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？
深圳市民政局深圳市公办养老机构入住轮候评估评估机构信息|养老服务数据集|机构评估数据集
收藏开放广东2025-11-05 更新2024-02-29 收录
下载链接：
https://gddata.gd.gov.cn/opdata/base/collect?chooseValue=collectForm
下载链接
链接失效反馈资源简介：
该信息类包含了自2021年深圳市民政局开放的深圳市公办养老机构入住轮候评估评估机构信息，主要包括机构名称、所属区、联系电话、联系人、地址、备注字段信息。
提供机构：
深圳市
创建时间：
2024-01-04
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
学生课堂行为数据集 (SCB-dataset3)
学生课堂行为数据集(SCB-dataset3)由成都东软学院创建，包含5686张图像和45578个标签，重点关注六种行为：举手、阅读、写作、使用手机、低头和趴桌。数据集覆盖从幼儿园到大学的不同场景，通过YOLOv5、YOLOv7和YOLOv8算法评估，平均精度达到80.3%。该数据集旨在为学生行为检测研究提供坚实基础，解决教育领域中学生行为数据集的缺乏问题。
arXiv 收录
MeSH
MeSH（医学主题词表）是一个用于索引和检索生物医学文献的标准化词汇表。它包含了大量的医学术语和概念，用于描述医学文献中的主题和内容。MeSH数据集包括主题词、副主题词、树状结构、历史记录等信息，广泛应用于医学文献的分类和检索。
www.nlm.nih.gov 收录
UIEB, U45, LSUI
本仓库提供了水下图像增强方法和数据集的实现，包括UIEB、U45和LSUI等数据集，用于支持水下图像增强的研究和开发。
github 收录
HyperGlobal-450K - 全球最大规模高光谱图像数据集
HyperGlobal-450K数据集由武汉大学联合国内外多所知名高校及研究机构共同构建，是迄今为止全球规模最大的高光谱图像数据集。该数据集包含约45万张高光谱图像，规模等价于超过2000万张不重叠的三波段图像，远超现有的同类数据集。数据集涵盖了全球范围内的高光谱遥感图像，包括来自地球观测一号（EO-1）Hyperion和高分五号（GF-5B）两种传感器的图像，光谱范围从可见光到短波及中波红外，具有从紫外到长波红外的330个光谱波段，空间分辨率为30米。每幅图像经过精心处理，去除了无效波段和水汽吸收波段，保留了具有实际应用价值的光谱信息。HyperGlobal-450K数据集不仅支持高光谱图像的基础研究，还能够用于开发和测试各种高光谱图像处理方法，比如图像分类、目标检测、异常检测、变化检测、光谱解混、图像去噪和超分辨率等任务。
github 收录