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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2512.15514 |
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| _version_ | 1866914206283988992 |
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| author | Ying, Lu Tang, Junxiu He, Tingying Fekete, Jean-Daniel |
| author_facet | Ying, Lu Tang, Junxiu He, Tingying Fekete, Jean-Daniel |
| contents | We propose a methodology to improve figures from the Intergovernmental Panel on Climate Change (IPCC), ensuring that all modifications remain scientifically rigorous. IPCC figures are notoriously difficult to understand, and although designers have proposed alternatives, these lack formal IPCC validation and can be dismissed by skeptics. To address this gap, our approach starts from official IPCC figures. We gather their associated learning objectives and devise tests to score a pool of figure readers to assess how well they learn the objectives.We define improvement as higher scores obtained by a comparable reader pool after viewing a revised figure, where all modifications undergo review to ensure scientific validity. This assessment gives freedom to designers, who can deviate from the original design while making sure the objectives are still met and improved. We demonstrate the methodology through a case study and describe unexpected challenges encountered during the process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15514 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Constructive Scientific Methodology to Improve Climate Figures from IPCC Ying, Lu Tang, Junxiu He, Tingying Fekete, Jean-Daniel Human-Computer Interaction We propose a methodology to improve figures from the Intergovernmental Panel on Climate Change (IPCC), ensuring that all modifications remain scientifically rigorous. IPCC figures are notoriously difficult to understand, and although designers have proposed alternatives, these lack formal IPCC validation and can be dismissed by skeptics. To address this gap, our approach starts from official IPCC figures. We gather their associated learning objectives and devise tests to score a pool of figure readers to assess how well they learn the objectives.We define improvement as higher scores obtained by a comparable reader pool after viewing a revised figure, where all modifications undergo review to ensure scientific validity. This assessment gives freedom to designers, who can deviate from the original design while making sure the objectives are still met and improved. We demonstrate the methodology through a case study and describe unexpected challenges encountered during the process. |
| title | A Constructive Scientific Methodology to Improve Climate Figures from IPCC |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2512.15514 |