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+ 
+dataset_infos.json ignore
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README.md

@@ -0,0 +1,68 @@
+---
+license: other
+#User-Defined Tags
+tags:
+  - Lagrangian
+  - CFD
+  - graph neural network
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;"> Lagrangian </span>
+  </strong>
+</p>
+
+## Dataset Overview
+
+The Lagrangian dataset is sourced from the DeepMind team's ICML 2020 paper [Learning to Simulate Complex Physics with Graph Networks](https://proceedings.mlr.press/v119/sanchez-gonzalez20a.html). It consists of the two-dimensional Water particle-dynamics data from the paper's Graph Network-based Simulator (GNS) benchmark. The data represents particles as graph nodes and describes fluid evolution over time through particle-position sequences and particle types. It can be used for Lagrangian particle-dynamics modeling, long-horizon fluid rollout prediction, and evaluation of graph-neural-network physics simulations.
+
+
+## Supported Tasks
+
+This standardized dataset repository organizes the training, validation, and test TFRecord files for Lagrangian Water, together with data metadata, a data schema, an integrity summary, and validation scripts. It can be used as data input for training, inference, evaluation, and visualization with the `OneScience/LagrangianMGN` model. The core data is placed uniformly under `data/Water/`; the training set contains 1,000 trajectories, while the validation and test sets each contain 30 trajectories.
+
+
+## Dataset Format and Structure
+
+The dataset uses the TFRecord SequenceExample format, with each record corresponding to one particle-motion trajectory. The number of particles, `num_particles`, varies by trajectory, and the spatial dimension is `dim=2`.
+
+| Feature | Component | shape | dtype | Source Encoding | Description |
+|---|---|---:|---|---|---|
+| `position` | sequence feature | `[1001, num_particles, 2]` | `float32` | `bytes` | Two-dimensional particle positions for 1,001 frames, corresponding to 1,000 evolution time steps |
+| `particle_type` | context feature | `[num_particles]` | `int64` | `bytes` | Type identifier for each particle |
+
+The data splits are as follows:
+
+| File | split | Number of Trajectories | Description |
+|---|---|---:|---|
+| `data/Water/train.tfrecord` | `train` | 1,000 | Used for model training |
+| `data/Water/valid.tfrecord` | `valid` | 30 | Used for model validation and hyperparameter selection |
+| `data/Water/test.tfrecord` | `test` | 30 | Used for model testing and result evaluation |
+
+`data/Water/metadata.json` also contains `bounds=[[0.1, 0.9], [0.1, 0.9]]`, `sequence_length=1000`, `default_connectivity_radius=0.015`, `dt=0.0025`, and normalization fields such as `vel_mean`, `vel_std`, `acc_mean`, and `acc_std`.
+
+## How to Use the Dataset
+
+This dataset is compatible with the `OneScience/LagrangianMGN` model.
+
+- Files and Download:
+
+```bash
+hf download --dataset OneScience-Sugon/lagrangian --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 Lagrangian Water paper: [Learning to Simulate Complex Physics with Graph Networks](https://proceedings.mlr.press/v119/sanchez-gonzalez20a/sanchez-gonzalez20a.pdf)
+- This dataset is organized from the Learning to Simulate project released by Google DeepMind. Before public distribution, confirm the licensing requirements of the upstream project.

README_zh.md

@@ -0,0 +1,68 @@
+---
+license: other
+#用户自定义标签
+tags:
+  - Lagrangian
+  - CFD
+  - graph neural network
+language:
+  - en
+  - zh
+---
+<p align="center">
+  <strong>
+    <span style="font-size: 30px;"> Lagrangian </span>
+  </strong>
+</p>
+
+## 数据集介绍
+
+Lagrangian 数据集来源于 DeepMind 团队发表于 ICML 2020 的论文 [《Learning to Simulate Complex Physics with Graph Networks》](https://proceedings.mlr.press/v119/sanchez-gonzalez20a.html)，是该论文 Graph Network-based Simulator（GNS）基准中的二维 Water 粒子动力学数据。数据以粒子为图节点，通过粒子位置序列和粒子类型描述流体随时间的演化，可用于拉格朗日粒子动力学建模、流体长期滚动预测以及图神经网络物理仿真评测。
+
+
+## 数据集支持的任务
+
+本标准化数据集仓库整理了 Lagrangian Water 的训练集、验证集和测试集 TFRecord 文件，以及数据元信息、数据模式、完整性汇总和校验脚本，可作为 `OneScience/LagrangianMGN` 模型的训练、推理、评测和可视化数据输入。核心数据统一放置在 `data/Water/` 下，其中训练集包含 1,000 条轨迹，验证集和测试集各包含 30 条轨迹。
+
+
+## 数据集的格式和结构
+
+数据集采用 TFRecord SequenceExample 格式，每条记录对应一条粒子运动轨迹。粒子数量 `num_particles` 随轨迹变化，空间维度 `dim=2`。
+
+| 特征 | 所属部分 | shape | dtype | 源编码 | 说明 |
+|---|---|---:|---|---|---|
+| `position` | sequence feature | `[1001, num_particles, 2]` | `float32` | `bytes` | 1,001 帧粒子二维位置，对应 1,000 个演化时间步 |
+| `particle_type` | context feature | `[num_particles]` | `int64` | `bytes` | 每个粒子的类型标识 |
+
+数据划分如下：
+
+| 文件 | split | 轨迹数 | 说明 |
+|---|---|---:|---|
+| `data/Water/train.tfrecord` | `train` | 1,000 | 用于模型训练 |
+| `data/Water/valid.tfrecord` | `valid` | 30 | 用于模型验证与参数选择 |
+| `data/Water/test.tfrecord` | `test` | 30 | 用于模型测试与结果评估 |
+
+`data/Water/metadata.json` 还包含 `bounds=[[0.1, 0.9], [0.1, 0.9]]`、`sequence_length=1000`、`default_connectivity_radius=0.015`、`dt=0.0025`，以及 `vel_mean`、`vel_std`、`acc_mean` 和 `acc_std` 等归一化字段。
+
+## 数据集使用方式
+
+本数据集适配 `OneScience-Sugon/LagrangianMGN` 模型。
+
+- 文件与下载：
+
+```bash
+hf download --dataset OneScience-Sugon/lagrangian --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
+
+- Lagrangian Water 原始论文：[Learning to Simulate Complex Physics with Graph Networks](https://proceedings.mlr.press/v119/sanchez-gonzalez20a/sanchez-gonzalez20a.pdf)
+- 本数据集整理于 Google DeepMind 发布的 Learning to Simulate 项目，公开分发前请根据上游项目确认许可证要求。

data/Water/metadata.json

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data_integrity_summary.json

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+{
+  "source_root": "cfd_dataset/Lagrangian_MGN/data",
+  "standardized_root": "modelscope_standardized/dataset/cfd_lagrangian/data",
+  "files": [
+    {
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+      "match": true
+    },
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+  "all_match": true
+}

files_sha256.jsonl

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metadata/lagrangian_water_schema.json

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+{
+  "dataset": "DeepMind Lagrangian Water",
+  "modelscope_repo_id": "OneScience/lagrangian",
+  "format": "tfrecord_sequence_example",
+  "splits": {
+    "train": {
+      "path": "data/Water/train.tfrecord",
+      "expected_sequences": 1000
+    },
+    "valid": {
+      "path": "data/Water/valid.tfrecord",
+      "expected_sequences": 30
+    },
+    "test": {
+      "path": "data/Water/test.tfrecord",
+      "expected_sequences": 30
+    }
+  },
+  "metadata_path": "data/Water/metadata.json",
+  "sequence_features": {
+    "position": {
+      "dtype": "float32",
+      "shape": ["sequence_length + 1", "num_particles", "dim"],
+      "source_encoding": "bytes"
+    }
+  },
+  "context_features": {
+    "particle_type": {
+      "dtype": "int64",
+      "shape": ["num_particles"],
+      "source_encoding": "bytes"
+    }
+  },
+  "water_metadata": {
+    "dim": 2,
+    "sequence_length": 1000,
+    "dt": 0.0025,
+    "default_connectivity_radius": 0.015,
+    "num_node_types": 6,
+    "normalization_stats": ["vel_mean", "vel_std", "acc_mean", "acc_std"]
+  }
+}

onescience_relations.yaml

@@ -0,0 +1,16 @@
+required_datasets: []
+optional_datasets: []
+compatible_models:
+  - id: OneScience/LagrangianMGN
+    role: train_data
+    required_for: [preflight, train, inference, evaluate, visualize]
+    resource_ref:
+      platform: modelscope
+      repo_id: OneScience/LagrangianMGN
+      repo_type: model
+      url: https://modelscope.cn/models/OneScience/LagrangianMGN
+      revision: main
+      readme_path: README.md
+      manifest_path: onescience_run_manifest.yaml
+    expected_local_path: data/Water
+    adapter: null

onescience_run_manifest.yaml

@@ -0,0 +1,271 @@
+onescience_manifest_version: "0.1"
+resource_type: dataset
+
+resource:
+  id: OneScience/lagrangian
+  name: lagrangian
+  domain: cfd
+  domain_tags: [cfd, particle_simulation, lagrangian_dynamics]
+  task: lagrangian_particle_simulation_dataset
+  task_tags: [train_data, eval_data, inference_input, dataset_validation]
+  modalities: [particle_trajectory]
+  input_formats: [tfrecord]
+  output_formats: [tfrecord]
+  summary: DeepMind Lagrangian Water 子数据集标准数据包，包含 train、valid、test 三个 TFRecord split 和 metadata.json。
+
+platform_resource:
+  primary:
+    platform: modelscope
+    repo_id: OneScience/lagrangian
+    repo_type: dataset
+    url: https://modelscope.cn/datasets/OneScience/lagrangian
+    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/lagrangian
+  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_LAGRANGIAN_DATA_DIR 指向本仓库 data/Water。
+
+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_LAGRANGIAN_DATA_DIR: <dataset_repo_root>/data/Water
+  dependencies:
+    python: ">=3.10"
+    python_packages: [tensorflow, 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/deepmind_lagrangian.py
+  compatibility:
+    examples_path: onescience/examples/cfd/Lagrangian_MGN
+    status: examples_compatible
+  datapipe: onescience.datapipes.cfd.DeepMindLagrangianDatapipe
+
+runtime_package:
+  kind: standard_runtime_package
+  package_root: .
+  standard_layout:
+    workdir: .
+    data_dir: data/Water
+    metadata_dir: metadata
+    output_dir: validation_outputs
+  apply_policy:
+    mode: direct_use
+    target: session_workdir
+    overwrite: false
+    protect_installed_onescience: true
+  entry_files:
+    - data/Water/metadata.json
+    - data/Water/train.tfrecord
+    - data/Water/valid.tfrecord
+    - data/Water/test.tfrecord
+    - metadata/lagrangian_water_schema.json
+    - scripts/validate_lagrangian_dataset.py
+    - files_sha256.jsonl
+  entrypoints:
+    preflight: scripts/validate_lagrangian_dataset.py
+    validate: scripts/validate_lagrangian_dataset.py
+    inference: null
+    train: null
+    finetune: null
+    evaluate: null
+    visualize: null
+    deploy: null
+
+files:
+  model_files: []
+  dataset_files:
+    - id: water_tfrecord_splits
+      path: data/Water/*.tfrecord
+      role: train_eval_inference_data
+      description_zh: Water 子数据集的 train、valid、test TFRecord 序列文件。
+      required: true
+      required_for: [dataset_validation, train, inference, evaluate]
+      source: {platform: modelscope, repo_id: OneScience/lagrangian, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: data/Water
+    - id: water_metadata
+      path: data/Water/metadata.json
+      role: schema_and_normalization_stats
+      description_zh: Water 数据的维度、时间步长、边界、半径、速度和加速度归一化统计。
+      required: true
+      required_for: [dataset_validation, train, inference, evaluate]
+      source: {platform: modelscope, repo_id: OneScience/lagrangian, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: data/Water/metadata.json
+    - id: integrity_inventory
+      path: files_sha256.jsonl
+      role: file_size_sha256_inventory
+      description_zh: 整理后数据文件大小和 SHA256 清单。
+      required: true
+      required_for: [dataset_validation]
+      source: {platform: modelscope, repo_id: OneScience/lagrangian, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: files_sha256.jsonl
+    - id: integrity_summary
+      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/lagrangian, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: data_integrity_summary.json
+  config_files:
+    - id: schema
+      path: metadata/lagrangian_water_schema.json
+      role: dataset_schema
+      description_zh: TFRecord SequenceExample 字段、dtype、shape 和 split 数量说明。
+      required: true
+      required_for: [dataset_validation, train, inference]
+      source: {platform: modelscope, repo_id: OneScience/lagrangian, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: metadata/lagrangian_water_schema.json
+  sample_files:
+    - id: validation_script
+      path: scripts/validate_lagrangian_dataset.py
+      role: dataset_validation
+      description_zh: 检查数据文件结构、metadata、TFRecord 首条样本 shape/dtype 和可选 SHA256。
+      required: true
+      required_for: [dataset_validation]
+      source: {platform: modelscope, repo_id: OneScience/lagrangian, repo_type: dataset, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: scripts/validate_lagrangian_dataset.py
+
+dataset:
+  format: [tfrecord_sequence_example]
+  sample_unit: 一个 SequenceExample 对应一条粒子仿真序列。
+  schema:
+    path: metadata/lagrangian_water_schema.json
+  splits:
+    train: {path: data/Water/train.tfrecord, expected_sequences: 1000}
+    validation: {path: data/Water/valid.tfrecord, expected_sequences: 30}
+    test: {path: data/Water/test.tfrecord, expected_sequences: 30}
+  metadata:
+    path: data/Water/metadata.json
+    dim: 2
+    sequence_length: 1000
+    dt: 0.0025
+    default_connectivity_radius: 0.015
+    normalization_stats: [vel_mean, vel_std, acc_mean, acc_std]
+
+relations:
+  required_datasets: []
+  optional_datasets: []
+  compatible_models:
+    - id: OneScience/LagrangianMGN
+      role: train_data
+      required_for: [preflight, train, inference, evaluate, visualize]
+      resource_ref:
+        platform: modelscope
+        repo_id: OneScience/LagrangianMGN
+        repo_type: model
+        url: https://modelscope.cn/models/OneScience/LagrangianMGN
+        revision: main
+        readme_path: README.md
+        manifest_path: onescience_run_manifest.yaml
+      expected_local_path: data/Water
+      adapter: null
+
+run_matrix:
+  scenarios:
+    - name: dataset_validation
+      default: true
+      capability: preflight
+      description: 验证 Water TFRecord 数据、metadata schema、文件清单和可读性。
+      required_datasets:
+        - id: OneScience/lagrangian
+          role: dataset_validation
+          local_path: .
+      required_model_files: []
+      required_dataset_files: [data/Water/metadata.json, data/Water/train.tfrecord, data/Water/valid.tfrecord, data/Water/test.tfrecord, files_sha256.jsonl]
+      preconditions:
+        - 已下载 OneScience/lagrangian 数据集仓库并 cd 到数据集仓库根目录。
+      command_refs: [commands.preflight.validate_dataset]
+      expected_outputs: [dataset_validation_ok]
+
+capabilities:
+  inference: false
+  train: false
+  finetune: false
+  evaluate: false
+  visualize: false
+  deploy: false
+  dataset_validation: true
+
+commands:
+  download:
+    - name: download_dataset
+      target: dataset
+      repo_id: OneScience/lagrangian
+      local_path: <session_workdir>/lagrangian
+      required: true
+      required_for: [dataset_validation, train, inference, evaluate]
+      command: modelscope download --dataset OneScience/lagrangian
+  preflight:
+    - name: validate_dataset
+      description: 验证 Water 数据集文件结构、metadata、TFRecord 首条样本和清单。
+      cwd: .
+      command: python scripts/validate_lagrangian_dataset.py --dataset-root .
+      required_files: [scripts/validate_lagrangian_dataset.py, data/Water/metadata.json, data/Water/train.tfrecord, data/Water/valid.tfrecord, data/Water/test.tfrecord, files_sha256.jsonl]
+      required_env: []
+      expected_outputs: [{path: stdout, type: text, contains: "Lagrangian Water dataset validation passed"}]
+      success_criteria: [exit_code == 0]
+  prepare: []
+  inference: []
+  train: []
+  finetune: []
+  evaluate: []
+  visualize: []
+  deploy: []
+
+expected_outputs:
+  - id: dataset_validation_ok
+    path: stdout
+    type: text
+    description_zh: 数据集验证输出 Lagrangian Water dataset validation passed。
+
+diagnostics:
+  - symptom: "missing TFRecord split"
+    cause: 数据集下载不完整或当前目录不是数据集仓库根目录。
+    fix: 重新执行 modelscope download --dataset OneScience/lagrangian，并 cd 到下载后的数据集根目录。
+  - symptom: "metadata dim or sequence_length mismatch"
+    cause: 使用了非 Water 或非本标���包的数据目录。
+    fix: 确认 ONESCIENCE_LAGRANGIAN_DATA_DIR 指向 OneScience/lagrangian 的 data/Water。
+  - symptom: "TensorFlow first-record check skipped"
+    cause: 当前环境缺少 TensorFlow。
+    fix: 安装 TensorFlow 后重跑验证；文件大小和 SHA256 仍可先用于完整性判断。
+
+domain_extension:
+  cfd:
+    dataset_family: DeepMind Learning to Simulate
+    subset: Water
+    simulation_type: lagrangian_particle_dynamics
+    dimensionality: 2
+    splits: [train, valid, test]

scripts/validate_lagrangian_dataset.py

@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""Validate the standardized DeepMind Lagrangian Water dataset package."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+
+
+EXPECTED_SPLITS = {
+    "train": 1000,
+    "valid": 30,
+    "test": 30,
+}
+
+
+def fail(message: str) -> None:
+    raise SystemExit(f"[FAIL] {message}")
+
+
+def sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as f:
+        for chunk in iter(lambda: f.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset-root", default=".", help="dataset package root")
+    parser.add_argument(
+        "--verify-sha256",
+        action="store_true",
+        help="recompute and verify SHA256 values from files_sha256.jsonl",
+    )
+    parser.add_argument(
+        "--skip-tfrecord-read",
+        action="store_true",
+        help="skip optional TensorFlow first-record readability check",
+    )
+    return parser.parse_args()
+
+
+def load_metadata(data_dir: Path) -> dict:
+    meta_path = data_dir / "metadata.json"
+    if not meta_path.is_file():
+        fail(f"missing metadata file: {meta_path}")
+    metadata = json.loads(meta_path.read_text(encoding="utf-8"))
+    if metadata.get("dim") != 2:
+        fail(f"expected metadata dim=2, got {metadata.get('dim')}")
+    if metadata.get("sequence_length") != 1000:
+        fail(f"expected sequence_length=1000, got {metadata.get('sequence_length')}")
+    for key in ["vel_mean", "vel_std", "acc_mean", "acc_std"]:
+        value = metadata.get(key)
+        if not isinstance(value, list) or len(value) != 2:
+            fail(f"metadata {key} must be a length-2 list")
+    return metadata
+
+
+def check_files(dataset_root: Path) -> dict:
+    data_dir = dataset_root / "data" / "Water"
+    if not data_dir.is_dir():
+        fail(f"missing data directory: {data_dir}")
+    sizes = {}
+    for split in EXPECTED_SPLITS:
+        path = data_dir / f"{split}.tfrecord"
+        if not path.is_file():
+            fail(f"missing TFRecord split: {path}")
+        size = path.stat().st_size
+        if size <= 0:
+            fail(f"empty TFRecord split: {path}")
+        sizes[str(path.relative_to(dataset_root))] = size
+    if not (data_dir / "metadata.json").is_file():
+        fail("missing data/Water/metadata.json")
+    sizes["data/Water/metadata.json"] = (data_dir / "metadata.json").stat().st_size
+    return sizes
+
+
+def verify_inventory(dataset_root: Path, sizes: dict, verify_sha256: bool) -> None:
+    inventory_path = dataset_root / "files_sha256.jsonl"
+    if not inventory_path.is_file():
+        fail("missing files_sha256.jsonl")
+    seen = {}
+    for line in inventory_path.read_text(encoding="utf-8").splitlines():
+        if not line.strip():
+            continue
+        item = json.loads(line)
+        rel_path = item["path"]
+        path = dataset_root / rel_path
+        if not path.is_file():
+            fail(f"inventory path missing on disk: {rel_path}")
+        if path.stat().st_size != item["size"]:
+            fail(f"size mismatch for {rel_path}")
+        if sizes.get(rel_path) != item["size"]:
+            fail(f"required file size mismatch for {rel_path}")
+        if verify_sha256:
+            actual = sha256_file(path)
+            if actual != item["sha256"]:
+                fail(f"sha256 mismatch for {rel_path}")
+        seen[rel_path] = item
+    missing = sorted(set(sizes) - set(seen))
+    if missing:
+        fail(f"inventory missing required files: {missing}")
+
+
+def check_tfrecord_first_record(data_dir: Path, metadata: dict) -> None:
+    try:
+        import numpy as np
+        import tensorflow.compat.v1 as tf
+    except Exception as exc:  # pragma: no cover - depends on runtime env
+        print(f"[WARN] TensorFlow first-record check skipped: {exc}")
+        return
+
+    feature_description = {"position": tf.io.VarLenFeature(tf.string)}
+    context_features = {
+        "key": tf.io.FixedLenFeature([], tf.int64, default_value=0),
+        "particle_type": tf.io.VarLenFeature(tf.string),
+    }
+    expected_steps = metadata["sequence_length"] + 1
+    dim = metadata["dim"]
+    for split in EXPECTED_SPLITS:
+        record_iter = iter(tf.data.TFRecordDataset(str(data_dir / f"{split}.tfrecord")).take(1))
+        try:
+            raw = next(record_iter)
+        except StopIteration:
+            fail(f"{split}.tfrecord contains no records")
+        context, features = tf.io.parse_single_sequence_example(
+            raw,
+            context_features=context_features,
+            sequence_features=feature_description,
+        )
+        position = np.frombuffer(features["position"].values[0].numpy(), dtype=np.float32)
+        if position.size % (expected_steps * dim) != 0:
+            fail(f"{split}.tfrecord first record position shape is incompatible with metadata")
+        particle_type = np.frombuffer(context["particle_type"].values[0].numpy(), dtype=np.int64)
+        num_particles = position.size // (expected_steps * dim)
+        if particle_type.shape[0] != num_particles:
+            fail(f"{split}.tfrecord particle_type length does not match position particles")
+        print(
+            f"[OK] {split}.tfrecord first record: "
+            f"position_shape=({expected_steps}, {num_particles}, {dim}), "
+            "position_dtype=float32, particle_type_dtype=int64"
+        )
+
+
+def main() -> None:
+    args = parse_args()
+    dataset_root = Path(args.dataset_root).resolve()
+    data_dir = dataset_root / "data" / "Water"
+    sizes = check_files(dataset_root)
+    metadata = load_metadata(data_dir)
+    verify_inventory(dataset_root, sizes, args.verify_sha256)
+    if not args.skip_tfrecord_read:
+        check_tfrecord_first_record(data_dir, metadata)
+    print("[OK] Lagrangian Water dataset validation passed")
+
+
+if __name__ == "__main__":
+    main()