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1. Accurately identify the topics of the course.
   1. Be specific to the level of the course
      1.  
2. Agree with common usage of scientific terms.
3. Conform to the content and formatting instructions below.
4. Fall between 5 and 15 in number.
5. Not create a large additional effort requirement for publication managers.

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1. 1. Course Title - This one is self-explanatory
   2. Course Home Page Description
      1. Example: http://ocw.mit.edu/OcwWeb/Economics/14-30Spring-2006/CourseHome/index.htm
         This course is a self-contained introduction to statistics with economic applications. Elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for http://ocw.mit.edu/OcwWeb/Economics/14-32Spring-2007/CourseHome/index.htm No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.
         
         Keyword possibilities: probability theory, sampling theory, statistical estimation, regression analysis, hypothesis testing, econometrics, statistical tools, economic data
         
         Do not use: statistics - too general, probability - too general and repetitive, economists - the course isn't about economists it is for economists, social scientists - same as economists, basic algebra - not what course is about, calculus - same as basic algebra
   3. Syllabus
      1. Example: http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-034Spring-2005/Syllabus/index.htm
         
         See the topics section of the syllabus, notice how it provides additional information to the course description.
   4. Calendar headings
      1. Example: http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-034Spring-2005/Calendar/index.htm
         
         Notice that there are terms in this calendar that do not appear in the course description or the syllabus (naïve bayes, feature and model selection, linear separators)
   5. Lecture Topics/Titles
      1. Example: http://ocw.mit.edu/OcwWeb/Economics/14-30Spring-2006/LectureNotes/index.htm
         
         Keyword possibilities: set theory, Random Variables, Probability Mass/Density Function, Cumulative Distribution Function (Univariate Model), and more
   6. Additionally, the Readings, Assignments, Exams, and Study Materials sections may be organized by useful subject headings.
2. Be specific to the level of the course
   1. If calendar or other sections are organized into topics and subtopics, do not include the subtopics.
   2. If there is a plethora of topics in the course description of other sources, start with the broadest topics.
   3. Don't slavishly follow the 5 to 15 rule for keywords.  If you need to add more to accurately describe the course, do so.
3. Recording keywords is not a simple process of copying and pasting content found in the course areas listed above.  Found words and phrases need to be adjusted.  Adjustment goals are:
   1. Use widely adopted words and phrases for particular topics
      1. Example: Brain and Cognitive Sciences
         
         The department name "Brain and Cognitive Sciences" is an idiosyncrasy of MIT.  Courses in this department cover a wide range of topics (from Psychology to Neurobiology), many of which are newly coined and inconsistently used.  You will frequently need to do a little research to identify the variant word or phrase that has the widest and most consistent use.
   2. Acronyms are okay
   3. Separate compound phrases.
      1. Example: feature and model selection - see http://ocw.mit.edu/OcwWeb/Electrical-Engineering-and-Computer-Science/6-034Spring-2005/Calendar/index.htm
         
         The use of the word "and" should cause you to consider the phrase more closely.  In this case "feature selection" and "model selection" are two different concepts.  See: http://en.wikipedia.org/wiki/Feature_selection and http://en.wikipedia.org/wiki/Model_selection
   4. If you're unsure, look the term up in Wikipedia.
      1. Example: hypothesis testing - see http://ocw.mit.edu/OcwWeb/Economics/14-30Spring-2006/CourseHome/index.htm
         
         This seems too general at first (hypothesis testing occurs in many scientific disciplines, there are many different kinds of hypothesis testing).  If you look up the term in Wikipedia (http://en.wikipedia.org/wiki/Hypothesis_testing) you'll find the particular kind of hypothesis testing that is explained in this course.
         Use: statistical hypothesis testing
   5. Apply Keyword Formatting Guidelines.

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