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README.md

@@ -0,0 +1,69 @@
+---
+license: other
+#User-Defined Tags
+tags:
+  - BENO
+  - CFD
+  - neural operator
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">BENO</span>
+  </strong>
+</p>
+
+## Dataset Description
+
+The BENO dataset originates from the ICLR 2024 paper *BENO: Boundary-Embedded Neural Operators for Elliptic PDEs* and is designed for solving elliptic partial differential equations under complex boundary conditions. The data contains random boundary geometries with four, three, two, one, or no corners, all standardized to a `32 x 32` grid resolution.
+
+Paper: [BENO: Boundary-Embedded Neural Operators for Elliptic PDEs](https://proceedings.iclr.cc/paper_files/paper/2024/file/218ca0d92e6ed8f9db00621e103dc70c-Paper-Conference.pdf)
+
+## Supported Tasks
+
+| Scenario | Description |
+|---|---|
+| Elliptic PDE solving | Predict the solution field from the boundary conditions and the right-hand side of the equation. |
+| Boundary-condition research | Compare solution performance under Dirichlet and Neumann boundary conditions. |
+| Geometry generalization evaluation | Evaluate model generalization across different random boundary shapes. |
+| Neural operator research | Provide standardized training and evaluation data for operator models such as BENO. |
+
+## Dataset Format and Structure
+
+The data is organized by boundary condition:
+
+```text
+data/
+  Dirichlet/
+  Neumann/
+```
+
+Each boundary-condition category contains the following six configurations: `N32_0c`, `N32_1c`, `N32_2c`, `N32_3c`, `N32_4c`, and `N32_mix`. Each configuration contains 1,000 `float64` samples:
+
+| File | shape | Description |
+|---|---|---|
+| `BC_<prefix>_all.npy` | `[1000, 128, 4]` | Boundary coordinates, boundary values, and boundary features. |
+| `RHS_<prefix>_all.npy` | `[1000, 1024, 4]` | Coordinates, source terms, and cell states on a 32×32 grid. |
+| `SOL_<prefix>_all.npy` | `[1000, 1024, 1]` | Elliptic PDE solution fields. |
+
+## How to Use the Dataset
+
+This dataset has been adapted for the `OneScience-Sugon/BENO` model. Download the dataset and model:
+
+```bash
+hf download --dataset OneScience-Sugon/beno --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 BENO paper: [BENO: Boundary-embedded Neural Operators for Elliptic PDEs](https://openreview.net/forum?id=ZZTkLDRmkg)
+- This dataset has been organized and converted from the original BENO dataset. Its use must comply with the licensing requirements published by the original project.

README_zh.md

@@ -0,0 +1,69 @@
+---
+license: other
+#用户自定义标签
+tags:
+  - BENO
+  - CFD
+  - neural operator
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;">BENO</span>
+  </strong>
+</p>
+
+## 数据集描述
+
+BENO 数据集来源于 ICLR 2024 论文 *BENO: Boundary-Embedded Neural Operators for Elliptic PDEs*，面向复杂边界条件下的椭圆型偏微分方程求解。数据包含四角、三角、两角、单角和无角等随机边界几何，并统一为 `32 x 32` 网格分辨率。
+
+论文：[BENO: Boundary-Embedded Neural Operators for Elliptic PDEs](https://proceedings.iclr.cc/paper_files/paper/2024/file/218ca0d92e6ed8f9db00621e103dc70c-Paper-Conference.pdf)
+
+## 数据集支持的任务
+
+| 场景 | 说明 |
+|---|---|
+| 椭圆 PDE 求解 | 根据边界条件和方程右端项预测解场。 |
+| 边界条件研究 | 比较 Dirichlet 和 Neumann 边界条件下的求解效果。 |
+| 几何泛化评测 | 评估模型在不同随机边界形状上的泛化能力。 |
+| 神经算子研究 | 为 BENO 等算子模型提供统一训练和评测数据。 |
+
+## 数据集的格式和结构
+
+数据按边界条件组织：
+
+```text
+data/
+  Dirichlet/
+  Neumann/
+```
+
+每类边界条件包含以下六种配置：`N32_0c`、`N32_1c`、`N32_2c`、`N32_3c`、`N32_4c` 和 `N32_mix`。每种配置包含 1,000 个 `float64` 样本：
+
+| 文件 | shape | 说明 |
+|---|---|---|
+| `BC_<prefix>_all.npy` | `[1000, 128, 4]` | 边界坐标、边界值及边界特征。 |
+| `RHS_<prefix>_all.npy` | `[1000, 1024, 4]` | 32×32 网格上的坐标、源项和单元状态。 |
+| `SOL_<prefix>_all.npy` | `[1000, 1024, 1]` | 椭圆 PDE 解场。 |
+
+## 数据集使用方式
+
+本数据集适配 `OneScience-Sugon/BENO` 模型。下载数据集和模型：
+
+```bash
+hf download --dataset OneScience-Sugon/beno --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
+
+- BENO 原始论文：[BENO: Boundary-embedded Neural Operators for Elliptic PDEs](https://openreview.net/forum?id=ZZTkLDRmkg)
+- 本数据集基于 BENO 原始数据集整理与转换，使用时应遵循原始项目公布的许可要求。

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metadata/beno_schema.json

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+{
+  "dataset": "BENO",
+  "format": "npy",
+  "boundary_types": ["Dirichlet", "Neumann"],
+  "prefixes": ["N32_0c", "N32_1c", "N32_2c", "N32_3c", "N32_4c", "N32_mix"],
+  "files": {
+    "BC_<prefix>_all.npy": {
+      "shape": [1000, 128, 4],
+      "dtype": "float64",
+      "description_zh": "边界坐标、边界值和边界相关特征；当前 OneScience BENO datapipe 使用通道 0:2 和通道 2，通道 3 在原始数据中为 NaN 占位"
+    },
+    "RHS_<prefix>_all.npy": {
+      "shape": [1000, 1024, 4],
+      "dtype": "float64",
+      "description_zh": "32x32 网格上的坐标、源项和单元状态"
+    },
+    "SOL_<prefix>_all.npy": {
+      "shape": [1000, 1024, 1],
+      "dtype": "float64",
+      "description_zh": "椭圆 PDE 解场"
+    }
+  },
+  "default_model_subset": {
+    "boundary_type": "Dirichlet",
+    "prefix": "N32_4c",
+    "train_samples": 900,
+    "test_samples": 100
+  }
+}

onescience_run_manifest.yaml

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+onescience_manifest_version: "0.1"
+resource_type: dataset
+
+resource:
+  id: OneScience/beno
+  name: beno
+  domain: cfd
+  domain_tags:
+    - cfd
+    - elliptic_pde
+    - boundary_condition
+  task: elliptic_pde_surrogate_dataset
+  task_tags:
+    - train_data
+    - eval_data
+    - inference_input
+    - dataset_validation
+  modalities:
+    - gridded_field
+    - boundary_condition
+  input_formats:
+    - npy
+  output_formats:
+    - npy
+  summary: BENO 椭圆 PDE 数据集，包含 Dirichlet 和 Neumann 边界下的 32x32 网格 RHS、SOL、BC NPY 文件。
+
+platform_resource:
+  primary:
+    platform: modelscope
+    repo_id: OneScience/beno
+    repo_type: dataset
+    url: https://modelscope.cn/datasets/OneScience/beno
+    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/beno
+  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_BENO_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_BENO_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/beno.py
+  compatibility:
+    examples_path: onescience/examples/cfd/BENO
+    status: examples_compatible
+  datapipe: onescience.datapipes.cfd.BENODatapipe
+
+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/Dirichlet/RHS_N32_4c_all.npy
+    - data/Dirichlet/SOL_N32_4c_all.npy
+    - data/Dirichlet/BC_N32_4c_all.npy
+    - metadata/beno_schema.json
+    - scripts/validate_beno_dataset.py
+    - files_sha256.jsonl
+  entrypoints:
+    preflight: scripts/validate_beno_dataset.py
+    validate: scripts/validate_beno_dataset.py
+    inference: null
+    train: null
+    finetune: null
+    evaluate: null
+    visualize: null
+    deploy: null
+
+files:
+  model_files: []
+  dataset_files:
+    - path: data/Dirichlet/*.npy
+      role: train_eval_inference_data
+      description_zh: Dirichlet 边界条件下的 BENO NPY 数据文件，包含 RHS、SOL、BC 三类数组和 6 个几何前缀。
+      required: true
+      required_for: [dataset_validation, train, inference, evaluate]
+      source:
+        platform: modelscope
+        repo_id: OneScience/beno
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: data/Dirichlet
+    - path: data/Neumann/*.npy
+      role: additional_boundary_data
+      description_zh: Neumann 边界条件下的 BENO NPY 数据文件，可用于扩展训练或对比实验。
+      required: true
+      required_for: [dataset_validation]
+      source:
+        platform: modelscope
+        repo_id: OneScience/beno
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: data/Neumann
+    - 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/beno
+        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/beno
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: data_integrity_summary.json
+  config_files:
+    - path: metadata/beno_schema.json
+      role: dataset_schema
+      description_zh: 描述 BENO 数据文件命名、shape、dtype 和默认模型子集。
+      required: true
+      required_for: [dataset_validation, train, inference]
+      source:
+        platform: modelscope
+        repo_id: OneScience/beno
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: metadata/beno_schema.json
+  sample_files:
+    - path: scripts/validate_beno_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、统计清单和可选 SHA256。
+      required: true
+      required_for: [dataset_validation]
+      source:
+        platform: modelscope
+        repo_id: OneScience/beno
+        repo_type: dataset
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: scripts/validate_beno_dataset.py
+
+relations:
+  required_datasets: []
+  optional_datasets: []
+  compatible_models:
+    - id: OneScience/BENO
+      role: train_data
+      required_for: [train, inference, evaluate, visualize]
+      resource_ref:
+        platform: modelscope
+        repo_id: OneScience/BENO
+        repo_type: model
+        url: https://modelscope.cn/models/OneScience/BENO
+        revision: main
+        readme_path: README.md
+        manifest_path: onescience_run_manifest.yaml
+      expected_local_path: session_workdir/data
+      expected_env: ONESCIENCE_BENO_DATA_DIR
+
+run_matrix:
+  scenarios:
+    - name: validate_structure
+      capability: dataset_validation
+      default: true
+      required_datasets:
+        - id: OneScience/beno
+          role: train_data
+          local_path: data
+      required_model_files: []
+      required_dataset_files:
+        - data/Dirichlet/*.npy
+        - data/Neumann/*.npy
+        - metadata/beno_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/beno
+          role: train_data
+          local_path: data
+      required_model_files: []
+      required_dataset_files:
+        - files_sha256.jsonl
+        - data/Dirichlet/*.npy
+        - data/Neumann/*.npy
+      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_beno_model
+      capability: train
+      default: true
+      required_datasets:
+        - id: OneScience/beno
+          role: train_data
+          local_path: data
+      required_model_files:
+        - OneScience/BENO:conf/beno.yaml
+      required_dataset_files:
+        - data/Dirichlet/RHS_N32_4c_all.npy
+        - data/Dirichlet/SOL_N32_4c_all.npy
+        - data/Dirichlet/BC_N32_4c_all.npy
+      preconditions:
+        - 模型仓库 OneScience/BENO 已下载。
+        - 设置 ONESCIENCE_BENO_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/beno
+      cwd: session_workdir
+    - name: download_model
+      command: modelscope download --model OneScience/BENO
+      cwd: session_workdir
+  prepare:
+    - name: export_dataset_env
+      command: export ONESCIENCE_BENO_DATA_DIR=/path/to/OneScience_beno/data
+      cwd: .
+  preflight:
+    - name: validate_dataset
+      command: python scripts/validate_beno_dataset.py
+      cwd: .
+    - name: validate_dataset_full_hash
+      command: python scripts/validate_beno_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: 36 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/beno 数据集仓库根目录，且 data 目录存在。
+  - name: missing_npy
+    symptom: missing required NPY files
+    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: 确认没有用其他分辨率或其他 BENO 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/beno。
+  - name: model_cannot_find_data
+    symptom: 模型侧报告 ONESCIENCE_BENO_DATA_DIR is not set 或 Data file not found
+    action: 将 ONESCIENCE_BENO_DATA_DIR 设置为本仓库 data 目录，而不是仓库根目录或 data/Dirichlet。
+
+domain_extension:
+  cfd:
+    dataset_family: BENO
+    physics: elliptic partial differential equation
+    boundary_types:
+      - Dirichlet
+      - Neumann
+    prefixes:
+      - N32_0c
+      - N32_1c
+      - N32_2c
+      - N32_3c
+      - N32_4c
+      - N32_mix
+    file_count: 36
+    total_size_bytes: 540676608
+    resolution: 32
+    samples_per_file: 1000
+    schema:
+      BC:
+        shape: [1000, 128, 4]
+        dtype: float64
+        note: 当前 OneScience BENO datapipe 使用通道 0:2 和通道 2；通道 3 在原始数据中为 NaN 占位，验证脚本会记录警告但不视为损坏。
+      RHS:
+        shape: [1000, 1024, 4]
+        dtype: float64
+      SOL:
+        shape: [1000, 1024, 1]
+        dtype: float64

scripts/validate_beno_dataset.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python3
+"""Validate the standardized BENO dataset package."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+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"
+BOUNDARIES = ("Dirichlet", "Neumann")
+PREFIXES = ("N32_0c", "N32_1c", "N32_2c", "N32_3c", "N32_4c", "N32_mix")
+KINDS = ("BC", "RHS", "SOL")
+EXPECTED_SHAPES = {
+    "BC": (1000, 128, 4),
+    "RHS": (1000, 1024, 4),
+    "SOL": (1000, 1024, 1),
+}
+
+
+def fail(message: str) -> None:
+    print(f"[FAIL] {message}")
+    raise SystemExit(1)
+
+
+def ok(message: str) -> None:
+    print(f"[OK] {message}")
+
+
+def warn(message: str) -> None:
+    print(f"[WARN] {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 expected_files() -> list[Path]:
+    return [
+        DATA_ROOT / boundary / f"{kind}_{prefix}_all.npy"
+        for boundary in BOUNDARIES
+        for prefix in PREFIXES
+        for kind in KINDS
+    ]
+
+
+def validate_structure() -> None:
+    if not DATA_ROOT.is_dir():
+        fail(f"dataset data root does not exist: {DATA_ROOT}")
+    missing = [str(path.relative_to(REPO_ROOT)) for path in expected_files() if not path.is_file()]
+    if missing:
+        fail("missing required NPY files: " + "; ".join(missing[:10]))
+    actual = sorted(path for path in DATA_ROOT.glob("*/*.npy"))
+    expected = sorted(expected_files())
+    if [p.relative_to(REPO_ROOT) for p in actual] != [p.relative_to(REPO_ROOT) for p in expected]:
+        fail(f"unexpected NPY file set: expected {len(expected)}, got {len(actual)}")
+    ok("dataset file names and directory layout are valid")
+
+
+def validate_arrays(sample_values: bool) -> None:
+    for path in expected_files():
+        kind = path.name.split("_", 1)[0]
+        arr = np.load(path, mmap_mode="r")
+        if tuple(arr.shape) != EXPECTED_SHAPES[kind]:
+            fail(f"{path.relative_to(REPO_ROOT)} shape mismatch: expected {EXPECTED_SHAPES[kind]}, got {tuple(arr.shape)}")
+        if arr.dtype != np.float64:
+            fail(f"{path.relative_to(REPO_ROOT)} dtype mismatch: expected float64, got {arr.dtype}")
+        if sample_values:
+            probe = np.asarray(arr[0])
+            if kind == "BC":
+                required_channels = probe[:, :3]
+                if not np.isfinite(required_channels).all():
+                    fail(f"{path.relative_to(REPO_ROOT)} contains non-finite values in BC channels 0:3")
+                optional_channel = probe[:, 3]
+                nonfinite = optional_channel.size - int(np.isfinite(optional_channel).sum())
+                if nonfinite:
+                    warn(f"{path.relative_to(REPO_ROOT)} BC channel 3 contains {nonfinite} non-finite placeholder values in first sample")
+            elif not np.isfinite(probe).all():
+                fail(f"{path.relative_to(REPO_ROOT)} contains non-finite values in first sample")
+    ok("dataset NPY files are readable and match expected shape/dtype")
+
+
+def verify_checksums(full_hash: bool) -> None:
+    if not CHECKSUM_PATH.exists():
+        warn(f"checksum manifest is not present: {CHECKSUM_PATH}")
+        return
+    records = []
+    for line in CHECKSUM_PATH.read_text(encoding="utf-8").splitlines():
+        if line.strip():
+            records.append(json.loads(line))
+    if len(records) != 36:
+        fail(f"checksum manifest must contain 36 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())