Modeling With Data: Tools and Techniques for Scientific Computing

Albyn C. Jones

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Modeling With Data: Tools and Techniques for Scientific Computing

机译：数据建模：科学计算的工具和技术

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There is much to like about this book, and the author has clearly put a tremendous amount of work into the project. The software, including the author's cutely named Apophenia library, is free and open source. The author has interesting and provocative opinions. I disagree profoundly with the main premise: that students should learn C for data analysis instead of a statistics package. To abandon interactive software for routine data analysis and revert to batch processing seems profoundly wrong-headed.

机译：这本书有很多值得喜欢的地方，并且作者显然已经在项目中投入了大量工作。该软件包括作者的可爱名字的Apophenia库，是免费的开放源代码。作者有有趣和挑衅的意见。我对主要前提深有不同意见：学生应该学习C进行数据分析，而不是使用统计软件包。放弃交互式软件进行常规数据分析，而转而使用批处理处理似乎是错误的。

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《The American statistician》 |2010年第1期|90-91|共2页
作者
Albyn C. Jones;
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Reed College;

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1. Modeling with data: tools and techniques for scientific computing [J] . Stefan Robila Computing reviews . 2010,第5期

机译：数据建模：科学计算的工具和技术
2. Modeling with data: tools and techniques for scientific computing [J] . Soubhik Chakraborty Computing reviews . 2010,第6期

机译：数据建模：科学计算的工具和技术
3. Data mining techniques for scientific computing: Application to asymptotic paraxial approximations to model ultrarelativistic particles [J] . Assous F., Chaskalovic J. Journal of Computational Physics . 2011,第12期

机译：用于科学计算的数据挖掘技术：在渐近近轴近似中建模超相对论粒子的应用
4. Enabling Interoperation of High Performance, Scientific Computing Applications: Modeling Scientific Data with the Sets Fields (SAF) Modeling System [C] . Mark C. Miller, James F. Reus, Robb P. Matzke, International Conference on Computational Science . 2001

机译：启用高性能的互操作，科学计算应用：使用集合和字段（SAF）建模系统建模科学数据
5. Using model-based techniques for improving performance and reliability in high performance scientific computing. [D] . Dubey, Abhishek. 2009

机译：使用基于模型的技术来提高高性能科学计算的性能和可靠性。
6. Applying Intelligent Computing Techniques to Modeling Biological Networks from Expression Data [O] . Wei-Po Lee, Kung-Cheng Yang 2008

机译：应用智能计算技术从表达数据构建生物网络模型
7. We develop a model of scientific creativity and test it in the field of rare diseases. Our model is based on the results of an in-depth case study of the Rett syndrome. Archival analysis, bibliometric techniques and expert surveys are combined with network analysis to identify the most creative scientists. First, alternative measures of generative and combinatorial creativity are compared. Then, we generalize our results and present a stochastic model of socio-semantic network evolution. The model predictions are tested with multiple networks of rare disease specialties. We find that new scientific collaborations among experts in a field enhance combinatorial creativity. Instead, high entry rates of novices are negatively related to generative creativity. By extending the set of useful concepts, creative scientists gain in centrality. At the same time, by increasing their centrality in the scientific community, scientists can replicate and generalize their results, thus contributing to a scientific paradigm. [O] . Massimo Riccaboni, Maria Laura Frigotto 100

机译：我们开发了一种科学创造力模型，并在稀有疾病领域进行测试。我们的模型基于对Rett综合征的深入案例研究的结果。档案分析，文献计量技术和专家调查与网络分析相结合，以确定最具创造力的科学家。首先，比较生成和组合创造力的替代措施。然后，我们推广了我们的结果，并提出了社会语义网络演化的随机模型。模型预测用多个罕见疾病专业网络进行测试。我们发现，一个领域的专家之间的新的科学合作增强了组合创造力。相反，新手的高入门率与生成创造力负相关。通过扩展这组有用的概念，创造性的科学家获得了中心地位。同时，通过增加科学界的中心地位，科学家们可以复制和推广他们的结果，从而促进科学范式的发展。

Modeling With Data: Tools and Techniques for Scientific Computing

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