<b>Reinterpreting Existing Data Through a Relational </b><b>Lens: Toward Meta-Dataset Science</b>
<p dir="ltr">This proposal invites researchers to <b>reinterpret existing scientific archives</b> through a <i>relational lens</i>.</p><p dir="ltr">Instead of gathering new data, we can <b>transform what already exists</b> into <b>meta-datasets</b> that capture relationships rather than isolated values—measuring how signals interact, not just what they are.</p><p><br></p><p dir="ltr">By extracting features such as <b>temporal lag</b>, <b>coupling strength</b>, and <b>coherence dynamics</b>, scientists across fields can uncover <b>cross-domain signatures of dynamic boundaries and emergent coherence</b>.</p><p dir="ltr">The goal is to spark a new wave of <b>low-cost, high-impact research</b> where physics, biology, and AI share a common language of relational metrics.</p><p><br></p><p dir="ltr">I offer this concept as an <b>open framework</b> for others to develop, test, and refine.</p>
