# FlareDB: A Database of Significant Flares in Solar Cycles 24 and 25 with SDO/HMI and SDO/AIA Observations

**Authors:** Nian Liu, Yasser Abduallah, Tanmay Sunil Kapure, Qin Li, Haimin Wang, Jason T. L. Wang

PMC · DOI: 10.1038/s41597-026-06607-7 · Scientific Data · 2026-01-24

## TL;DR

FlareDB is a database containing solar flare data from 2010 to 2025, offering magnetic field and UV/EUV observations to help predict solar eruptions.

## Contribution

FlareDB introduces a standardized, machine-learning-ready database of significant solar flares with aligned SDO/HMI and SDO/AIA observations.

## Key findings

- FlareDB includes 151 significant flares with M5.0 and larger magnitudes within 50° of the solar disk center.
- The database provides AIA AR patches in multiple projections aligned with HMI magnetograms for consistent analysis.
- A web interface and quick-look movies enhance data accessibility and visualization for machine learning applications.

## Abstract

We present FlareDB, a database that provides comprehensive magnetic field information, ultraviolet/extreme ultraviolet (UV/EUV) emissions, and white light continuum images for solar active regions (ARs) associated with 151 significant flares from May 2010 to May 2025. The data, sourced from the Solar Dynamics Observatory (SDO) via the Joint Science Operations Center (JSOC), were processed with SunPy and stored in standardized JSOC FITS format. FlareDB includes all M5.0 and larger flares within 50° of the solar disk center. Key features include (1) Atmospheric Imaging Assembly (AIA) AR patches in Helioprojective Cartesian(HPC) and Lambert Cylindrical Equal-Area (CEA) projections, aligned with corresponding HMI magnetogram patches; (2) quick-look movies with uniform value ranges that ensure consistent visualization, maintain data uniformity, and enhance readiness for machine learning studies; (3) a supplementary web interface that allows the entire dataset of a flare to be downloaded for large flare analysis. One of FlareDB’s primary objectives is to support scientists in predicting and understanding the onset of solar eruptions, including flares and coronal mass ejections. The data set is machine-learning ready for this purpose.

## Full-text entities

- **Diseases:** solar eruptions (MESH:D000092130)

## Full text

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## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12920639/full.md

## References

7 references — full list in the complete paper: https://tomesphere.com/paper/PMC12920639/full.md

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Source: https://tomesphere.com/paper/PMC12920639