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    <identifier>10.57760/sciencedb.36822</identifier>
    <datestamp>2026-02-03T20:03:32Z</datestamp>
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  <dc:date>2026-02-03</dc:date>
  <dc:title>Precision Abacus‑based Representation Learning (PEARL)</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.36822</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>We present the Precision Abacus-based Representation Learning (PEARL) dataset, an ultra-dense longitudinal resource tracking three adults over 280 days of Abacus-based Mental Calculation (AMC) learning. Each participant completed 52 to 55 MRI scans, yielding nearly 4,000 minutes of high-quality multi-echo multiband fMRI data (resting-state, task-based, and natural stimuli) alongside daily behavioral assessments. In the current study, AMC, as an ideal model for study learning-induced plasticity, transitioning from embodied tool use to abstract mental manipulation, provides both ecological validity and a clear staged learning progression.&amp;nbsp;</dc:description>
  <dc:subject>skill acquisition; longitudinal fMRI; brain plasticity; individual differences; precision fMRI</dc:subject>
  <dc:creator>Jiali Mu</dc:creator>
  <dc:creator>Tianyong Xu</dc:creator>
  <dc:creator>Feiyan Chen</dc:creator>
  <dc:rights>RESTRICTED</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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