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.gitattributes

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+ 
+dataset_infos.json ignore
+*.csv filter=lfs diff=lfs merge=lfs -text
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+
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README.md

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+---
+license: cc-by-4.0
+#User-Defined Tags
+tags:
+  - 2D Steady-State Laminar Flow
+  - Flow Field Prediction
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">DeepCFD</span>
+  </strong>
+</p>
+
+## Dataset Overview
+The DeepCFD dataset is a publicly available two-dimensional steady-state laminar-flow computational fluid dynamics dataset created by a research team affiliated with the German Research Center for Artificial Intelligence (DFKI). The dataset was generated with OpenFOAM's simpleFoam solver and contains approximately 1,000 samples of flow around randomly generated obstacles in a channel. It is suitable for CFD solver acceleration, evaluation of flow-field generalization across different obstacle configurations, prediction-error analysis, and flow-field visualization.
+
+
+## Supported Tasks
+
+This standardized dataset repository organizes DeepCFD's input geometry and signed distance fields in `dataX.pkl` and the corresponding ground-truth CFD solutions in `dataY.pkl`. Each sample contains steady-state flow variables including computational mesh coordinates, horizontal velocity (U_x), vertical velocity (U_y), and pressure (p). It also provides a signed distance function (SDF) and flow-region class labels to represent obstacle geometry, the computational domain, and boundary conditions. The data can be used as input for training, inference, evaluation, and visualization with the `OneScience/DeepCFD` model.
+
+
+
+## Data Format
+
+| File | shape | dtype | Channel Description |
+|---|---:|---|---|
+| `data/dataX.pkl` | `[981, 3, 172, 79]` | `float32` | Channel 0: signed distance to the obstacle surface; Channel 1: fluid-region label; Channel 2: signed distance to the top/bottom surfaces |
+| `data/dataY.pkl` | `[981, 3, 172, 79]` | `float32` | Channel 0: horizontal velocity `Ux`; Channel 1: vertical velocity `Uy`; Channel 2: pressure `pressure` |
+
+Each sample in this dataset is represented on a `172 × 79` regular grid. The input data consists of the signed distance to the obstacle surface, fluid-region labels, and signed distances to the top/bottom surfaces. The output data consists of the corresponding horizontal velocity `Ux`, vertical velocity `Uy`, and pressure `pressure`.
+
+## How to Use the Dataset
+
+This dataset is compatible with the `OneScience-Sugon/DeepCFD` model. During DeepCFD runs, scripts in the model package can organize the data from this repository into the model's working directory.
+
+Download the dataset:
+
+```bash
+hf download --dataset OneScience-Sugon/deepcfd --local-dir ./data
+```
+
+## Official OneScience Information
+
+| Platform | OneScience Main Repository | Skills Repository |
+|---|---|---|
+| Gitee  | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub  | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+## Citation and License
+
+- Original DeepCFD paper: [DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks](https://arxiv.org/abs/2004.08826)
+- This dataset was converted from the original DeepCFD dataset and is licensed under the same CC-BY-4.0 license. The original copyright notice and license text must be retained when using, modifying, or redistributing it.

README_zh.md

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+---
+license: CC-BY-4.0
+#用户自定义标签
+tags:
+  - 2D Steady-State Laminar Flow
+  - Flow Field Prediction
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">DeepCFD</span>
+  </strong>
+</p>
+
+## 数据集介绍
+DeepCFD 数据集是由德国人工智能研究中心（DFKI）相关研究团队构建并公开的二维稳态层流计算流体力学数据集。该数据集采用 OpenFOAM 的 simpleFoam 求解器生成约 1,000 组通道内随机障碍物绕流样本，适用于 CFD 求解器加速、不同障碍物构型下的流场泛化评估、预测结果误差分析及流场可视化。
+
+
+## 数据集支持的任务
+
+本标准化数据集仓库整理了 DeepCFD 的输入几何与符号距离场 `dataX.pkl`、对应 CFD 真实解 `dataY.pkl`，每个样本包含计算网格坐标、水平速度 (U_x)、垂直速度 (U_y) 和压力 (p) 等稳态流场变量，并提供符号距离函数（SDF）及流动区域类别标记，用于表征障碍物几何、计算区域和边界条件，可作为 `OneScience/DeepCFD` 模型的训练、推理、评测和可视化数据输入。
+
+
+
+## 数据格式
+
+| 文件 | shape | dtype | 通道说明 |
+|---|---:|---|---|
+| `data/dataX.pkl` | `[981, 3, 172, 79]` | `float32` | 通道 0：障碍物表面符号距离；通道 1：流体区域标签；通道 2：顶部/底部表面符号距离 |
+| `data/dataY.pkl` | `[981, 3, 172, 79]` | `float32` | 通道 0：水平速度 `Ux`；通道 1：垂直速度 `Uy`；通道 2：压力 `pressure` |
+
+本数据集中的每个样本采用 `172 × 79` 的规则网格表示。输入数据由障碍物表面符号距离、流体区域标签和顶部/底部表面符号距离组成，输出数据为对应的水平速度 `Ux`、垂直速度 `Uy` 和压力 `pressure`
+
+## 数据集使用方式
+
+本数据集适配 `OneScience-Sugon/DeepCFD` 模型。DeepCFD运行时可通过模型包脚本把本仓库的数据组织到模型运行目录。
+
+下载数据集：
+
+```bash
+hf download --dataset OneScience-Sugon/deepcfd --local-dir ./data
+```
+
+## OneScience 官方信息
+
+| 平台 | OneScience 主仓库 | Skills 仓库 |
+|---|---|---|
+| Gitee  | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub  | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+## 引用与许可证
+
+- DeepCFD 原始论文：[DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks](https://arxiv.org/abs/2004.08826)
+- 本数据集基于DeepCFD原始数据集进行转换，许可证同 CC-BY-4.0，使用、修改或再分发时，须保留原始版权声明和许可证文本。

data/dataX.pkl

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data/dataY.pkl

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data_integrity_summary.json

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+{
+  "source_root": "cfd_dataset/DeepCFD",
+  "standardized_root": "modelscope_standardized/dataset/cfd_deepcfd/data",
+  "files": [
+    {
+      "path": "data/dataX.pkl",
+      "source_path": "cfd_dataset/DeepCFD/dataX.pkl",
+      "size": 159958102,
+      "sha256": "49eb753a343367070607d5314ca2b341dadb56cfb86918506726e61bbc6587e5",
+      "source_size": 159958102,
+      "source_sha256": "49eb753a343367070607d5314ca2b341dadb56cfb86918506726e61bbc6587e5",
+      "matches_source": true,
+      "shape": [
+        981,
+        3,
+        172,
+        79
+      ],
+      "dtype": "float32"
+    },
+    {
+      "path": "data/dataY.pkl",
+      "source_path": "cfd_dataset/DeepCFD/dataY.pkl",
+      "size": 159958102,
+      "sha256": "70b0f9a614ded93d2771daf9b208048218ba69fccbcfb312072ad2c9b29d2b34",
+      "source_size": 159958102,
+      "source_sha256": "70b0f9a614ded93d2771daf9b208048218ba69fccbcfb312072ad2c9b29d2b34",
+      "matches_source": true,
+      "shape": [
+        981,
+        3,
+        172,
+        79
+      ],
+      "dtype": "float32"
+    }
+  ],
+  "all_match": true
+}

dataset_infos.json

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+{"default": {"features": {"source_root": {"_type": "Value"}, "standardized_root": {"_type": "Value"}, "files": {"_type": "Value"}, "all_match": {"_type": "Value"}}, "splits": {"train": {"name": "train", "dataset_name": "deepcfd"}}}}

files_sha256.jsonl

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+{"path": "data/dataX.pkl", "size": 159958102, "sha256": "49eb753a343367070607d5314ca2b341dadb56cfb86918506726e61bbc6587e5"}
+{"path": "data/dataY.pkl", "size": 159958102, "sha256": "70b0f9a614ded93d2771daf9b208048218ba69fccbcfb312072ad2c9b29d2b34"}

metadata/deepcfd_schema.json

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+{
+  "dataset_family": "DeepCFD",
+  "files": [
+    {
+      "path": "data/dataX.pkl",
+      "role": "input_geometry_and_sdf",
+      "format": "pickle",
+      "array_shape": [981, 3, 172, 79],
+      "dtype": "float32",
+      "channels": [
+        "obstacle_surface_signed_distance",
+        "fluid_region_label",
+        "top_bottom_surface_signed_distance"
+      ]
+    },
+    {
+      "path": "data/dataY.pkl",
+      "role": "cfd_solution",
+      "format": "pickle",
+      "array_shape": [981, 3, 172, 79],
+      "dtype": "float32",
+      "channels": [
+        "Ux",
+        "Uy",
+        "pressure"
+      ]
+    }
+  ],
+  "default_split": {
+    "split_ratio": 0.7,
+    "seed": 0,
+    "train_samples": 686,
+    "test_samples": 295
+  },
+  "grid": {
+    "nx": 172,
+    "ny": 79
+  }
+}

onescience_run_manifest.yaml

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+onescience_manifest_version: "0.1"
+resource_type: dataset
+
+resource:
+  id: OneScience/deepcfd
+  name: deepcfd
+  domain: cfd
+  domain_tags:
+    - cfd
+    - laminar_flow
+    - channel_flow
+  task: steady_laminar_flow_surrogate_dataset
+  task_tags:
+    - train_data
+    - eval_data
+    - inference_input
+    - dataset_validation
+  modalities:
+    - gridded_field
+    - signed_distance_field
+    - flow_solution
+  input_formats:
+    - pkl
+  output_formats:
+    - pkl
+  summary: DeepCFD 稳态层流代理建模数据集，包含 dataX.pkl 几何输入和 dataY.pkl CFD 真实解。
+
+platform_resource:
+  primary:
+    platform: modelscope
+    repo_id: OneScience/deepcfd
+    repo_type: dataset
+    url: https://modelscope.cn/datasets/OneScience/deepcfd
+    revision: main
+    readme_path: README.md
+    manifest_path: onescience_run_manifest.yaml
+  mirrors: []
+  access:
+    visibility: public
+    license: unknown
+
+website_integration:
+  enabled: true
+  click_target:
+    platform: modelscope
+    resource_url: https://modelscope.cn/datasets/OneScience/deepcfd
+  llm_handoff:
+    readme_required: true
+    manifest_required: true
+    download_readme_first: true
+    resolve_related_models: true
+    default_run_goal: dataset_validation
+    cwd_note: 如果使用 modelscope download --cache_dir 下载数据集，请先 cd 到实际下载后的数据集仓库根目录；模型侧将 ONESCIENCE_DEEPCFD_DATA_DIR 指向本仓库 data 目录。
+
+runtime:
+  enabled: true
+  onescience_domain: cfd
+  min_onescience_version: null
+  supported_execution:
+    - local_dataset_validation
+    - model_training_input
+    - model_inference_input
+  environment:
+    exported_env:
+      ONESCIENCE_DEEPCFD_DATA_DIR: <dataset_repo_root>/data
+  dependencies:
+    python: ">=3.10"
+    python_packages:
+      - numpy
+      - pyyaml
+
+onescience:
+  repo: https://gitee.com/onescience-ai/onescience
+  official_links:
+    gitee:
+      doc: https://gitee.com/onescience-ai/onescience-doc
+      onescience: https://gitee.com/onescience-ai/onescience
+      skills: https://gitee.com/onescience-ai/oneskills
+    github:
+      doc: https://github.com/onescience-ai/OneScience-doc
+      onescience: https://github.com/onescience-ai/OneScience
+      skills: https://github.com/onescience-ai/oneskills
+  install:
+    required_by_default: false
+    command: bash install.sh cfd
+  source_paths:
+    - onescience/src/onescience/datapipes/cfd/deepcfd.py
+  compatibility:
+    examples_path: onescience/examples/cfd/DeepCFD
+    status: examples_compatible
+  datapipe: onescience.datapipes.cfd.DeepCFDDatapipe
+
+runtime_package:
+  kind: standard_runtime_package
+  package_root: .
+  standard_layout:
+    workdir: .
+    data_dir: data
+    metadata_dir: metadata
+    output_dir: validation_outputs
+  apply_policy:
+    mode: direct_use
+    target: session_workdir
+    overwrite: false
+    protect_installed_onescience: true
+  entry_files:
+    - data/dataX.pkl
+    - data/dataY.pkl
+    - metadata/deepcfd_schema.json
+    - scripts/validate_deepcfd_dataset.py
+    - files_sha256.jsonl
+  entrypoints:
+    preflight: scripts/validate_deepcfd_dataset.py
+    validate: scripts/validate_deepcfd_dataset.py
+    inference: null
+    train: null
+    finetune: null
+    evaluate: null
+    visualize: null
+    deploy: null
+
+files:
+  model_files: []
+  dataset_files:
+    - path: data/dataX.pkl
+      role: input_geometry_and_sdf
+      description_zh: DeepCFD 输入数据，包含障碍物 SDF、流体区域标签和顶部/底部 SDF 三个通道。
+      required: true
+      required_for: [dataset_validation, train, inference, evaluate]
+      source:
+        platform: modelscope
+        repo_id: OneScience/deepcfd
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: data/dataX.pkl
+    - path: data/dataY.pkl
+      role: cfd_solution
+      description_zh: DeepCFD 标签数据，包含 Ux、Uy、pressure 三个通道。
+      required: true
+      required_for: [dataset_validation, train, inference, evaluate]
+      source:
+        platform: modelscope
+        repo_id: OneScience/deepcfd
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: data/dataY.pkl
+    - path: files_sha256.jsonl
+      role: file_size_sha256_inventory
+      description_zh: 整理后数据文件的大小和 SHA256 清单，用于完整性校验。
+      required: true
+      required_for: [dataset_validation]
+      source:
+        platform: modelscope
+        repo_id: OneScience/deepcfd
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: files_sha256.jsonl
+    - path: data_integrity_summary.json
+      role: source_to_standardized_integrity_summary
+      description_zh: 原始数据与整理后数据的文件名、大小、SHA256 对照摘要。
+      required: true
+      required_for: [dataset_validation]
+      source:
+        platform: modelscope
+        repo_id: OneScience/deepcfd
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: data_integrity_summary.json
+  config_files:
+    - path: metadata/deepcfd_schema.json
+      role: dataset_schema
+      description_zh: 描述 DeepCFD 数据文件角色、shape、dtype、通道和默认切分。
+      required: true
+      required_for: [dataset_validation, train, inference]
+      source:
+        platform: modelscope
+        repo_id: OneScience/deepcfd
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: metadata/deepcfd_schema.json
+  sample_files:
+    - path: scripts/validate_deepcfd_dataset.py
+      role: dataset_validation_entry
+      description_zh: 数据集读取验证脚本，检查文件结构、shape、dtype、可读性和 SHA256。
+      required: true
+      required_for: [dataset_validation]
+      source:
+        platform: modelscope
+        repo_id: OneScience/deepcfd
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: scripts/validate_deepcfd_dataset.py
+
+relations:
+  required_datasets: []
+  optional_datasets: []
+  compatible_models:
+    - id: OneScience/DeepCFD
+      role: train_eval_inference_model
+      required_for: [train, inference, evaluate, visualize]
+      resource_ref:
+        platform: modelscope
+        repo_id: OneScience/DeepCFD
+        repo_type: model
+        url: https://modelscope.cn/models/OneScience/DeepCFD
+        revision: main
+        readme_path: README.md
+        manifest_path: onescience_run_manifest.yaml
+      expected_local_path: session_workdir/data
+      expected_env: ONESCIENCE_DEEPCFD_DATA_DIR
+
+run_matrix:
+  scenarios:
+    - name: validate_structure
+      capability: dataset_validation
+      default: true
+      required_datasets:
+        - id: OneScience/deepcfd
+          role: train_eval_inference_data
+          local_path: data
+      required_model_files: []
+      required_dataset_files:
+        - data/dataX.pkl
+        - data/dataY.pkl
+        - metadata/deepcfd_schema.json
+      preconditions:
+        - 当前工作目录为数据集仓库根目录。
+      command_refs:
+        - commands.preflight.validate_dataset
+      outputs:
+        - 控制台输出 [OK] dataset validation completed
+    - name: validate_integrity_full_hash
+      capability: dataset_validation
+      default: false
+      required_datasets:
+        - id: OneScience/deepcfd
+          role: train_eval_inference_data
+          local_path: data
+      required_model_files: []
+      required_dataset_files:
+        - files_sha256.jsonl
+        - data/dataX.pkl
+        - data/dataY.pkl
+      preconditions:
+        - 当前工作目录为数据集仓库根目录。
+      command_refs:
+        - commands.preflight.validate_dataset_full_hash
+      outputs:
+        - 控制台输出 checksum manifest verified in size+sha256 mode
+    - name: provide_to_deepcfd_model
+      capability: train
+      default: true
+      required_datasets:
+        - id: OneScience/deepcfd
+          role: train_data
+          local_path: data
+      required_model_files:
+        - OneScience/DeepCFD:conf/deepcfd.yaml
+      required_dataset_files:
+        - data/dataX.pkl
+        - data/dataY.pkl
+      preconditions:
+        - 模型仓库 OneScience/DeepCFD 已下载。
+        - 设置 ONESCIENCE_DEEPCFD_DATA_DIR 为本仓库 data 目录。
+      command_refs:
+        - commands.prepare.export_dataset_env
+      outputs:
+        - 模型侧 scripts/preflight_check.py 可以读取数据集。
+
+capabilities:
+  dataset_validation: true
+  train_input: true
+  inference_input: true
+  evaluation_input: true
+  visualization_input: true
+  preflight: true
+  inference: false
+  train: false
+  finetune: false
+  evaluate: false
+  visualize: false
+  deploy: false
+
+commands:
+  download:
+    - name: download_dataset
+      command: modelscope download --dataset OneScience/deepcfd
+      cwd: session_workdir
+    - name: download_model
+      command: modelscope download --model OneScience/DeepCFD
+      cwd: session_workdir
+  prepare:
+    - name: export_dataset_env
+      command: export ONESCIENCE_DEEPCFD_DATA_DIR=/path/to/OneScience_deepcfd/data
+      cwd: .
+  preflight:
+    - name: validate_dataset
+      command: python scripts/validate_deepcfd_dataset.py
+      cwd: .
+    - name: validate_dataset_full_hash
+      command: python scripts/validate_deepcfd_dataset.py --full-hash
+      cwd: .
+  inference: []
+  train: []
+  finetune: []
+  evaluate: []
+  visualize: []
+  deploy: []
+
+expected_outputs:
+  - name: validate_dataset
+    paths: []
+    success_criteria:
+      - 控制台输出 [OK] dataset validation completed
+  - name: validate_dataset_full_hash
+    paths:
+      - files_sha256.jsonl
+    success_criteria:
+      - 控制台输出 checksum manifest verified in size+sha256 mode: 2 files
+  - name: model_consumption
+    paths: []
+    success_criteria:
+      - 模型包 scripts/preflight_check.py 通过数据路径和 schema 检查。
+
+diagnostics:
+  - name: missing_data_root
+    symptom: dataset data root does not exist
+    action: 确认当前目录为 OneScience/deepcfd 数据集仓库根目录，且 data 目录存在。
+  - name: missing_pkl
+    symptom: unexpected data file set 或 missing required data file
+    action: 对照 files_sha256.jsonl 重新上传或重新下载缺失文件。
+  - name: shape_mismatch
+    symptom: shape mismatch 或 dtype mismatch
+    action: 确认没有用其他 DeepCFD 数据覆盖当前文件。
+  - name: checksum_mismatch
+    symptom: sha256 mismatch
+    action: 删除损坏文件后重新下载 OneScience/deepcfd。
+  - name: model_cannot_find_data
+    symptom: 模型侧报告 ONESCIENCE_DEEPCFD_DATA_DIR is not set 或 Data files not found
+    action: 将 ONESCIENCE_DEEPCFD_DATA_DIR 设置为本仓库 data 目录，而不是仓库根目录。
+
+domain_extension:
+  cfd:
+    dataset_family: DeepCFD
+    physics: steady-state incompressible laminar flow
+    geometry: channel flow with obstacles
+    file_count: 2
+    total_size_bytes: 319916204
+    samples: 981
+    grid_shape: [172, 79]
+    schema:
+      dataX:
+        shape: [981, 3, 172, 79]
+        dtype: float32
+        channels:
+          - obstacle_surface_signed_distance
+          - fluid_region_label
+          - top_bottom_surface_signed_distance
+      dataY:
+        shape: [981, 3, 172, 79]
+        dtype: float32
+        channels:
+          - Ux
+          - Uy
+          - pressure
+

scripts/validate_deepcfd_dataset.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python3
+"""Validate the standardized DeepCFD dataset package."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import pickle
+import sys
+from pathlib import Path
+
+import numpy as np
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+DATA_ROOT = REPO_ROOT / "data"
+CHECKSUM_PATH = REPO_ROOT / "files_sha256.jsonl"
+EXPECTED_FILES = ("dataX.pkl", "dataY.pkl")
+EXPECTED_SHAPE = (981, 3, 172, 79)
+EXPECTED_DTYPE = np.float32
+
+
+def fail(message: str) -> None:
+    print(f"[FAIL] {message}")
+    raise SystemExit(1)
+
+
+def ok(message: str) -> None:
+    print(f"[OK] {message}")
+
+
+def sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def validate_structure() -> None:
+    if not DATA_ROOT.is_dir():
+        fail(f"dataset data root does not exist: {DATA_ROOT}")
+    actual = sorted(path.name for path in DATA_ROOT.iterdir() if path.is_file())
+    if actual != sorted(EXPECTED_FILES):
+        fail(f"unexpected data file set: expected {sorted(EXPECTED_FILES)}, got {actual}")
+    ok("dataset file names and directory layout are valid")
+
+
+def load_pickle(path: Path) -> np.ndarray:
+    with path.open("rb") as handle:
+        arr = np.asarray(pickle.load(handle))
+    return arr
+
+
+def validate_arrays(sample_values: bool) -> None:
+    arrays = {}
+    for name in EXPECTED_FILES:
+        path = DATA_ROOT / name
+        arr = load_pickle(path)
+        arrays[name] = arr
+        if arr.shape != EXPECTED_SHAPE:
+            fail(f"data/{name} shape mismatch: expected {EXPECTED_SHAPE}, got {arr.shape}")
+        if arr.dtype != EXPECTED_DTYPE:
+            fail(f"data/{name} dtype mismatch: expected {EXPECTED_DTYPE}, got {arr.dtype}")
+        if sample_values and not np.isfinite(arr[0]).all():
+            fail(f"data/{name} first sample contains non-finite values")
+    if arrays["dataX.pkl"].shape != arrays["dataY.pkl"].shape:
+        fail("dataX.pkl and dataY.pkl shapes are inconsistent")
+    ok("dataset PKL files are readable and match expected shape/dtype")
+
+
+def verify_checksums(full_hash: bool) -> None:
+    if not CHECKSUM_PATH.exists():
+        fail(f"checksum manifest is not present: {CHECKSUM_PATH}")
+    records = [json.loads(line) for line in CHECKSUM_PATH.read_text(encoding="utf-8").splitlines() if line.strip()]
+    if len(records) != len(EXPECTED_FILES):
+        fail(f"checksum manifest must contain {len(EXPECTED_FILES)} records, got {len(records)}")
+    for record in records:
+        path = REPO_ROOT / record["path"]
+        if not path.is_file():
+            fail(f"checksum entry points to missing file: {path}")
+        size = path.stat().st_size
+        if size != record["size"]:
+            fail(f"size mismatch for {path}: expected {record['size']}, got {size}")
+        if full_hash:
+            digest = sha256_file(path)
+            if digest != record["sha256"]:
+                fail(f"sha256 mismatch for {path}")
+    mode = "size+sha256" if full_hash else "size"
+    ok(f"checksum manifest verified in {mode} mode: {len(records)} files")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--full-hash", action="store_true")
+    parser.add_argument("--skip-value-sample", action="store_true")
+    args = parser.parse_args()
+    validate_structure()
+    validate_arrays(sample_values=not args.skip_value_sample)
+    verify_checksums(full_hash=args.full_hash)
+    ok("dataset validation completed")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())