# Presentation: 215456

## The BODC Parameter Markup and Usage Vocabulary Semantic Model

**Roy Lowry**
**British Oceanographic Data Centre**

**GO-ESSP Meeting, RAL, June 2005**

## Presentation Outline

**Parameter codes and their metadata load **
**EnParDis Project**
**BODC PMUV Semantic Model **
**Issues: Synonyms and Tooling**
**Points to Ponder **

## What is a Parameter Code?

**According to the original oceanographic data standard (GF3) a parameter code is a key attached to a data value that:**
**Specifies:**
**What was measured**
**How it was measured**
**Actual (not canonical) units of measurement **
**Is defined through the attributes of a parameter dictionary**
** ****Includes semantics (e.g. TEMP7RTD, TEMP7STD)**
**The data models of IODE data centres were strongly influenced by GF3 concepts (even if the format was little used)**
**Parameter codes are therefore endemic in legacy oceanographic data **

## What is a Parameter Code?

**Parameter codes have been grossly abused by the oceanographic data management community**
**Codes mapped to free text fields causing**
**Compromised semantic purity (e.g. vague spatio-temporal co-ordinates like ‘sea-surface’ introduced)**
**Incomplete or ambiguous specifications. Sample GF3 parameters:**
**Sea temperature (estuaries?)**
**Potential temperature (of what?)**
**Potential air temperature (OK!)**
**Wet bulb temperature (could be in water!)**
**Metadata overload (e.g. taxon names included in parameter – what was measured - descriptions)**
**Random scatterings of synonyms**
**Parameter semantics in unit definitions (e.g. per gram dry weight)**

## What is a Parameter Code?

**BODC joined in this abuse through a Parameter Dictionary following the GF3 model**
**BODC Parameter Dictionary originally mapped code to:**
**Two plain-text fields of what measured (parameter) and how (parameter subgroup)**
**Units specification**
**Valid data range**
**Formatting information**
**Abbreviated description label**
**As chemistry and biology were added the plain-text fields evolved into a total mess**

## Enabling Parameter Discovery (EnParDis)

**EnParDis was a one-off injection of NERC funding aiming to:**
**Integrate taxonomic knowledge into the BODC Parameter Dictionary**
**Include data on penguins in a query for ‘birds’**
**Based on ITIS and works providing taxa are in ITIS**
**Totally overhaul the parameter plaintext descriptions**
**Standardise terms and syntax**
**Eliminate implied semantics**
**Plaintext field overhaul achieved by using structured text (concatenated elements from a semantic model)**

## Semantic Model

**The Semantic Model maps each parameter code to a set of atomic metadata elements populated by entries from controlled vocabularies **
**The parameter description is built by structured concatenation of the Semantic Model elements**
**The Semantic Model forms a flexible interface between legacy systems based on parameter codes and/or semantically poor text descriptions and modern (meta)data content models**
**The Parameter Dictionary becomes a registry of valid Semantic Model element combinations (insurance against the introduction of the green dog)**

## Semantic Model

**The parameter description is built up as three themes:**
**What theme – what was measured**
**Where theme – where it was measured (sphere NOT spatio-temporal co-ordinates or their textual representation like sea surface)**
**How theme – how it was measured**
**Example**
**Temperature (What)**
**of the water column (Where)**
**by CTD (How)**

## What Theme

**Parameter Entity of Measurand Entity (chemical, physical or biological) by Measurand Entity**
**Examples**
**Clearance rate of Dinophycae by Acartia**
**Concentration of nitrate+nitrite**
**Temperature**

## Parameter Entity

**Three Semantic Elements**
**Parameter Name**
**Example: concentration**
**Parameter Statistic**
**Example: standard deviation**
**Parameter Subgroup**
**Example: v/v**

## Biological Entity

**Nine Semantic Elements**
**Taxon Name**
**ITIS code for taxon**
**Taxon size**
**Taxon gender**
**Taxon development stage**
**Taxon morphology (shape terms)**
**Taxon subcomponent (body parts)**
**Taxon colour**
**Taxon subgroup (subdivision ‘bucket’)**

## Chemical Entity

**Two Semantic Elements**
**Chemical name**
**Example: carbon**
**Chemical subgroup**
**Example: organic**

## Physical Entity

**Three Semantic Elements**
**Physical Name**
**Examples: sea surface elevation, temperature**
**Physical subgroup**
**Example: IPTS-68**
**Datum**
**Example: Ordnance Datum Newlyn**

## Where Theme

**Relationship and a Sphere Entity or Biological Entity**
**Examples**
**Per unit volume of the water column**
**Of the atmosphere**
**Per unit wet weight of Mytilus edulis flesh**
**Per unit dry weight of sediment**
**Per unit area of the water column **

## Sphere Entity

**Four Semantic Elements**
**Sphere Name**
**Example: sediment**
**Sphere subgroup**
**Example:****	****<63um**
**Sphere phase**
**Examples: particulate, aerosol, gaseous, dissolved plus reactive particulate**
**Sphere phase subgroup**
**Examples: >GF/F, 2-20um**

## How Theme

**Sample processing entity**
**Example: radiotracer inoculation and incubation in natural sunlight**
**Analysis entity**
**Example: proportional counting**
**Data processing entity**
**Example: conversion to carbon using unspecified algorithm**

## How Theme

**Sampling and Data Processing Entities are single entities**
**Analysis Entity has two Semantic Elements**
**Analysis Description**
**Analysis Instance Discriminator (multiple sensors) **

## Issues

**Synonyms**
**What synonyms do we need?**
**How do we store them?**
**How do we utilise them?**
**Tooling**
**Tools for code assignment**
**Tools for dictionary expansion**

## Synonyms

**Synonyms required for:**
**What Theme**
**Parameter Name**
**Parameter Entity**
**Chemical Name**
**Chemical Entity**
**Physical Name **
**Physical Entity**
**Biological Entity **
**Taxon name**
**Parameter Description**
**Probably more as well**

## Synonyms

**The following information needs to be known for each synonym**
**The Semantic Model entity type**
**The primary term **
**The secondary term**
**Could be managed through a conventional relational schema, but RDF seems more attractive as there is more to relationships than ‘synonymous’ **
**Current thinking on synonym exposure is to produce multiple parameter descriptions for a single parameter code incorporating all synonym combinations **

## Tooling

**Web services to give access to code definitions, model elements, controlled vocabularies, mappings and synonyms**
**Automated data markup tooling based on model semantic element specification**
**Automated request mechanism for dictionary population extension with efficient moderation mechanism**
**Could be configured as a single tool sitting on a common set of services**

## Points to Ponder

**What is the mapping between components of the Semantic Model and a CF Standard Name?**
**How can the CF Standard Name list and the BODC Data Markup vocabulary be integrated into a unified resource covering both the oceanographic and atmospheric domains?**