And so far when reviewing the developmental data we've talked about two of them: physics and people.

到目前为止,当我们回顾发展的数据时,我们讨论了其中的两个主题,物理学和人类

耶鲁公开课 - 心理学导论课程节选

This is useful especially when we get to details like forensics and looking at data on a hard drive, - 'cause if you don't know how much store-- how many bits are composing your file, you're never gonna recover that data or be able to access what you're looking for.

这是很有用的，尤其是在法庭上,查看硬盘驱动器里的数据时，如果你连-,文件有多少比特都不知道，你如何去恢复数据,获取你想要的信息呢。

哈佛公开课 - 计算机科学课程节选

whether it was the mental health workers, whether it was the philosophers,psychologists, praise the program as the best,as highly effective, when they looked at the raw objective data, the results were shocking.

无论是心理健康学者，哲学家，心理学家，都说这是最佳，最高效的研究方案，但当他们看到原始的样本数据，调研结果让他们震惊。

哈佛公开课 - 幸福课课程节选

They live much longer lives and they live to die of something else and the leading causes of death currently haven't changed very much since 1997 when this data was published: they die of heart disease and cancer primarily.

人们活的更久了,不过会死于另一些疾病,如今导致死亡的主要原因,并没有太大的变化,自一九九七年有相关数据公开开始,其显示人们主要死于心脏病和癌症

耶鲁公开课 - 生物医学工程探索课程节选

and what we saw, just to cut to the data, is that when people anticipated making money we saw increased activation and the region not far from the region that Olds and Milner stimulated in 1954.

而我们所见的则转换成了数据，当人们预期会赚钱时,我们看见大脑活动的增加,而这块活动增加的区域,和奥兹和米尔纳在1954年试验时的大脑区域相隔不远。

斯坦福公开课 - 7个颠覆你思想的演讲课程节选

Scheme And I happen to like Lisp and Scheme, it's a great language when you're trying to deal with problems where you have arbitrarily structured data sets.

而且我很偶然的喜欢上了Lisp和，这些是当你要试图解决你,曾经很武断的设置数据,的地方的问题时很棒的语言。

麻省理工公开课 - 计算机科学及编程导论课程节选

You can take a problem that might be relatively intuitive to solve but when you scale this thing up as is increasingly the case in the web, in large data systems, and so forth, you actually have to now think smart, you actually have to think efficiently and you have to solve this problem effectively.

你可以把一个问题用比较直观的方法解决,但如果你把此类问题的数量增大,正如越来越多的互联网，和大规模数据系统中出现的问题等等,你应该考虑怎样才能更简便，怎样才能更高效,你应该用行之有效的方法处理问题。

哈佛公开课 - 计算机科学课程节选

But one of the teaching fellas also passed long to us recently, a little real world example of what happens when you're not mindful of various data types and you're not mindful of the imprecision that's inherent in representing data in a computer, at least using a language like C and low level primitives like floats and even doubles.

最近有个助教告诉我们，一个现实世界中的例子,当你不注意各种各样的数据类型,也不注意在计算机中表示数据时,其内在的不精确性，至少在用像C语言,和float甚至double型数据时，那将会发生什么？

哈佛公开课 - 计算机科学课程节选