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An event extraction framework is developed that leverages parallel computing, efficient memory management, and vectorization, yielding significant performance enhancement. The newly developed\n                    <jats:italic>abf\u2010ultra\u2010simple function<\/jats:italic>\n                    extracts key parameters from the header critical for the operation of open\u2010seek\u2010read\u2010close data loading architecture running on multiple cores. This underpins the swift analysis of large files where an \u2248\u00a0\u00d7\u00a018 improvement is found for a 100\u00a0min\u2010long file (\u22484.5\u00a0GB) compared to the more traditional single (cell) array data loading method. The application is benchmarked against five other analysis platforms showcasing significant performance enhancement (&gt;2\u00a0\u00d7\u20131120\u00a0\u00d7). The integrated provisions for batch analysis enable concurrently analyzing multiple files (vital for high\u2010bandwidth experiments). Furthermore, the application is equipped with multi\u2010level data fitting based on abrupt changes in the event waveform. The application condenses the extracted events to a single binary file improving data portability (e.g., 16\u00a0GB file with 28\u00a0182 events reduces to 47.9\u00a0MB\u2013343\u00a0\u00d7 size reduction) and enables a multitude of post\u2010analysis extractions to be done efficiently.\n                  <\/jats:p>","DOI":"10.1002\/smtd.202400045","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T07:51:30Z","timestamp":1720165890000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Robust Parallel Computing Data Extraction Framework for Nanopore Experiments"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1921-8467","authenticated-orcid":false,"given":"Y. M. N. D. 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