---
language:
- en
license: apache-2.0
task_categories:
- image-classification
- image-to-text
- tabular-classification
tags:
- mathematics
- number-theory
- elliptic-curves
- bsd-conjecture
- scientific-computing
- algebraic-geometry
- multimodal
- time-lapse
- geometry
- number-theory
-  elliptic-curves
size_categories:
- 100K<n<1M
---

[![Website](https://img.shields.io/badge/webXOS.netlify.app-Explore_Apps-00d4aa?style=for-the-badge&logo=netlify&logoColor=white)](https://webxos.netlify.app)
[![GitHub](https://img.shields.io/badge/GitHub-webxos/webxos-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/webxos/webxos)
[![Hugging Face](https://img.shields.io/badge/Hugging_Face-🤗_webxos-FFD21E?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/webxos)
[![Follow on X](https://img.shields.io/badge/Follow_@webxos-1DA1F2?style=for-the-badge&logo=x&logoColor=white)](https://x.com/webxos)

## BSD Conjecture Dataset

The BSD conjecture dataset concerns the Birch and Swinnerton-Dyer (BSD) Conjecture, a Millennium Prize problem in number 
theory. It typically contains numerical data on elliptic curves, such as coefficients, ranks, and L-function values, relevant for 
computational verification or machine learning applications in arithmetic geometry. 

This Dataset is a collection of computational data relating to elliptic curves and their associated L-functions. The dataset is designed 
to support machine learning research in arithmetic geometry, specifically for predicting properties like the rank of an elliptic 
curve from its analytic invariants.

## Dataset Summary

This dataset provides the necessary numerical features (coefficients, conductors, and L-values) to explore these relationships 
empirically. 

## Supported Tasks Rank Regression: 

Predict the algebraic rank of an elliptic curve as a continuous or integer value based on analytic data.

## Analytic Rank Classification: 

Classify curves into rank categories (e.g., Rank 0 vs. Rank 1).

## Feature Exploration: 

Study the correlation between coefficients \(a_{1},a_{2},\dots \) and the curve's global invariants.

## Dataset Structure

The data is typically provided in a tabular format (CSV or Parquet).