Datasets:

nram97
/

wireless_taxonomy

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`	Metadata describing qualifying wireless datasets, including dataset names, OSI layer coverage, modalities, and collection environments.
`papers`	`Wireless_Papers.csv`	A structured index of research papers analyzed, including authors, venues, years, dataset usage, and taxonomy keys.
`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

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()