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<p>Catalyst performance in dry methane reforming (DMR) depends on metals, supports, promoters, and synthesis methods, yet inconsistent reporting limits reproducibility and comparability. This review introduces the Data-Friendly Article (DFA) framework, a FAIR-aligned guideline designed to enhance transparency, standardization, and machine readability in catalysis studies. The DFA defines essential elements such as activity and stability outputs, data-point count, quantitative characterization, and consistent reporting of experimental parameters. Applying this framework to 149 Ni–Al₂O₃ DMR studies showed that only 82 (55%) met DFA suitability criteria, while 67 were excluded due to missing parameters or inconsistent presentation, with stability outputs reported more consistently than activity metrics. By distinguishing between DFA-compliant and excluded studies, this work highlights the need for standardized reporting to facilitate benchmarking and accelerate the integration of catalysis data into machine-learning workflows.</p>

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Toward efficient reporting for catalytic methane reforming data: a framework and review for standardized scientific data reporting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月13日

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  • Ismail Salim,
  • Abdallah S. Berrouk

摘要

Catalyst performance in dry methane reforming (DMR) depends on metals, supports, promoters, and synthesis methods, yet inconsistent reporting limits reproducibility and comparability. This review introduces the Data-Friendly Article (DFA) framework, a FAIR-aligned guideline designed to enhance transparency, standardization, and machine readability in catalysis studies. The DFA defines essential elements such as activity and stability outputs, data-point count, quantitative characterization, and consistent reporting of experimental parameters. Applying this framework to 149 Ni–Al₂O₃ DMR studies showed that only 82 (55%) met DFA suitability criteria, while 67 were excluded due to missing parameters or inconsistent presentation, with stability outputs reported more consistently than activity metrics. By distinguishing between DFA-compliant and excluded studies, this work highlights the need for standardized reporting to facilitate benchmarking and accelerate the integration of catalysis data into machine-learning workflows.