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| 1 | 国内外科学数据管理FAIR原则研究进展及应用综述显示文摘[目的/意义]科学数据管理FAIR原则从2016年正式发布到目前为止引起了国内外学者的广泛关注和重视,围绕FAIR原则开展了诸多探索和研究,有力地推动了FAIR原则的实施和推广,本文旨在对相关学术成果进行系统梳理和深入总结。[方法/过程]通过文献调研和内容分析,从FAIR原则组织保障、FAIR的4个基本原则、FAIR原则的实践探索、FAIR原则的学科应用及FAIR原则的区域应用5个角度梳理国内外对FAIR原则的研究进展和实践,总结研究现状和已有成果。[结果/结论]国外对于FAIR原则的研究集中在理论、实施策略、评估方法等方面,并已在医学等学科领域展开了深入探索,相比之下,国内的研究尚在起步状态,亟需国家的政策支持和相关组织的跟进。 | 陈书贤 刘桂锋 刘琼 | 2022 | 农业图书情报学报2022，34，8： | 6 |
| 2 | HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction显示文摘Machine learning(ML)applications in weather and climate are gaining momentum as big data and the immense increase in High-performance computing(HPC)power are paving the way.Ensuring FAIR data and reproducible ML practices are significant challenges for Earth system researchers.Even though the FAIR principle is well known to many scientists,research communities are slow to adopt them.Canonical Workflow Framework for Research(CWFR)provides a platform to ensure the FAIRness and reproducibility of these practices without overwhelming researchers.This conceptual paper envisions a holistic CWFR approach towards ML applications in weather and climate,focusing on HPC and big data.Specifically,we discuss Fair Digital Object(FDO)and Research Object(RO)in the DeepRain project to achieve granular reproducibility.DeepRain is a project that aims to improve precipitation forecast in Germany by using ML.Our concept envisages the raster datacube to provide data harmonization and fast and scalable data access.We suggest the Juypter notebook as a single reproducible experiment.In addition,we envision JuypterHub as a scalable and distributed central platform that connects all these elements and the HPC resources to the researchers via an easy-to-use graphical interface. | Amirpasha Mozaffari Michael Langguth Bing Gong Jessica Ahring Adrian Rojas Campos Pascal Nieters Otoniel Jose Campos Escobar Martin Wittenbrink Peter Baumann Martin G.Schultz | 2022 | Data Intelligence2022，4，2： | 1 |
| 3 | Canonical Workflows in Simulation-based Climate Sciences显示文摘In this paper we present the derivation of Canonical Workflow Modules from current workflows in simulation-based climate science in support of the elaboration of a corresponding framework for simulationbased research.We first identified the different users and user groups in simulation-based climate science based on their reasons for using the resources provided at the German Climate Computing Center(DKRZ).What is special about this is that the DKRZ provides the climate science community with resources like high performance computing(HPC),data storage and specialised services,and hosts the World Data Center for Climate(WDCC).Therefore,users can perform their entire research workflows up to the publication of the data on the same infrastructure.Our analysis shows,that the resources are used by two primary user types:those who require the HPC-system to perform resource intensive simulations to subsequently analyse them and those who reuse,build-on and analyse existing data.We then further subdivided these top-level user categories based on their specific goals and analysed their typical,idealised workflows applied to achieve the respective project goals.We find that due to the subdivision and further granulation of the user groups,the workflows show apparent differences.Nevertheless,similar'Canonical Workflow Modules'can be clearly made out.These modules are'Data and Software(Re)use','Compute','Data and Software Storing','Data and Software Publication','Generating Knowledge'and in their entirety form the basis for a Canonical Workflow Framework for Research(CWFR).It is desirable that parts of the workflows in a CWFR act as FDOs,but we view this aspect critically.Also,we reflect on the question whether the derivation of Canonical Workflow modules from the analysis of current user behaviour still holds for future systems and work processes. | Ivonne Anders Karsten Peters-von Gehlen Hannes Thiemann | 2022 | Data Intelligence2022，4，2： | 0 |
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