The IESR Content Manager

1
The IESR Content Manager

• Leigh Morris
• MIMAS,
• The University of Manchester, UK

2
Outline

• What the job involves
• Metadata quality
• Why isnt it easy to ensure quality?
• Processes
• Quality Assurance
• The future

3
IESR Content Manager is people!

• Who am I?
• Job title
• Information Technology Officer,Development and
  Service Support Joint Information Systems
  CommitteeInformation Environment Service
  RegistryContent Manager

4
What am I supposed to do?

• Manage content of IESR
• Project plan
• Consistent, relevant metadata in the IESR
• Up to date data creation guidelines
• Quality Assurance procedures
• In addition, run IESR website

5
So I ensure quality then

• Project plan in more detail
• Data checking
• Check consistency of data submissions, implement
  quality assurance procedures and liaise with data
  contributors to ensure data quality
• Maintain and update data creation guidelines
• Make changes to guidelines as required
• Contacting contributors
• Contacting potential content providers and
  encouraging the creation of metadata for
  appropriate services

6
What is quality metadata?

• What is quality?
• Hard to define
• Subjective, semantics
• For the IESR, dont know what quality means yet
• Probably means Fit for purpose
• IESR purpose Resource discovery

7
Characteristics of quality metadata

• According to Hillman and Bruce, quality metadata
  has
• Completeness
• Accuracy
• Provenance
• Conformance to expectations
• Logical consistency and coherence
• Timeliness
• Accessibility
• The Continuum of Metadata Quality. In Metadata
  in Practice ALA, 2004.

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Why is quality important?

• If the data is not quality then
• Trust in IESR data lost
• No use of IESR
• The Information Environment will break down!
• Resource discovery - quality ensures
• Resource can be found
• Resource can then be used

9
Why isnt it easy to ensure quality?

• Collection description involves cataloguing
  resources
• Librarian skills
• Subjective
• Service description requires technical knowledge
  about the service
• Who creates descriptions?
• What skills do they have?
• Time Money

10
Problem properties Collection Description

• Use
• Used in searching
• Might be displayed by e.g. portal
• What makes a good description?
• Can only make an informed judgement
• Define criteria for what a good Description
  should contain
• Currency a problem

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Problem properties Collection Subject

• Subjective
• Which controlled vocabulary?
• Specificity
• Number of terms
• Dewey
• Backbone for searching
• Requires licence

12
Problem properties Service properties

• Interface information for Z39.50, SOAP, SRW,
  web-cgi services
• Probably needs to be manually created currently
• Output
• Supports Standard

13
Getting data

• How do we get data?
• Will be just via the data editing interface
• Data suppliers must register
• Manual process
• One record at a time
• This makes it a bit easier
• Not accepting XML
• Not harvesting from other sources

14
How do we ensure metadata quality?

• Direct contact with data suppliers
• Support
• Helpdesk (email, telephone)
• Guidelines for data/content creation
• Quality Assurance procedures
• Ownership - its your data

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How do we help data suppliers?

• Find out about their
• cataloguing skills
• Technical knowledge about the services
• Why does this make it easier?
• Get personal contact with data suppliers
• Very important
• Be there for them
• IESR Content Management is people

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Support to create IESR data descriptions

• Data/Content creation guidelines
• Help in the data editing interface
• Examples very important
• Lack of examples as little data in IESR
• Written guidelines can only do so much
• Need direct support too

17
Cataloguing support

• Help with creating subject terms
• What standard vocabularies do data suppliers have
  access to?
• Some easier to get to then others
• Dewey terms?
• Added by Content Manager if data supplier does
  not have licence

18
Quality Assurance

• Get data via data editing interface
• Data checked by data editing interface
• Check for missing data
• Data validated
• Manually check initial submissions
• Feedback, re-submit data
• When were both happy, let them loose without
  supervision

19
QA what the software does

• Checks for missing data
• Mandatory properties
• Validates some properties
• Date values in correct formatYYYY-MM-DD,
  YYYY-MM, YYYY (W3CDTF)
• Check email addresses contain _at_
• Check URLs start with http//
• Might check language values look like a language
  code (RFC 3066)

20
QA What a human does

• Checks Title, Alternative Title explain any
  acronyms, abbreviations
• Checks Collection Description manually
• Checks subject terms
• Checks rights information
• etc

21
Update frequency

• When do records go out of date??
• Up to you, the data suppliers
• System to email you at regular intervals
• Default will be 3 months, but you can change this
  to
• 1 month
• 3 months
• 6 months
• 12 months too much time between updates

22
Ongoing Quality Assurance

• Dump a random selection of records out of IESR
  every month
• How many?
• Check properties manually
• Title explains any acronyms/abbreviations
• Check description against criteria for a good
  description
• Check subjects
• Automatic checking
• Already check Z39.50 targets weekly

23
The future

• Harvesting of records
• No personal contact with data suppliers
• How will this affect data quality, quality
  assurance procedures?
• How many records?
• IESR Kite mark
• Might mark records to say they meet IESR quality
  expectations
• Decay, use by date, need for frequent checking,
  updating of record

24
Contact details

• Leigh Morris
• leigh.morris_at_manchester.ac.uk
• IESR Helpdesk
• iesr_at_mimas.ac.uk
• 0161 275 7179
• IESR website
• http//iesr.ac.uk/
• Who is hungry?
• Retire to MBS for lunch