Considerations for the construction of lichen databases
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Considerations for the Construction of Lichen Databases. Data Management. Relational Database Platforms. Excel MS Access, Paradox, etc. SQL Server Oracle / Sybase. Excel. Not recommended for long-term storage No data-typing enforcement. Excel. MS Access, Paradox, Etc.

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Considerations for the Construction of Lichen Databases

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Considerations for the construction of lichen databases

Considerations for the Construction of Lichen Databases

Data Management


Relational database platforms

Relational Database Platforms

  • Excel

  • MS Access, Paradox,etc.

  • SQL Server

  • Oracle / Sybase


Excel

Excel

  • Not recommended for long-term storage

  • No data-typing enforcement


Excel1

Excel


Ms access paradox etc

MS Access, Paradox, Etc.

  • Desktop database systems

  • Do not scale well

  • Front-end application development

  • Free / cheap and fairly beginner friendly


Sql server

SQL Server

  • Microsoft’s database engine for larger scale databases

  • Serving live data on the web possible


Oracle sybase

Oracle / Sybase

  • Enterprise size database systems

  • High cost and maintenance

  • Support for data exchange

  • Recommended if provided


General database design consideration

General Database Design Consideration

  • Databases should be designed to serve the data best not a particular question


General database design consideration1

General Database Design Consideration

  • Avoid storing redundant information by designing several tables, linking information as necessary


General database design consideration2

General Database Design Consideration

  • Keep information as consistent as possible (e.g. spelling of collector names, description of places)

    • Authority tables

    • Input masks


General database design consideration3

General Database Design Consideration


General database design consideration4

General Database Design Consideration

  • Take advantage of data-typing

    • Dates in date fields

    • Numbers in number fields

    • Don’t mix letters and numbers if possible (collection number, lat long)


General database design consideration5

General Database Design Consideration

  • For any kind of descriptive information (e.g. substrate) consider developing and keyword taxonomy (e.g. bark, coniferous tree, Juniperus deppeana)

  • The better the keywords the more efficient the information retrieval


General database design consideration6

General Database Design Consideration

  • Develop a species checklist for your area or use an existing one to assure highest taxonomic accuracy possible


Asu s data model

Annotations

Secondary Compounds

TLC Records

Types

Specimens

Exsiccati

Localities

Multiple Identifications

ASU’s Data Model

R. Schoeninger


Asu s data model1

ASU’s Data Model

  • Authority tables

    • Species check list (accepted names, synonyms, authors)

    • List of ecological keywords

    • List of substrates

    • List of collectors and determiners

    • List of localities


Database types and implications for use

Database Types and Implications for Use

  • Collections or taxonomic databases

  • Observation databases

  • Measurement databases


Collections or taxonomic databases

Collections or Taxonomic Databases

  • Based on collected specimens

  • Highest degree of taxonomic information

  • Information on distribution varies

  • No information on abundance


Observation databases

Observation Databases

  • Based on a sampling design for observations

  • Emphasis on absence/presence or abundance

  • Taxonomic value varies


Measurement databases

Measurement Databases

  • Data on the ecology of a species

    • Laboratory measurements

    • Data from a literature search


Interoperability

Interoperability

Distributed

Databases

HTML

Search

Application

Registering

Service

&

Data

Discovery

Tool

&

Target

Manager

Meta data

Query

Results


Meta data

Meta Data

  • Data about data

    • Taxonomic: NBII, ISO

    • Spatial: FGDC, ISO

    • Ecological: EML

    • Air management?


Standardization efforts

Standardization Efforts

  • Geo-referencing the data

  • Metadata standard

  • Keyword standard

  • Taxonomic thesaurus (ITIS)

  • Geographic thesaurus (Alexandria)


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