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Open Data in Catalysis: From Today's Big Picture to the Future of Small Data
ChemCatChem ( IF 4.5 ) Pub Date : 2020-10-23 , DOI: 10.1002/cctc.202001132
Pedro S. F. Mendes 1 , Sébastien Siradze 1 , Laura Pirro 1 , Joris W. Thybaut 1
Affiliation  

Open science and data are yet to make a real breakthrough and research policies will have a critical role in it. The history and general context around open data is hence firstly addressed, including how researchers perceive the existing incentives, leading to recommendations on how to foster data sharing. Subsequently, the focus is on catalysis, with a particular emphasis on benchmarking the data sharing practices against other fields and surveying the type of data currently being shared. The current infrastructure, including data repositories, and standards formats is maped. The striking differences among different disciplines are discussed, serving as a basis to propose specific actions to promote data sharing in catalysis. Short‐term initiatives are needed to boost the amount of openly available data, particularly in heterogeneous catalysis, but a high degree of standardization in data formats will be needed to ensure optimal and automated data mining in the long run. Because of its unique, central role in understanding the catalytic action, kinetic catalytic data is of particular interest. As formats and mining tools are dependant on the type of data, kinetic catalytic data is firstly characterized. Guidelines for a standardized sharing format are proposed, taking into account the small, well‐structured nature of this type of data. To maximize the extraction of information, the low volume of kinetic catalytic data will be compensated by incorporating fundamental knowledge into statistics‐based tools. Whencoupled with knowledge generation tools, i. e. kinetic models, new insights at the active site and mechanism levels will be reached in an ever more automated and powerful way.

中文翻译:

催化中的开放数据:从当今的大局到小数据的未来

开放的科学和数据尚未取得真正的突破,研究政策将在其中发挥关键作用。因此,首先要解决开放数据的历史和一般背景,包括研究人员如何看待现有的激励机制,并就如何促进数据共享提出建议。随后,重点是催化,特别着重于对照其他领域对数据共享实践进行基准测试并调查当前共享的数据类型。映射了当前基础结构,包括数据存储库和标准格式。讨论了不同学科之间的显着差异,以此为基础,提出了促进催化数据共享的具体措施。需要采取短期措施来增加公开可用的数据量,尤其是在多相催化方面,但是从长远来看,将需要高度标准化的数据格式以确保最佳的自动化数据挖掘。由于其在理解催化作用方面具有独特的中心作用,因此动力学催化数据特别受关注。由于格式和挖掘工具取决于数据类型,因此首先对动力学催化数据进行了表征。考虑到此类数据的细小,结构良好的性质,提出了标准化共享格式的准则。为了最大程度地提取信息,通过将基础知识纳入基于统计的工具中,可以补偿少量的动力学催化数据。与知识生成工具结合使用时,i。e。动力学模型,在活动场所和机理层面的新见解将以更加自动化和强大的方式获得。
更新日期:2020-10-23
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