<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Journal Article</dc:product_type><dc:title>FlareDB: A Database of Significant Flares in Solar Cycles 24 and 25 with SDO/HMI and SDO/AIA Observations</dc:title><dc:creator>Liu, Nian (ORCID:0000000260183799); Abduallah, Yasser; Kapure, Tanmay Sunil; Li, Qin; Wang, Haimin (ORCID:000000025233565X); Wang, Jason_T L</dc:creator><dc:corporate_author/><dc:editor/><dc:description>&lt;title&gt;Abstract&lt;/title&gt; &lt;p&gt;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.&lt;/p&gt;</dc:description><dc:publisher>Springer Nature</dc:publisher><dc:date>2026-01-12</dc:date><dc:nsf_par_id>10675222</dc:nsf_par_id><dc:journal_name>Scientific Data</dc:journal_name><dc:journal_volume>13</dc:journal_volume><dc:journal_issue>1</dc:journal_issue><dc:page_range_or_elocation/><dc:issn>2052-4463</dc:issn><dc:isbn/><dc:doi>https://doi.org/10.1038/s41597-026-06607-7</dc:doi><dcq:identifierAwardId>2504860; 2206886</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>