ASAS-NANP研讨会:动物营养中的数学建模-大数据和机器学习的理解:开源代码如何促进动物科学家的培训
Jameson R Brennan1, Hector M Menendez1, Krista Ehlert2
1Department of Animal Science, South Dakota State University West River Research and Extension Center, Rapid City, SD 57701, USA.
Journal of animal science
|November 24, 2023
概括
开源工具和教程对于克服精密畜牧管理中的数据分析瓶至关重要. 常规使用开源代码有助于培训未来的动物科学家,促进研究.
科学领域:
- 动物科学动物科学
- 数据科学数据科学数据科学
- 农业技术 农业技术
背景情况:
- 精密畜牧技术产生了大量的动物数据.
- 数据分析的瓶阻碍了这些数据在畜牧管理中的有效使用.
- 从头开始开发数据管道是耗时和资源密集的.
研究的目的:
- 概述开源代码如何解决精确畜牧数据分析中的瓶问题.
- 展示开源教程对简化数据处理和模型培训的有用性.
- 强调开源代码在培训下一代动物科学家中的作用.
主要方法:
- 使用开源编程语言 (R/Python) 进行数据自动化.
- 开发和介绍用公开可用的数据和代码进行案例研究.
- 整合机器学习模型与动物营养模型.
主要成果:
- 开源代码有效地自动化了数据清理,聚合和集成.
- 案例研究证明了简化数据处理和机器学习模型培训.
- 开源教程有助于将预测集成到数学模型中.
结论:
- 开源解决方案对于实现精密畜牧技术的潜力至关重要.
- 开源代码的发布和常规使用加速了科学发现和教育.
- 协作开源存储库增强动物科学研究和数据可访问性.
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