Data · dataset · 2026
The Deep Learning–based Dual-branch Multimodal Fusion Model for Solar Flare Prediction
Listed in ScienceDB
The dataset is associated with solar flare forecasting research.
Description
It includes SDO/HMI SHARP active-region magnetograms, the corresponding magnetic-field parameters, a supplemented solar flare catalog, and solar flare forecasting labels.The Magnetogram data include active-region magnetograms in the cylindrical equal-area (CEA) projection, recorded from May 2010 to May 2023 at a cadence of 96 minutes.The Flare-catalog was constructed from the NOAA/NCEI GOES X-ray Flare Catalog.
For flare events without associated NOAA active-region numbers, the corresponding NOAA active-region numbers were supplemented using the SolarSoft event archive and corresponding SDO observations.The Flare label file was generated from the Flare-catalog using a predefined 24-hour forecasting window. Samples are labeled as flare or non-flare according to whether a C-class or stronger flare occurs within the subsequent 24 hours.
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For multiclass forecasting, flare samples are further categorized as C-, M-, or X-class according to the highest flare class within the forecasting window.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.space.00328 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Machine learning 71%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.space.00328 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4611 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (71%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |