LinxSci/arxiv-paper-insights

---
license: cc-by-4.0
pretty_name: arXiv Paper Insights
language:
- en
tags:
- arxiv
- paper-insights
- scientific-llm
- summarization
- research
- linxsci
task_categories:
- text-generation
- feature-extraction
- sentence-similarity
size_categories:
- 1K<n<10K
---

# arXiv Paper Insights

## Overview

arXiv Paper Insights is a public dataset for arXiv paper discovery, recommendations, and structured research insights.

The dataset is derived from LinxSci (`https://linxsci.com`), a paper reading and insight platform focused on arXiv papers.

For any paper in this dataset, you can open:

`https://linxsci.com/pdf/{arxiv_id}`

to read the paper and view the corresponding insights on LinxSci.

## Related Links

- LinxSci: `https://linxsci.com`
- GitHub project page: `https://github.com/LinxSci/arxiv-paper-insights`
- Kaggle dataset page: `https://www.kaggle.com/datasets/linxsci/arxiv-paper-insights`
- Hugging Face dataset page: `https://huggingface.co/datasets/LinxSci/arxiv-paper-insights`

## Data Source

- Source platform: LinxSci (`https://linxsci.com`)
- Upstream corpus: arXiv papers
- Enrichment: recommendation pipeline outputs and structured insights derived by LinxSci

## Files

- `recommendations.parquet` / `recommendations.jsonl`
- `insights.parquet` / `insights.jsonl`
- `SCHEMA.md`

## What Is Included

- recommendation records with lightweight paper metadata
- structured and sanitized paper insights
- dataset fields designed for ranking, retrieval, summarization, and scientific IE

## Update Schedule

- the dataset is updated every Monday
- each update covers the previous week's recommended arXiv papers
- corresponding insights are included when available in LinxSci's release pipeline

## What Is Excluded

- full paper text
- PDF files
- markdown copies of papers
- raw chunk provenance
- figure and table image binaries

## Field Guide

### recommendations

- `date`: recommendation date
- `arxiv_id`: arXiv paper identifier
- `rank`: rank within the recommendation list
- `title`: paper title
- `abstract`: paper abstract
- `tldr`: short summary
- `arxiv_categories`: arXiv categories
- `github_links`: extracted GitHub links
- `hf_links`: extracted Hugging Face links
- `github_stars`: GitHub stars snapshot when available
- `figures_count`: number of figures detected
- `tables_count`: number of tables detected
- `has_insight`: whether a sanitized insight record exists

### insights

- `arxiv_id`: arXiv paper identifier
- `title`: paper title
- `abstract`: paper abstract
- `paper_type`: coarse paper type label
- `keywords_extracted`: extracted keywords
- `project_page`: project page URL
- `github_repo`: GitHub repository URL
- `demo_url`: demo URL
- `problem_statement`: concise problem statement
- `proposed_solution_overview`: concise solution summary
- `key_contributions`: list of contribution statements
- `limitations`: list of limitation statements
- `future_work`: future work summary
- `research_questions_or_hypotheses`: list of research questions
- `dataset_names`: dataset names mentioned in the paper
- `evaluation_metric_names`: names of evaluation metrics
- `baseline_method_names`: baseline or comparison method names
- `implementation_details`: concise implementation summary
- `key_references_summary`: summarized key references
- `main_findings`: main findings
- `sota_comparison`: summary of comparison to prior work
- `ablation_findings`: ablation summaries
- `failure_cases`: failure case summaries
- `is_code_available`: whether code availability is indicated
- `code_url`: code URL when available
- `is_data_available`: whether data availability is indicated
- `gpu_requirement`: GPU requirement summary when available
- `training_time`: training time summary when available
- `personal_summary`: reader-oriented summary
- `strengths`: strengths list
- `weaknesses`: weaknesses list
- `questions_and_ideas`: follow-up questions or ideas
- `tags`: tag list
- `prerequisite_knowledge`: prerequisite knowledge list
- `figures_count`: number of figures
- `tables_count`: number of tables
- `formulas_count`: number of formulas

## How To Use

Typical use cases:

- paper recommendation
- scientific information extraction
- research-agent memory
- retrieval and summarization

To inspect a specific paper on LinxSci, open:

`https://linxsci.com/pdf/{arxiv_id}`

Example:

`https://linxsci.com/pdf/2401.01234`

## Loading Examples

### Load with pandas

```python
import pandas as pd

recommendations = pd.read_parquet("recommendations.parquet")
insights = pd.read_parquet("insights.parquet")
```

### Load JSONL

```python
import pandas as pd

recommendations = pd.read_json("recommendations.jsonl", lines=True)
insights = pd.read_json("insights.jsonl", lines=True)
```

### Join recommendations and insights

```python
merged = recommendations.merge(insights, on="arxiv_id", how="left")
```

## Limitations

- some snapshot-based fields can become stale
- recommendation and insight outputs are derived artifacts
- structured insights may contain extraction errors
- not all papers include external project links or code links

## License Notes

License: `CC BY 4.0` for derived dataset artifacts only.

Original paper contents, PDFs, and other third-party source materials remain subject to their respective licenses and rights. Users are responsible for complying with upstream source terms.

## Citation

If you use this dataset, cite the project and link back to LinxSci:

```bibtex
@misc{arxiv-paper-insights,
title = {arXiv Paper Insights},
author = {LinxSci},
year = {2026},
howpublished = {\url{https://linxsci.com}},
note = {Public dataset for arXiv paper insights and recommendation-ready metadata}
}
```

## Version History

- `2026-04-20`: initial public release
- Weekly cadence: every Monday we publish the previous week's recommended papers

---
license: CC BY 4.0
pretty_name: arXiv论文洞察数据集（arXiv Paper Insights）
language:
- en
tags:
- arXiv
- 论文洞察
- 科学大语言模型（Scientific LLM）
- 文本摘要
- 研究
- LinxSci
task_categories:
- 文本生成
- 特征提取
- 句子相似度
size_categories:
- 1000<n<10000
---

# arXiv论文洞察数据集（arXiv Paper Insights）

## 概览
arXiv论文洞察数据集是面向arXiv（arXiv）论文发现、推荐与结构化研究洞察的公开数据集。
本数据集源自专注于arXiv论文阅读与洞察提取的平台LinxSci（LinxSci），其官方网址为`https://linxsci.com`。对于本数据集中的任意一篇论文，您可通过访问`https://linxsci.com/pdf/{arxiv_id}`来阅读论文并查看LinxSci平台上对应的洞察内容。

## 相关链接
- LinxSci：`https://linxsci.com`
- GitHub项目页面：`https://github.com/LinxSci/arxiv-paper-insights`
- Kaggle数据集页面：`https://www.kaggle.com/datasets/linxsci/arxiv-paper-insights`
- Hugging Face数据集页面：`https://huggingface.co/datasets/LinxSci/arxiv-paper-insights`

## 数据来源
- 来源平台：LinxSci（LinxSci），官方网址为`https://linxsci.com`
- 上游语料：arXiv论文
- 补充内容：LinxSci通过推荐流水线生成的输出结果与结构化研究洞察

## 数据集文件
- `recommendations.parquet` / `recommendations.jsonl`
- `insights.parquet` / `insights.jsonl`
- `SCHEMA.md`

## 数据集包含内容
- 附带轻量级论文元数据的推荐记录
- 经过清洗与结构化处理的论文洞察内容
- 专为排序、检索、摘要生成与科学信息提取设计的数据集字段

## 更新周期
- 本数据集每周一进行更新
- 每次更新涵盖上周的推荐arXiv论文
- 若LinxSci发布流水线中存在对应洞察内容，则会一并包含在更新包中

## 数据集不包含内容
- 完整论文文本
- PDF文件
- 论文的Markdown副本
- 原始分块的来源信息
- 图表图片的二进制文件

## 字段说明
### 推荐记录（recommendations）
- `date`：推荐日期
- `arxiv_id`：arXiv论文标识符
- `rank`：推荐列表中的排名位次
- `title`：论文标题
- `abstract`：论文摘要
- `tldr`：简短摘要
- `arxiv_categories`：arXiv论文分类标签
- `github_links`：提取得到的GitHub链接
- `hf_links`：提取得到的Hugging Face链接
- `github_stars`：提取时的GitHub星标快照（若可用）
- `figures_count`：检测到的图表数量
- `tables_count`：检测到的表格数量
- `has_insight`：是否存在经过清洗的洞察记录

### 洞察内容（insights）
- `arxiv_id`：arXiv论文标识符
- `title`：论文标题
- `abstract`：论文摘要
- `paper_type`：粗粒度论文类型标签
- `keywords_extracted`：自动提取的关键词
- `project_page`：项目页面URL
- `github_repo`：GitHub仓库URL
- `demo_url`：演示页面URL
- `problem_statement`：简洁的问题阐述
- `proposed_solution_overview`：解决方案概述
- `key_contributions`：贡献声明列表
- `limitations`：局限性声明列表
- `future_work`：未来工作计划总结
- `research_questions_or_hypotheses`：研究问题或假设列表
- `dataset_names`：论文中提及的数据集名称
- `evaluation_metric_names`：评估指标名称
- `baseline_method_names`：基线方法或对比方法名称
- `implementation_details`：简洁的实现细节总结
- `key_references_summary`：关键参考文献总结
- `main_findings`：核心研究发现
- `sota_comparison`：与当前前沿研究的对比总结
- `ablation_findings`：消融实验结果总结
- `failure_cases`：失败案例总结
- `is_code_available`：是否标注了代码可获取
- `code_url`：代码获取URL（若可用）
- `is_data_available`：是否标注了数据可获取
- `gpu_requirement`：GPU资源需求总结（若可用）
- `training_time`：训练耗时总结（若可用）
- `personal_summary`：面向读者的个性化总结
- `strengths`：研究优势列表
- `weaknesses`：研究劣势列表
- `questions_and_ideas`：后续研究问题或创新思路
- `tags`：自定义标签列表
- `prerequisite_knowledge`：前置知识列表
- `figures_count`：论文图表数量
- `tables_count`：论文表格数量
- `formulas_count`：论文公式数量

## 使用方式
典型应用场景包括：
- 学术论文推荐
- 科学信息提取
- AI智能体（AI Agent）记忆库构建
- 学术文本检索与摘要生成

若要查看LinxSci平台上的特定论文，请访问以下链接：
`https://linxsci.com/pdf/{arxiv_id}`
示例链接：`https://linxsci.com/pdf/2401.01234`

## 加载示例
### 使用Pandas加载Parquet格式数据
python
import pandas as pd

recommendations = pd.read_parquet("recommendations.parquet")
insights = pd.read_parquet("insights.parquet")

### 加载JSONL格式数据
python
import pandas as pd

recommendations = pd.read_json("recommendations.jsonl", lines=True)
insights = pd.read_json("insights.jsonl", lines=True)

### 合并推荐记录与洞察内容
python
merged = recommendations.merge(insights, on="arxiv_id", how="left")

## 数据集局限性
- 部分基于快照生成的字段可能随时间过时
- 推荐结果与洞察内容均为衍生加工产物
- 结构化洞察内容可能存在自动提取误差
- 并非所有论文都附带外部项目链接或代码获取链接

## 许可声明
本数据集衍生产物的许可证为CC BY 4.0。
原始论文内容、PDF文件及其他第三方源材料仍受其各自的许可协议与版权条款约束。使用者需自行遵守上游数据源的相关使用条款。

## 引用方式
若您使用本数据集，请引用该项目并链接至LinxSci官方网站：
bibtex
@misc{arxiv-paper-insights,
title = {arXiv论文洞察数据集},
author = {LinxSci},
year = {2026},
howpublished = {url{https://linxsci.com}},
note = {arXiv论文洞察与可用于推荐的元数据公开数据集}
}

## 版本历史
- `2026-04-20`：首次公开发布
- 更新周期：每周一发布上周的推荐论文数据集