有没有相关的论文或文献参考？
这个数据集是基于什么背景创建的？
数据集的作者是谁？
能帮我联系到这个数据集的作者吗？
这个数据集如何下载？
Titanic Anchors Front Back|泰坦尼克号数据集|3D建模数据集
收藏Mendeley Data2024-05-10 更新2024-06-27 收录
下载链接：
https://zenodo.org/records/10285134
下载链接
链接失效反馈资源简介：
Anchor asset of titanic ship Source: Objaverse 1.0 / Sketchfab
创建时间：
2024-01-08
数据主题
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集
aqcat25
<h1 align="center" style="font-size: 36px;"> <span style="color: #FFD700;">AQCat25 Dataset:</span> Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis </h1>  This repository contains the **AQCat25 dataset**. AQCat25-EV2 models can be accessed [here](https://huggingface.co/SandboxAQ/aqcat25-ev2). The AQCat25 dataset provides a large and diverse collection of **13.5 million** DFT calculation trajectories, encompassing approximately 5K materials and 47K intermediate-catalyst systems. It is designed to complement existing large-scale datasets by providing calculations at **higher fidelity** and including critical **spin-polarized** systems, which are essential for accurately modeling many industrially relevant catalysts. Please see our [website](https://www.sandboxaq.com/aqcat25) and [paper](https://cdn.prod.website-files.com/622a3cfaa89636b753810f04/68ffc1e7c907b6088573ba8c_AQCat25.pdf) for more details about the impact of the dataset and [models](https://huggingface.co/SandboxAQ/aqcat25-ev2). ## 1. AQCat25 Dataset Details This repository uses a hybrid approach, providing lightweight, queryable Parquet files for each split alongside compressed archives (`.tar.gz`) of the raw ASE database files. More details can be found below. ### Queryable Metadata (Parquet Files) A set of Parquet files provides a "table of contents" for the dataset. They can be loaded directly with the `datasets` library for fast browsing and filtering. Each file contains the following columns: | Column Name | Data Type | Description | Example | | :--- | :--- | :--- | :--- | | `frame_id` | string | **Unique ID for this dataset**. Formatted as `database_name::index`. | `data.0015.aselmdb::42` | | `adsorption_energy`| float | **Key Target**. The calculated adsorption energy in eV. | -1.542 | | `total_energy` | float | The raw total energy of the adslab system from DFT (in eV). | -567.123 | | `fmax` | float | The maximum force magnitude on any single atom in eV/Å. | 0.028 | | `is_spin_off` | boolean | `True` if the system is non-magnetic (VASP ISPIN=1). | `false` | | `mag` | float | The total magnetization of the system (µB). | 32.619 | | `slab_id` | string | Identifier for the clean slab structure. | `mp-1216478_001_2_False` | | `adsorbate` | string | SMILES or chemical formula of the adsorbate. | `*NH2N(CH3)2` | | `is_rerun` | boolean | `True` if the calculation is a continuation. | `false` | | `is_md` | boolean | `True` if the frame is from a molecular dynamics run. | `false` | | `sid` | string | The original system ID from the source data. | `vadslabboth_82` | | `fid` | integer | The original frame index (step number) from the source VASP calculation. | 0 | --- #### Understanding `frame_id` and `fid` | Field | Purpose | Example | | :--- | :--- | :--- | | `fid` | **Original Frame Index**: This is the step number from the original VASP relaxation (`ionic_steps`). It tells you where the frame came from in its source simulation. | `4` (the 5th frame of a specific VASP run) | | `frame_id` | **Unique Dataset Pointer**: This is a new ID created for this specific dataset. It tells you exactly which file (`data.0015.aselmdb`) and which row (`101`) to look in to find the full atomic structure. | `data.0015.aselmdb::101` | --- ## Downloadable Data Archives The full, raw data for each split is available for download in compressed `.tar.gz` archives. The table below provides direct download links. The queryable Parquet files for each split can be loaded directly using the `datasets` library as shown in the "Example Usage" section. The data currently available for download (totaling ~11.1M frames, as listed in the table below) is the initial dataset version (v1.0) released on September 10, 2025. The 13.5M frame count mentioned in our paper and the introduction includes additional data used to rebalance non-magnetic element systems and add a low-fidelity spin-on dataset. These new data splits will be added to this repository soon. | Split Name | Structures | Archive Size | Download Link | | :--- | :--- | :--- | :--- | | ***In-Domain (ID)*** | | | | | Train | `7,386,750` | `23.8 GB` | [`train_id.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/train_id.tar.gz) | | Validation | `254,498` | `825 MB` | [`val_id.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/val_id.tar.gz) | | Test | `260,647` | `850 MB` | [`test_id.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/test_id.tar.gz) | | Slabs | `898,530` | `2.56 GB` | [`id_slabs.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/id_slabs.tar.gz) | | ***Out-of-Distribution (OOD) Validation*** | | | | | OOD Ads (Val) | `577,368` | `1.74 GB` | [`val_ood_ads.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/val_ood_ads.tar.gz) | | OOD Materials (Val) | `317,642` | `963 MB` | [`val_ood_mat.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/val_ood_mat.tar.gz) | | OOD Both (Val) | `294,824` | `880 MB` | [`val_ood_both.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/val_ood_both.tar.gz) | | OOD Slabs (Val) | `28,971` | `83 MB` | [`val_ood_slabs.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/val_ood_slabs.tar.gz) | | ***Out-of-Distribution (OOD) Test*** | | | | | OOD Ads (Test) | `346,738` | `1.05 GB` | [`test_ood_ads.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/test_ood_ads.tar.gz) | | OOD Materials (Test) | `315,931` | `993 MB` | [`test_ood_mat.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/test_ood_mat.tar.gz) | | OOD Both (Test) | `355,504` | `1.1 GB` | [`test_ood_both.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/test_ood_both.tar.gz) | | OOD Slabs (Test) | `35,936` | `109 MB` | [`test_ood_slabs.tar.gz`](https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset/resolve/main/test_ood_slabs.tar.gz) | --- ## 2. Dataset Usage Guide This guide outlines the recommended workflow for accessing and querying the AQCat25 dataset. ### 2.1 Initial Setup Before you begin, you need to install the necessary libraries and authenticate with Hugging Face. This is a one-time setup. ```bash pip install datasets pandas ase tqdm requests huggingface_hub ase-db-backends ``` **1. Create a Hugging Face Account:** If you don't have one, create an account at [huggingface.co](https://huggingface.co/join). **2. Create an Access Token:** Navigate to your **Settings -> Access Tokens** page or click [here](https://huggingface.co/settings/tokens). Create a new token with at least **`read`** permissions. Copy this token to your clipboard. **3. Log in via the Command Line:** Open your terminal and run the following command: ```bash hf auth login ``` ### 2.2 Get the Helper Scripts You may copy the scripts directly from this repository, or download them by running the following in your local python environment: ```python from huggingface_hub import snapshot_download snapshot_download( repo_id="SandboxAQ/aqcat25", repo_type="dataset", allow_patterns=["scripts/*", "README.md"], local_dir="./aqcat25" ) ``` This will create a local folder named aqcat25 containing the scripts/ directory. ### 2.3 Download Desired Dataset Splits Data splits may be downloaded directly via the Hugging Face UI, or via the `download_split.py` script (found in `aqcat25/scripts/`). ```bash python aqcat25/scripts/download_split.py --split val_id ``` This will download `val_id.tar.gz` and extract it to a new folder named `aqcat_data/val_id/`. ### 2.4 Query the Dataset Use the `query_aqcat.py` script to filter the dataset and extract the specific atomic structures you need. It first queries the metadata on the Hub and then extracts the full structures from your locally downloaded files. **Example 1: Find all CO and OH structures in the test set:** ```bash python aqcat25/scripts/query_aqcat.py \ --split test_id \ --adsorbates "*CO" "*OH" \ --data-root ./aqcat_data/test_id ``` **Example 2: Find structures on metal slabs with low adsorption energy:** ```bash python aqcat25/scripts/query_aqcat.py \ --split val_ood_both \ --max-energy -2.0 \ --material-type nonmetal \ --magnetism magnetic \ --data-root ./aqcat_data/val_ood_both \ --output-file low_energy_metals.extxyz ``` **Example 3: Find CO on slabs containing both Ni AND Se with adsorption energy between -2.5 and -1.5 eV with a miller index of 011** ```bash python aqcat25/scripts/query_aqcat.py \ --split val_ood_ads \ --adsorbates "*COCH2OH" \ --min-energy -2.5 \ --max-energy -1.5 \ --contains-elements "Ni" "Se" \ --element-filter-mode all \ --facet 011 \ --data-root ./aqcat_data/val_ood_ads \ --output-file COCH2OH_on_ni_and_se.extxyz ``` --- ## 3. How to Cite If you use the AQCat25 dataset or the models in your research, please cite the following paper: ``` Omar Allam, Brook Wander, & Aayush R. Singh. (2025). AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis. arXiv preprint arXiv:XXXX.XXXXX. ``` ### BibTeX Entry ```bibtex @article{allam2025aqcat25, title={{AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis}}, author={Allam, Omar and Wander, Brook and Singh, Aayush R}, journal={arXiv preprint arXiv:2510.22938}, year={2025}, eprint={2510.22938}, archivePrefix={arXiv}, primaryClass={cond-mat.mtrl-sci} } ```
魔搭社区 收录
China Health and Nutrition Survey (CHNS)
China Health and Nutrition Survey（CHNS）是一项由美国北卡罗来纳大学人口中心与中国疾病预防控制中心营养与健康所合作开展的长期开放性队列研究项目，旨在评估国家和地方政府的健康、营养与家庭计划政策对人群健康和营养状况的影响，以及社会经济转型对居民健康行为和健康结果的作用。该调查覆盖中国15个省份和直辖市的约7200户家庭、超过30000名个体，采用多阶段随机抽样方法，收集了家庭、个体以及社区层面的详细数据，包括饮食、健康、经济和社会因素等信息。自2011年起，CHNS不断扩展，新增多个城市和省份，并持续完善纵向数据链接，为研究中国社会经济变化与健康营养的动态关系提供了重要的数据支持。
www.cpc.unc.edu 收录
THCHS-30
“THCHS30是由清华大学语音与语言技术中心（CSLT）发布的开放式汉语语音数据库。原始录音是2002年在清华大学国家重点实验室的朱晓燕教授的指导下，由王东完成的。清华大学计算机科学系智能与系统，原名“TCMSD”，意思是“清华连续普通话语音数据库”，时隔13年出版，由王东博士发起，并得到了教授的支持。朱小燕。我们希望为语音识别领域的新研究人员提供一个玩具数据库。因此，该数据库对学术用户完全免费。整个软件包包含建立中文语音识别所需的全套语音和语言资源系统。”
OpenDataLab 收录
O*NET
O*NET（Occupational Information Network）是一个综合性的职业信息数据库，提供了关于各种职业的详细描述，包括技能要求、工作活动、知识领域、工作环境等。该数据集被广泛用于职业分析、教育和劳动力市场研究。
www.onetonline.org 收录
PlantVillage
在这个数据集中，39 种不同类别的植物叶子和背景图像可用。包含 61,486 张图像的数据集。我们使用了六种不同的增强技术来增加数据集的大小。这些技术是图像翻转、伽玛校正、噪声注入、PCA 颜色增强、旋转和缩放。
OpenDataLab 收录