Create README.md

README.md

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+---
+tags:
+  - wireless
+  - taxonomy
+  - communication-systems
+  - datasets
+  - metadata
+  - benchmarking
+  - reproducibility
+license: mit
+language:
+  - en
+pretty_name: Wireless Taxonomy Dataset
+---
+
+# 📶 Wireless Taxonomy Dataset
+
+The **Wireless Taxonomy Dataset** is a structured corpus of wireless communication research metadata, created to support the development of a standardized benchmark for the wireless research community.  
+It captures the relationships between **datasets**, **papers**, and **citations**, emphasizing **data provenance**, **collection environments**, and **modality-level detail** across the OSI stack.
+
+This taxonomy was curated as part an effort to systematically identify and classify datasets collected from real-world, lab-based, or high-fidelity wireless environments.
+
+---
+
+## 🧭 Overview
+
+The dataset provides a unified reference for wireless research data sources, including:
+
+- Publications from **ACM SIGCOMM**, **IMC**, and **CoNEXT** (2022–2025)
+- Descriptions of **datasets used or generated** in these papers
+- Mappings between datasets, publications, and BibTeX references
+
+The curation emphasizes datasets that involve **physical or trace-driven wireless environments**, such as operational LTE/5G systems, SDR testbeds, or validated wireless emulations.
+
+---
+
+## 🗂️ Dataset Structure
+
+This repository consists of **three interlinked CSV tables**, each available as a configuration:
+
+| Config | File | Description |
+|---------|------|-------------|
+| **`datasets`** | [`Wireless_Datasets.csv`](./datasets/Wireless_Datasets.csv) | Metadata describing qualifying wireless datasets, including dataset names, OSI layer coverage, modalities, and collection environments. |
+| **`papers`** | [`Wireless_Papers.csv`](./papers/Wireless_Papers.csv) | A structured index of research papers analyzed, including authors, venues, years, dataset usage, and taxonomy keys. |
+| **`bibtex`** | [`Bibtex.csv`](./bibtex/Bibtex.csv) | Canonical citation metadata linking publications to datasets via shared BibTeX keys. |
+
+---
+
+## 🔗 Linking and Relational Schema
+
+Each table contains a shared relational variable: **`bibtex_citation_key`**.  
+This key enables relational joins across the three tables.
+
+- A single dataset may link to **multiple papers** (e.g., reused benchmarks).  
+- Papers may list **multiple datasets**.  
+- Merging on `bibtex_citation_key` reconstructs the complete dataset–paper–citation graph.
+
+---
+
+## 🧪 Methodology
+
+The taxonomy was constructed through a combination of structured corpus analysis and manual validation.  
+Publications from major networking and wireless conferences between 2022 and 2025 were reviewed to identify papers containing datasets from qualifying wireless environments — namely, **real-world deployments**, **physical testbeds**, or **high-fidelity simulations/emulations**.
+
+---
+
+## 📑 Schema Description
+
+| Field | Description |
+|--------|-------------|
+| `dataset_name` | Name or descriptive identifier of the dataset. |
+| `bibtex_citation_key` | Shared key linking datasets to papers and citations. |
+| `osi_layers` | OSI layers represented in the dataset (e.g., L1, L4). |
+| `modalities` | Collected data types (e.g., RF traces, latency, throughput). |
+| `availability` | Indicates whether the dataset is open, closed, or n/a. |
+| `collection_environment` | Describes how the dataset was collected: Real-world deployment, Physical Testbed, or High-Fidelity Simulation. |
+
+---
+
+## ⚙️ Example Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset taxonomy
+datasets_table = load_dataset("your-hf-username/wireless_taxonomy", name="datasets", split="train")
+
+# Explore papers and linked citations
+papers_table = load_dataset("your-hf-username/wireless_taxonomy", name="papers", split="train")
+bib_table    = load_dataset("your-hf-username/wireless_taxonomy", name="bibtex", split="train")
+
+# Join tables via the BibTeX citation key
+import pandas as pd
+
+df = pd.merge(datasets_table.to_pandas(), papers_table.to_pandas(), on="bibtex_citation_key")
+df.head()