| --- |
| license: gpl-3.0 |
| task_categories: |
| - text-retrieval |
| - text-classification |
| language: |
| - zh |
| - en |
| tags: |
| - papers |
| - academic |
| - bibliography |
| - nlp |
| - computer-vision |
| - machine-learning |
| pretty_name: PaperVault |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: papers |
| data_files: "cache/cache.jsonl.gz" |
| default: true |
| - config_name: abstract_backfill_progress |
| data_files: "cache/abstract_backfill_progress.jsonl.gz" |
| --- |
| |
| # PaperVault Dataset · 论文元数据库 |
|
|
| ## 🔎 项目简介 · Overview |
|
|
| PaperVault 是一份**持续自动更新**的统一论文元数据库，覆盖自然语言处理、计算机视觉、机器学习、数据挖掘、数据库、语音、系统、网络、安全、理论计算机科学、人机交互、计算机图形学与多媒体等方向的顶级会议与期刊。PaperVault is a **continuously-updated, unified metadata database** of papers from top-tier conferences and journals across NLP, Computer Vision, Machine Learning, Data Mining, Databases, Speech, Systems, Networking, Security, Theory, HCI, Graphics, and Multimedia. |
|
|
| > 🌐 源仓库 / Source: **[github.com/youngfish42/PaperVault](https://github.com/youngfish42/PaperVault)** — Web UI、REST API、采集流水线、Issues / PRs 全部在那里 · Web UI, REST API, crawling pipelines and issues/PRs all live there. |
| > |
| > 🌐 在线搜索 / Live search: **[papervault.top](https://papervault.top)** — 直接使用 Web 检索界面，无需下载数据 · Use the web search UI directly without downloading the dataset. |
|
|
| --- |
|
|
| ## 🆕 最近更新 · Recent Update |
|
|
| <!-- hf-recent-update-start --> |
| - 📅 **最近更新 · Last updated**: 2026-10-01 (Asia/Shanghai) |
| - 📊 **数据库规模 · Database size**: 712,484 篇论文 / 129 个刊物系列 / 624,023 篇含摘要 / 111,924 篇含开源代码（712,484 papers / 129 venue series / 624,023 with abstract / 111,924 with code） |
| <!-- hf-recent-update-end --> |
|
|
| --- |
|
|
| ## 📈 数据看板 · Statistics at a Glance |
|
|
| 下列 4 张统计图与 `cache.jsonl.gz` 同源同步，反映本数据集的最新状态。The four charts below are generated from the same `cache.jsonl.gz` and always reflect the latest state of this dataset. |
|
|
| <p align="center"> |
| <img src="pics/stats/stats_overview.svg" alt="Statistics Overview" width="850" /> |
| </p> |
|
|
| <p align="center"> |
| <img src="pics/stats/papers_by_category.svg" alt="Papers by Research Field" width="850" /> |
| </p> |
|
|
| <p align="center"> |
| <img src="pics/stats/papers_by_year.svg" alt="Annual Paper Collection Trend" width="850" /> |
| </p> |
|
|
| <p align="center"> |
| <img src="pics/stats/wordcloud.svg" alt="Publication Series Word Cloud" width="900" /> |
| </p> |
|
|
| --- |
|
|
| ## 📦 数据集内容 · What's in this dataset |
|
|
| | 路径 Path | 子集 Subset | 格式 Format | 说明 Description | |
| |---|---|---|---| |
| | `cache/cache.jsonl.gz` | `papers`（默认 / default） | gzip-compressed JSON Lines (UTF-8) | 每行一篇论文 · One paper per line; one JSON object per line | |
| | `cache/abstract_backfill_progress.jsonl.gz` | `abstract_backfill_progress` | gzip-compressed JSON Lines (UTF-8) | 摘要回填流水线的进度/断点记录，**不是论文元数据**；仅供工作流恢复使用 · Append-only progress log of the abstract-backfill pipeline (**not paper records**); used by the workflow to resume between runs | |
|
|
| Hugging Face 会自动为 `cache.jsonl.gz` 生成 Parquet 视图，也可直接用 `datasets.load_dataset(...)` 读取，无需手动解压。Hugging Face also exposes an auto-generated Parquet view, so `datasets.load_dataset(...)` works out of the box. |
|
|
| > 💡 Dataset Viewer 与 `datasets.load_dataset("youngfish42/PaperVault")` 默认展示/加载的都是 `papers` 子集（即 `cache/cache.jsonl.gz`）。如需查看回填进度，请在 Viewer 顶部下拉框切换到 `abstract_backfill_progress`，或调用 `load_dataset("youngfish42/PaperVault", name="abstract_backfill_progress")`。The Dataset Viewer and `datasets.load_dataset("youngfish42/PaperVault")` both default to the `papers` subset (`cache/cache.jsonl.gz`). To inspect backfill progress, switch the Viewer's subset dropdown to `abstract_backfill_progress` or call `load_dataset("youngfish42/PaperVault", name="abstract_backfill_progress")`. |
|
|
| ### 📐 字段 Schema |
|
|
| | Field 字段 | Type 类型 | Notes 说明 | |
| |---|---|---| |
| | `paper_name` | string | 论文标题（已归一化）· Normalised paper title | |
| | `paper_authors` | list[string] | 作者列表，按原始顺序 · Author names in order | |
| | `paper_url` | string | 论文在原始平台的链接（PDF 或落地页）· Canonical URL on the venue's site (PDF or landing page) | |
| | `paper_abstract` | string | 摘要；未回填时为空字符串 · Abstract; may be empty when not yet backfilled | |
| | `paper_code` | string | 从摘要中抽取出的 GitHub 仓库 URL；`"#"` 是「未发现代码链接」的占位符 · GitHub repository URL extracted from the abstract; `"#"` is the sentinel for "no code link discovered" | |
| | `conf` | string | 会议+年份标识，如 `ACL2024`、`NIPS2023`、`CVPR2025`；去掉末尾四位数字即可得到会议系列。注意 NeurIPS Proceedings 沿用历史命名 `NIPS{year}`。Venue + year identifier (e.g. `ACL2024`, `NIPS2023`, `CVPR2025`). Strip the trailing 4-digit year to recover the venue series. Note that NeurIPS Proceedings entries use the historical name `NIPS{year}`. | |
|
|
| > 缺失字段请按空字符串处理。Treat missing fields as empty strings. |
|
|
| --- |
|
|
| ## ⬇️ 获取方式 · How to download |
|
|
| 下面三种方式任选其一即可，**无需克隆 GitHub 仓库**。Pick any one of the three options below — **no GitHub clone is required**. |
|
|
| #### 方式 A · Option A — `huggingface_hub`（推荐 / recommended） |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
| import gzip, json |
|
|
| path = hf_hub_download( |
| repo_id="youngfish42/PaperVault", |
| filename="cache/cache.jsonl.gz", |
| repo_type="dataset", |
| ) |
| |
| with gzip.open(path, "rt", encoding="utf-8") as f: |
| for line in f: |
| record = json.loads(line) |
| # 在这里处理一条记录 · do something with the record |
| ``` |
| |
| #### 方式 B · Option B — `datasets` |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("youngfish42/PaperVault") |
| print(ds[next(iter(ds))][0]) |
| ``` |
|
|
| > 数据集只有一个默认 split（非 ML 训练集），不要传 `split="train"`。Single default split — do **not** pass `split="train"`. |
|
|
| #### 方式 C · Option C — `huggingface-cli` / 直接 HTTPS · Plain HTTPS |
|
|
| ```bash |
| huggingface-cli download youngfish42/PaperVault \ |
| cache/cache.jsonl.gz --repo-type dataset --local-dir ./data |
| ``` |
|
|
| > 💡 文件压缩后约 120 MB（会随数据持续增长），解压后是 GB 级 JSONL 流，请按行流式读取，不要整体载入内存。The file is ~120 MB compressed (and growing) and decompresses to a multi-GB JSONL stream. Stream it line-by-line rather than loading the whole thing into memory. |
|
|
| --- |
|
|
| ## 🔁 更新节奏 · Update cadence |
|
|
| 数据集由三个 GitHub Actions 工作流负责重建并推送到本 Hub 仓库 / The dataset is rebuilt and pushed to this Hub repo by three GitHub Actions workflows： |
|
|
| | 工作流 Workflow | 触发节奏 Schedule | 推送的内容 What it pushes | |
| |---|---|---| |
| | `collect_papers` | 每月 15 号 16:00 UTC + 手动触发 · 15th of every month at 16:00 UTC + `workflow_dispatch` | 增量抓取新发现的会议/年份组合 · Incremental crawl of newly-discovered conference/year combinations | |
| | `backfill_abstracts` | 每月 1 号 00:00 UTC + 手动触发 · 1st of every month at 00:00 UTC + `workflow_dispatch` | 为已有论文回填 `paper_abstract` · Adds `paper_abstract` for papers that were collected without one | |
| | `update_readme` | 仅手动触发 (`workflow_dispatch`) · Manual only (`workflow_dispatch`) | 默认仅刷新 README 与统计；当输入参数 `mode=force` 时执行全量重建 · Refreshes the README and statistics by default; performs a full rebuild only when invoked with `mode=force` | |
|
|
| 每次推送都使用 Hugging Face 的 `parent_commit` 乐观锁机制，避免并发覆盖。Each push uses Hugging Face's `parent_commit` optimistic-lock mechanism to avoid silently overwriting concurrent updates. |
|
|
| --- |
|
|
| ## 🌐 线上服务 · Live Service |
|
|
| 无需下载数据集即可直接体验 Web 检索： |
| No need to download the dataset — try the web search directly: |
|
|
| 👉 **[papervault.top](https://papervault.top)** |
|
|
| --- |
|
|
| ## 🔗 关联仓库 · Related repository |
|
|
| 如果你需要完整的搜索 Web UI（智能搜索 + Web of Science 风格的高级查询 DSL）、REST API（`/api/v1/*`）、抓取 / 合并 / 摘要回填流水线源码、收录会议范围、统计仪表盘、项目截图或贡献指南，请前往 GitHub 项目仓库。If you are looking for the full search Web UI (smart search + Web-of-Science-style advanced query DSL), the REST API (`/api/v1/*`), the crawling / merging / abstract-backfill pipelines source code, conference coverage, statistics dashboards, screenshots or contribution guidelines, please visit the GitHub repository. |
|
|
| 👉 **[github.com/youngfish42/PaperVault](https://github.com/youngfish42/PaperVault)** |
|
|
| --- |
|
|
| ## 📜 许可证 · License |
|
|
| 代码以 [GPL-3.0](https://github.com/youngfish42/PaperVault/blob/main/LICENSE) 发布；每条论文记录的著作权仍属于原作者 / 出版方，本数据集仅重新分发公开可获取的元数据与链接。Code is released under [GPL-3.0](https://github.com/youngfish42/PaperVault/blob/main/LICENSE); individual paper records remain the IP of their authors/publishers — this dataset only redistributes publicly available bibliographic metadata and links. |
|
|
