Dataset Viewer (First 5GB)
crawl_id
string | timestamp
timestamp[ns, tz=UTC] | url
string | source_url
string | html
string |
|---|---|---|---|---|
a0dde408-769a-44e8-ba44-5b16cdc93ccc | 2025-06-13T10:36:59.338000 | https://apps.apple.com/jp/app/expo%E3%82%B0%E3%83%AA%E3%83%BC%E3%83%B3%E3%83%81%E3%83%A3%E3%83%AC%E3%83%B3%E3%82%B8/id6475059135 | https://expo2025-greenchallenge.com/ | "<!DOCTYPE html><html dir=\"ltr\" lang=\"ja-JP\"><head>\n <meta charset=\"utf-8\">\n <meta ht(...TRUNCATED) |
89d3ba84-c82f-4336-9530-4742e02efbec | 2025-06-13T10:36:59.445000 | https://play.google.com/store/apps/details?id=jp.global.ebookset.app1.ozcaf2 | https://expo2025-greenchallenge.com/ | "<!doctype html><html lang=\"ja\" dir=\"ltr\"><head><base href=\"https://play.google.com/\"><link re(...TRUNCATED) |
8f6864e2-82b7-4ddb-a05b-c780c2cd471c | 2025-06-13T10:36:59.613000 | https://www.fc-osaka.com/ | https://expo2025-greenchallenge.com/ | "<!DOCTYPE html>\n<html lang=\"ja\">\n<head>\n \n <meta charset='utf-8'>\n <meta name=\"viewport\(...TRUNCATED) |
72cd5ee4-8fb0-41ed-85b3-929fb15a05d4 | 2025-06-13T10:36:59.662000 | https://youtu.be/ | https://youtu.be/uQy1TeylKNA | "<!DOCTYPE html><html style=\"font-size: 10px;font-family: Roboto, Arial, sans-serif;\" lang=\"ja-JP(...TRUNCATED) |
5859c2f4-cafb-4a8d-8d62-05bb0fd7b076 | 2025-06-13T10:36:59.687000 | https://www.facebook.com/recover/initiate?lwv=110&ars=royal_blue_bar | http://www.facebook.com/share.php?u=https://japan.kantei.go.jp/103/diplomatic/202506/13kosovo.html | "<!DOCTYPE html>\n<html lang=\"ja\" id=\"facebook\" class=\"no_js\">\n<head><meta charset=\"utf-8\" (...TRUNCATED) |
0c69e7a7-dcf2-46d8-a837-5778af0339f7 | 2025-06-13T10:36:59.730000 | https://terms.line.me/line_communication_privacy/ | https://www.lycorp.co.jp/ja/company/privacypolicy/ | "<!DOCTYPE html>\n<html dir=\"ltr\"\n lang=\"ja\">\n\n<head>\n <meta charset=\"UTF-8\">\n (...TRUNCATED) |
ccbc4c2b-a433-47e3-9c7b-cbdbf7c3a0d2 | 2025-06-13T10:36:59.969000 | http://www.facebook.com/share.php?u=https://japan.kantei.go.jp/103/actions/202506/13kosovo.html | https://japan.kantei.go.jp/103/actions/202506/13kosovo.html | "<!DOCTYPE html>\n<html lang=\"ja\" id=\"facebook\" class=\"no_js\">\n<head><meta charset=\"utf-8\" (...TRUNCATED) |
8eb6f58d-843c-461a-a9eb-e4206d55e6e3 | 2025-06-13T10:36:59.991000 | https://www.youtube.com/watch?v=aN2iBhmOVxw | https://team.expo2025.or.jp/ja/article/1496#tep | "<!DOCTYPE html><html style=\"font-size: 10px;font-family: Roboto, Arial, sans-serif;\" lang=\"ja-JP(...TRUNCATED) |
396c972c-2eb1-488f-a648-0da572863b88 | 2025-06-13T10:37:00.003000 | https://help.peatix.com/?hl=ja | https://peatix.com | "<!DOCTYPE html><!-- Last Published: Fri Feb 28 2025 03:34:08 GMT+0000 (Coordinated Universal Time) (...TRUNCATED) |
28a041b5-1b56-45b5-aa41-972f82a2119f | 2025-06-13T10:37:00.012000 | http://www.trlosaka.co.jp/topics/expo_greenchallenge.html | https://expo2025-greenchallenge.com/ | "<!doctype html>\n<!--[if lt IE 7]> <html class=\"no-js lt-ie9 lt-ie8 lt-ie7\"> <![endif]-->\n<(...TRUNCATED) |
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in Data Studio
TakaraSpider Japanese Web Crawl Dataset
Dataset Summary
TakaraSpider is a large-scale web crawl dataset specifically designed to capture Japanese web content alongside international sources. The dataset contains 257,900 web pages collected through systematic crawling, with a primary focus on Japanese language content (78.5%) while maintaining substantial international representation (21.5%). This makes it ideal for Japanese-English comparative studies, cross-cultural web analysis, and multilingual NLP research.
The dataset was generated by the TakaraSpider crawler, which was specifically engineered to capture high-quality Japanese web content while maintaining broad international coverage.
Supported Tasks and Leaderboards
- Text Retrieval: Large-scale web document retrieval and indexing
- Language Detection: Japanese-English-multilingual classification
- Content Classification: Web page categorization (blogs, e-commerce, news, etc.)
- Cross-Cultural Analysis: Comparative studies between Japanese and international web content
- HTML Processing: Benchmarking for web scraping and content extraction tools
- Japanese NLP: Training and evaluation for Japanese language models
Languages
- Japanese (ja): 78.5% of content - Primary focus with rich representation
- English (en): 5.3% of content - International perspective
- Other/Unknown: 16.2% of content - Diverse multilingual representation
Dataset Structure
Data Instances
{
"crawl_id": "a0dde408-769a-44e8-ba44-5b16cdc93ccc",
"timestamp": "2025-06-13T10:36:59.338661+00:00",
"url": "https://www.example.co.jp/page",
"source_url": "https://www.example.co.jp/",
"html": "<!DOCTYPE html><html lang=\"ja\">..."
}
Data Fields
crawl_id(string): Unique identifier for each crawl sessiontimestamp(timestamp): ISO 8601 formatted crawl timestamp with timezoneurl(string): Target URL that was crawledsource_url(string): Referring/source URL (when available)html(string): Complete raw HTML content of the page
Data Splits
| Split | Examples |
|---|---|
| train | 257,900 |
Dataset Creation
Curation Rationale
TakaraSpider was created to address the lack of high-quality, large-scale Japanese web crawl datasets for research purposes. Key objectives:
- Japanese Language Focus: Capture substantial Japanese web content for NLP research
- Cultural Representation: Include diverse Japanese web content types (blogs, news, e-commerce)
- International Balance: Maintain global perspective with international content
- Research Quality: Ensure clean, structured data suitable for academic and commercial research
- Temporal Consistency: Single-session crawl for temporal consistency
Source Data
Initial Data Collection and Normalization
The data was collected through systematic web crawling using the TakaraSpider crawler during a concentrated crawling session on June 13, 2025. The crawler was configured to:
- Prioritize Japanese (.jp) domains while maintaining international diversity
- Capture complete HTML content with metadata
- Ensure broad domain coverage (10,590+ unique domains)
- Maintain crawl provenance through unique session IDs
Who are the source language producers?
The source content represents natural web usage across:
- Japanese web users: Content creators, bloggers, businesses, news organizations
- International web users: Global content accessible to Japanese audiences
- Mixed demographics: Spanning individual users to large organizations
Considerations for Using the Data
Social Impact of Dataset
Positive Impacts:
- Enables Japanese NLP research and development
- Supports cross-cultural digital humanities research
- Facilitates web technology development and benchmarking
- Promotes understanding of Japanese digital culture
Potential Concerns:
- May contain biased content reflecting web demographics
- Temporal snapshot may not represent evolving web trends
- Domain concentration could skew research findings
Discussion of Biases
Identified Biases:
- Geographic Bias: 50.9% Japanese domains may not represent global web diversity
- Temporal Bias: Single-day crawl (June 13, 2025) captures specific moment in time
- Domain Concentration: Top 10 domains represent 13.4% of dataset (improved diversity)
- Language Detection: 15.9% of content requires language identification
- Content Type Skew: Structured webpages (64.1%) over-represented
Mitigation Strategies:
- Clearly document dataset composition and limitations
- Encourage diverse evaluation across content types
- Recommend supplementary datasets for global research
- Provide detailed analytics for informed usage decisions
Other Known Limitations
- Temporal Scope: Single-session crawl may miss temporal variations
- Robots.txt Compliance: Limited to publicly accessible content
- Dynamic Content: JavaScript-rendered content may be incomplete
- Scale vs. Depth: Broad coverage may sacrifice deep domain-specific content
Additional Information
Dataset Curators
- Primary Curator: [Dataset Author Name]
- Organization: [Organization Name]
- Technical Contact: [Contact Email]
Licensing Information
This dataset is released under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). Users are free to:
- Share and redistribute the material
- Adapt, remix, transform, and build upon the material
- Use for any purpose, including commercial applications
Attribution Required: Please cite this dataset when using it in research or applications.
Citation Information
@dataset{takaraspider2025,
title={TakaraSpider: Large-Scale Japanese Web Crawl Dataset},
author={[Author Names]},
year={2025},
publisher={Hugging Face},
doi={[DOI if available]},
url={https://huggingface.co/datasets/takarajordan/takaraspider}
}
Contributions
Thanks to @takarajordan for creating and sharing this dataset with the research community.
Technical Specifications
Computational Requirements
- Storage: ~2.5GB compressed, ~8GB uncompressed
- Memory: 4GB+ RAM recommended for full dataset loading
- Processing: Optimized for streaming with 🤗 Datasets library
Data Quality Metrics
| Metric | Value | Description |
|---|---|---|
| Duplicate URLs | 0.0% | No duplicate URLs detected in sample |
| Content Completeness | 99%+ | HTML content available for virtually all records |
| Metadata Completeness | 100% | All required fields populated |
| Average Content Size | 198KB | Substantial content per page |
| Domain Diversity | 0.205 | Strong domain-to-page ratio |
Getting Started
Quick Start
from datasets import load_dataset
# Load the full dataset
dataset = load_dataset("takarajordan/takaraspider")
# Or stream for memory efficiency
dataset = load_dataset("takarajordan/takaraspider", streaming=True)
# Sample for testing
sample = dataset["train"].select(range(1000))
Example Usage
# Filter Japanese content
japanese_pages = dataset["train"].filter(
lambda x: 'lang="ja"' in x['html'][:500].lower()
)
# Extract large content pages
rich_content = dataset["train"].filter(
lambda x: len(x['html']) > 100000
)
# Domain analysis
from urllib.parse import urlparse
domains = [urlparse(url).netloc for url in dataset["train"]['url']]
Analytics and Visualizations
Complete analytics and visualizations are available in the analytics_output/ directory:
- Domain Distribution: Top domains by page count
- Geographic Analysis: TLD-based geographic distribution
- Content Analysis: Size distribution and content types
- Language Breakdown: Detailed language detection results
- URL Structure: Path depth and navigation patterns
This dataset card was generated using comprehensive analytics based on a 51,580-sample representative subset (20% of full dataset). Last updated: June 18, 2025.
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