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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2506.18586 |
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| _version_ | 1866918068061470720 |
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| author | Yang, Zijie Zhou, Qiji Guo, Fang Zhang, Sijie Xi, Yexun Nie, Jinglei Zhu, Yudian Huang, Liping Wu, Chou Xia, Yonghe Ma, Xiaoyu Pu, Yingming Lu, Panzhong Pan, Junshu Chen, Mingtao Guo, Tiannan Dou, Yanmei Chen, Hongyu Zeng, Anping Huang, Jiaxing Xu, Tian Zhang, Yue |
| author_facet | Yang, Zijie Zhou, Qiji Guo, Fang Zhang, Sijie Xi, Yexun Nie, Jinglei Zhu, Yudian Huang, Liping Wu, Chou Xia, Yonghe Ma, Xiaoyu Pu, Yingming Lu, Panzhong Pan, Junshu Chen, Mingtao Guo, Tiannan Dou, Yanmei Chen, Hongyu Zeng, Anping Huang, Jiaxing Xu, Tian Zhang, Yue |
| contents | Research data are the foundation of Artificial Intelligence (AI)-driven science, yet current AI applications remain limited to a few fields with readily available, well-structured, digitized datasets. Achieving comprehensive AI empowerment across multiple disciplines is still out of reach. Present-day research data collection is often fragmented, lacking unified standards, inefficiently managed, and difficult to share. Creating a single platform for standardized data digitization needs to overcome the inherent challenge of balancing between universality (supporting the diverse, ever-evolving needs of various disciplines) and standardization (enforcing consistent formats to fully enable AI). No existing platform accommodates both facets. Building a truly multidisciplinary platform requires integrating scientific domain knowledge with sophisticated computing skills. Researchers often lack the computational expertise to design customized and standardized data recording methods, whereas platform developers rarely grasp the intricate needs of multiple scientific domains. These gaps impede research data standardization and hamper AI-driven progress. In this study, we address these challenges by developing Airalogy (https://airalogy.com), the world's first AI- and community-driven platform that balances universality and standardization for digitizing research data across multiple disciplines. Airalogy represents entire research workflows using customizable, standardized data records and offers an advanced AI research copilot for intelligent Q&A, automated data entry, analysis, and research automation. Already deployed in laboratories across all four schools of Westlake University, Airalogy has the potential to accelerate and automate scientific innovation in universities, industry, and the global research community-ultimately benefiting humanity as a whole. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18586 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Airalogy: AI-empowered universal data digitization for research automation Yang, Zijie Zhou, Qiji Guo, Fang Zhang, Sijie Xi, Yexun Nie, Jinglei Zhu, Yudian Huang, Liping Wu, Chou Xia, Yonghe Ma, Xiaoyu Pu, Yingming Lu, Panzhong Pan, Junshu Chen, Mingtao Guo, Tiannan Dou, Yanmei Chen, Hongyu Zeng, Anping Huang, Jiaxing Xu, Tian Zhang, Yue Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Research data are the foundation of Artificial Intelligence (AI)-driven science, yet current AI applications remain limited to a few fields with readily available, well-structured, digitized datasets. Achieving comprehensive AI empowerment across multiple disciplines is still out of reach. Present-day research data collection is often fragmented, lacking unified standards, inefficiently managed, and difficult to share. Creating a single platform for standardized data digitization needs to overcome the inherent challenge of balancing between universality (supporting the diverse, ever-evolving needs of various disciplines) and standardization (enforcing consistent formats to fully enable AI). No existing platform accommodates both facets. Building a truly multidisciplinary platform requires integrating scientific domain knowledge with sophisticated computing skills. Researchers often lack the computational expertise to design customized and standardized data recording methods, whereas platform developers rarely grasp the intricate needs of multiple scientific domains. These gaps impede research data standardization and hamper AI-driven progress. In this study, we address these challenges by developing Airalogy (https://airalogy.com), the world's first AI- and community-driven platform that balances universality and standardization for digitizing research data across multiple disciplines. Airalogy represents entire research workflows using customizable, standardized data records and offers an advanced AI research copilot for intelligent Q&A, automated data entry, analysis, and research automation. Already deployed in laboratories across all four schools of Westlake University, Airalogy has the potential to accelerate and automate scientific innovation in universities, industry, and the global research community-ultimately benefiting humanity as a whole. |
| title | Airalogy: AI-empowered universal data digitization for research automation |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language |
| url | https://arxiv.org/abs/2506.18586 |