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？
Relief
收藏Mendeley Data2024-05-10 更新2024-06-27 收录
下载链接：
https://zenodo.org/records/10306856
下载链接
链接失效反馈资源简介：
Relief representation of a visit to a patient. The stone relief, which is reminiscent of the former Mautner Markhof'sche children's hospital, shows a doctor taking the pulse of a girl. The depiction is located within a window-like frame decorated with flower garlands and a woman's mask. The relief was created by Carl Kundmann and dates from 1873. 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
中国近海台风路径集合数据集(1945-2024)
1945-2024年度，中国近海台风路径数据集，包含每个台风的真实路径信息、台风强度、气压、中心风速、移动速度、移动方向。 数据源为获取温州台风网(http://www.wztf121.com/)的真实观测路径数据，经过处理整合后形成文件，如使用csv文件需使用文本编辑器打开浏览，否则会出现乱码，如要使用excel查看数据，请使用xlsx的格式。
国家海洋科学数据中心 收录
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
红外谱图数据库
收集整理红外谱图实验手册等数据，建成了红外谱图数据库。本数据库收录了常见化合物的红外谱图。主要包括化合物数据和对应的红外谱图数据。其中，原始红外谱图都进行了数字化处理，从而使谱峰检索成为可能。用户可以在数据库中检索指定化合物的谱图，也可以提交谱图/谱峰数据，以检索与之相似的谱图数据，以协助进行谱图鉴定。
国家基础学科公共科学数据中心 收录
糖尿病预测数据集
糖尿病相关的医学研究或者健康数据
AI_Studio 收录