图 书 馆
在复杂数据分析中提高实验的再现性和研究成果的可重用性Improving the reproducibility of experiments and reusability of research outputs in complex data analysis |
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| 课程网址: | http://videolectures.net/icgeb_panov_complex_data_analysis/ |
| 主讲教师: | Panče Panov |
| 开课单位: | Joíef Stefan学院知识技术部 |
| 开课时间: | 2019-06-28 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The advances in science are heavily based on the premise of the concept of a trusted discovery, provided that the performed research is done correctly, and reproducible by other scientists. In order to increase the reusability of research outputs, such as developed models and produced data, they should be Findable, Accessible, Interoperable and Reusable (FAIR principles). The main point of the FAIR is to ensure that research outputs are reusable and will actually be used by others, thus becoming more valuable. The research outputs that wish to fulfil the FAIR principles must be represented with a wide accepted machine-readable framework. Currently, a popular solution to data sharing that fulfils the FAIR requirements is the use of semantic web technologies and ontologies. Complex data analysis methods, originating from machine learning and data mining, are increasingly being used in applications from various domains of science (e.g., life sciences, space research, etc). In order to provide reproducibility of experiments (e.g., executions of methods) and reuse of research outputs (e.g., predictive models), one needs to formally describe the entities involved in the process of analysis, and store them together with their descriptions (e.g., metadata) as a digital objects in a database like structure. Having a “semantically aware” stores of entities for complex data analytics enhanced with automatic reasoning capabilities would be beneficial for improving the reproducibility of experiments and reuse of research outputs. In this way, we would move closer to a FAIR data analysis process. In this talk, I will show and discuss the recent advances in the domain that are aimed towards improving the reproducibility of experiments and reusability of research outputs in complex data analysis. |
| 关 键 词: | 复杂数据分析; 提高实验的再现性; MTP的半监督方法; 模型学习; 化合物筛选 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-10-14：cyh |
| 最后编审: | 2022-10-14：cyh |
| 阅读次数: | 28 |