oslo: restructure the data cards to the common layout

oslo/A_cardiac/README.md

@@ -1,4 +1,5 @@
 ---
+name: oslo-a-cardiac
 pretty_name: "USTB - In-vivo Cardiac (Verasonics P4-2)"
 license: cc-by-4.0
 task_categories:
@@ -17,18 +18,19 @@ size_categories:
 
 # USTB - In-vivo Cardiac (Verasonics P4-2)
 
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
 ## Dataset Description
 
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** (see the [collection card](../README.md)).
+
 In-vivo human cardiac channel-capture data acquired with a Verasonics Vantage 256 research scanner and a P4-2 phased-array probe. The collection contains parasternal long-axis and apical four-chamber views recorded with focused transmit beams (sector scan). The data is pre-beamformed RF channel data intended for research into generalized beamforming, adaptive imaging and cardiac reconstruction.
 
-## Dataset Contributors
+## Dataset Contributor(s)
 
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+- Ole Marius Hoel Rindal <omrindal@ifi.uio.no> (primary contact)
+- Yucel Karabiyik
+- Sven Peter Nasholm
+- Andreas Austeng
+- University of Oslo (UiO), Department of Informatics
 
 ## Dataset Creation Date
 
@@ -36,9 +38,7 @@ University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius
 
 ## License / Terms of Use
 
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+[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
 
@@ -48,18 +48,17 @@ Generalized reconstruction and adaptive beamforming of cardiac ultrasound (RFP t
 
 - **Data Collection Method:** clinical
 - **Labeling Method:** N/A (raw channel data; no annotations).
-- **Acquisition system:** probe(s) P4-2;
-  element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling
-  frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+- **Acquisition system:** probe(s) P4-2; element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+
+## Processing the Dataset
+
+The 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
 
-All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single
-acquisition with raw channel data `/data/raw_data` of shape
-`(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit
-sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1).
-No demodulation or decimation was applied during packaging beyond conversion from the USTB
-Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single acquisition with raw channel data `/data/raw_data` of shape `(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1). No demodulation or decimation was applied during packaging beyond conversion from the USTB Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
 
 ## Dataset Quantification
 
@@ -100,20 +99,9 @@ Healthy adult volunteer(s). Anatomy: heart (parasternal long-axis, apical four-c
 
 ## Data Validation
 
-A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct.
-
-The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the
-UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction
-used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with
-scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry.
-These are the recommended reference reconstructions for visual verification.
+A Delay-And-Sum `zea.Pipeline` (`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`; RF is demodulated in-pipeline, IQ uses a baseband pipeline) reconstructs every acquisition as a portable check that the recorded geometry and timing are correct (see *Processing the Dataset*).
+
+The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry. These are the recommended reference reconstructions for visual verification.
 
 ## Known Issues
 
@@ -122,3 +110,7 @@ Focused sector acquisition: lateral resolution and field of view follow the tran
 ## Ethical Considerations
 
 In-vivo data recorded from healthy adult volunteers at the University of Oslo with written informed consent for research use and data sharing, and approval from the Regional Committee for Medical and Health Research Ethics (REK), Norway. The files contain only backscattered RF channel data and acquisition parameters - no patient name, identifier, date of birth, acquisition date, facial image, or any other HHS Safe Harbor identifier is present (de-identified by construction).
+
+## Citation
+
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).

oslo/B_carotid/README.md

@@ -1,4 +1,5 @@
 ---
+name: oslo-b-carotid
 pretty_name: "USTB - In-vivo Carotid (Verasonics L7-4)"
 license: cc-by-4.0
 task_categories:
@@ -18,18 +19,19 @@ size_categories:
 
 # USTB - In-vivo Carotid (Verasonics L7-4)
 
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
 ## Dataset Description
 
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** (see the [collection card](../README.md)).
+
 In-vivo human carotid-artery channel-capture data acquired with a Verasonics Vantage 256 and an L7-4 linear-array probe, cross-sectional views, focused transmit imaging. Pre-beamformed RF channel data for vascular imaging and beamforming research.
 
-## Dataset Contributors
+## Dataset Contributor(s)
 
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+- Ole Marius Hoel Rindal <omrindal@ifi.uio.no> (primary contact)
+- Yucel Karabiyik
+- Sven Peter Nasholm
+- Andreas Austeng
+- University of Oslo (UiO), Department of Informatics
 
 ## Dataset Creation Date
 
@@ -37,9 +39,7 @@ University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius
 
 ## License / Terms of Use
 
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+[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
 
@@ -49,18 +49,17 @@ Generalized reconstruction and adaptive beamforming of vascular ultrasound (RFP
 
 - **Data Collection Method:** clinical
 - **Labeling Method:** N/A (raw channel data; no annotations).
-- **Acquisition system:** probe(s) L7-4;
-  element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling
-  frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+- **Acquisition system:** probe(s) L7-4; element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+
+## Processing the Dataset
+
+The 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
 
-All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single
-acquisition with raw channel data `/data/raw_data` of shape
-`(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit
-sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1).
-No demodulation or decimation was applied during packaging beyond conversion from the USTB
-Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single acquisition with raw channel data `/data/raw_data` of shape `(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1). No demodulation or decimation was applied during packaging beyond conversion from the USTB Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
 
 ## Dataset Quantification
 
@@ -102,20 +101,9 @@ Healthy adult volunteer(s). Anatomy: carotid artery (cross-section). Scanner: Ve
 
 ## Data Validation
 
-A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct.
-
-The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the
-UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction
-used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with
-scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry.
-These are the recommended reference reconstructions for visual verification.
+A Delay-And-Sum `zea.Pipeline` (`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`; RF is demodulated in-pipeline, IQ uses a baseband pipeline) reconstructs every acquisition as a portable check that the recorded geometry and timing are correct (see *Processing the Dataset*).
+
+The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry. These are the recommended reference reconstructions for visual verification.
 
 ## Known Issues
 
@@ -124,3 +112,7 @@ Single/few-frame acquisitions; focused linear imaging.
 ## Ethical Considerations
 
 In-vivo data recorded from healthy adult volunteers at the University of Oslo with written informed consent for research use and data sharing, and approval from the Regional Committee for Medical and Health Research Ethics (REK), Norway. Files contain only RF channel data and acquisition parameters; no HHS Safe Harbor identifiers are present.
+
+## Citation
+
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).

oslo/C_verasonics_phantom/README.md

@@ -1,4 +1,5 @@
 ---
+name: oslo-c-verasonics-phantom
 pretty_name: "USTB - Phantom (Verasonics L7-4 / P4)"
 license: cc-by-4.0
 task_categories:
@@ -17,18 +18,19 @@ size_categories:
 
 # USTB - Phantom (Verasonics L7-4 / P4)
 
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
 ## Dataset Description
 
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** (see the [collection card](../README.md)).
+
 Tissue-mimicking and table-top phantom channel-capture data acquired on a Verasonics Vantage 256 with L7-4 linear and P4 phased-array probes. The collection spans coherent plane-wave compounding (CPWC), focused imaging (FI), synthetic transmit aperture (STA) and diverging-wave (DW) sequences for resolution, contrast, dynamic-range and point-spread-function evaluation, including CIRS tissue-mimicking phantom targets.
 
-## Dataset Contributors
+## Dataset Contributor(s)
 
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+- Ole Marius Hoel Rindal <omrindal@ifi.uio.no> (primary contact)
+- Yucel Karabiyik
+- Sven Peter Nasholm
+- Andreas Austeng
+- University of Oslo (UiO), Department of Informatics
 
 ## Dataset Creation Date
 
@@ -36,9 +38,7 @@ University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius
 
 ## License / Terms of Use
 
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+[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
 
@@ -48,18 +48,17 @@ Generalized reconstruction, image-quality assessment (resolution, contrast, dyna
 
 - **Data Collection Method:** phantom
 - **Labeling Method:** Derived (known phantom geometry, e.g. CIRS Model 040GSE where applicable).
-- **Acquisition system:** probe(s) L7-4, P4-1, P4-2;
-  element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling
-  frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+- **Acquisition system:** probe(s) L7-4, P4-1, P4-2; element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+
+## Processing the Dataset
+
+The 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
 
-All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single
-acquisition with raw channel data `/data/raw_data` of shape
-`(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit
-sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1).
-No demodulation or decimation was applied during packaging beyond conversion from the USTB
-Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single acquisition with raw channel data `/data/raw_data` of shape `(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1). No demodulation or decimation was applied during packaging beyond conversion from the USTB Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
 
 ## Dataset Quantification
 
@@ -113,20 +112,9 @@ No human or animal subjects. Targets: CIRS tissue-mimicking phantoms and lab pha
 
 ## Data Validation
 
-A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct.
-
-The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the
-UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction
-used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with
-scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry.
-These are the recommended reference reconstructions for visual verification.
+A Delay-And-Sum `zea.Pipeline` (`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`; RF is demodulated in-pipeline, IQ uses a baseband pipeline) reconstructs every acquisition as a portable check that the recorded geometry and timing are correct (see *Processing the Dataset*).
+
+The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry. These are the recommended reference reconstructions for visual verification.
 
 ## Known Issues
 
@@ -135,3 +123,7 @@ Mixed transmit schemes across files (CPWC / FI / STA / DW). Single-frame acquisi
 ## Ethical Considerations
 
 Phantom / table-top acquisitions; no human or animal subjects are involved. No ethical considerations beyond standard laboratory practice.
+
+## Citation
+
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).

oslo/D_alpinion_phantom/README.md

@@ -1,4 +1,5 @@
 ---
+name: oslo-d-alpinion-phantom
 pretty_name: "USTB - Phantom (Alpinion L3-8)"
 license: cc-by-4.0
 task_categories:
@@ -17,18 +18,19 @@ size_categories:
 
 # USTB - Phantom (Alpinion L3-8)
 
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
 ## Dataset Description
 
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** (see the [collection card](../README.md)).
+
 Phantom channel-capture data acquired on an Alpinion E-Cube 12R research scanner with an L3-8 linear-array probe. Hypoechoic and hyperechoic targets imaged with focused (FI) and coherent plane-wave compounding (CPWC) sequences.
 
-## Dataset Contributors
+## Dataset Contributor(s)
 
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+- Ole Marius Hoel Rindal <omrindal@ifi.uio.no> (primary contact)
+- Yucel Karabiyik
+- Sven Peter Nasholm
+- Andreas Austeng
+- University of Oslo (UiO), Department of Informatics
 
 ## Dataset Creation Date
 
@@ -36,9 +38,7 @@ University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius
 
 ## License / Terms of Use
 
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+[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
 
@@ -48,18 +48,17 @@ Generalized reconstruction and image-quality assessment on a second hardware pla
 
 - **Data Collection Method:** phantom
 - **Labeling Method:** Derived (known phantom target types).
-- **Acquisition system:** probe(s) L3-8;
-  element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling
-  frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+- **Acquisition system:** probe(s) L3-8; element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+
+## Processing the Dataset
+
+The 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
 
-All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single
-acquisition with raw channel data `/data/raw_data` of shape
-`(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit
-sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1).
-No demodulation or decimation was applied during packaging beyond conversion from the USTB
-Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single acquisition with raw channel data `/data/raw_data` of shape `(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1). No demodulation or decimation was applied during packaging beyond conversion from the USTB Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
 
 ## Dataset Quantification
 
@@ -102,20 +101,9 @@ No human or animal subjects. Targets: hypoechoic/hyperechoic phantom inclusions.
 
 ## Data Validation
 
-A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct.
-
-The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the
-UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction
-used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with
-scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry.
-These are the recommended reference reconstructions for visual verification.
+A Delay-And-Sum `zea.Pipeline` (`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`; RF is demodulated in-pipeline, IQ uses a baseband pipeline) reconstructs every acquisition as a portable check that the recorded geometry and timing are correct (see *Processing the Dataset*).
+
+The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry. These are the recommended reference reconstructions for visual verification.
 
 ## Known Issues
 
@@ -124,3 +112,7 @@ Single-frame acquisitions; two transmit schemes (FI, CPWC).
 ## Ethical Considerations
 
 Phantom acquisitions; no human or animal subjects. No ethical considerations beyond standard laboratory practice.
+
+## Citation
+
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).

oslo/E_simulation/README.md

@@ -1,4 +1,5 @@
 ---
+name: oslo-e-simulation
 pretty_name: "USTB - Simulation (Field II)"
 license: cc-by-4.0
 task_categories:
@@ -18,18 +19,19 @@ size_categories:
 
 # USTB - Simulation (Field II)
 
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
 ## Dataset Description
 
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** (see the [collection card](../README.md)).
+
 Physics-based synthetic channel-capture data generated with the Field II ultrasound simulation framework. The collection covers point scatterers, cysts, speckle, dynamic-range targets and blocked-array (aperture-apodized) configurations, using linear (L7-4-like) and phased (P4-like) virtual probes with CPWC, FI and STA sequences. Because the scattering medium is fully defined, exact ground-truth scatterer positions and medium parameters are known. One numerical calibration acquisition (PICMUS_numerical_calib_v2) was created in collaboration with our group as part of the PICMUS effort; it is included here while the other PICMUS datasets are excluded (see Known Issues).
 
-## Dataset Contributors
+## Dataset Contributor(s)
 
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+- Ole Marius Hoel Rindal <omrindal@ifi.uio.no> (primary contact)
+- Yucel Karabiyik
+- Sven Peter Nasholm
+- Andreas Austeng
+- University of Oslo (UiO), Department of Informatics
 
 ## Dataset Creation Date
 
@@ -37,9 +39,7 @@ University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius
 
 ## License / Terms of Use
 
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+[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
 
@@ -49,18 +49,17 @@ Generalized reconstruction, beamformer development and validation with known gro
 
 - **Data Collection Method:** synthetic
 - **Labeling Method:** Synthetic ground truth (known scatterer positions and medium parameters).
-- **Acquisition system:** probe(s) L7-4, P4-1;
-  element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling
-  frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+- **Acquisition system:** probe(s) L7-4, P4-1; element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+
+## Processing the Dataset
+
+The 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
 
-All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single
-acquisition with raw channel data `/data/raw_data` of shape
-`(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit
-sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF/IQ (n_ch in [1, 2]).
-No demodulation or decimation was applied during packaging beyond conversion from the USTB
-Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single acquisition with raw channel data `/data/raw_data` of shape `(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF/IQ (n_ch in [1, 2]). No demodulation or decimation was applied during packaging beyond conversion from the USTB Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
 
 ## Dataset Quantification
 
@@ -110,20 +109,9 @@ No human or animal subjects. Synthetic media simulated with Field II. Virtual pr
 
 ## Data Validation
 
-A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct.
-
-The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the
-UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction
-used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with
-scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry.
-These are the recommended reference reconstructions for visual verification.
+A Delay-And-Sum `zea.Pipeline` (`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`; RF is demodulated in-pipeline, IQ uses a baseband pipeline) reconstructs every acquisition as a portable check that the recorded geometry and timing are correct (see *Processing the Dataset*).
+
+The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html), with scanline transmit apodization for focused/sector acquisitions and correct sector-scan geometry. These are the recommended reference reconstructions for visual verification.
 
 ## Known Issues
 
@@ -132,3 +120,7 @@ PICMUS calibration: 'PICMUS_numerical_calib_v2' was created in collaboration wit
 ## Ethical Considerations
 
 Fully synthetic data generated with the Field II simulation framework; no human or animal subjects. No personal data is present. The included numerical calibration file was produced in collaboration with our group as part of the PICMUS effort (IEEE IUS 2016) and is released here under CC BY 4.0.
+
+## Citation
+
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).

oslo/F_motion/README.md

@@ -1,4 +1,5 @@
 ---
+name: oslo-f-motion
 pretty_name: "USTB - Motion Estimation (SWE / ARFI, Verasonics L7-4)"
 license: cc-by-4.0
 task_categories:
@@ -18,18 +19,19 @@ size_categories:
 
 # USTB - Motion Estimation (SWE / ARFI, Verasonics L7-4)
 
-Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** contributed to the
-[OpenH-RF](https://github.com/open-h/OpenH-RF) initiative. All acquisitions are stored in the
-*zea* HDF5 file format (zea_version 0.1.6) and contain raw pre-beamformed
-channel data (`/data/raw_data`).
-
 ## Dataset Description
 
+Part of the **UltraSound ToolBox (USTB) Channel Capture Collection** (see the [collection card](../README.md)).
+
 Shear-wave elastography (SWE) and acoustic-radiation-force-impulse (ARFI) push-tracking phantom acquisitions on a Verasonics Vantage 256 with an L7-4 linear array. Each acquisition contains many high-frame-rate tracking frames following an acoustic push, suitable for tissue-motion and shear-wave-velocity estimation.
 
-## Dataset Contributors
+## Dataset Contributor(s)
 
-University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius Hoel Rindal (omrindal@ifi.uio.no). Team: Ole Marius Hoel Rindal, Yucel Karabiyik, Sven Peter Nasholm, Andreas Austeng.
+- Ole Marius Hoel Rindal <omrindal@ifi.uio.no> (primary contact)
+- Yucel Karabiyik
+- Sven Peter Nasholm
+- Andreas Austeng
+- University of Oslo (UiO), Department of Informatics
 
 ## Dataset Creation Date
 
@@ -37,9 +39,7 @@ University of Oslo (UiO), Department of Informatics. Primary contact: Ole Marius
 
 ## License / Terms of Use
 
-Released under **Creative Commons Attribution 4.0 International (CC BY 4.0)** — see the `LICENCE`
-file at the submission root (this license is also declared in the YAML frontmatter above). The
-contributed data is cleared for this license. The UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo. Contributed to OpenH-RF. Zenodo record 20261898.
+[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
 
@@ -49,18 +49,17 @@ Motion estimation (RFP task 6.4): shear-wave velocity estimation, ARFI displacem
 
 - **Data Collection Method:** phantom
 - **Labeling Method:** Derived (elastography phantom of known/typical stiffness classes).
-- **Acquisition system:** probe(s) L7-4;
-  element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling
-  frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+- **Acquisition system:** probe(s) L7-4; element positions stored in `/probe/probe_geometry` (meters); center frequency, sampling frequency and sound speed stored per acquisition in `/scan` (see per-sample feature table).
+
+## Processing the Dataset
+
+The 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.
 
 ## Dataset Format
 
-All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single
-acquisition with raw channel data `/data/raw_data` of shape
-`(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit
-sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1).
-No demodulation or decimation was applied during packaging beyond conversion from the USTB
-Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
+[zea v0.1.6](https://github.com/tue-bmd/zea)
+
+All acquisitions are stored in the **zea** HDF5 file format. Each `.hdf5` file is a single acquisition with raw channel data `/data/raw_data` of shape `(n_frames, n_tx, n_ax, n_el, n_ch)` and a fully populated `/scan` group describing the transmit sequence (delays, focus distances, steering angles, apodization, timing). Data type: RF (n_ch=1). No demodulation or decimation was applied during packaging beyond conversion from the USTB Ultrasound File Format (UFF) to zea; RF data is demodulated inside the reconstruction pipeline.
 
 ## Dataset Quantification
 
@@ -103,20 +102,9 @@ No human or animal subjects. Elastography phantoms. Scanner: Verasonics Vantage
 
 ## Data Validation
 
-A Delay-And-Sum `zea.Pipeline` is provided in the **`pipeline.yaml` at the submission root** and
-run by the single **`reconstruct.py` at the submission root**:
-`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`
-(RF is demodulated in-pipeline; IQ uses a baseband pipeline). The script is geometry-driven and
-recurses into every sub-dataset folder; running `python reconstruct.py` from the root reconstructs
-every `.hdf5` in the collection (or pass a folder-qualified path for a single acquisition) and writes
-`<name>_zea_bmode.png` next to each file as a portable check that the recorded geometry and timing
-are correct. These plane-wave tracking acquisitions use the non-focused `compound` pipeline, and the
-reference reconstruction shows a single tracking frame.
-
-The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the
-UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction
-used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html). These are
-the recommended reference reconstructions for visual verification.
+A Delay-And-Sum `zea.Pipeline` (`cast -> demodulate -> delay-and-sum beamform -> envelope detect -> normalize -> log compress`; RF is demodulated in-pipeline, IQ uses a baseband pipeline) reconstructs every acquisition as a portable check that the recorded geometry and timing are correct (see *Processing the Dataset*).
+
+The reference B-mode images committed alongside the data (`<name>_bmode.png`) are produced with the UltraSound ToolBox (USTB) MATLAB Delay-And-Sum beamformer — the exact per-dataset reconstruction used in the public USTB dataset catalog (https://unioslo.github.io/USTB/datasets.html). These are the recommended reference reconstructions for visual verification.
 
 ## Known Issues
 
@@ -125,3 +113,7 @@ Push-track sequences contain many frames at a high frame rate; the B-mode refere
 ## Ethical Considerations
 
 Phantom acquisitions; no human or animal subjects. No ethical considerations beyond standard laboratory practice.
+
+## Citation
+
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898).

oslo/README.md

@@ -1,5 +1,6 @@
 ---
-pretty_name: "OpenH-RF — UltraSound ToolBox (USTB) Channel Capture Collection"
+name: oslo
+pretty_name: "UltraSound ToolBox (USTB) Channel Capture Collection"
 license: cc-by-4.0
 task_categories:
   - image-to-image
@@ -15,25 +16,17 @@ size_categories:
   - n<1K
 ---
 
-# UltraSound ToolBox (USTB) Channel Capture Collection — OpenH-RF Submission
+# UltraSound ToolBox (USTB) Channel Capture Collection
 
-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 (`zea_version 0.1.6`,
-meeting the OpenH-RF minimum format version `0.1.4`) and released under **CC BY 4.0**.
+![Focused sector B-mode of an apical four-chamber view](assets/Verasonics_P2-4_apical_four_chamber_subject_1_zea_bmode.png)
 
-## OpenH-RF Release Inventory
+*Apical four-chamber view reconstructed from the raw channel data, [`A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1.hdf5`](https://huggingface.co/datasets/nvidia/OpenH-RF/blob/main/oslo/A_cardiac/Verasonics_P2-4_apical_four_chamber_subject_1.hdf5).*
 
-**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.
+## Dataset Description
 
-## Contents
+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**.
 
-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).
+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).
 
 | Folder | Sub-dataset | Tier | RFP task | Acq. |
 |---|---|---|---|---|
@@ -46,23 +39,37 @@ across folders), and the single LICENCE covers the whole collection (each data c
 
 Total: **39 acquisitions**, 8.61 GB of stored HDF5 data.
 
-## How to reconstruct
+## Dataset Contributor(s)
+
+- Ole Marius Hoel Rindal <omrindal@ifi.uio.no> (primary contact)
+- Yucel Karabiyik
+- Sven Peter Nasholm
+- Andreas Austeng
+- University of Oslo (UiO), Department of Informatics
+
+## Dataset Creation Date
+
+06/23/2026 (packaging date; original acquisitions/simulations were produced between 2016 and 2023).
+
+## 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/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.
 
-Every acquisition is reconstructable from the file alone — all acquisition parameters live in the
-zea `/scan` and `/probe` groups. A single `reconstruct.py` at the submission root reconstructs
-every acquisition in every sub-dataset folder:
+## Dataset Format
 
-```bash
-export KERAS_BACKEND=jax
-python reconstruct.py                          # standard recon for every .hdf5
-python reconstruct.py --refocus                # also emit REFoCUS where enabled
-python reconstruct.py A_cardiac/<file>.hdf5    # a single acquisition
-```
+[zea v0.1.6](https://github.com/tue-bmd/zea)
 
-All reconstruction choices live in **`parameters.yaml`** — one entry per acquisition giving its
-`pipeline` (which `zea.Pipeline` to use), display window (`zlims`/`xlims` in mm) and `dynamic_range`.
-Every acquisition uses the same workflow (load parameters → run the pipeline → plot); there is no
-per-file logic in the script. Each `pipeline` value maps to a `zea.Pipeline` YAML at the root:
+## 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.
+
+## Reconstruction Details
+
+All reconstruction choices live in **`parameters.yaml`** — one entry per acquisition giving its `pipeline` (which `zea.Pipeline` to use), display window (`zlims`/`xlims` in mm) and `dynamic_range`. Every acquisition uses the same workflow (load parameters → run the pipeline → plot); there is no per-file logic in the script. Each `pipeline` value maps to a `zea.Pipeline` YAML at the root:
 
 | `pipeline` | Used for | `zea.Pipeline` |
 |---|---|---|
@@ -71,44 +78,18 @@ per-file logic in the script. Each `pipeline` value maps to a `zea.Pipeline` YAM
 | `iq` | baseband IQ (`n_ch == 2`, e.g. PICMUS) | `pipeline_iq.yaml` (no demodulation step). |
 | `compound` | non-focused linear (plane-wave / diverging / STA / SWE-ARFI) | `pipeline.yaml`: coherent compounding, no pfield (these insonify the whole field of view). |
 
-The pipelines share the same shape as zea's regular B-mode pipeline (`cast → apply_window →
-demodulate → beamform → envelope → normalize → log_compress`). Scanline imaging
-uses the same `beamform` op with a scanline grid plus receive apodization rather than a dedicated
-scanline-specific operation. Two zea features used in this submission are scanline receive
-apodization (`enable_receive_apodization`) and `focal_region_length` (focal-region delay blending,
-Rindal et al., IUS 2018).
-
-An acquisition may also set `refocus: true` in `parameters.yaml`. When `reconstruct.py` is run
-with the `--refocus` flag (off by default), it additionally runs a REFoCUS reconstruction for
-those acquisitions — transmit-encoding recovery (Bottenus, 2018) that
-inverts the transmit-encoding matrix to recover the multistatic (full-matrix-capture) dataset
-before pressure-field-weighted DAS — and writes a `<name>_zea_refocus_bmode.png` alongside the
-standard reconstruction. REFoCUS is enabled for **every acquisition with enough encoded transmits
-for the inversion to be well posed (≥ 8 transmit events)** — coherent plane-wave compounding
-(CPWC), diverging waves (DW), and focused linear (FI) and phased-array sector scans. Phased-array
-`sector` acquisitions use `pipeline_refocus_sector.yaml` (polar grid + scan conversion, preserving
-the sector geometry); every other geometry uses the linear/cartesian `pipeline_refocus.yaml`.
-REFoCUS is **not** enabled where the inversion is ill-posed or undefined: single/few-transmit
-acquisitions (the `n_tx = 1` plane-wave tracking sets and single-angle simulations, PICMUS's
-5-angle IQ) and synthetic transmit aperture (STA) acquisitions, which are already multistatic.
+The pipelines share the same shape as zea's regular B-mode pipeline (`cast → apply_window → demodulate → beamform → envelope → normalize → log_compress`). Scanline imaging uses the same `beamform` op with a scanline grid plus receive apodization rather than a dedicated scanline-specific operation. Two zea features used in this submission are scanline receive apodization (`enable_receive_apodization`) and `focal_region_length` (focal-region delay blending, Rindal et al., IUS 2018).
+
+An acquisition may also set `refocus: true` in `parameters.yaml`. With `REFOCUS = True` (off by default), `reconstruct.py` additionally runs a REFoCUS reconstruction for those acquisitions — transmit-encoding recovery (Bottenus, 2018) that inverts the transmit-encoding matrix to recover the multistatic (full-matrix-capture) dataset before pressure-field-weighted DAS — and writes a `<name>_zea_refocus_bmode.png` alongside the standard reconstruction. REFoCUS is enabled for **every acquisition with enough encoded transmits for the inversion to be well posed (≥ 8 transmit events)** — coherent plane-wave compounding (CPWC), diverging waves (DW), and focused linear (FI) and phased-array sector scans. Phased-array `sector` acquisitions use `pipeline_refocus_sector.yaml` (polar grid + scan conversion, preserving the sector geometry); every other geometry uses the linear/cartesian `pipeline_refocus.yaml`. REFoCUS is **not** enabled where the inversion is ill-posed or undefined: single/few-transmit acquisitions (the `n_tx = 1` plane-wave tracking sets and single-angle simulations, PICMUS's 5-angle IQ) and synthetic transmit aperture (STA) acquisitions, which are already multistatic.
 
 ## Reference images
 
 Each acquisition ships with:
 - `<name>_zea_bmode.png` — produced by the root `reconstruct.py` (zea); the reproducible reference.
 - `<name>_zea_bmode_old.png` — the initial v2 zea reconstruction (kept for comparison).
-- `<name>_bmode.png` — the canonical USTB MATLAB Delay-And-Sum reconstruction from the public
-  [USTB dataset catalog](https://unioslo.github.io/USTB/datasets.html), for cross-validation.
-- `<name>_zea_refocus_bmode.png` — REFoCUS variant, for acquisitions with `refocus: true`
-  (CPWC / DW / focused FI / sector with ≥ 8 transmits; not STA or single/few-transmit sets).
-
-## Licensing & attribution
-
-Released under **CC BY 4.0**. Please cite the UltraSound ToolBox (USTB), University of Oslo
-(Zenodo record 20261898). `PICMUS_numerical_calib_v2` (group E) was created **in collaboration
-with our group** as part of the PICMUS effort and is included on that basis; the other PICMUS
-datasets are excluded.
+- `<name>_bmode.png` — the canonical USTB MATLAB Delay-And-Sum reconstruction from the public [USTB dataset catalog](https://unioslo.github.io/USTB/datasets.html), for cross-validation.
+- `<name>_zea_refocus_bmode.png` — REFoCUS variant, for acquisitions with `refocus: true` (CPWC / DW / focused FI / sector with ≥ 8 transmits; not STA or single/few-transmit sets).
 
-## Contact
+## Citation
 
-Ole Marius Hoel Rindal — omrindal@ifi.uio.no (UiO, USTB).
+Please cite the UltraSound ToolBox (USTB) Channel Capture Collection, University of Oslo (Zenodo record 20261898). `PICMUS_numerical_calib_v2` (group E) was created **in collaboration with our group** as part of the PICMUS effort and is included on that basis; the other PICMUS datasets are excluded.