Update README.md

README.md

@@ -19,214 +19,7 @@ tags:
 - biology
 - finance
 - legal
-pretty_name: OpenAlex Scholarly Knowledge Graph
+pretty_name: Openalex Jun 2025 Snapshot
 size_categories:
-- 100M<n<1B
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: doi
-    dtype: string
-  - name: title
-    dtype: string
-  - name: publication_year
-    dtype: int64
-  - name: cited_by_count
-    dtype: int64
-  - name: is_oa
-    dtype: bool
-  - name: type
-    dtype: string
-  - name: abstract
-    dtype: string
-  splits:
-  - name: train
-    num_examples: 174000000
----
-
-# 🎓 OpenAlex: The World's Scholarly Knowledge Graph
-
-<center>
-    <img src="https://cdn-uploads.huggingface.co/production/uploads/6749e82a5d3c9fa088c5da9f/q2V8CvbQNrHE_bZNNxGJb.png" alt="OpenAlex: Open access to the global research system" width="600">
-</center>
-
-> **174M scholarly works** from the world's largest open bibliographic database
-
-**OpenAlex Homepage:** https://openalex.org  
-**API Documentation:** https://docs.openalex.org  
-**Paper:** https://arxiv.org/abs/2205.01833
-
-## What is OpenAlex?
-
-🎓 **OpenAlex** is a free and open catalog of the global research system, containing metadata for **250M+ scholarly works**, **90M+ authors**, **120K+ venues**, and **100K+ institutions**. Named after the ancient Library of Alexandria, it serves as the successor to Microsoft Academic Graph.
-
-This Hugging Face dataset provides a streamlined subset of **174M scholarly works** with rich metadata, making it easy to:
-- 📊 Analyze research trends across disciplines
-- 🔗 Build citation networks and knowledge graphs  
-- 🤖 Train models for academic text understanding
-- 🔍 Develop scholarly search and recommendation systems
-
-## Dataset Overview
-
-### Quick Stats
-- **174M scholarly works** (papers, books, datasets, etc.)
-- **Multi-disciplinary coverage**: Medicine, Biology, Chemistry, Physics, Computer Science, Social Sciences, and more
-- **Rich metadata**: Titles, abstracts, authors, institutions, citations, concepts, and open access status
-- **Time span**: Publications from 1800s to present
-- **Languages**: Primarily English, with multilingual content
-
-### Key Features
-
-✅ **Comprehensive Metadata**
-- Unique identifiers (DOI, OpenAlex ID, arXiv, PubMed)
-- Publication details (title, date, venue, language)
-- Author and institutional affiliations
-- Citation counts and referenced works
-
-✅ **Open Access Information**
-- OA status and licensing
-- Repository locations
-- Full-text availability
-
-✅ **Concept Tagging**
-- Hierarchical subject classification
-- Auto-generated topic tags
-- Cross-disciplinary connections
-
-✅ **Quality Indicators**
-- Publication types
-- Peer review status
-- Citation metrics
-
-## Usage
-
-### Loading the Dataset
-
-```python
-from datasets import load_dataset
-
-# Load a sample (recommended for exploration)
-dataset = load_dataset("sumuks/openalex", split="train", streaming=True)
-
-# Iterate through examples
-for paper in dataset.take(100):
-    print(f"Title: {paper['title']}")
-    print(f"Year: {paper['publication_year']}")
-    print(f"Citations: {paper['cited_by_count']}")
-    print("---")
-```
-
-### Data Schema
-
-Each record contains:
-
-| Field | Description | Type |
-|-------|-------------|------|
-| `id` | OpenAlex work ID | string |
-| `doi` | Digital Object Identifier | string |
-| `title` | Work title | string |
-| `display_name` | Formatted display title | string |
-| `publication_year` | Year of publication | integer |
-| `publication_date` | Full publication date | date |
-| `type` | Publication type (article, book, etc.) | string |
-| `cited_by_count` | Number of citations | integer |
-| `is_oa` | Open access status | boolean |
-| `concepts` | Subject area tags | list |
-| `authorships` | Author information | list |
-| `institutions` | Affiliated institutions | list |
-| `referenced_works` | Citations/references | list |
-| `abstract` | Work abstract (when available) | string |
-
-### Example Use Cases
-
-#### 1. Research Trend Analysis
-```python
-# Analyze publication trends in machine learning
-ml_papers = dataset.filter(lambda x: any('machine learning' in c['display_name'].lower() 
-                                       for c in x['concepts']))
-```
-
-#### 2. Citation Network Analysis
-```python
-# Build citation graphs
-citation_network = {}
-for paper in dataset:
-    citation_network[paper['id']] = paper['referenced_works']
-```
-
-#### 3. Open Access Research
-```python
-# Find open access papers in climate science
-oa_climate = dataset.filter(lambda x: x['is_oa'] and 
-                          any('climate' in c['display_name'].lower() 
-                              for c in x['concepts']))
-```
-
-#### 4. Academic Search Systems
-```python
-# Create embeddings for semantic search
-from sentence_transformers import SentenceTransformer
-
-model = SentenceTransformer('all-MiniLM-L6-v2')
-embeddings = model.encode([paper['title'] for paper in dataset.take(1000)])
-```
-
-## Considerations for Use
-
-### Scope and Limitations
-
-- **Subset of Full OpenAlex**: This dataset contains 174M of the 250M+ works in the complete OpenAlex database
-- **Metadata Quality**: While extensive, some fields may be incomplete for older publications
-- **Abstract Availability**: Not all works include abstracts due to copyright restrictions
-- **Dynamic Updates**: This is a static snapshot; the live OpenAlex API has more recent data
-
-### Ethical Considerations
-
-- **Citation Bias**: Citation counts may reflect systemic biases in academic publishing
-- **Geographic Representation**: May have uneven coverage across global regions
-- **Language Bias**: English-language publications are overrepresented
-- **Open Access**: Respect publisher policies when using full-text information
-
-### Recommended Practices
-
-1. **Use Streaming**: Given the dataset size, use `streaming=True` to avoid memory issues
-2. **Filter Early**: Apply filters during loading to work with relevant subsets
-3. **Respect Rate Limits**: If combining with API calls, follow OpenAlex rate limits
-4. **Cite Properly**: Include proper attribution to OpenAlex and original authors
-
-## License
-
-This dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0**, following OpenAlex's commitment to open science. You are free to share and adapt this data with proper attribution.
-
-## Citation
-
-If you use this dataset, please cite:
-
-```bibtex
-@article{priem2022openalex,
-  title={OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts},
-  author={Priem, Jason and Piwowar, Heather and Orr, Richard},
-  journal={arXiv preprint arXiv:2205.01833},
-  year={2022}
-}
-
-@misc{shashidhar2024openalex-hf,
-  author = {Shashidhar, Sumuk},
-  title = {OpenAlex Scholarly Knowledge Graph Dataset},
-  year = {2024},
-  publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/datasets/sumuks/openalex}}
-}
-```
-
-## Acknowledgments
-
-Special thanks to:
-- The [OurResearch](https://ourresearch.org/) team for creating and maintaining OpenAlex
-- [Arcadia Fund](https://www.arcadiafund.org.uk/) for funding OpenAlex development
-- The academic community for supporting open science initiatives
-
----
-
-**Questions or Issues?** Open a discussion in the [Community tab](https://huggingface.co/datasets/sumuks/openalex/discussions) or visit [OpenAlex Help](https://help.openalex.org/)
+- 10M<n<100M
+---