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计算科学中的可复制研究:数据和代码共享的问题和解决方案


Reproducible Research in Computational Science: Problems and Solutions For Data and Code Sharing

课程网址: http://videolectures.net/icml2010_stodden_rric/  
主讲教师: Victoria Stodden
开课单位: 耶鲁大学法学院
开课时间: 2010-07-20
课程语种: 英语
中文简介:
科学计算正在成为科学方法的绝对核心,但非常宽松的做法的盛行正导致一场信誉危机。可复制的计算研究,其中计算代码和数据的所有细节都可以方便地提供给其他人,是应对这场危机的必要措施。2009年对机器学习社区(NIPS参与者)进行的调查结果旨在阐明影响数据和代码共享的因素。知识产权问题对共享造成了重大障碍,我还将介绍“可复制研究标准”的工作,该标准提供了开放的许可选择,旨在为科学家创建符合长期科学规范的知识产权框架,并促进可复制研究。
课程简介: Scientific computation is emerging as absolutely central to the scientific method, but the prevalence of very relaxed practices is leading to a credibility crisis. Reproducible computational research, in which all details of computations—code and data—are made conveniently available to others, is a necessary response to this crisis. Results from a 2009 survey of the Machine Learning community (NIPS participants) designed to elucidate factors that affect data and code sharing will be presented. Intellectual property concerns create a significant barrier to sharing, and I will also present work on the “Reproducible Research Standard” giving open licensing options designed to create an intellectual property framework for scientists consonant with longstanding scientific norms and facilitating reproducible research.
关 键 词: 科学计算; 机器学习; 可复制研究
课程来源: 视频讲座网
数据采集: 2022-12-01:chenjy
最后编审: 2022-12-01:chenjy
阅读次数: 35