Publication Manager Metadata Production Participation

Instructions, guidelines, and advice for Publication Manager contributions to metadata production for OCW courses and sections.

Publication Managers will make their contributions to Course and Section metadata in the Filemaker spec for each Course.

Course Metadata

Metadata Element

FM Table

FM Field

Instructions

Title

d_Course

zzd_courseTitle

Keep doing what you're doing now.

Description

d_Course

A field will be created that will contain only the course descriptions.

Keep doing what you're doing now.

Course Instructors

d_Course

Major changes to FM contributor tables will be made.

Keep doing what you're doing now.  Further instructions will be provided.

Semester

d_Course

zzd_semester, zzd_year

Keep doing what you're doing now.

Master Course Number

d_Course

There is a field that concatenates the course number and suffix, this is the field we want.

Keep doing what you're doing now.

Cross-listed Course Number

d_Course

zzd_otherCourseNumbers

Keep doing what you're doing now.

Educational Context

d_Course

zzd_courseLevel

Keep doing what you're doing now.

Other MIT Version of the Course

d_Course

zzd_urlCurrentSite

Use a full URL (include http://) if it exists, otherwise leave blank.

Other kinds of relations

d_Course

We want to start capturing a number of different kinds of relations (Prerequisites, GIRs, etc.)  This will require some FM development.

Instructions forthcoming.

Instructional Model

d_Course

zzd_zciInstructionalModel

See content inventory

Keywords

d_Course

zzd_keywords

See Keyword Instructions

Keywords

Objectives

Course keywords should:

  1. Accurately identify the topics of the course.
    1. Be specific to the level of the course 
  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.

Keyword Content Instructions

  1. Look for terms in course content
    1. Course Title - This may contain one or more topics
    2. Course Home Page Description
      1. Example: http://ocw.mit.edu.ezproxyberklee.flo.org/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.ezproxyberklee.flo.org/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.ezproxyberklee.flo.org/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.ezproxyberklee.flo.org/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.ezproxyberklee.flo.org/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.
  1. 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.
  2. 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.ezproxyberklee.flo.org/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.ezproxyberklee.flo.org/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.

Keyword Formatting Instructions

  1. Both words and phrases are okay
  2. Concepts are singular
    1. Examples: sampling theory, neurobiology
  3. Objects are plural
    1. Examples: economic data, statistical tools
  4. Only capitalize proper nouns
  5. Don't invert adjective and noun
    1. Example: sampling theory NOT theory, sampling
  6. Prepositional phrases are okay
    1. Example: analysis of variance, see: http://ocw.mit.edu.ezproxyberklee.flo.org/OcwWeb/Brain-and-Cognitive-Sciences/9-07Spring-2004/CourseHome/index.htm and http://en.wikipedia.org/wiki/Variance_analysis
  7. Proper Nouns are okay
    1. Examples: United States of America, Thomas Jefferson, Abraham Lincoln, see: http://ocw.mit.edu.ezproxyberklee.flo.org/OcwWeb/History/21H-101Fall-2005/CourseHome/index.htm
  8. Don't use subheadings
    1. Example: Don't use something like "neuroscience--neurolinguistics"

Section Metadata

Metadata Element

FM Table

FM Field

Instructions

Description

d_Section

Field to be created.

See Section Description Instructions

Section Descriptions

Objectives

Section descriptions identify the contents of a section and describe the context in which the contents were prepared and used.

Instructions

  1. Descriptions are often included in the content of the section.  These descriptions should also be included in the metadata.  You will often need to edit these descriptions.
  2. Identify the kind of resources included in the section.
    1. Example: Lecture Notes

      Lecture notes take many forms--handouts, slides, outlines, etc.  This is candidate information for a section description.

    2. Example: Assignments

      Same as lecture notes, provide more specific information about the content.  Problem sets, solutions, essay assignments

    3. Example: Readings

      Some readings sections are lists of citations, some are chapter assignments in the course textbook, some or mandatory, some are not.

  3. Generally characterize content.  You do not need to list all of the resources, nor provide a count.
  4. Do indicate whether the contents of a section are complete for the course.
  5. Use full sentences.

Examples

More forthcoming

  • No labels