<p>In the data-driven discovery of high-performance thermoelectric (TE) materials, the lack of high-quality data remains a key bottleneck. Addressing this issue, we introduce the Systematically Verified Thermoelectric (<Emphasis FontCategory="NonProportional">sysTEm</Emphasis>) dataset. Leveraging the physical relationships between transport properties, we curated and validated over 8,600 experimental data points, spanning more than 1,400 unique TE materials and 70 elements. Each entry includes the composition, temperature, and up to seven key transport properties: the figure of merit (<i>zT</i>), Seebeck coefficient (<i>S</i>), electrical conductivity (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\sigma \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>σ</mi> </math></EquationSource> </InlineEquation>), power factor (PF), total (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\kappa \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>κ</mi> </math></EquationSource> </InlineEquation>), electronic (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\kappa _e\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>κ</mi> <mi>e</mi> </msub> </math></EquationSource> </InlineEquation>), and lattice thermal conductivities (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\kappa _l\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>κ</mi> <mi>l</mi> </msub> </math></EquationSource> </InlineEquation>). The dataset is formatted as a data table, with little preprocessing needed for machine learning. Initial analysis shows that doped materials, defined via a compositional threshold, exhibit a larger number of high <i>zT</i> outliers compared to undoped materials, highlighting that doping can improve TE performance. Overall, the publicly available <Emphasis FontCategory="NonProportional">sysTEm</Emphasis> dataset and its accompanying code are intended to accelerate data-driven TE research and benchmarking.</p>