---
license: mit
task_categories:
- text-retrieval
- text-classification
language:
- en
- multilingual
tags:
- research
- academic
- data-collection
- multi-source
- machine-learning
- ai
pretty_name: Research Collector Dataset
size_categories:
- n<1K
---

# Research Collector Dataset

This dataset contains research results aggregated from multiple sources by the Research-Collector tool. Each item is enriched with comprehensive metadata, ML subfield classifications, quality scores, and temporal features.

## Dataset Details

- **Topic**: artificial super intelligence OR superintelligent AI OR ASI
- **Time Range**: 2026-04-12T16:58:38.679122 to 2026-04-26T16:58:38.679128
- **Sources**: pubmed, crossref, semantic_scholar, paperswithcode, arxiv, medium, kaggle, stackoverflow, github, reddit, hackernews, gdelt
- **Total Items**: 39
- **Exported At**: 2026-04-26T16:59:07.690129

## Dataset Structure

### Core Fields
- `id`: Unique identifier
- `title`: Title of the research item
- `source`: Source platform (e.g., pubmed, arxiv, github, reddit, stackoverflow)
- `url`: URL to original content
- `author`: Author(s)
- `published_date`: Publication date (ISO 8601 format)
- `citations`: Number of citations (if available)
- `upvotes`: Number of upvotes (if available)
- `downloads`: Number of downloads (if available)
- `comments`: Number of comments (if available)
- `content`: Content/abstract/description
- `score`: Relevance score

### Enriched Metadata Fields
- `metadata_year`: Publication year
- `metadata_month`: Publication month
- `metadata_day`: Publication day
- `metadata_week`: Week of year
- `metadata_quarter`: Quarter of year
- `metadata_days_since`: Days since publication
- `metadata_ml_subfields`: ML subfield classifications (JSON array)
- `metadata_subfield_count`: Number of ML subfields
- `metadata_keywords`: Extracted keywords (JSON array)
- `metadata_keyword_count`: Number of keywords
- `metadata_quality_scores`: Quality score metrics (JSON dict)
- `metadata_content_type`: Content type (paper, preprint, repository, discussion, qa, news)
- `metadata_has_code`: Whether item contains code
- `metadata_has_doi`: Whether item has DOI
- `metadata_sentiment_polarity`: Sentiment polarity score (-1 to 1)
- `metadata_sentiment_subjectivity`: Sentiment subjectivity score (0 to 1)
- `metadata_sentiment_category`: Sentiment category (positive, negative, neutral)
- `metadata_summary`: Automatic summary of content (extractive)
- `metadata_summary_length`: Length of summary in characters
- `metadata_data_quality`: Data quality metrics (JSON dict)
  - `completeness_score`: Field completeness percentage (0-100)
  - `consistency_score`: Internal consistency score (0-100)
  - `validity_score`: Data validity score (0-100)
  - `overall_quality_score`: Overall data quality score (0-100)
- `metadata_trending_score`: Engagement velocity score
- `metadata_trending_category`: Trending category (hot, warm, cool, cold)
- `metadata_engagement_score`: Raw engagement score
- `metadata_related_items`: Related items with similarity scores (JSON array)
- `metadata_related_count`: Number of related items

### Source-Specific Metadata
- **PubMed**: `metadata_journal`, `metadata_doi`, `metadata_mesh_terms`, `metadata_publication_types`, `metadata_abstract_length`
- **arXiv**: `metadata_arxiv_id`, `metadata_primary_category`, `metadata_categories`, `metadata_journal_ref`
- **GitHub**: `metadata_stars`, `metadata_forks`, `metadata_language`, `metadata_license`, `metadata_topics`, `metadata_has_readme`
- **Reddit**: `metadata_subreddit`, `metadata_link_flair_text`, `metadata_upvote_ratio`, `metadata_total_awards`, `metadata_is_gilded`
- **Stack Overflow**: `metadata_tags`, `metadata_answer_count`, `metadata_has_accepted_answer`, `metadata_view_count`, `metadata_owner_reputation`
- **Semantic Scholar**: `metadata_citation_count`, `metadata_influential_citation_count`, `metadata_fields_of_study`, `metadata_has_open_access`
- **Medium**: `metadata_author`, `metadata_publication`, `metadata_read_time`, `metadata_claps`
- **Kaggle**: `metadata_votes`, `metadata_usability_rating`, `metadata_file_count`

## Usage Examples

```python
from datasets import load_dataset

# Load dataset
dataset = load_dataset("nellaivijay/asi-research-daily")
train_data = dataset["train"]

# Filter by source
pubmed_items = train_data.filter(lambda x: x["source"] == "pubmed")
github_items = train_data.filter(lambda x: x["source"] == "github")

# Filter by content type
papers = train_data.filter(lambda x: x.get("metadata_content_type") == "paper")
repositories = train_data.filter(lambda x: x.get("metadata_content_type") == "repository")

# Filter by ML subfield
cv_papers = train_data.filter(lambda x: "computer-vision" in x.get("metadata_ml_subfields", []))

# Filter by quality
high_quality = train_data.filter(lambda x: x.get("metadata_quality_scores", {}).get("overall_quality_score", 0) > 0.7)

# Sort by score
sorted_items = train_data.sort("score", reverse=True)

# Filter by date
recent_items = train_data.filter(lambda x: x.get("metadata_days_since", 999) < 30)

# Filter by trending category
trending_items = train_data.filter(lambda x: x.get("metadata_trending_category") == "hot")

# Filter by data quality
high_quality = train_data.filter(lambda x: x.get("metadata_data_quality", {}).get("overall_quality_score", 0) > 0.7)

# Filter by sentiment
positive_items = train_data.filter(lambda x: x.get("metadata_sentiment_category") == "positive")

# Get related items
item_with_related = train_data[0]
related_items = item_with_related.get("metadata_related_items", [])
```

## Data Quality Features

- **Standardized Dates**: All dates normalized to ISO 8601 format
- **ML Subfield Classification**: Automatic classification into 15+ ML subfields
- **Quality Scoring**: Multi-dimensional quality assessment (abstract length, code availability, DOI, engagement, recency)
- **Temporal Features**: Year, month, week, quarter, days since publication
- **Keyword Extraction**: Automatic extraction of technical keywords
- **Content Type Detection**: Automatic classification of item type
- **Sentiment Analysis**: Sentiment polarity, subjectivity, and category classification
- **Automatic Summarization**: Extractive summaries for quick content overview
- **Data Quality Metrics**: Completeness, consistency, and validity scores for each item
- **Trending Metrics**: Engagement velocity analysis with trending categories
- **Cross-References**: Related item detection based on shared subfields, keywords, and tags
- **Fuzzy Deduplication**: Intelligent duplicate detection with metadata merging
- **Metadata Completeness**: Fallback logic to infer missing metadata fields

## Data Sources

This dataset aggregates research from:
- **Academic**: PubMed, arXiv, Semantic Scholar, Crossref, Papers with Code
- **Professional**: GitHub, Stack Overflow, Kaggle
- **Social**: Reddit, Hacker News
- **News**: GDELT
- **Blogs**: Medium, Towards Data Science

## Limitations

- Data is limited to the specified time range
- Some sources may have rate limits or API restrictions
- Citation counts may vary between sources
- ML subfield classification is based on keyword matching and may not be perfect

## Source

Generated by [Research-Collector](https://github.com/nellaivijay/research-collector), an educational multi-source research aggregation tool.

## License

MIT License

## Citation

If you use this dataset, please cite the repository URL: https://huggingface.co/datasets/nellaivijay/asi-research-daily