EDI Training Module 9: Explore EML with XML Editors
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This is module 9 in the EDI Data Publishing training course. In this module, you will learn now to view an EML document using jEdit or OxygenXML.
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EDI Training Module 9: Explore EML with XML Editors
- 1. Explore EML with XML Editors 1 (Phase 3)
- 2. 2 Background Sometimes manual editing of an XML document is necessary. XML editors provide a human friendly interface to understanding and modifying EML metadata.
- 3. 3 Here is the greenish title slide Objectives Become familiar with a few XML editors and the most commonly used features Navigate the EML schema within an XML editor
- 4. XML (what is is?) A set of hierarchical custom elements for a community’s use ● Platform-independent, human and machine readable ● Common exchange format for information, like metadata ● Relatively verbose, requires more storage space than some other formats ● Does not have strong data typing or access control, so schemas have to include this ● Schema language requires training 4
- 5. XML basics XML Schema ● Describes the structure of an XML document ● Also referred to as “XML Schema Definition” or XSD So XML is (kind of) a language, and EML is a dialect. A community will write up its own specification for a standard in XML Schema. 5
- 6. XML basics 6 General: Components: Prolog “Root” element Validate (an XML doc) XML element XML attribute Schema, schema location Namespace A little bit of XML vocabulary:
- 7. XML editors 7 https://en.wikipedia.org/wiki/Comparison_of_XML_editors jEdit oXygen Completely free (GPL 2.0) Good general text editor XML plug-in Schema validation Auto-complete Commercial product Academic pricing, or free (strings attached) Schema validation Instant auto-complete Other view modes http://www.jedit.org/ https://www.oxygenxml.com/
- 8. EML editing demo OxygenXML First choice: one of your EML files, from the google drive (or example package) 8
- 9. oXygen 9 Download: https://www.oxygenxml.com/xml_editor/download_oxygenxml_editor.html Request a 1-year license: https://www.oxygenxml.com/support_life.html They will ask that you post their logo Not free, but reasonable: $100 Academic license, no expiration
- 10. jEdit 10 Open source: http://www.jedit.org > Download Add Plugins for XML:
- 13. 13 Here is the greenish title slide Summary Summarize answers from objectives slide
- 14. EML details 14
- 15. Data Table metadata Some components can be automated Attribute list: your knowledge of the data is essential
- 16. Attributes & Units ● Attribute: a “property” of an object (data table) ○ In databases, a table column is called an “attribute” ○ Often referred to as “variables”, “parameters”, “columns” or “field names” ● Unit: a particular physical quantity ○ Defined and adopted by convention ○ Comparable To describe a data table you need a moderate understanding of A. how to define the table’s attributes, B. when and how to define a unit, and C. the relationship between the two.
- 17. EML Attribute components 1 Attribute Name: Usually the name you would give that column in a script 2 Attribute Label: Longer, for display, Use whole words, capitals, etc. 3 Attribute Definition: As complete and unambiguous as you need them to be, for the data to be understood 4 Measurement Scale: ● Nominal - attribute can be considered a category ● Ordinal - categories that have a logical or ordered relationship to one another ● Interval - the magnitude between the steps is known; equidistant points ● Ratio - have a meaningful zero, which allows ratios between values to have meaning ● Datetime - Gregorian dates and times 5 Unit: Interval and Ratio measurements only Choose from: TBD
- 18. EML Attributes - Measurement Scale Nominal Values are members of a category string Place and taxon names, coded values (eg, 1=male, 2=female), text comments Ordinal Nominal categories that have a logical or ordered relationship to one another string Academic grades, quality rankings (eg, 1=high, 2=medium, 3=low) Interval Ordinal, but the magnitude between the steps is known; equidistant points numeric Celsius scale, pH Ratio Interval, with a meaningful zero, so ratios between values to have meaning numeric Temperature in Kelvin, lengths, concentrations, organism densities Datetime Gregorian dates and times datetime Points in time, e.g., with formats like YYYY-MM-DD, hh:mm:ss.s
- 19. EML Attributes - code lists Nominal Values are members of a category Place and taxon names, coded values (eg, 1=male, 2=female) <codeDefinition> <code>ABUR</code> <definition>Arroyo Burro Reef</definition> </codeDefinition> <codeDefinition> <code>NAPL</code> <definition>Naples Reef</definition> </codeDefinition> ... Ordinal Nominal categories that have a logical or ordered relationship to one another Academic grades, quality rankings (eg, 1=high, 2=medium, 3=low) <codeDefinition> <code>A</code> <definition>scored higher than 90%/definition> </codeDefinition> <codeDefinition> <code>B</code> <definition>score 80 - 89%</definition> </codeDefinition> <codeDefinition> <code>C</code> <definition>score 70 - 79%</definition> </codeDefinition> ...
Editor's Notes
- A high level view of EML schema, so you knows where to look Describe how to work with XML editors
- The deep end… only if you need this to answer questions.
- If needed, could replace image, and map columns to EML. ?? to do??
- In general use, the term ‘attribute’ defines a property of an object, element or (in computer science) a file. In database vocabulary, a table column is called an ‘attribute’. If data were arranged in rows instead, then a row name could also be called an attribute. In ecology and environmental sciences where data are often arranged in tables or arrays, attributes may be referred to as variables, parameters, columns or field names. A ‘unit’ is “a particular physical quantity, defined and adopted by convention, with which other particular quantities of the same kind are compared to express their value.” (quoted from eml-docs, find a ref). There is often a blending or overlap between units and attributes in local laboratory conventions. But on a structural level and for an unambiguous comparison of measurements, the attribute and unit must be distinguished. Units may be one of the most problematic categories of metadata. For instance, there are many attributes that clearly have no unit, such as named places and letter grades. There are other attributes for which a unit is difficult to identify, despite a suspicion that one should exist (e.g. pH, dates, times). In still other cases, a unit may be meaningful, but apparently absent due to dimensional analysis (e.g. grams of carbon per grams of soil). Anyone describing a data table will need a moderate understanding of a) how to define the table’s attributes, b) when and how to define a unit, and c) the relationship between the two.
- 5 basic parts to an attribute: Name: BP is to make this match the table header. And to keep those clean (only ascii alpha-nums, please, no wonky chars). Label, definition, pretty self explanatory (see next slide) The measurement scales: a typology. This is where EML differs from other specs, DC has a variable-value model (which is by design uncontrolled for flexibility). ISO-19115 generally relies on external lists. EML wanted to encode at least some of the attribute info so that metadata could have some control over data values, and data packages could be self-contained. Meas scales range from simple to complex, and build on each other. This measurement scale model comes from Statistics, and has been around since the 1940s. It’s not perfect, and there are others. But it works pretty well for the kinds of measurements we have in environmental data. Nominal: values that can be considered categories. Values are assigned to distinguish them from other observations. Simple strings. Ordinal: values are categories that have a logical or ordered relationship to one another, but the magnitude of the difference between values is irregular or is not defined. Scores, like high/med/low. Interval: is used for data which consist of equidistant points on a scale, i.e., it is ordinal but now, the magnitude between the steps is known, and quantified. This is the first one of the series that is numeric Ratio: builds further - now those equidistant points also have a meaningful zero point, which allows ratios between values to have meaning. The 5th is dateTime. Not part of the original measurement scale model, but essential for environmental data. These are labels for points in time, and adhere to a convention - the Gregorian calendar. datetimes have characteristics of both the ordinal type (in that they are ordered categories) and interval type (equidistant points on a scale). By making dateTime a separate category and providing a mechanism for describing date formats, datasets contain the information needed to parse date values into their appropriate components (e.g., days, months, years). Unit: assigned only for two numeric types, for Interval and ratio. Cannot quite use this for EML 2.2: http://unit.lternet.edu
- So having this typology means that values can be typed differently: eg, the first two are strings, interval and ratio are numeric. Here are some examples of the way you would categorize measurements in this typology Nominal: values that can be considered categories. Values are assigned to distinguish them from other observations. Examples: using the number 1 for male and 2 for female, a species code or binomial, or the name of the site where the observation was made. Columns that contain strings or simple text are nominal type. Ordinal: values are categories that have a logical or ordered relationship to one another, but the magnitude of the difference between values is irregular or is not defined. Examples: academic grades: A, B, C, D, F, or ranking quality 1=high, 2=medium, 3=low. Interval: is used for data which consist of equidistant points on a scale, i.e., it is ordinal but the magnitude between the steps is equdistant. Examples: the Celsius scale is an interval scale, since degrees are equally spaced but there is no natural zero point. Since the ‘0’ of the Celsius scale is tied to a property of water, 20 C is not twice as hot as 10 C. Another example is pH. Ratio: is used for data which consists of equidistant points that also have a meaningful zero point, which allows ratios between values to have meaning. Examples of a ratio scale include the Kelvin temperature scale (200K is half as hot as 400K) and length in meters (e.g., 10 meters is twice as long as 5 meters). Concentrations are of ratio type. dateTime: A label for a point in time. Not a duration.
- In EML, Nominal and ordinal types can be either free text, or can have code lists, where you define the meaning of the categories. Code lists are used if the incoming data was “controlled”, eg, was part of a FK constraint in a database, or you know what values are allowed and you want to keep in “controlled” in the data package. Even for data that has never been explicitly controlled (ie, did not come out of a database, listing the allowable codes and their definitions will help a user (or even you) later on. It’s a good way to keep track of what your codes mean. Ordinal types: (side note: I have seen very few datasets that use ordinal type, but when they do, they have code lists. Almost all my datasets have had at least one nominal attribute with a code list) In R, things that have code lists are typed as “factor” If you have a lot of codes, or they are reused a lot, or if the definition is longer than simple text, you could put the codes and definitions into a separate table (getting into bp here. Talk to Kristin)