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.gitattributes

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+*.raw filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+*.tif filter=lfs diff=lfs merge=lfs -text
+*.pvsm filter=lfs diff=lfs merge=lfs -text
+*.vtk filter=lfs diff=lfs merge=lfs -text
+*.ex2 filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.avi filter=lfs diff=lfs merge=lfs -text
+*.glb filter=lfs diff=lfs merge=lfs -text
+*.vtr filter=lfs diff=lfs merge=lfs -text
+*.nc filter=lfs diff=lfs merge=lfs -text

LICENSE

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+Copyright (c) 2025 University of Notre Dame  
+All rights reserved.
+
+Permission is hereby granted, free of charge, to designated collaborators of the SciVisAgentBench project, to use this software and associated documentation files (the "Software") solely for the purposes of research and collaboration agreed upon with the University of Notre Dame.
+
+Any redistribution, modification, sublicensing, or commercial use of this Software is strictly prohibited without prior written permission from the copyright holders.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

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+# SciVisAgentBench Tasks
+
+This repository is a secondary repo of [SciVisAgentBench](https://github.com/KuangshiAi/SciVisAgentBench), contains scientific visualization datasets and tasks for benchmarking scientific visualization agents.
+
+## Download Volume Datasets
+`download_and_organize.py` downloads and organizes datasets under 512MB. Make sure you run the following script before you evaluate your agents through [SciVisAgentBench](https://github.com/KuangshiAi/SciVisAgentBench):
+```shell
+python download_and_organize.py
+```
+
+## Data Organization
+
+All the volume datasets from http://klacansky.com/open-scivis-datasets/ have been organized into a consistent structure.
+
+### Directory Structure
+
+The datasets and tasks for ParaView-MCP and ChatVis are organized into the `main` and `sci_volume_data` folders, while `napari_mcp_evals` holds tasks and datasets for napari-MCP.
+
+
+Each dataset in the `main` and `sci_volume_data` folders follows this structure:
+```
+dataset_name/
+├── data/
+│   ├── dataset_file.raw         # The actual data file
+│   └── dataset_name.txt         # Metadata about the dataset
+├── GS/                          # Ground truth folder (ParaView state + pvpython code)
+├── task_description.txt         # ParaView visualization task
+└── visualization_goals.txt      # Evaluation criteria for the visualization
+```
+
+### Available Volume Datasets
+
+- **37 datasets under 512MB** are suggested to be downloaded
+- **18 datasets over 512MB** are listed but not downloaded
+
+See `datasets_list.md` for a complete list with specifications. And `datasets_info.json` is the complete JSON file with all dataset metadata.
+
+### Task Descriptions
+
+Each dataset has:
+1. **Task descriptions** - Based on dataset type (medical, simulation, molecular, etc.)
+2. **Visualization goals** - Evaluation criteria tailored to the dataset characteristics
+3. **Ground Truth** - Ground truth pvpython code, ParaView state and screenshots
+
+## Acknowledgement
+
+SciVisAgentBench was mainly created by Kuangshi Ai (kai@nd.edu), Shusen Liu (liu42@llnl.gov), and Haichao Miao (miao1@llnl.gov). Some of the test cases are provided by Kaiyuan Tang (ktang2@nd.edu). We sincerely thank the open-source community for their invaluable contributions. This project is made possible thanks to the following outstanding projects:
+
+- [ParaView-MCP](https://github.com/LLNL/paraview_mcp)
+- [Napari-MCP](https://github.com/LLNL/napari-mcp)
+
+## License
+
+Copyright (c) 2025 University of Notre Dame
+Released under the [License](./LICENSE).
+All rights reserved.
+
+Permission is hereby granted, free of charge, to designated collaborators of the SciVisAgentBench project, to use this software and associated documentation files (the "Software") solely for the purposes of research and collaboration agreed upon with the University of Notre Dame.
+
+Any redistribution, modification, sublicensing, or commercial use of this Software is strictly prohibited without prior written permission from the copyright holders.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

datasets_info.json

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+[
+  {
+    "id": "fuel",
+    "name": "Fuel",
+    "description": "Simulation of fuel injection into a combustion chamber. The higher the density value, the less presence of air.",
+    "dimensions": "64x64x64",
+    "width": 64,
+    "height": 64,
+    "depth": 64,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 0,
+    "size_str": "64x64x64 (256.0 kB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/fuel/fuel_64x64x64_uint8.raw",
+    "filename": "fuel_64x64x64_uint8.raw"
+  },
+  {
+    "id": "marschner_lobb",
+    "name": "Marschner-Lobb",
+    "description": "High frequencies where 99% of the sinusoids are right below the Nyquist frequency.",
+    "dimensions": "41x41x41",
+    "width": 41,
+    "height": 41,
+    "depth": 41,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 0,
+    "size_str": "41x41x41 (67.3 kB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/marschner_lobb/marschner_lobb_41x41x41_uint8.raw",
+    "filename": "marschner_lobb_41x41x41_uint8.raw"
+  },
+  {
+    "id": "neghip",
+    "name": "Neghip",
+    "description": "Simulation of the spatial probability distribution of the electrons in a high potential protein molecule.",
+    "dimensions": "64x64x64",
+    "width": 64,
+    "height": 64,
+    "depth": 64,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 0,
+    "size_str": "64x64x64 (256.0 kB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/neghip/neghip_64x64x64_uint8.raw",
+    "filename": "neghip_64x64x64_uint8.raw"
+  },
+  {
+    "id": "nucleon",
+    "name": "Nucleon",
+    "description": "Simulation of the two-body distribution probability of a nucleon in the atomic nucleus 16O if a second nucleon is known to be positioned at r'=(2 fm,0,0).",
+    "dimensions": "41x41x41",
+    "width": 41,
+    "height": 41,
+    "depth": 41,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 0,
+    "size_str": "41x41x41 (67.3 kB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/nucleon/nucleon_41x41x41_uint8.raw",
+    "filename": "nucleon_41x41x41_uint8.raw"
+  },
+  {
+    "id": "silicium",
+    "name": "Silicium",
+    "description": "Simulation of a silicium grid.",
+    "dimensions": "98x34x34",
+    "width": 98,
+    "height": 34,
+    "depth": 34,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 0,
+    "size_str": "98x34x34 (110.6 kB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/silicium/silicium_98x34x34_uint8.raw",
+    "filename": "silicium_98x34x34_uint8.raw"
+  },
+  {
+    "id": "tooth",
+    "name": "Tooth",
+    "description": "",
+    "dimensions": "103x94x161",
+    "width": 103,
+    "height": 94,
+    "depth": 161,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 1.5,
+    "size_str": "103x94x161 (1.5 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/tooth/tooth_103x94x161_uint8.raw",
+    "filename": "tooth_103x94x161_uint8.raw"
+  },
+  {
+    "id": "blunt_fin",
+    "name": "Blunt Fin",
+    "description": "",
+    "dimensions": "256x128x64",
+    "width": 256,
+    "height": 128,
+    "depth": 64,
+    "data_type": "uint8",
+    "spacing": "1x0.75x1",
+    "size_mb": 2.0,
+    "size_str": "256x128x64 (2.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/blunt_fin/blunt_fin_256x128x64_uint8.raw",
+    "filename": "blunt_fin_256x128x64_uint8.raw"
+  },
+  {
+    "id": "hydrogen_atom",
+    "name": "Hydrogen Atom",
+    "description": "Simulation of the spatial probability distribution of the electron in an hydrogen atom, residing in a strong magnetic field.",
+    "dimensions": "128x128x128",
+    "width": 128,
+    "height": 128,
+    "depth": 128,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 2.0,
+    "size_str": "128x128x128 (2.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/hydrogen_atom/hydrogen_atom_128x128x128_uint8.raw",
+    "filename": "hydrogen_atom_128x128x128_uint8.raw"
+  },
+  {
+    "id": "shockwave",
+    "name": "Shockwave",
+    "description": "Simulation of an unsteady interaction of a planar shockwave with a randomly-perturbed contact discontinuity.",
+    "dimensions": "64x64x512",
+    "width": 64,
+    "height": 64,
+    "depth": 512,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 2.0,
+    "size_str": "64x64x512 (2.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/shockwave/shockwave_64x64x512_uint8.raw",
+    "filename": "shockwave_64x64x512_uint8.raw"
+  },
+  {
+    "id": "frog",
+    "name": "Frog",
+    "description": "MRI scan of a frog as part of the Whole Frog Project.",
+    "dimensions": "256x256x44",
+    "width": 256,
+    "height": 256,
+    "depth": 44,
+    "data_type": "uint8",
+    "spacing": "0.5x0.5x1",
+    "size_mb": 2.8,
+    "size_str": "256x256x44 (2.8 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/frog/frog_256x256x44_uint8.raw",
+    "filename": "frog_256x256x44_uint8.raw"
+  },
+  {
+    "id": "lobster",
+    "name": "Lobster",
+    "description": "CT scan of a lobster contained in a block of resin.",
+    "dimensions": "301x324x56",
+    "width": 301,
+    "height": 324,
+    "depth": 56,
+    "data_type": "uint8",
+    "spacing": "1x1x1.4",
+    "size_mb": 5.2,
+    "size_str": "301x324x56 (5.2 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/lobster/lobster_301x324x56_uint8.raw",
+    "filename": "lobster_301x324x56_uint8.raw"
+  },
+  {
+    "id": "mri_ventricles",
+    "name": "Head MRI CISS",
+    "description": "1.5T MRT 3D CISS dataset of a human head that highlights the CSF (Cerebro-Spinal-Fluid) filled cavities of the head.",
+    "dimensions": "256x256x124",
+    "width": 256,
+    "height": 256,
+    "depth": 124,
+    "data_type": "uint8",
+    "spacing": "0.9x0.9x0.9",
+    "size_mb": 7.8,
+    "size_str": "256x256x124 (7.8 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/mri_ventricles/mri_ventricles_256x256x124_uint8.raw",
+    "filename": "mri_ventricles_256x256x124_uint8.raw"
+  },
+  {
+    "id": "engine",
+    "name": "Engine",
+    "description": "CT scan of two cylinders of an engine block.",
+    "dimensions": "256x256x128",
+    "width": 256,
+    "height": 256,
+    "depth": 128,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 8.0,
+    "size_str": "256x256x128 (8.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/engine/engine_256x256x128_uint8.raw",
+    "filename": "engine_256x256x128_uint8.raw"
+  },
+  {
+    "id": "vis_male",
+    "name": "Head (Visible Male)",
+    "description": "Male head scan",
+    "dimensions": "128x256x256",
+    "width": 128,
+    "height": 256,
+    "depth": 256,
+    "data_type": "uint8",
+    "spacing": "1.57774x0.995861x1.00797",
+    "size_mb": 8.0,
+    "size_str": "128x256x256 (8.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/vis_male/vis_male_128x256x256_uint8.raw",
+    "filename": "vis_male_128x256x256_uint8.raw"
+  },
+  {
+    "id": "statue_leg",
+    "name": "Leg of Statue",
+    "description": "CT scan of a leg of a bronze statue.",
+    "dimensions": "341x341x93",
+    "width": 341,
+    "height": 341,
+    "depth": 93,
+    "data_type": "uint8",
+    "spacing": "1x1x4",
+    "size_mb": 10.3,
+    "size_str": "341x341x93 (10.3 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/statue_leg/statue_leg_341x341x93_uint8.raw",
+    "filename": "statue_leg_341x341x93_uint8.raw"
+  },
+  {
+    "id": "boston_teapot",
+    "name": "Boston Teapot",
+    "description": "CT scan of the SIGGRAPH 1989 teapot with a small version of the AVS lobster inside.",
+    "dimensions": "256x256x178",
+    "width": 256,
+    "height": 256,
+    "depth": 178,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 11.1,
+    "size_str": "256x256x178 (11.1 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/boston_teapot/boston_teapot_256x256x178_uint8.raw",
+    "filename": "boston_teapot_256x256x178_uint8.raw"
+  },
+  {
+    "id": "mri_woman",
+    "name": "MRI Woman",
+    "description": "MRI scan of a woman's head",
+    "dimensions": "256x256x109",
+    "width": 256,
+    "height": 256,
+    "depth": 109,
+    "data_type": "uint16",
+    "spacing": "1x1x1.5",
+    "size_mb": 13.6,
+    "size_str": "256x256x109 (13.6 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/mri_woman/mri_woman_256x256x109_uint16.raw",
+    "filename": "mri_woman_256x256x109_uint16.raw"
+  },
+  {
+    "id": "aneurism",
+    "name": "Aneurism",
+    "description": "Rotational C-arm x-ray scan of the arteries of the right half of a human head. A contrast agent was injected into the blood and an aneurism is present.",
+    "dimensions": "256x256x256",
+    "width": 256,
+    "height": 256,
+    "depth": 256,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 16.0,
+    "size_str": "256x256x256 (16.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/aneurism/aneurism_256x256x256_uint8.raw",
+    "filename": "aneurism_256x256x256_uint8.raw"
+  },
+  {
+    "id": "bonsai",
+    "name": "Bonsai",
+    "description": "CT scan of a bonsai tree.",
+    "dimensions": "256x256x256",
+    "width": 256,
+    "height": 256,
+    "depth": 256,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 16.0,
+    "size_str": "256x256x256 (16.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/bonsai/bonsai_256x256x256_uint8.raw",
+    "filename": "bonsai_256x256x256_uint8.raw"
+  },
+  {
+    "id": "foot",
+    "name": "Foot",
+    "description": "Rotational C-arm x-ray scan of a human foot. Tissue and bone are present in the dataset.",
+    "dimensions": "256x256x256",
+    "width": 256,
+    "height": 256,
+    "depth": 256,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 16.0,
+    "size_str": "256x256x256 (16.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/foot/foot_256x256x256_uint8.raw",
+    "filename": "foot_256x256x256_uint8.raw"
+  },
+  {
+    "id": "skull",
+    "name": "Skull",
+    "description": "Rotational C-arm x-ray scan of phantom of a human skull.",
+    "dimensions": "256x256x256",
+    "width": 256,
+    "height": 256,
+    "depth": 256,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 16.0,
+    "size_str": "256x256x256 (16.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/skull/skull_256x256x256_uint8.raw",
+    "filename": "skull_256x256x256_uint8.raw"
+  },
+  {
+    "id": "csafe_heptane",
+    "name": "CSAFE Heptane Gas",
+    "description": "A single time step from a computational simulation of a jet of heptane gas undergoing combustion.",
+    "dimensions": "302x302x302",
+    "width": 302,
+    "height": 302,
+    "depth": 302,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 26.3,
+    "size_str": "302x302x302 (26.3 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/csafe_heptane/csafe_heptane_302x302x302_uint8.raw",
+    "filename": "csafe_heptane_302x302x302_uint8.raw"
+  },
+  {
+    "id": "mrt_angio",
+    "name": "Head MRT Angiography",
+    "description": "3T MRT Time-of-Flight Angiography dataset of a human head. The dataset has been resampled into an isotropic voxel grid (hence the peculiar slice size).",
+    "dimensions": "416x512x112",
+    "width": 416,
+    "height": 512,
+    "depth": 112,
+    "data_type": "uint16",
+    "spacing": "0.412x0.412x0.412",
+    "size_mb": 45.5,
+    "size_str": "416x512x112 (45.5 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/mrt_angio/mrt_angio_416x512x112_uint16.raw",
+    "filename": "mrt_angio_416x512x112_uint16.raw"
+  },
+  {
+    "id": "carp",
+    "name": "Carp",
+    "description": "CT scan of a carp fish",
+    "dimensions": "256x256x512",
+    "width": 256,
+    "height": 256,
+    "depth": 512,
+    "data_type": "uint16",
+    "spacing": "0.78125x0.390625x1",
+    "size_mb": 64.0,
+    "size_str": "256x256x512 (64.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/carp/carp_256x256x512_uint16.raw",
+    "filename": "carp_256x256x512_uint16.raw"
+  },
+  {
+    "id": "tacc_turbulence",
+    "name": "Isotropic Turbulence",
+    "description": "The dataset represents a time step from an isotropic turbulence simulation. A single variable, enstrophy, is represented on a Cartesian grid.",
+    "dimensions": "256x256x256",
+    "width": 256,
+    "height": 256,
+    "depth": 256,
+    "data_type": "float32",
+    "spacing": "1x1x1",
+    "size_mb": 64.0,
+    "size_str": "256x256x256 (64.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/tacc_turbulence/tacc_turbulence_256x256x256_float32.raw",
+    "filename": "tacc_turbulence_256x256x256_float32.raw"
+  },
+  {
+    "id": "stent",
+    "name": "Stented Abdominal Aorta",
+    "description": "CT Scan of the abdomen and pelvis. The dataset contains also a stent in the abdominal aorta. No contrast agent was used to enhance the blood vessels.",
+    "dimensions": "512x512x174",
+    "width": 512,
+    "height": 512,
+    "depth": 174,
+    "data_type": "uint16",
+    "spacing": "0.8398x0.8398x3.2",
+    "size_mb": 87.0,
+    "size_str": "512x512x174 (87.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/stent/stent_512x512x174_uint16.raw",
+    "filename": "stent_512x512x174_uint16.raw"
+  },
+  {
+    "id": "neocortical_layer_1_axons",
+    "name": "Neocortical Layer 1 Axons",
+    "description": "Axons in layer 1 of the mouse barrel cortex imaged in vivo.",
+    "dimensions": "1464x1033x76",
+    "width": 1464,
+    "height": 1033,
+    "depth": 76,
+    "data_type": "uint8",
+    "spacing": "1x1x3.4",
+    "size_mb": 109.6,
+    "size_str": "1464x1033x76 (109.6 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/neocortical_layer_1_axons/neocortical_layer_1_axons_1464x1033x76_uint8.raw",
+    "filename": "neocortical_layer_1_axons_1464x1033x76_uint8.raw"
+  },
+  {
+    "id": "pancreas",
+    "name": "Pancreas",
+    "description": "First scan. The National Institutes of Health Clinical Center performed 82 abdominal contrast enhanced 3D CT scans (~70 seconds after intravenous contrast injection in portal-venous) from 53 male and 27 female subjects. Seventeen of the subjects are healthy kidney donors scanned prior to nephrectomy. The remaining 65 patients were selected by a radiologist from patients who neither had major abdominal pathologies nor pancreatic cancer lesions. Subjects' ages range from 18 to 76 years with a mean age of 46.8 \u00b1 16.7. The CT scans have resolutions of 512x512 pixels with varying pixel sizes and slice thickness between 1.5 - 2.5 mm, acquired on Philips and Siemens MDCT scanners (120 kVp tube voltage). A medical student manually performed slice-by-slice segmentations of the pancreas as ground-truth and these were verified/modified by an experienced radiologist.",
+    "dimensions": "240x512x512",
+    "width": 240,
+    "height": 512,
+    "depth": 512,
+    "data_type": "int16",
+    "spacing": "1.16x1.0x1.0",
+    "size_mb": 120.0,
+    "size_str": "240x512x512 (120.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/pancreas/pancreas_240x512x512_int16.raw",
+    "filename": "pancreas_240x512x512_int16.raw"
+  },
+  {
+    "id": "duct",
+    "name": "Duct Flow",
+    "description": "A wall-bounded flow in a duct.",
+    "dimensions": "193x194x1000",
+    "width": 193,
+    "height": 194,
+    "depth": 1000,
+    "data_type": "float32",
+    "spacing": "1x1x1",
+    "size_mb": 142.8,
+    "size_str": "193x194x1000 (142.8 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/duct/duct_193x194x1000_float32.raw",
+    "filename": "duct_193x194x1000_float32.raw"
+  },
+  {
+    "id": "bunny",
+    "name": "Bunny",
+    "description": "A CT scan of the Stanford Bunny. The greyscale units are Hounsfield units, denoting electron-density of the subject; the scale units are in millimeters. The scan was completed 28 January 2000.",
+    "dimensions": "512x512x361",
+    "width": 512,
+    "height": 512,
+    "depth": 361,
+    "data_type": "uint16",
+    "spacing": "0.337891x0.337891x0.5",
+    "size_mb": 180.5,
+    "size_str": "512x512x361 (180.5 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/bunny/bunny_512x512x361_uint16.raw",
+    "filename": "bunny_512x512x361_uint16.raw"
+  },
+  {
+    "id": "backpack",
+    "name": "Backpack Scan",
+    "description": "CT scan of a backpack filled with items.",
+    "dimensions": "512x512x373",
+    "width": 512,
+    "height": 512,
+    "depth": 373,
+    "data_type": "uint16",
+    "spacing": "0.9766x0.9766x1.25",
+    "size_mb": 186.5,
+    "size_str": "512x512x373 (186.5 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/backpack/backpack_512x512x373_uint16.raw",
+    "filename": "backpack_512x512x373_uint16.raw"
+  },
+  {
+    "id": "present",
+    "name": "Christmas Present",
+    "description": "An industrial CT scan of a christmas present.",
+    "dimensions": "492x492x442",
+    "width": 492,
+    "height": 492,
+    "depth": 442,
+    "data_type": "uint16",
+    "spacing": "1x1x1",
+    "size_mb": 204.1,
+    "size_str": "492x492x442 (204.1 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/present/present_492x492x442_uint16.raw",
+    "filename": "present_492x492x442_uint16.raw"
+  },
+  {
+    "id": "prone",
+    "name": "Colon Prone",
+    "description": "CT scan of abdomen in prone orientation (back faces ceiling, belly faces table).",
+    "dimensions": "512x512x463",
+    "width": 512,
+    "height": 512,
+    "depth": 463,
+    "data_type": "uint16",
+    "spacing": "0.625x0.625x1.0",
+    "size_mb": 231.5,
+    "size_str": "512x512x463 (231.5 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/prone/prone_512x512x463_uint16.raw",
+    "filename": "prone_512x512x463_uint16.raw"
+  },
+  {
+    "id": "christmas_tree",
+    "name": "Christmas Tree",
+    "description": "The Christmas tree model was scanned with a Siemens Somatom Plus 4 Volume Zoom Multislice-CT scanner at the general hospital in Vienna.",
+    "dimensions": "512x499x512",
+    "width": 512,
+    "height": 499,
+    "depth": 512,
+    "data_type": "uint16",
+    "spacing": "1x1x1",
+    "size_mb": 249.5,
+    "size_str": "512x499x512 (249.5 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/christmas_tree/christmas_tree_512x499x512_uint16.raw",
+    "filename": "christmas_tree_512x499x512_uint16.raw"
+  },
+  {
+    "id": "vertebra",
+    "name": "Head Aneurism",
+    "description": "Rotational angiography scan of a head with an aneurysm. Only contrasted blood vessels are visible.",
+    "dimensions": "512x512x512",
+    "width": 512,
+    "height": 512,
+    "depth": 512,
+    "data_type": "uint16",
+    "spacing": "0.1953x0.1953x0.1953",
+    "size_mb": 256.0,
+    "size_str": "512x512x512 (256.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/vertebra/vertebra_512x512x512_uint16.raw",
+    "filename": "vertebra_512x512x512_uint16.raw"
+  },
+  {
+    "id": "zeiss",
+    "name": "Zeiss",
+    "description": "Car part reconstructed from projections.",
+    "dimensions": "680x680x680",
+    "width": 680,
+    "height": 680,
+    "depth": 680,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 299.9,
+    "size_str": "680x680x680 (299.9 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/zeiss/zeiss_680x680x680_uint8.raw",
+    "filename": "zeiss_680x680x680_uint8.raw"
+  },
+  {
+    "id": "marmoset_neurons",
+    "name": "Neurons in Marmoset Visual Cortex",
+    "description": "Pyramidal neurons in the marmoset primary visual cortex (V1) labeled with green fluorescent protein (GFP) after injection of a psuedotyped G-deleted rabies virus in area V2. The tissue was cleared using the Sca/e technique and imaged on a Olympus 2-photon microscope at 20x magnification.",
+    "dimensions": "1024x1024x314",
+    "width": 1024,
+    "height": 1024,
+    "depth": 314,
+    "data_type": "uint8",
+    "spacing": "0.497x0.497x1.5",
+    "size_mb": 314.0,
+    "size_str": "1024x1024x314 (314.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/marmoset_neurons/marmoset_neurons_1024x1024x314_uint8.raw",
+    "filename": "marmoset_neurons_1024x1024x314_uint8.raw"
+  },
+  {
+    "id": "magnetic_reconnection",
+    "name": "Magnetic Reconnection Simulation",
+    "description": "A single time step from a computational simulation of magnetic reconnection.",
+    "dimensions": "512x512x512",
+    "width": 512,
+    "height": 512,
+    "depth": 512,
+    "data_type": "float32",
+    "spacing": "1x1x1",
+    "size_mb": 512.0,
+    "size_str": "512x512x512 (512.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/magnetic_reconnection/magnetic_reconnection_512x512x512_float32.raw",
+    "filename": "magnetic_reconnection_512x512x512_float32.raw"
+  },
+  {
+    "id": "stag_beetle",
+    "name": "Stag Beetle",
+    "description": "The stag beetle from Georg Glaeser, Vienna University of Applied Arts, Austria, was scanned with an industrial CT by Johannes Kastner, Wels College of Engineering, Austria, and Meister Eduard Gr\u00f6ller, Vienna University of Technology, Austria.",
+    "dimensions": "832x832x494",
+    "width": 832,
+    "height": 832,
+    "depth": 494,
+    "data_type": "uint16",
+    "spacing": "1x1x1",
+    "size_mb": 652.2,
+    "size_str": "832x832x494 (652.2 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/stag_beetle/stag_beetle_832x832x494_uint16.raw",
+    "filename": "stag_beetle_832x832x494_uint16.raw"
+  },
+  {
+    "id": "hcci_oh",
+    "name": "Homogeneous Charge Compression Ignition OH",
+    "description": "The first timestep of direct numerical simulation of an autoignition phenomena in stratified dimethyl-ether/air turbulent mixtures.",
+    "dimensions": "560x560x560",
+    "width": 560,
+    "height": 560,
+    "depth": 560,
+    "data_type": "float32",
+    "spacing": "1x1x1",
+    "size_mb": 669.9,
+    "size_str": "560x560x560 (669.9 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/hcci_oh/hcci_oh_560x560x560_float32.raw",
+    "filename": "hcci_oh_560x560x560_float32.raw"
+  },
+  {
+    "id": "kingsnake",
+    "name": "Kingsnake",
+    "description": "Scan of a Lampropeltis getula egg (captive bred by Travis LaDuc; laid on 7 July 2003, growth terminated on 29 August 2003, 54 days after oviposition) for Dr. Timothy Rowe of the Department of Geological Sciences, The University of Texas at Austin.",
+    "dimensions": "1024x1024x795",
+    "width": 1024,
+    "height": 1024,
+    "depth": 795,
+    "data_type": "uint8",
+    "spacing": "0.03174x0.03174x0.0688",
+    "size_mb": 795.0,
+    "size_str": "1024x1024x795 (795.0 MB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/kingsnake/kingsnake_1024x1024x795_uint8.raw",
+    "filename": "kingsnake_1024x1024x795_uint8.raw"
+  },
+  {
+    "id": "pawpawsaurus",
+    "name": "Pawpawsaurus Campbelli",
+    "description": "This specimen, the holotype, was collected from the Paw Paw Formation, SMU Loc. No. 263, Tarrant County, Texas. The specimen was scanned along the coronal axis for a total of 1088 slices. Voxel size is 0.2275 mm.",
+    "dimensions": "958x646x1088",
+    "width": 958,
+    "height": 646,
+    "depth": 1088,
+    "data_type": "uint16",
+    "spacing": "0.2275x0.2275x0.2275",
+    "size_mb": 1331.2,
+    "size_str": "958x646x1088 (1.3 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/pawpawsaurus/pawpawsaurus_958x646x1088_uint16.raw",
+    "filename": "pawpawsaurus_958x646x1088_uint16.raw"
+  },
+  {
+    "id": "spathorhynchus",
+    "name": "Spathorhynchus Fossorium",
+    "description": "This specimen, the holotype, was collected from the Middle Eocene Green River Formation of Sweetwater County, Wyoming on 27 July 1967 by Frank L. Pearce. The specimen was scanned along the coronal axis for a total of 750 slices. Each 1024x1024 pixel slice is 0.047 mm thick, with an interslice spacing of 0.047 mm and a field of reconstruction of 22 mm.",
+    "dimensions": "1024x1024x750",
+    "width": 1024,
+    "height": 1024,
+    "depth": 750,
+    "data_type": "uint16",
+    "spacing": "0.0215x0.0215x0.047",
+    "size_mb": 1536.0,
+    "size_str": "1024x1024x750 (1.5 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/spathorhynchus/spathorhynchus_1024x1024x750_uint16.raw",
+    "filename": "spathorhynchus_1024x1024x750_uint16.raw"
+  },
+  {
+    "id": "chameleon",
+    "name": "Chameleon",
+    "description": "CT scan of a chameleon.",
+    "dimensions": "1024x1024x1080",
+    "width": 1024,
+    "height": 1024,
+    "depth": 1080,
+    "data_type": "uint16",
+    "spacing": "0.09228515625x0.09228515625x0.105",
+    "size_mb": 2150.4,
+    "size_str": "1024x1024x1080 (2.1 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/chameleon/chameleon_1024x1024x1080_uint16.raw",
+    "filename": "chameleon_1024x1024x1080_uint16.raw"
+  },
+  {
+    "id": "beechnut",
+    "name": "Beechnut",
+    "description": "A microCT scan of a dried beechnut.",
+    "dimensions": "1024x1024x1546",
+    "width": 1024,
+    "height": 1024,
+    "depth": 1546,
+    "data_type": "uint16",
+    "spacing": "2e-05x2e-05x2e-05",
+    "size_mb": 3072.0,
+    "size_str": "1024x1024x1546 (3.0 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/beechnut/beechnut_1024x1024x1546_uint16.raw",
+    "filename": "beechnut_1024x1024x1546_uint16.raw"
+  },
+  {
+    "id": "miranda",
+    "name": "Rayleigh-Taylor Instability",
+    "description": "A time step of a density field in a simulation of the mixing transition in Rayleigh-Taylor instability.",
+    "dimensions": "1024x1024x1024",
+    "width": 1024,
+    "height": 1024,
+    "depth": 1024,
+    "data_type": "float32",
+    "spacing": "1x1x1",
+    "size_mb": 4096.0,
+    "size_str": "1024x1024x1024 (4.0 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/miranda/miranda_1024x1024x1024_float32.raw",
+    "filename": "miranda_1024x1024x1024_float32.raw"
+  },
+  {
+    "id": "jicf_q",
+    "name": "Jet In Crossflow",
+    "description": "Q-criterion of a jet in crossflow created by a direct numerical simulation.",
+    "dimensions": "1408x1080x1100",
+    "width": 1408,
+    "height": 1080,
+    "depth": 1100,
+    "data_type": "float32",
+    "spacing": "1x1x1",
+    "size_mb": 6348.8,
+    "size_str": "1408x1080x1100 (6.2 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/jicf_q/jicf_q_1408x1080x1100_float32.raw",
+    "filename": "jicf_q_1408x1080x1100_float32.raw"
+  },
+  {
+    "id": "synthetic_truss_with_five_defects",
+    "name": "Synthetic Truss Scan",
+    "description": "A simulated CT scan of a 8x8x8 octet truss with five defects on the front side of the object. The defects are bent strut, broken strut, missing strut, dross, and thin strut.",
+    "dimensions": "1200x1200x1200",
+    "width": 1200,
+    "height": 1200,
+    "depth": 1200,
+    "data_type": "float32",
+    "spacing": "1x1x1",
+    "size_mb": 6553.6,
+    "size_str": "1200x1200x1200 (6.4 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/synthetic_truss_with_five_defects/synthetic_truss_with_five_defects_1200x1200x1200_float32.raw",
+    "filename": "synthetic_truss_with_five_defects_1200x1200x1200_float32.raw"
+  },
+  {
+    "id": "richtmyer_meshkov",
+    "name": "Richtmyer-Meshkov Instability",
+    "description": "Entropy field (timestep 160) of Richtmyer-Meshkov instability simulation.",
+    "dimensions": "2048x2048x1920",
+    "width": 2048,
+    "height": 2048,
+    "depth": 1920,
+    "data_type": "uint8",
+    "spacing": "1x1x1",
+    "size_mb": 7680.0,
+    "size_str": "2048x2048x1920 (7.5 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/richtmyer_meshkov/richtmyer_meshkov_2048x2048x1920_uint8.raw",
+    "filename": "richtmyer_meshkov_2048x2048x1920_uint8.raw"
+  },
+  {
+    "id": "3d_neurons_15_sept_2016",
+    "name": "3DNeurons15Sept2016",
+    "description": "The neurons are macaque visual cortical neurons labeled with TdTomato fluorescent proteins.",
+    "dimensions": "2048x2048x1718",
+    "width": 2048,
+    "height": 2048,
+    "depth": 1718,
+    "data_type": "uint16",
+    "spacing": "0.267345x0.267345x0.5",
+    "size_mb": 13721.6,
+    "size_str": "2048x2048x1718 (13.4 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/3d_neurons_15_sept_2016/3d_neurons_15_sept_2016_2048x2048x1718_uint16.raw",
+    "filename": "3d_neurons_15_sept_2016_2048x2048x1718_uint16.raw"
+  },
+  {
+    "id": "woodbranch",
+    "name": "Wood Branch",
+    "description": "A microCT scan of dried wood branch (hazelnut).",
+    "dimensions": "2048x2048x2048",
+    "width": 2048,
+    "height": 2048,
+    "depth": 2048,
+    "data_type": "uint16",
+    "spacing": "1.8e-05x1.8e-05x1.8e-05",
+    "size_mb": 16384.0,
+    "size_str": "2048x2048x2048 (16.0 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/woodbranch/woodbranch_2048x2048x2048_uint16.raw",
+    "filename": "woodbranch_2048x2048x2048_uint16.raw"
+  },
+  {
+    "id": "pig_heart",
+    "name": "Cardiac Volume (Porcine)",
+    "description": "Volumes were obtained by way of computed tomography (CT) imaging on excised, postmortem porcine hearts. Alginate curing agents were injected into ventricles to provide rigidity and radiopaque agents were injected into the coronary arteries to distinguish microvasculature from the rest of the tissue.",
+    "dimensions": "2048x2048x2612",
+    "width": 2048,
+    "height": 2048,
+    "depth": 2612,
+    "data_type": "int16",
+    "spacing": "0.03557x0.03557x0.03557",
+    "size_mb": 20889.6,
+    "size_str": "2048x2048x2612 (20.4 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/pig_heart/pig_heart_2048x2048x2612_int16.raw",
+    "filename": "pig_heart_2048x2048x2612_int16.raw"
+  },
+  {
+    "id": "isotropic_pressure",
+    "name": "Forced Isotropic Turbulence",
+    "description": "Pressure field of a direct numerical simulation of forced isotropic turbulence.",
+    "dimensions": "4096x4096x4096",
+    "width": 4096,
+    "height": 4096,
+    "depth": 4096,
+    "data_type": "float32",
+    "spacing": "1x1x1",
+    "size_mb": 262144.0,
+    "size_str": "4096x4096x4096 (256.0 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/isotropic_pressure/isotropic_pressure_4096x4096x4096_float32.raw",
+    "filename": "isotropic_pressure_4096x4096x4096_float32.raw"
+  },
+  {
+    "id": "rotstrat_temperature",
+    "name": "Rotating Stratified Turbulence",
+    "description": "Temperature field of a direct numerical simulation of rotating stratified turbulence.",
+    "dimensions": "4096x4096x4096",
+    "width": 4096,
+    "height": 4096,
+    "depth": 4096,
+    "data_type": "float32",
+    "spacing": "1x1x1",
+    "size_mb": 262144.0,
+    "size_str": "4096x4096x4096 (256.0 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/rotstrat_temperature/rotstrat_temperature_4096x4096x4096_float32.raw",
+    "filename": "rotstrat_temperature_4096x4096x4096_float32.raw"
+  },
+  {
+    "id": "dns",
+    "name": "Turbulent Channel Flow",
+    "description": "A pressure field of a direct numerical simulation of fully developed flow at different Reynolds numbers in a plane channel have been performed with POONGBACK code which uses the spectral numerical method of Kim, Moin and Moser (J. Fluid Mech. vol 177, page 133).",
+    "dimensions": "10240x7680x1536",
+    "width": 10240,
+    "height": 7680,
+    "depth": 1536,
+    "data_type": "float64",
+    "spacing": "1x1x1",
+    "size_mb": 921600.0,
+    "size_str": "10240x7680x1536 (900.0 GB)",
+    "download_url": "http://klacansky.com/open-scivis-datasets/dns/dns_10240x7680x1536_float64.raw",
+    "filename": "dns_10240x7680x1536_float64.raw"
+  }
+]

datasets_list.md

@@ -0,0 +1,66 @@
+# Open SciVis Datasets Information
+
+## Datasets under 512MB (will be downloaded)
+
+| Name | Description | Dimensions | Data Type | Size | Spacing |
+|------|-------------|------------|-----------|------|----------|
+| Fuel | Simulation of fuel injection into a combustion chamber. The higher the density value, the less presence of air. | 64x64x64 | uint8 | 64x64x64 (256.0 kB) | 1x1x1 |
+| Marschner-Lobb | High frequencies where 99% of the sinusoids are right below the Nyquist frequency. | 41x41x41 | uint8 | 41x41x41 (67.3 kB) | 1x1x1 |
+| Neghip | Simulation of the spatial probability distribution of the electrons in a high potential protein molecule. | 64x64x64 | uint8 | 64x64x64 (256.0 kB) | 1x1x1 |
+| Nucleon | Simulation of the two-body distribution probability of a nucleon in the atomic nucleus 16O if a second nucleon is known to be positioned at r'=(2 fm,0,0). | 41x41x41 | uint8 | 41x41x41 (67.3 kB) | 1x1x1 |
+| Silicium | Simulation of a silicium grid. | 98x34x34 | uint8 | 98x34x34 (110.6 kB) | 1x1x1 |
+| Tooth |  | 103x94x161 | uint8 | 103x94x161 (1.5 MB) | 1x1x1 |
+| Blunt Fin |  | 256x128x64 | uint8 | 256x128x64 (2.0 MB) | 1x0.75x1 |
+| Hydrogen Atom | Simulation of the spatial probability distribution of the electron in an hydrogen atom, residing in a strong magnetic field. | 128x128x128 | uint8 | 128x128x128 (2.0 MB) | 1x1x1 |
+| Shockwave | Simulation of an unsteady interaction of a planar shockwave with a randomly-perturbed contact discontinuity. | 64x64x512 | uint8 | 64x64x512 (2.0 MB) | 1x1x1 |
+| Frog | MRI scan of a frog as part of the Whole Frog Project. | 256x256x44 | uint8 | 256x256x44 (2.8 MB) | 0.5x0.5x1 |
+| Lobster | CT scan of a lobster contained in a block of resin. | 301x324x56 | uint8 | 301x324x56 (5.2 MB) | 1x1x1.4 |
+| Head MRI CISS | 1.5T MRT 3D CISS dataset of a human head that highlights the CSF (Cerebro-Spinal-Fluid) filled cavities of the head. | 256x256x124 | uint8 | 256x256x124 (7.8 MB) | 0.9x0.9x0.9 |
+| Engine | CT scan of two cylinders of an engine block. | 256x256x128 | uint8 | 256x256x128 (8.0 MB) | 1x1x1 |
+| Head (Visible Male) | Male head scan | 128x256x256 | uint8 | 128x256x256 (8.0 MB) | 1.57774x0.995861x1.00797 |
+| Leg of Statue | CT scan of a leg of a bronze statue. | 341x341x93 | uint8 | 341x341x93 (10.3 MB) | 1x1x4 |
+| Boston Teapot | CT scan of the SIGGRAPH 1989 teapot with a small version of the AVS lobster inside. | 256x256x178 | uint8 | 256x256x178 (11.1 MB) | 1x1x1 |
+| MRI Woman | MRI scan of a woman's head | 256x256x109 | uint16 | 256x256x109 (13.6 MB) | 1x1x1.5 |
+| Aneurism | Rotational C-arm x-ray scan of the arteries of the right half of a human head. A contrast agent was injected into the blood and an aneurism is present. | 256x256x256 | uint8 | 256x256x256 (16.0 MB) | 1x1x1 |
+| Bonsai | CT scan of a bonsai tree. | 256x256x256 | uint8 | 256x256x256 (16.0 MB) | 1x1x1 |
+| Foot | Rotational C-arm x-ray scan of a human foot. Tissue and bone are present in the dataset. | 256x256x256 | uint8 | 256x256x256 (16.0 MB) | 1x1x1 |
+| Skull | Rotational C-arm x-ray scan of phantom of a human skull. | 256x256x256 | uint8 | 256x256x256 (16.0 MB) | 1x1x1 |
+| CSAFE Heptane Gas | A single time step from a computational simulation of a jet of heptane gas undergoing combustion. | 302x302x302 | uint8 | 302x302x302 (26.3 MB) | 1x1x1 |
+| Head MRT Angiography | 3T MRT Time-of-Flight Angiography dataset of a human head. The dataset has been resampled into an isotropic voxel grid (hence the peculiar slice size). | 416x512x112 | uint16 | 416x512x112 (45.5 MB) | 0.412x0.412x0.412 |
+| Carp | CT scan of a carp fish | 256x256x512 | uint16 | 256x256x512 (64.0 MB) | 0.78125x0.390625x1 |
+| Isotropic Turbulence | The dataset represents a time step from an isotropic turbulence simulation. A single variable, enstrophy, is represented on a Cartesian grid. | 256x256x256 | float32 | 256x256x256 (64.0 MB) | 1x1x1 |
+| Stented Abdominal Aorta | CT Scan of the abdomen and pelvis. The dataset contains also a stent in the abdominal aorta. No contrast agent was used to enhance the blood vessels. | 512x512x174 | uint16 | 512x512x174 (87.0 MB) | 0.8398x0.8398x3.2 |
+| Neocortical Layer 1 Axons | Axons in layer 1 of the mouse barrel cortex imaged in vivo. | 1464x1033x76 | uint8 | 1464x1033x76 (109.6 MB) | 1x1x3.4 |
+| Pancreas | First scan. The National Institutes of Health Clinical Center performed 82 abdominal contrast enhanced 3D CT scans (~70 seconds after intravenous contrast injection in portal-venous) from 53 male and 27 female subjects. Seventeen of the subjects are healthy kidney donors scanned prior to nephrectomy. The remaining 65 patients were selected by a radiologist from patients who neither had major abdominal pathologies nor pancreatic cancer lesions. Subjects' ages range from 18 to 76 years with a mean age of 46.8 ± 16.7. The CT scans have resolutions of 512x512 pixels with varying pixel sizes and slice thickness between 1.5 - 2.5 mm, acquired on Philips and Siemens MDCT scanners (120 kVp tube voltage). A medical student manually performed slice-by-slice segmentations of the pancreas as ground-truth and these were verified/modified by an experienced radiologist. | 240x512x512 | int16 | 240x512x512 (120.0 MB) | 1.16x1.0x1.0 |
+| Duct Flow | A wall-bounded flow in a duct. | 193x194x1000 | float32 | 193x194x1000 (142.8 MB) | 1x1x1 |
+| Bunny | A CT scan of the Stanford Bunny. The greyscale units are Hounsfield units, denoting electron-density of the subject; the scale units are in millimeters. The scan was completed 28 January 2000. | 512x512x361 | uint16 | 512x512x361 (180.5 MB) | 0.337891x0.337891x0.5 |
+| Backpack Scan | CT scan of a backpack filled with items. | 512x512x373 | uint16 | 512x512x373 (186.5 MB) | 0.9766x0.9766x1.25 |
+| Christmas Present | An industrial CT scan of a christmas present. | 492x492x442 | uint16 | 492x492x442 (204.1 MB) | 1x1x1 |
+| Colon Prone | CT scan of abdomen in prone orientation (back faces ceiling, belly faces table). | 512x512x463 | uint16 | 512x512x463 (231.5 MB) | 0.625x0.625x1.0 |
+| Christmas Tree | The Christmas tree model was scanned with a Siemens Somatom Plus 4 Volume Zoom Multislice-CT scanner at the general hospital in Vienna. | 512x499x512 | uint16 | 512x499x512 (249.5 MB) | 1x1x1 |
+| Head Aneurism | Rotational angiography scan of a head with an aneurysm. Only contrasted blood vessels are visible. | 512x512x512 | uint16 | 512x512x512 (256.0 MB) | 0.1953x0.1953x0.1953 |
+| Zeiss | Car part reconstructed from projections. | 680x680x680 | uint8 | 680x680x680 (299.9 MB) | 1x1x1 |
+| Neurons in Marmoset Visual Cortex | Pyramidal neurons in the marmoset primary visual cortex (V1) labeled with green fluorescent protein (GFP) after injection of a psuedotyped G-deleted rabies virus in area V2. The tissue was cleared using the Sca/e technique and imaged on a Olympus 2-photon microscope at 20x magnification. | 1024x1024x314 | uint8 | 1024x1024x314 (314.0 MB) | 0.497x0.497x1.5 |
+
+## Datasets over 512MB (not downloaded)
+
+| Name | Description | Dimensions | Data Type | Size | Spacing |
+|------|-------------|------------|-----------|------|----------|
+| Magnetic Reconnection Simulation | A single time step from a computational simulation of magnetic reconnection. | 512x512x512 | float32 | 512x512x512 (512.0 MB) | 1x1x1 |
+| Stag Beetle | The stag beetle from Georg Glaeser, Vienna University of Applied Arts, Austria, was scanned with an industrial CT by Johannes Kastner, Wels College of Engineering, Austria, and Meister Eduard Gröller, Vienna University of Technology, Austria. | 832x832x494 | uint16 | 832x832x494 (652.2 MB) | 1x1x1 |
+| Homogeneous Charge Compression Ignition OH | The first timestep of direct numerical simulation of an autoignition phenomena in stratified dimethyl-ether/air turbulent mixtures. | 560x560x560 | float32 | 560x560x560 (669.9 MB) | 1x1x1 |
+| Kingsnake | Scan of a Lampropeltis getula egg (captive bred by Travis LaDuc; laid on 7 July 2003, growth terminated on 29 August 2003, 54 days after oviposition) for Dr. Timothy Rowe of the Department of Geological Sciences, The University of Texas at Austin. | 1024x1024x795 | uint8 | 1024x1024x795 (795.0 MB) | 0.03174x0.03174x0.0688 |
+| Pawpawsaurus Campbelli | This specimen, the holotype, was collected from the Paw Paw Formation, SMU Loc. No. 263, Tarrant County, Texas. The specimen was scanned along the coronal axis for a total of 1088 slices. Voxel size is 0.2275 mm. | 958x646x1088 | uint16 | 958x646x1088 (1.3 GB) | 0.2275x0.2275x0.2275 |
+| Spathorhynchus Fossorium | This specimen, the holotype, was collected from the Middle Eocene Green River Formation of Sweetwater County, Wyoming on 27 July 1967 by Frank L. Pearce. The specimen was scanned along the coronal axis for a total of 750 slices. Each 1024x1024 pixel slice is 0.047 mm thick, with an interslice spacing of 0.047 mm and a field of reconstruction of 22 mm. | 1024x1024x750 | uint16 | 1024x1024x750 (1.5 GB) | 0.0215x0.0215x0.047 |
+| Chameleon | CT scan of a chameleon. | 1024x1024x1080 | uint16 | 1024x1024x1080 (2.1 GB) | 0.09228515625x0.09228515625x0.105 |
+| Beechnut | A microCT scan of a dried beechnut. | 1024x1024x1546 | uint16 | 1024x1024x1546 (3.0 GB) | 2e-05x2e-05x2e-05 |
+| Rayleigh-Taylor Instability | A time step of a density field in a simulation of the mixing transition in Rayleigh-Taylor instability. | 1024x1024x1024 | float32 | 1024x1024x1024 (4.0 GB) | 1x1x1 |
+| Jet In Crossflow | Q-criterion of a jet in crossflow created by a direct numerical simulation. | 1408x1080x1100 | float32 | 1408x1080x1100 (6.2 GB) | 1x1x1 |
+| Synthetic Truss Scan | A simulated CT scan of a 8x8x8 octet truss with five defects on the front side of the object. The defects are bent strut, broken strut, missing strut, dross, and thin strut. | 1200x1200x1200 | float32 | 1200x1200x1200 (6.4 GB) | 1x1x1 |
+| Richtmyer-Meshkov Instability | Entropy field (timestep 160) of Richtmyer-Meshkov instability simulation. | 2048x2048x1920 | uint8 | 2048x2048x1920 (7.5 GB) | 1x1x1 |
+| 3DNeurons15Sept2016 | The neurons are macaque visual cortical neurons labeled with TdTomato fluorescent proteins. | 2048x2048x1718 | uint16 | 2048x2048x1718 (13.4 GB) | 0.267345x0.267345x0.5 |
+| Wood Branch | A microCT scan of dried wood branch (hazelnut). | 2048x2048x2048 | uint16 | 2048x2048x2048 (16.0 GB) | 1.8e-05x1.8e-05x1.8e-05 |
+| Cardiac Volume (Porcine) | Volumes were obtained by way of computed tomography (CT) imaging on excised, postmortem porcine hearts. Alginate curing agents were injected into ventricles to provide rigidity and radiopaque agents were injected into the coronary arteries to distinguish microvasculature from the rest of the tissue. | 2048x2048x2612 | int16 | 2048x2048x2612 (20.4 GB) | 0.03557x0.03557x0.03557 |
+| Forced Isotropic Turbulence | Pressure field of a direct numerical simulation of forced isotropic turbulence. | 4096x4096x4096 | float32 | 4096x4096x4096 (256.0 GB) | 1x1x1 |
+| Rotating Stratified Turbulence | Temperature field of a direct numerical simulation of rotating stratified turbulence. | 4096x4096x4096 | float32 | 4096x4096x4096 (256.0 GB) | 1x1x1 |
+| Turbulent Channel Flow | A pressure field of a direct numerical simulation of fully developed flow at different Reynolds numbers in a plane channel have been performed with POONGBACK code which uses the spectral numerical method of Kim, Moin and Moser (J. Fluid Mech. vol 177, page 133). | 10240x7680x1536 | float64 | 10240x7680x1536 (900.0 GB) | 1x1x1 |

download_and_organize.py

@@ -0,0 +1,91 @@
+import json
+import os
+import subprocess
+import time
+import zipfile
+
+# Load dataset information
+with open('datasets_info.json', 'r') as f:
+    datasets = json.load(f)
+
+# Filter datasets under 512MB, excluding existing ones
+existing_datasets = []
+datasets_to_download = [d for d in datasets if d['size_mb'] < 512 and d['id'] not in existing_datasets]
+
+print(f"Will download and organize {len(datasets_to_download)} datasets")
+
+# Process each dataset
+for i, dataset in enumerate(datasets_to_download):
+    dataset_id = dataset['id']
+    print(f"\n[{i+1}/{len(datasets_to_download)}] Processing {dataset_id}...")
+    
+    # Create directory structure
+    os.makedirs(f"sci_volume_data/{dataset_id}/data", exist_ok=True)
+    
+    # Download the dataset
+    url = dataset['download_url']
+    output_file = f"sci_volume_data/{dataset_id}/data/{dataset['filename']}"
+    
+    if not os.path.exists(output_file):
+        print(f"  Downloading {dataset['filename']} ({dataset['size_str']})...")
+        result = subprocess.run(['curl', '-o', output_file, url], capture_output=True)
+        if result.returncode != 0:
+            print(f"  ERROR downloading {dataset_id}")
+            continue
+        time.sleep(0.5)  # Be nice to the server
+    else:
+        print(f"  File already exists, skipping download")
+    
+    # Create metadata file
+    metadata_file = f"sci_volume_data/{dataset_id}/data/{dataset_id}.txt"
+    with open(metadata_file, 'w') as f:
+        f.write(f"{dataset['name']}\n")
+        f.write(f"Description: {dataset['description']}\n")
+        f.write(f"Data Type: {dataset['data_type']}\n")
+        f.write(f"Data Byte Order: little Endian\n")
+        f.write(f"Data Spacing: {dataset['spacing']}\n")
+        f.write(f"Data Extent: {dataset['dimensions']}\n")
+    
+    print(f"  Created directory structure and metadata")
+
+print(f"\nCompleted processing {len(datasets_to_download)} datasets")
+
+# Download BBBC012 dataset for napari_mcp_evals
+print(f"\nDownloading BBBC012 dataset for napari_mcp_evals...")
+
+# Create the napari_mcp_evals/data directory
+napari_data_dir = "napari_mcp_evals/data"
+os.makedirs(napari_data_dir, exist_ok=True)
+
+# Download the BBBC012 dataset
+bbbc_url = "https://data.broadinstitute.org/bbbc/BBBC012/BBBC012_v1_images.zip"
+bbbc_zip_file = os.path.join(napari_data_dir, "BBBC012_v1_images.zip")
+
+if not os.path.exists(os.path.join(napari_data_dir, "BBBC012_v1_images")):
+    print(f"  Downloading BBBC012_v1_images.zip...")
+    result = subprocess.run(['curl', '-o', bbbc_zip_file, bbbc_url], capture_output=True)
+    
+    if result.returncode == 0:
+        print(f"  Download completed successfully")
+        
+        # Unzip the file
+        print(f"  Extracting BBBC012_v1_images.zip...")
+        try:
+            with zipfile.ZipFile(bbbc_zip_file, 'r') as zip_ref:
+                zip_ref.extractall(napari_data_dir)
+            print(f"  Extraction completed")
+            
+            # Delete the zip file
+            os.remove(bbbc_zip_file)
+            print(f"  Cleaned up zip file")
+            
+        except zipfile.BadZipFile:
+            print(f"  ERROR: Downloaded file is not a valid zip archive")
+        except Exception as e:
+            print(f"  ERROR during extraction: {e}")
+    else:
+        print(f"  ERROR downloading BBBC012 dataset")
+else:
+    print(f"  BBBC012 dataset already exists, skipping download")
+
+print(f"\nAll processing completed!")

main/bonsai/GS/bonsai_gs.pvsm

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa03b91b2e71ffbfef15c26dce85b93887ee4ffd1461759c11a46f640f604ac0
+size 235372

main/bonsai/GS/bonsai_gs.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env pvpython
+
+import os
+from paraview.simple import *
+
+def create_bonsai_visualization():
+    # — Paths & setup —
+    base      = os.path.abspath(os.path.join(__file__, '..', '..'))
+    raw_file  = os.path.join(base, 'data', 'bonsai_256x256x256_uint8.raw')
+    state_dir = os.path.join(base, 'results', 'pvpython_state')
+    state     = os.path.join(state_dir, 'bonsai.pvsm')
+    os.makedirs(state_dir, exist_ok=True)
+    if not os.path.isfile(raw_file):
+        raise FileNotFoundError(f"Missing raw: {raw_file}")
+
+    # — 1) Load the RAW image —
+    reader = ImageReader(FileNames=[raw_file])
+    reader.DataScalarType     = 'unsigned char'
+    reader.DataByteOrder      = 'LittleEndian'
+    reader.DataExtent         = [0, 255, 0, 255, 0, 255]
+    reader.DataSpacing        = [1.0, 1.0, 1.0]
+    reader.FileDimensionality = 3
+    reader.UpdatePipeline()
+
+    # — 2) Volume render setup —
+    view = GetActiveViewOrCreate('RenderView')
+    view.BackgroundColorMode = 'Single Color'
+    view.Background          = [1, 1, 1]
+
+    disp = Show(reader, view)
+    disp.SetRepresentationType('Volume')
+    disp.ColorArrayName = ['POINTS', 'ImageFile']
+    view.ResetCamera()
+
+    # — 3) Transfer functions from extracted GS state —
+    ctf = GetColorTransferFunction('ImageFile')
+    ctf.ColorSpace = 'RGB'
+    ctf.NumberOfTableValues = 1024
+    ctf.RGBPoints = [
+        0.000,     0.780, 0.522, 0.000,
+        37.564,    0.847, 0.565, 0.000,
+        61.402,    0.796, 0.757, 0.722,
+        88.853,    0.753, 0.753, 0.753,
+        118.470,   0.804, 0.737, 0.694,
+        129.306,   0.686, 0.357, 0.047,
+        156.756,   0.678, 0.345, 0.024,
+        239.108,   0.667, 0.333, 0.000,
+        255.000,   0.706, 0.016, 0.149
+    ]
+
+    otf = GetOpacityTransferFunction('ImageFile')
+    otf.Points = [
+        0.000,     0.000, 0.5, 0.0,
+        32.507,    0.000, 0.5, 0.0,
+        32.507,    0.360, 0.5, 0.0,
+        39.731,    0.455, 0.5, 0.0,
+        41.176,    0.000, 0.5, 0.0,
+        63.569,    0.000, 0.5, 0.0,
+        63.569,    0.511, 0.5, 0.0,
+        89.575,    0.412, 0.5, 0.0,
+        100.411,   0.000, 0.5, 0.0,
+        163.980,   0.002, 0.5, 0.0,
+        163.980,   0.567, 0.5, 0.0,
+        231.161,   0.649, 0.5, 0.0,
+        241.275,   0.433, 0.5, 0.0,
+        255.000,   1.000, 0.5, 0.0
+    ]
+
+    disp.LookupTable           = ctf
+    disp.ScalarOpacityFunction = otf
+
+    # — 4) Camera & save —
+    cam = view.GetActiveCamera()
+    cam.SetPosition(400, 350, 450)
+    cam.SetFocalPoint(128, 128, 128)
+    cam.SetViewUp(1, 0, 0)
+    view.ResetCamera()
+    cam.Elevation(15)
+    cam.Azimuth(30)
+    cam.Zoom(1.0)
+
+    view.StillRender()
+    SaveState(state)
+    print(f"[✔] Saved gold‑standard PVSM:\n    {state}")
+
+if __name__ == '__main__':
+    create_bonsai_visualization()

main/bonsai/data/bonsai.txt

@@ -0,0 +1,5 @@
+Bonsai (Scalar)
+Data Scalar Type: unsigned char
+Data Byte Order: little Endian
+Data Spacing: 1x1x1
+Data Extent: 256x256x256

main/bonsai/data/bonsai_256x256x256_uint8.raw

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:288d2818cffb50d2e2c9e3b5b0a2520d09b0f5051bb63cca315f3fc7a894af80
+size 16777216

main/bonsai/task_description.txt

@@ -0,0 +1,14 @@
+Task:
+
+Load the bonsai dataset from "bonsai/data/bonsai_256x256x256_uint8.raw", the information about this dataset:
+Bonsai (Scalar)
+Data Scalar Type: unsigned char
+Data Byte Order: little Endian
+Data Spacing: 1x1x1
+Data Extent: 256x256x256
+
+Then visualize it with volume rendering, modify the transfer function and reach the visualization goal as: "A potted tree with brown pot silver branch and golden leaves."
+
+Please think step by step and make sure to fulfill all the visualization goals mentioned above.
+
+Finally, save the paraview state as "bonsai/results/{agent_mode}/bonsai.pvsm"

main/bonsai/visualization_goals.txt

@@ -0,0 +1,7 @@
+1. Overall Visualization Goal: How well does the result achieve the overall goal of showing a potted tree with the specified colors?
+
+2. Brown Pot Visualization: Does the result show the pot portion in brown color?
+
+3. Silver Branch Visualization: Does the result show the branch/trunk portion in silver color?
+
+4. Golden Leaves Visualization: Does the result show the leaves portion in golden color?

main/carp/GS/answers.txt

@@ -0,0 +1,13 @@
+Q1: How many distinct fins are visible in the carp skeleton? List their anatomical names and corresponding counts.
+
+A1: There are 7 distinct fins in total. They are:
+1 Dorsal fin (on the top, mid-back region)
+2 Pectoral fins (one on each side, near the skull)
+2 Pelvic fins (smaller, paired fins on the underside, behind pectorals)
+1 Anal fin (on the underside, near the tail)
+1 Caudal fin (tail fin, fan-shaped)
+
+
+Q2: Estimate the ratio of skull length to overall body length, based on the visualization.
+
+A2: Skull length ≈ 20-22% of overall body length. The ratio of skull length to overall body length is approximately 1:4.5

main/carp/GS/carp_gs.pvsm

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b6b1f8ef7f14cc0939313dece7a3f73725efa29f0b3242827a3205ee27d5d55
+size 234004

main/carp/data/carp.txt

@@ -0,0 +1,5 @@
+Carp (Scalar)
+Data Scalar Type: unsigned short
+Data Byte Order: little Endian
+Data Spacing: 0.78125x0.390625x1
+Data Extent: 256x256x512

main/carp/data/carp_256x256x512_uint16.raw

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0744e565584393e8e2e561af60d128d68426832faade48d70c2a49df23907f38
+size 67108864

main/carp/task_description.txt

@@ -0,0 +1,26 @@
+Task:
+
+Load the carp dataset from "carp/data/carp_256x256x512_uint16.raw", the information about this dataset:
+Carp (Scalar)
+Data Scalar Type: unsigned short
+Data Byte Order: little Endian
+Data Spacing: 0.78125x0.390625x1
+Data Extent: 256x256x512
+
+Instructions:
+
+1. Load the dataset into ParaView.
+
+2. Apply volume rendering to visualize the carp skeleton.
+
+3. Adjust the transfer function to highlight only the bony structures in an X-ray style (suppressing soft tissue).
+
+4. Optimize the viewpoint to display the full skeleton, ensuring the head, spine, and fins are all clearly visible in a single frame.
+
+5. Analyze the visualization and answer the following questions:
+Q1: How many distinct fins are visible in the carp skeleton? List their anatomical names and corresponding counts.
+Q2: Estimate the ratio of skull length to overall body length, based on the visualization.
+
+6. Save your work:
+Save the ParaView state as "carp/results/{agent_mode}/carp.pvsm".
+Save the answers to the analysis questions in plain text as "carp/results/{agent_mode}/answers.txt".

main/carp/visualization_goals.txt

@@ -0,0 +1,7 @@
+1. Bone Isolation: Are the bones clearly visible while soft tissue and background are suppressed? Thin fin rays should be distinguishable without major loss.
+
+2. Viewpoint Selection: Does the chosen viewpoint display the entire carp skeleton (head, spine, ribs, fins, tail) without critical occlusion?
+
+3. X-ray Appearance: Does the visualization resemble an X-ray (monochrome or grayscale, transparent look, consistent lighting)?
+
+4. Correct Data Setup: Was the dataset loaded with proper spacing (0.78125 × 0.390625 × 1.0)? The carp skeleton should appear in its correct proportions without distortion (i.e., the fish shape looks anatomically normal).

main/chameleon/GS/chameleon.pvsm

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f19cabc7c0a6fd449d07b11fd99e45838aeacdc11ce082d010fd522b3151a4d
+size 248359

main/chameleon/data/chameleon.txt

@@ -0,0 +1,5 @@
+chameleon (Scalar)
+Data Scalar Type: float
+Data Byte Order: little Endian
+Data Extent: 256x256x270
+Number of Scalar Components: 1

main/chameleon/data/chameleon_256x256x270_float32.raw

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bff2380bd60471fedc7e67d016387d08073dbe119733944b9cbb8c6196201cc4
+size 70778880

main/chameleon/task_description.txt

@@ -0,0 +1,19 @@
+Task:
+
+Load the chameleon dataset from "chameleon/data/chameleon_256x256x270_float32.raw", the information about this dataset:
+chameleon (Scalar)
+Data Scalar Type: float
+Data Byte Order: little Endian
+Data Extent: 256x256x270
+Number of Scalar Components: 1
+Data loading is very important, make sure you correctly load the dataset according to their features.
+
+Apply the volume rendering to visualize the chameleon dataset
+
+Adjust the transfer function to highlight the bony structures and skin in an X-ray style.
+
+Adjust the camera position and focus on the head part of the chameleon
+
+Please think step by step and make sure to fulfill all the visualization goals mentioned above.
+
+Finally, save the paraview state as "chameleon/results/{agent_mode}/chameleon.pvsm"

main/chameleon/visualization_goals.txt

@@ -0,0 +1,7 @@
+1. Overall Visualization Goal: Does the result present a clean, X-ray–style volume rendering where the chameleon’s bony structures are clearly emphasized and soft tissue is faint but discernible?
+
+2. Data Loading Correctness: Is the RAW volume loaded with the specified metadata (float32, little-endian, 256×256×270, 1 component) so that the histogram looks reasonable and the anatomy is not flipped or distorted?
+
+3. Transfer Function Quality: Does the grayscale transfer function make low-intensity tissue mostly transparent while assigning higher opacity to bones/skin ridges, yielding good depth cues without over-saturation or banding?
+
+4. Camera & Framing: Is the camera positioned and zoomed to focus on the chameleon’s head, keeping it sharply framed (no clipping), with a stable viewpoint that highlights key anatomical details?

main/engine/GS/engine.pvsm

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57d77bac59a328eb14ed6e2eed84a20ad93582462278d4e30301b8a342dc7045
+size 259002

main/engine/data/engine.txt

@@ -0,0 +1,5 @@
+engine (Scalar)
+Data Scalar Type: float
+Data Byte Order: little Endian
+Data Extent: 256x256x128
+Number of Scalar Components: 1

main/engine/data/engine_256x256x128_uint8.raw

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15cf29cdde69cd6c8b5ab9eeef4d1bb21f276056da4111cb5069ebfbe5b6ff90
+size 8388608

main/engine/task_description.txt

@@ -0,0 +1,19 @@
+Task:
+
+Load the vortex dataset from "engine/data/engine_256x256x128_uint8.raw", the information about this dataset:
+engine (Scalar)
+Data Scalar Type: float
+Data Byte Order: little Endian
+Data Extent: 256x256x128
+Number of Scalar Components: 1
+
+Instructions:
+
+1. Load the dataset into ParaView.
+
+2. Apply the volume rendering to visualize the engine dataset
+
+3. Adjust the transfer function, let the outer part more transparent and the inner part more solid. Use light blue for the outer part and orange for the inner part.
+
+4. Save your work:
+Save the ParaView state as "engine/results/{agent_mode}/engine.pvsm".

main/engine/visualization_goals.txt

@@ -0,0 +1,7 @@
+1. Overall Visualization Goal: How well does the result use volume rendering to clearly present the internal and external structures of the engine dataset?
+
+2. Structural Clarity: Does the visualization emphasize depth so that the outer layers do not obscure the inner structures?
+
+3. Transfer Function Transparency: Is the outer region rendered with higher transparency and the inner region more solid, achieving a clear layering effect?
+
+4. Transfer Function Color Mapping: Are colors correctly assigned so that the outer part is light blue and the inner part is orange, enhancing structural contrast?

main/foot/GS/foot_gs.pvsm

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f75a4a23f18a31173f1dbeca7f27cd6c4e384e533c4c3f14f190f5577c394b2
+size 211051

main/foot/data/foot.txt

@@ -0,0 +1,6 @@
+Foot
+Description: Rotational C-arm x-ray scan of a human foot. Tissue and bone are present in the dataset.
+Data Type: uint8
+Data Byte Order: little Endian
+Data Spacing: 1x1x1
+Data Extent: 256x256x256

main/foot/data/foot_256x256x256_uint8.raw

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5b296aa009eb1e84316bc8694dd4ec196f0ad9e1442fb4f08cf44c70e3b7dc4
+size 16777216

main/foot/task_description.txt

@@ -0,0 +1,20 @@
+Task:
+
+Load the Foot dataset from "foot/data/foot_256x256x256_uint8.raw", the information about this dataset:
+Foot
+Description: Rotational C-arm x-ray scan of a human foot. Tissue and bone are present in the dataset.
+Data Type: uint8
+Data Byte Order: little Endian
+Data Spacing: 1x1x1
+Data Extent: 256x256x256
+Data loading is very important, make sure you correctly load the dataset according to their features.
+
+Visualize the anatomical structures:
+1. Apply volume rendering with an X-ray transfer function that distinguishes soft tissues and bones. Bones with darker color, and soft tissue with lighter color.
+
+2. Analyze the visualization and answer the following questions:
+Q1: In the visualization, which structures are fully visible: the phalanges, the metatarsals, or both?
+
+3. Save your work:
+Save the ParaView state as "foot/results/{agent_mode}/foot.pvsm".
+Save the answers to the analysis questions in plain text as "foot/results/{agent_mode}/answers.txt".

main/foot/visualization_goals.txt

@@ -0,0 +1,3 @@
+1. Overall Goal: Does the visualization effectively distinguish between different tissue types in the foot dataset?
+
+2. QA: The phalanges (toe bones) are clearly visible in full, but the metatarsals are only partially visible.

main/get_tf_from_gs.py

@@ -0,0 +1,44 @@
+#!/usr/bin/env pvpython
+
+from paraview.simple import *
+import os
+
+def load_state_and_inspect_transfer_functions(state_path):
+    if not os.path.isfile(state_path):
+        raise FileNotFoundError(f"State file not found: {state_path}")
+
+    # Load the ParaView state file
+    LoadState(state_path)
+    print(f"[✔] Loaded state from: {state_path}")
+
+    # Access the active view
+    view = GetActiveViewOrCreate('RenderView')
+
+    # Iterate through all sources to find volume display
+    for source in GetSources().values():
+        display = GetDisplayProperties(source, view=view)
+        if hasattr(display, 'LookupTable') and display.LookupTable:
+            lut = display.LookupTable
+            otf = display.ScalarOpacityFunction
+
+            print("\n🎨 Color Transfer Function (LUT) — RGBPoints:")
+            for i in range(0, len(lut.RGBPoints), 4):
+                val = lut.RGBPoints[i]
+                r, g, b = lut.RGBPoints[i+1:i+4]
+                print(f"  {val:.3f}: R={r:.3f}, G={g:.3f}, B={b:.3f}")
+
+            print("\n🌫️ Opacity Transfer Function (OTF) — Points:")
+            for i in range(0, len(otf.Points), 4):
+                val = otf.Points[i]
+                alpha = otf.Points[i+1]
+                print(f"  {val:.3f}: α={alpha:.3f}")
+
+            return lut, otf
+
+    print("❌ No volume rendering transfer function found.")
+    return None, None
+
+if __name__ == '__main__':
+    base_path = os.path.abspath(os.path.join(__file__, '..'))
+    state_file = os.path.join(base_path, 'bonsai_gs.pvsm')
+    load_state_and_inspect_transfer_functions(state_file)

main/lobster/GS/lobster_gs.pvsm

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4850853e90dc12c5b72c08a48dde7972da869ed6a1ffca9b43fae04e85618057
+size 378290