107,19 @@ Age and sex were not recorded for these acquisitions.
 
 ## Data Validation
 
-`reconstruct.py` reconstructs a B-mode from `raw_data` using the `zea.Pipeline`
-defined in `pipeline.yaml`: delay-and-sum beamforming on a polar scanline grid
-(one image line per transmit, receive dynamic focusing) → envelope
-detection → normalization → log compression → sector scan conversion. Run it on
-any file to reproduce a reference frame:
-
-```
-python reconstruct.py
-```
+`reconstruct.py` reconstructs a B-mode from `raw_data` using the `zea.Pipeline` defined in `pipeline.yaml`: delay-and-sum beamforming on a polar scanline grid (one image line per transmit, receive dynamic focusing) → envelope detection → normalization → log compression → sector scan conversion.
 
-Reference output: `bmode.png` — frame 30 of `data/a1.hdf5` (in `assets/`). The
-pipeline matches the acquisition's own receive-beamforming geometry
-(`code/processing/`), so the reconstruction reproduces the expected sector
-B-mode.
+Reference output: `bmode.png` — frame 30 of `data/a1.hdf5` (in `assets/`). The pipeline matches the acquisition's own receive-beamforming geometry (`code/processing/`), so the reconstruction reproduces the expected sector B-mode.
 
 ## Known Issues
 
-- **No paired image target** (unlike the cardiac set); the B-mode is derived from
-  the channel data, not supplied.
-- **Transmit fundamental (1.6 MHz) not stored** — only the 3.44 MHz demodulation
-  frequency is in the files, so `center_frequency` equals the demodulation
-  frequency.
+- **No paired image target** (unlike the cardiac set); the B-mode is derived from the channel data, not supplied.
+- **Transmit fundamental (1.6 MHz) not stored** — only the 3.44 MHz demodulation frequency is in the files, so `center_frequency` equals the demodulation frequency.
 
 ## Ethical Considerations
 
-**Privacy safeguards (HIPAA and GDPR).** Pre-beamformed RF channel data contains
-no facial or otherwise identifying imagery. All records are de-identified to the
-HIPAA Safe Harbor standard, with direct identifiers removed and any dates
-generalized to bands. As an EU institution we additionally comply with GDPR,
-holding any pseudonymized subject identifiers separately on access-controlled
-storage and never sharing them. The released data are de-identified and contain
-only the channel signals and acquisition metadata.
+**Privacy safeguards (HIPAA and GDPR).** Pre-beamformed RF channel data contains no facial or otherwise identifying imagery. All records are de-identified to the HIPAA Safe Harbor standard, with direct identifiers removed and any dates generalized to bands. As an EU institution we additionally comply with GDPR, holding any pseudonymized subject identifiers separately on access-controlled storage and never sharing them. The released data are de-identified and contain only the channel signals and acquisition metadata.
+
+**Ethics.** The data were collected under ethical best practices on healthy volunteers.
 
-**Ethics.** The data were collected under ethical best practices on healthy
-volunteers.
+The contributors confirm intent to release under CC BY 4.0 with no third-party IP encumbrances (proposal §8).

technion/cardiac/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF — Technion Cardiac Pre-Beamformed Channel Data"
+name: technion-cardiac
+pretty_name: "Technion Cardiac Pre-Beamformed Channel Data"
 license: cc-by-4.0
 task_categories:
   - image-to-image
@@ -16,36 +17,23 @@ size_categories:
   - n<1K
 ---
 
-# OpenH-RF — Cardiac pre-beamformed RF channel data (paired with DAS targets)
+# Technion Cardiac Pre-beamformed RF Channel Data (paired with DAS targets)
 
 ![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).
-
-`zea` renders it straight from the Hub with the
-`pipeline.yaml` in this folder. Try it out with the following command:
-
-```bash
-zea process \
-  --dataset hf://nvidia/OpenH-RF/technion/cardiac/data/c1.hdf5 \
-  --config hf://nvidia/OpenH-RF/technion/cardiac/pipeline.yaml \
-  --n-frames 32
-```
+*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).*
 
 ## Dataset Description
 
-Real, **in-vivo human** pre-beamformed ultrasound **channel data** for cardiac
-imaging: per-element I/Q recorded before receive beamforming on a 64-element
-phased array — a sector scan of 140 transmit beams steered over ±37.5°, one image
-line per transmit (steering angles in `scan.polar_angles`). Each frame is **paired with
-its conventional delay-and-sum reconstruction** (stored as `beamformed_data`),
-making this a ready-made input→target set for learned reconstruction /
-beamforming. 777 frames across 25 cine loops from six subjects (a–f).
+Real, **in-vivo human** pre-beamformed ultrasound **channel data** for cardiac imaging: per-element I/Q recorded before receive beamforming on a 64-element phased array — a sector scan of 140 transmit beams steered over ±37.5°, one image line per transmit (steering angles in `scan.polar_angles`). Each frame is **paired with its conventional delay-and-sum reconstruction** (stored as `beamformed_data`), making this a ready-made input→target set for learned reconstruction / beamforming. 777 frames across 25 cine loops from six subjects (a–f).
 
 ## Dataset Contributor(s)
 
-Sanketh Vedula, Ortal Senouf, Dean Zadok, Alex M. Bronstein (PI) —
-Technion – Israel Institute of Technology. Primary contact: sanketh@campus.technion.ac.il.
+- Sanketh Vedula <sanketh@campus.technion.ac.il> (primary contact)
+- Ortal Senouf
+- Dean Zadok
+- Alex M. Bronstein (PI)
+- Technion – Israel Institute of Technology
 
 ## Dataset Creation Date
 
@@ -53,45 +41,45 @@ Acquired 2018; converted to the OpenH-RF (zea) format 07/16/2026.
 
 ## License / Terms of Use
 
-CC BY 4.0. The data is the contributors' own research acquisition, cleared for
-CC BY 4.0 with no third-party IP encumbrances.
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
 
 ## Intended Usage
 
-Primary: **generalized reconstruction** (§6.1) — learning to map raw per-element
-channel data to a focused image (learned receive/transmit beamforming,
-super-resolution, clutter suppression), trained and evaluated against the paired
-delay-and-sum target. Secondary: motion estimation across the cardiac cine loops
-(§6.4) and anatomy/cohort interpretation (§6.5).
+Primary: **generalized reconstruction** (§6.1) — learning to map raw per-element channel data to a focused image (learned receive/transmit beamforming, super-resolution, clutter suppression), trained and evaluated against the paired delay-and-sum target. Secondary: motion estimation across the cardiac cine loops (§6.4) and anatomy/cohort interpretation (§6.5).
 
 ## Dataset Characterization
 
-- **Data Collection Method:** in-vivo human (research platform) — GE Vivid S70
-  scanner with raw per-element channel access.
-- **Labeling Method:** derived ground truth — the paired `beamformed_data` is the
-  conventional delay-and-sum reconstruction of each frame.
-- **Acquisition system:** GE Vivid S70 scanner; GE 3Sc-RS 64-element phased-array
-  probe, 0.30 mm pitch; sector scan, 140 acquisition lines over a ~75° sector
-  (±37.5°); 2.5 MHz transmit; apical four-chamber view (A4C).
+- **Data Collection Method:** in-vivo human (research platform) — GE Vivid S70 scanner with raw per-element channel access.
+- **Labeling Method:** derived ground truth — the paired `beamformed_data` is the conventional delay-and-sum reconstruction of each frame.
+- **Acquisition system:** GE Vivid S70 scanner; GE 3Sc-RS 64-element phased-array probe, 0.30 mm pitch; sector scan, 140 acquisition lines over a ~75° sector (±37.5°); 2.5 MHz transmit; apical four-chamber view (A4C).
+
+## Processing the Dataset
+
+The acquisitions can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/technion/cardiac/data/c1.hdf5 \
+  --config hf://nvidia/OpenH-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).
 
 ## Dataset Format
 
-zea file format, one HDF5 file per cine loop (`data/<subject><clip>.hdf5`, e.g.
-`a1.hdf5` = subject a, clip 1; `f2.hdf5` = patient-set subject f). The source
-complex `int16` samples were repackaged to `float32` I/Q with I and Q on the
-final channel axis (`n_ch = 2`); values are otherwise verbatim. Each file carries
-`metadata/subject/{id,type=human}`, `metadata/credit`, and
-`metadata/annotations/{anatomy=cardiac, label=in vivo, view=apical four-chamber (A4C)}`. Probe
-model (`probe.name = GE 3Sc-RS`) and scanner (`us_machine = GE Vivid S70`) are
-stored too.
+[zea v0.1.4](https://github.com/tue-bmd/zea)
+
+zea file format, one HDF5 file per cine loop (`data/<subject><clip>.hdf5`, e.g. `a1.hdf5` = subject a, clip 1; `f2.hdf5` = patient-set subject f). The source complex `int16` samples were repackaged to `float32` I/Q with I and Q on the final channel axis (`n_ch = 2`); values are otherwise verbatim. Each file carries `metadata/subject/{id,type=human}`, `metadata/credit`, and `metadata/annotations/{anatomy=cardiac, label=in vivo, view=apical four-chamber (A4C)}`. Probe model (`probe.name = GE 3Sc-RS`) and scanner (`us_machine = GE Vivid S70`) are stored too.
 
 ## 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.
 
 - **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).
+- **Train / val / test split:** N/A (contributor to define; the `f2` patient set is a natural held-out cine).
 - **Stored HDF5 size:** 14.99 GB (14,992,998,400 bytes).
 
 | Field | Shape | dtype | Units | Description |
@@ -107,47 +95,23 @@ stored too.
 
 ## Subject Metadata
 
-Six subjects (a–e main set, f patient set), 777 frames across 25 cine loops.
-In-vivo human; no PHI stored (only `subject.id` a1…f2, `subject.type = human`,
-`anatomy = cardiac`, `view = apical four-chamber (A4C)`). Age and sex were not recorded for these
-acquisitions.
+Six subjects (a–e main set, f patient set), 777 frames across 25 cine loops. In-vivo human; no PHI stored (only `subject.id` a1…f2, `subject.type = human`, `anatomy = cardiac`, `view = apical four-chamber (A4C)`). Age and sex were not recorded for these acquisitions.
 
 ## Data Validation
 
-`reconstruct.py` reconstructs a B-mode from `raw_data` using the `zea.Pipeline`
-defined in `pipeline.yaml`: delay-and-sum on a polar scanline grid (one image line
-per acquisition line, receive dynamic focusing) → envelope detection →
-normalization → log compression → sector scan conversion. Run:
-
-```
-python reconstruct.py
-```
+`reconstruct.py` reconstructs a B-mode from `raw_data` using the `zea.Pipeline` defined in `pipeline.yaml`: delay-and-sum on a polar scanline grid (one image line per acquisition line, receive dynamic focusing) → envelope detection → normalization → log compression → sector scan conversion.
 
-Reference output: `bmode.png` — frame 8 of `data/c1.hdf5` (in `assets/`). Each
-frame is also paired with its conventional delay-and-sum reconstruction in
-`beamformed_data` (the target for the raw→image learning task) — note its depth
-scale is approximate because the acquisition axial rate is not stored (see Known
-Issues).
+Reference output: `bmode.png` — frame 8 of `data/c1.hdf5` (in `assets/`). Each frame is also paired with its conventional delay-and-sum reconstruction in `beamformed_data` (the target for the raw→image learning task) — note its depth scale is approximate because the acquisition axial rate is not stored (see Known Issues).
 
 ## Known Issues
 
-- **Axial sample rate not stored.** The consolidated source `.mat` files do not
-  carry the acquisition header, so `sampling_frequency` (6.0 MHz) is a best
-  estimate and the reconstructed depth scale is approximate. This applies both to
-  the raw→image reconstruction and to the paired `beamformed_data` target (exact
-  in value, approximate in depth axis).
-- **Sector-angle convention.** Lines are stored as ±37.5° centred about
-  boresight, the physically correct convention for a phased array.
+- **Axial sample rate not stored.** The consolidated source `.mat` files do not carry the acquisition header, so `sampling_frequency` (6.0 MHz) is a best estimate and the reconstructed depth scale is approximate. This applies both to the raw→image reconstruction and to the paired `beamformed_data` target (exact in value, approximate in depth axis).
+- **Sector-angle convention.** Lines are stored as ±37.5° centred about boresight, the physically correct convention for a phased array.
 
 ## Ethical Considerations
 
-**Privacy safeguards (HIPAA and GDPR).** Pre-beamformed RF channel data contains
-no facial or otherwise identifying imagery. All records are de-identified to the
-HIPAA Safe Harbor standard, with direct identifiers removed and any dates
-generalized to bands. As an EU institution we additionally comply with GDPR,
-holding any pseudonymized subject identifiers separately on access-controlled
-storage and never sharing them. The released data are de-identified and contain
-only the channel signals and acquisition metadata.
+**Privacy safeguards (HIPAA and GDPR).** Pre-beamformed RF channel data contains no facial or otherwise identifying imagery. All records are de-identified to the HIPAA Safe Harbor standard, with direct identifiers removed and any dates generalized to bands. As an EU institution we additionally comply with GDPR, holding any pseudonymized subject identifiers separately on access-controlled storage and never sharing them. The released data are de-identified and contain only the channel signals and acquisition metadata.
+
+**Ethics.** The data were collected under ethical best practices on healthy volunteers.
 
-**Ethics.** The data were collected under ethical best practices on healthy
-volunteers.
+The data is the contributors' own research acquisition, cleared for CC BY 4.0 with no third-party IP encumbrances.

technion/phantom/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF — Technion Phantom Pre-Beamformed Channel Data"
+name: technion-phantom
+pretty_name: "Technion Phantom Pre-Beamformed Channel Data"
 license: cc-by-4.0
 task_categories:
   - image-to-image
@@ -16,36 +17,23 @@ size_categories:
   - n<1K
 ---
 
-# OpenH-RF — Tissue-mimicking phantom pre-beamformed RF channel data
+# Technion Tissue-mimicking Phantom Pre-beamformed RF Channel Data
 
 ![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`.
-
-`zea` renders it straight from the Hub with the
-`pipeline.yaml` in this folder. Try it out with the following command:
-
-```bash
-zea process \
-  --dataset hf://nvidia/OpenH-RF/technion/phantom/data/ph.hdf5 \
-  --config hf://nvidia/OpenH-RF/technion/phantom/pipeline.yaml \
-  --n-frames 1
-```
+*Frame 6 of [`data/ph.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/technion/phantom/data/ph.hdf5), reconstructed by `reconstruct.py`.*
 
 ## Dataset Description
 
-Pre-beamformed ultrasound **channel data** from a tissue-mimicking phantom,
-acquired on the same 64-element phased-array sector scheme as the in-vivo
-collection (180 transmit beams steered over ±45.13°, one image line per
-transmit), for **calibration and verification**. Contains
-resolvable point targets and an anechoic cyst — a clean reference for validating
-beamforming and reconstruction. 12 frames, one acquisition.
+Pre-beamformed ultrasound **channel data** from a tissue-mimicking phantom, acquired on the same 64-element phased-array sector scheme as the in-vivo collection (180 transmit beams steered over ±45.13°, one image line per transmit), for **calibration and verification**. Contains resolvable point targets and an anechoic cyst — a clean reference for validating beamforming and reconstruction. 12 frames, one acquisition.
 
 ## Dataset Contributor(s)
 
-Sanketh Vedula, Ortal Senouf, Dean Zadok, Alex M. Bronstein (PI) —
-Technion – Israel Institute of Technology. Primary contact: sanketh@campus.technion.ac.il.
+- Sanketh Vedula <sanketh@campus.technion.ac.il> (primary contact)
+- Ortal Senouf
+- Dean Zadok
+- Alex M. Bronstein (PI)
+- Technion – Israel Institute of Technology
 
 ## Dataset Creation Date
 
@@ -53,30 +41,38 @@ Source data 2018; converted to the OpenH-RF (zea) format 07/16/2026.
 
 ## License / Terms of Use
 
-CC BY 4.0 (proposal §8).
+[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode.en). Retain attribution and identify modifications when reusing the data.
 
 ## Intended Usage
 
-Calibration and end-to-end verification of the beamforming/reconstruction
-pipeline (point-target resolution, cyst contrast). Phantom tier (×1).
+Calibration and end-to-end verification of the beamforming/reconstruction pipeline (point-target resolution, cyst contrast). Phantom tier (×1).
 
 ## Dataset Characterization
 
-- **Data Collection Method:** phantom — tissue-mimicking phantom (Gammex 403GS LE,
-  Gammex Inc., Middleton, WI, USA), acquired on the same scanner/probe as the
-  in-vivo collection for calibration.
+- **Data Collection Method:** phantom — tissue-mimicking phantom (Gammex 403GS LE, Gammex Inc., Middleton, WI, USA), acquired on the same scanner/probe as the in-vivo collection for calibration.
 - **Labeling Method:** N/A (calibration target; known phantom geometry).
-- **Acquisition system:** GE Vivid S70 scanner; GE 3Sc-RS 64-element phased-array
-  probe, 0.30 mm pitch; sector scan, 180 transmit beams steered over ±45.13°
-  (≈90.25° FOV), one image line per transmit; IQ demodulated at 3.44 MHz.
+- **Acquisition system:** GE Vivid S70 scanner; GE 3Sc-RS 64-element phased-array probe, 0.30 mm pitch; sector scan, 180 transmit beams steered over ±45.13° (≈90.25° FOV), one image line per transmit; IQ demodulated at 3.44 MHz.
+
+## Processing the Dataset
+
+The acquisitions can be processed with the `pipeline.yaml` definition in this folder and the [zea library](https://github.com/tue-bmd/zea).
+
+`zea` streams the data from the Hugging Face Hub and processes it according to the pipeline. You can try it out with the following command:
+
+```bash
+zea process \
+  --dataset hf://nvidia/OpenH-RF/technion/phantom/data/ph.hdf5 \
+  --config hf://nvidia/OpenH-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).
 
 ## Dataset Format
 
-zea file format, a single HDF5 file `data/ph.hdf5`. Source complex samples
-repackaged to `float32` I/Q (`n_ch = 2`), values verbatim. Carries
-`metadata/subject/{id=ph, type=phantom}`, `metadata/credit`, probe model
-(`probe.name = GE 3Sc-RS`) and scanner (`us_machine = GE Vivid S70`). ("phantom"
-is recorded only as `subject.type`, not as an anatomy or label.)
+[zea v0.1.4](https://github.com/tue-bmd/zea)
+
+zea file format, a single HDF5 file `data/ph.hdf5`. Source complex samples repackaged to `float32` I/Q (`n_ch = 2`), values verbatim. Carries `metadata/subject/{id=ph, type=phantom}`, `metadata/credit`, probe model (`probe.name = GE 3Sc-RS`) and scanner (`us_machine = GE Vivid S70`). ("phantom" is recorded only as `subject.type`, not as an anatomy or label.)
 
 ## Dataset Quantification
 
@@ -100,21 +96,13 @@ N/A — inanimate phantom (GAMMEX 403GS LE); `subject.type = phantom`.
 
 ## Data Validation
 
-`reconstruct.py` reconstructs a B-mode from `raw_data` using the `zea.Pipeline`
-in `pipeline.yaml` (delay-and-sum on a polar scanline grid → envelope →
-normalization → log compression → sector scan conversion). Run:
-
-```
-python reconstruct.py
-```
+`reconstruct.py` reconstructs a B-mode from `raw_data` using the `zea.Pipeline` in `pipeline.yaml` (delay-and-sum on a polar scanline grid → envelope → normalization → log compression → sector scan conversion).
 
-Reference output: `bmode.png` — frame 6 of `data/ph.hdf5`, shown above:
-resolvable point targets and a well-defined anechoic cyst at ~65 mm.
+Reference output: `bmode.png` — frame 6 of `data/ph.hdf5`, shown above: resolvable point targets and a well-defined anechoic cyst at ~65 mm.
 
 ## Known Issues
 
-- Same scan scheme and probe as the in-vivo bladder collection (GE
-  tissue-harmonic); acquired as its calibration reference. GAMMEX 403GS LE.
+- Same scan scheme and probe as the in-vivo bladder collection (GE tissue-harmonic); acquired as its calibration reference. GAMMEX 403GS LE.
 
 ## Ethical Considerations