1. MEOW: Metadata Enhancement and OAI Workshop

3. Metadata Enhancement, Sharing and OAI-PMH

4. What does this record describe?
identifier:
publisher: Museum of Zoology, Fish Field Notes
format: jpeg
rights: These pages may be freely searched and displayed. Permission must be received for subsequent distribution in print or electronically.
type: image
subject: ; 1926; 0812; 18; Trib. to Sixteen Cr. Trib. Pine River, Manistee R.; JAM26-460; 05; 1926/05/18; R10W; S26; S27; T21N
language: UND
source: Michigan 1926 Metzelaar, ;
description: Flora and Fauna of the Great Lakes Region

6. Creating descriptive metadata
“Digital library” systems
ContentDM
ExLibris Digitool
Greenstone
Library catalogs
Spreadsheets & databases
XML

7. Some Emerging Trends in Metadata Creation
“Schema-agnostic” metadata
Metadata that is both shareable and re-purposeable
Harvestable metadata (OAI-PMH)
“Non-exclusive”/”cross-cultural” metadata--i.e. it’s okay to combine standards from different metadata communities--e.g. MARC and CCO, DACS and AACR, DACS and CCO, EAD and CDWA Lite, etc.
Importance of authorities--and difficulties in “bringing along” the power of authorities with shareable metadata records
The need for practical, economically feasible approaches to metadata creation

8. Building “Good digital collections”
Interoperable – with the important goal of cross-collection searching
Persistent – reliably accessible
Re-usable – repositories of digital objects that can be used for multiple purposes

9. Issues Many significant digital collections lack standardized metadata
Such inconsistent metadata causes access problems, especially when collections are aggregated
Enhancing metadata for existing collections is often difficult to undertake because of a lack of tools to automate the process

10. Issues
IMLS grant: “Enhancing and Remediating Legacy Metadata for Effective Resource Sharing.”
“Metadata today is likely to be created by people without any metadata training … metadata records are also created by automated means … unsurprisingly, the metadata resulting form these processes varies strikingly in quality and often does not play well together .. Nevertheless, many metadata aggregators use this metadata to build services for end users, thus contributing to criticisms that metadata is of limited value, can’t be trusted or that it’s demonstrably so incomplete as to be worthless”--Diane Hillman.

11. Issues
Useful services depend on good metadata, but most metadata is not very good
Human created metadata is expensive
Automated crawling strategies are limited by:
Accessibility barriers (rights issues, technical issues)
Variable results with crawling technologies for non-text
Best metadata does not rely solely on information contained within the resource itself
Ex.: Controlled vocabularies, descriptions, links

12. What is shareable metadata?
Is quality metadata
Promotes search interoperability
“the ability to perform a search over diverse sets of metadata records and obtain meaningful results.”
Is human understandable outside of its local context
Is useful outside of its local context
Is machine processable

13. Shareable metadata defined
Promotes search interoperability - “the ability to perform a search over diverse sets of metadata records and obtain meaningful results” (Priscilla Caplan)
Is human understandable outside of its local context
Is useful outside of its local context
Preferably is machine processable

14. Why share metadata anyway?
Benefits to users
Single search of a variety of digital resources
Aggregation of subject-specific resources
Higher quality resources
Benefits to institutions
Increased user access to collection by allowing metadata to appear in other places
Exposure to broader audience, new users
Surfacing rare, unknown, or scattered collections

15. Different shapes and sizes…
Range of different aggregations:
focused subject area v. comprehensive
specialized audience v. general audience
Range of different displays

16. Metadata as a view of the resource
There is no monolithic, one-size-fits-all metadata record
Metadata for the same thing is different depending on use and audience
Affected by format, content, and context
Descriptive vs. administrative vs. technical, etc. data

17. Metadata is a view of a resource
No monolithic, one-size-fits-all metadata record
The view might be different depending on use and audience as well as format, content, and context
Content standard is a view
Metadata standard is a view
Vocabulary used is a view

18. Choice of metadata format(s) as a view
Many factors affect choice of metadata formats
Many different formats may all be appropriate for a single item
High-quality metadata in a format not common in your community of practice is not shareable

19. Focus of description as a view
Link between records for analog and digital
Hierarchical record with all versions
Physical with link to digital
All versions in flat record
Content but not carrier

20. Finding the right balance
Metadata providers know the materials
Document encoding schemes and controlled vocabularies
Document practices
Ensure record validity
Aggregators have the processing power
Format conversion
Reconcile known vocabularies
Normalize data
Batch metadata enhancement

21. 6 Cs and lots of Ss of shareable metadata
Content
Consistency
Coherence
Context
Communication
Conformance
Metadata standards Vocabulary and encoding standards
Descriptive content standards Technical standards

22. Content Choose appropriate vocabularies Choose appropriate granularity
Make it obvious what to display
Make it obvious what to index
Exclude unnecessary “filler”
Make it clear what links point to

23. Consistency Records in a set should all reflect the same practice
Fields used
Vocabularies
Syntax encoding schemes
Allows aggregators to apply same enhancement logic to an entire group of records

24. Coherence
Metadata format chosen makes sense for materials and managing institution
Not just Dublin Core!
Record should be self-explanatory
Values must appear in appropriate elements
Repeat fields instead of “packing” to explicitly indicate where one value ends and another begins

25. Context Include information not used locally
Exclude information only used locally
Current safe assumptions
Users discover material through shared record
User then delivered to your environment for full context
Context driven by intended use

26. Communication Method for creating shared records
Vocabularies and content standards used in shared records
Record updating practices and schedules
Accrual practices and schedules
Existence of analytical or supplementary materials
Provenance of materials

27. Conformance to Standards
Metadata standards (and not just DC)
Vocabulary and encoding standards
Descriptive content standards (AACR2, CCO, DACS)
Technical standards (XML, Character encoding, etc)

28. Before you share… Check your metadata Appropriate view? Consistent?
Context provided?
Does the aggregator have what they need?
Documented?
Can a stranger tell you what the record describes?

29. The reality of sharing metadata
We can no longer afford to only think about our local users
Creating shareable metadata will require more work on your part
Creating shareable metadata will require our vendors to support (more) standards
Creating shareable metadata is no longer an option, it’s a requirement

30. So where does RDA fit in? RDA is a content standard
MODS is a metadata standard
RDA is closely aligned with MARC and MODS
Useful to have a RDA – MODS examples particularly as MODS is shifting away from MARC
Although because of its origins in the library world RDA presupposes MARC as a vehicle for cataloging records, this emergent cataloging code could also be used with MODS, Dublin Core, or other metadata schemas.
Lack of engagement with the MODS community beyond the Library of Congress

31. DLF Aquifer Guidelines
Does NOT recommend any one content standard over another
“Choice and format of titles should be governed by a content standard such as the Anglo-American Cataloging Rules, 2nd edition (AACR2), Cataloguing Cultural Objects (CCO), or Describing Archives: A Content Standard (DACS). Details such as capitalization, choosing among the forms of titles presented on an item, and use of abbreviations should be determined based on the rules in a content standard. One standard should be chosen and used consistently for all records in an OAI set.”

32. Metadata aggregators
CIC Metadata Portal
Records and digital resources shared by consortium of institutions, provided for
Educators, researchers, and general public
Benefits:
Single comprehensive search of multiple collections and a variety of disciplines

36. Metadata aggregators National Science Digital Library http://nsdl.org
Online resources and records pertaining to science & math education and research, vetted for inclusion, provided for
Educators, researchers, policy makers, and the general public
Benefits:
Single portal serving a range of resources on a specialized topic to a diverse audience

40. OAI-PMH Open Archives Initiative-Protocol for Metadata Harvesting
OAI-PMH defines a mechanism for harvesting records containing metadata from repositories.
The OAI-PMH gives a simple technical option for data providers to make their metadata available to services, based on the open standards HTTP (Hypertext Transport Protocol) and XML (Extensible Markup Language).
The metadata that is harvested may be in any format that is agreed by a community (or by any discrete set of data and service providers), although unqualified Dublin Core is specified to provide a basic level of interoperability

41. OAI-PMH
Metadata from many sources can be gathered together in one database, and services can be provided based on this centrally harvested, or "aggregated" data.
Data Provider: a Data Provider maintains one or more repositories (web servers) that support the OAI-PMH as a means of exposing metadata.
Service Provider: a Service Provider issues OAI-PMH requests to data providers and uses the metadata as a basis for building value-added services. A Service Provider in this manner is "harvesting" the metadata exposed by Data Providers

42. OAI-PMH Structure Intentionally designed to be simple Data providers
Have metadata they want to share
“Expose” their metadata to be harvested
Service providers
Harvest metadata from data providers
Provide searching of harvested metadata from multiple sources
Can also provide other value-added services

43. Data Providers Set up a server that responds to harvesting requests
Required to expose metadata in simple Dublin Core (DC) format
Can also expose metadata in any other format expressible with an XML schema

44. Service Providers Harvest and store metadata
Generally provide search/browse access to this metadata
Can be general or domain-specific
Can choose to collect metadata in formats other than DC
Generally link out to holding institutions for access to digital content
OAIster is a good example

45. Pop Quiz What is the OAI-PMH? (Select one answer)
(a) The OAI-PMH is a protocol for sharing metadata.
(b) The OAI-PMH is a low-barrier protocol for searching across repositories and retrieving resources from them.

46. Multiple Service Providers can harvest from multiple Data Providers.

47. Basic functioning of OAI-PMH

50. Finding the right balance
Metadata providers know the materials
Document encoding schemes and controlled vocabularies
Document practices
Ensure record validity
Aggregators have the processing power
Format conversion
Reconcile known vocabularies
Normalize data
Batch metadata enhancement

51. Why share metadata? Benefits to users Benefits to institutions
One-stop searching
Aggregation of subject-specific resources
Benefits to institutions
Increased exposure for collections
Broader user base
Bringing together of distributed collections
Don’t expect users will know about your collection and remember to visit it.

52. Why share metadata with OAI?
“Low barrier” protocol
Shares metadata only, not content, simplifying rights issues
Same effort on your part to share with one or a hundred service providers (basically)
Wide adoption in the cultural heritage sector
Quickly eclipsed older methods such as Z39.50

53. Common Problems with Metadata in Aggregation
Consistency
Sufficiency
Compatibility

54. Consistency problems Appearance of data Application of format
Granularity of records
Vocabulary usage
Result:
Service Provider must normalize data (if can determine what “normal” is)

55. Sufficiency problems
Too little info for understanding what resource is, especially outside of local context
Result:
Users don’t know whether a resource is relevant or not

56. Compatibility problems
Information in records is
Erroneous
Unnecessary
Incompatible
Result:
Interferes with harvesting and indexing

57. Common content mistakes
No indication of vocabulary used
Names
LCNAF: Michelangelo Buonarroti,
ULAN: Buonarroti, Michelangelo
Places
LCSH: Jakarta (Indonesia)
TGN: Jakarta
Subjects
LCSH: Neo-impressionism (Art)
AAT: Pointillism
Shared record for a single page in a book
Link goes to search interface rather than item being described
“Unknown” or “N/A” in metadata record

58. Common context mistakes
Leaving out information that applies to an entire collection (“On a horse”)
Location information lacking parent institution
Geographic information lacking higher-level jurisdiction
Inclusion of administrative metadata

59. 1. Duplication problem
Different collections describing same source:

61. Duplication problems Duplicates can be identical records,
can describe the same source, but with different metadata, and
can describe the same source but with links to different or slightly different location identifiers (e.g. index page vs. splash page).

62. 2. Record quality could be different
No date.
No format, type, grade level, language, rights, etc.
Description might be based on the then table of contents.

63. No date.
Description might be based on the then cover page. Description is no longer relevant.
Format missed images.
No grade level, rights.

64. No date.
Description is more general and can last longer. +
Recorded grade level, format, type +
No language, rights.

65. Completeness Search & Display
3. Incomplete data causes low recall
Completeness Search & Display
No FORMAT information

66. Completeness Search & Display (cont.)
Collections which did not provide FORMAT information are excluded from being searched at advanced searches

67. Completeness Search & Display (cont.)
Collections did not use EDUCATIONLEVEL or AUDIENCE element would not be in the pool when a user searches by Grade Level.

68. Expectations for discovery systems are rising
Growth of cutting-edge systems outside of libraries affecting user expectations
Higher user expectations are a good thing!
Many expected functions will be easier with robust structured metadata
Genre access
Faceted browsing
Limiting scope by time, place, etc.

69. Libraries are having trouble meeting those expectations
Non-textual resources are more difficult to search
Legacy metadata isn’t always structured in ways that allow high-level services
Legacy metadata doesn’t always include enough information to allow high-level services
Creating new metadata needed to provide high-level services is prohibitively expensive

70. Enter automated enhancement methods
Much research has been done
Little of it has been put into production systems in library metadata creation environments
Still requires human intervention
Fear of human skills becoming devalued
Metadata aggregators, out of necessity, are among the first implementers
Automatic enhancement holds great promise for standardizing and streamlining metadata creation and aggregation activities

71. Why Enhance Metadata at All?
Four categories of problems limit metadata usefulness:
Missing data: elements not present
Incorrect data: values not conforming to proper usage
Confusing data: embedded html tags, improper separation of multiple elements, etc.
Insufficient data: no indication of controlled vocabularies, formats, etc.

72. Solving the problems -- Enriching and enhancing harvested records
Metadata Repository
Enriched record
Aggregation

73. Aggregation
It is possible that these problems be eliminated to certain level through a process called ‘aggregation’ in a metadata repository.
The notion behind this process is that a metadata record, “a series of statements about resources,” can be aggregated to build a more complete profile of a resource.

75. Incorrect element mapping
Data-conversion-related problems
Incorrect element mapping
OPTIONS mapped to SUBJECT, missing all KEYWORDS
missed?!
missing keywords

76. AUTHOR mapped to DESCRIPTION, no CREATOR

77. Only physical description, no content description

78. Inappropriate mapping
CLASSIFICATION mapped to SUBJECT and missed all the KEYWORDs.

79. Solving the problems -- Correcting the errors
Checking and testing crosswalks!!!
Re-harvesting
Training how to use OAI tools

80. Value space that should follow standardized rules: Examples from values associated with DATE element
1979
T21:48.00Z
200003
C1999, 2000
January, 1919
May, 1919
1987, c2000
?1999
1952 (issued)
(1982)
1930?]
Between 1680 and 1896?
dc.date: Recommended best practice for encoding the date value
is defined in a profile of ISO 8601 [W3CDTF] and includes
(among others) dates of the form YYYY-MM-DD

81. German LOCLANGUAGE:: German
Value space that should apply standard controlled vocabularies: Examples from values associated with LANGUAGE element en
eng
en-GB
en-US
English
engfre
new
Korean
Deutsch
German LOCLANGUAGE:: German
dc.language: Recommended best practice is to use RFC 3066 [RFC3066] which, in conjunction with ISO639 [ISO639]), defines two- and three-letter primary language tags with optional subtags.

82. Two efforts to promote shareable metadata
Best Practices for OAI Data Provider Implementations and Shareable Metadata
Digital Library Federation / Aquifer Implementation Guidelines for Shareable MODS Records

83. Everyone could use better metadata!?

84. Metadata Enhancement Clustering and classification
Automated name authority control
Date normalization
Thumbnail generation and creating actionable URLs

85. Clustering and classification
UC-Irvine and Michigan
Evaluate topic/subject-based metadata enhancement
Clustering: “learning the topics” (pre-process)
Classification: using the learned topics to determine topics in records and records in topics

86. Topic Model State-of-the-art statistical algorithm
Learns a set of topics or subjects covered by a collection of text records
Works by finding patterns of co-occurring words
Determines the mix of topics associated with each record

87. Clustering and classification
Mix of scientific repositories
Average of 75 words per record
Used words from <title>, <description>, <subject> for clustering
Only kept words that occurred in more than 10 records
Result: a final vocabulary of 90,000 words
Cluster words into topics: ended up with 500 topics

88. Clustering and classification
500 topics too many to look at
Needed to organize topics under broad topical categories
Cluster the clusters (automatic)
Use pre-defined categories
Classify group of keywords (manual + automatic)
Create hierarchy by hand (manual)

89. clustering is learning the topics
vocab-
ulary
Cluster
preprocess
topic
model
(cluster/learn)
topics
OAI
records
vocab
-ulary
Classify
preprocess
topic
model
(classify)
1. topics in records
2. records in topics
OAI
records
classification is using the learned topics

90. Preprocessing Example
<ID=oai:CiteSeerPSU:44072>
<title>Reinforcement Learning: A Survey
<description>This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." …
<subject>Leslie Pack Kaelbling, Michael Littman, Andrew Moore. Reinforcement Learning: A Survey
vocab
-ulary
<ID=oai:CiteSeerPSU:44072>
reinforcement learning survey
survey field reinforcement learning computer science perspective written accessible researcher familiar machine learning historical basis field broad selection current summarized reinforcement learning faced agent learn behavior trial error interaction dynamic environment resemblance psychology differ considerably detail word reinforcement …
leslie pack kaelbling littman andrew moore reinforcement learning survey
preprocess

91. Example Topics (1) Words in Topic Topic Label
gene sequence genes sequences cdna region amino_acid clones encoding cloned coding dna genomic cloning clone
gene sequencing
social cultural political culture conflict identity society economic context gender contemporary politic world examines tradition sociology institution ethic discourse
cultural identity
general_relativity gravity gravitational solution black_hole tensor einstein horizon spacetime equation field metric vacuum scalar matter energy relativity
relativity
house garden houses dwelling housing homes terrace estate home building architecture residence homestead residences road cottage domestic fences lawn historic
domestic architecture

92. Example Topics (2) Words in Topic Usefulness
large small size larger smaller sizes scale sized largest
Reasonable but unusable
foi para pacientes por foram dos doen resultados grupo das tratamento entre
Topic about patient treatment, in Spanish
building street visible santa_ana view avenue public_library front orange corner
Not usable: mix of concept words and specific geographic location words

93. Topics Assigned to a Record
Metadata Record
Topic Labels
(% words assigned)
Aggregating sets of judgments: two impossibility results compared (C. List and P. Pettit)
May's celebrated theorem (1952) shows that, if a group of individuals wants to make a choice between two alternatives (say x and y), then majority voting is the unique decision procedure satisfying a set of attractive minimal conditions ...
game theory (21%)
argument (12%)
criteria (7%)

96. Clustering and classification
Selected useful topics
[ t482 ]     labor worker employment wage market labour job unemployment wages earning panel find evidence individual participation skill
[ t372 ]   firm investment capital productivity innovation industry sector economic industrial foreign industries corporate ownership technological companies evidence

97. Clustering and classification
Selected less useful topics
[ t255 ]     journal author chapter vol notes editor publication issue special bibliography reader references appendix literature submitted topic
[ t013 ]   university department mail edu institute science california usa computer york fax college press center address

98. Broad Topical Categories (BTCs)
By clustering the clusters
Worked well
Can choose desired number of BTCs
By classifying groups of keywords
Worked well too
Then review and manually edit
Include or exclude any subtopic

101. Clustering and classification: Further evaluation
Need to test non-English and cultural heritage repositories
Need usability testing
“On the horse” problem more prevalent
When to re-cluster?

102. Automated Name Authority Control (ANAC)
Johns Hopkins University: research only; never implemented
29,000 Levy sheet music records
13,764 unique names

103. ANAC
The evidence used to determine the probability of a match between a name to an LC record is a set of Boolean tests involving the name, the Levy metadata associated with that name, and the LC record.
The following fields were used by ANAC:
Levy record:
Given name: often abbreviated
Middle names: often abbreviated
Family name
Modifiers: titles and suffixes
Date: publication year
Location: publication location (city)
LC record:
Given name: includes abbreviations
Middle names: includes abbreviations
Birth: year of birth
Death: year of death
Context: miscellaneous data

104. ANAC
The tests used are: first name equality and consistency, middle name equality and consistency, music terms present in LC record context, name modifier consistency, Levy sheet music publication consistent with LC author birth and death, and Levy record publication location in LC record context

105. ANAC
In order to train the system, the Cataloging Department at the Sheridan Libraries generated ground truth data.
For each name in 2,000 randomly selected Levy metadata records, catalogers recorded the authorized form of the name when a matching authority record was available.
The entire process required 311 hours (approximately seven minutes per name).
The human catalogers used much the same type of evidence as ANAC in establishing matches. Catalogers examined name similarity; compared publication dates from the Levy records to birth and death dates in the authority records; and examined authority record note fields for musical terms.
In addition, the catalogers often searched for bibliographic records of other editions of a particular title to determine the authoritative name assigned to the subject.

106. ANAC
Overall, ANAC was successful 58% of the time. When a name had an LC record, ANAC was successful 77% of the time, but when an LC record did not exist for a name ANAC was successful only 12% of them time. The reason for this discrepancy is that ANAC cannot learn whether or not a name has been added to the LC authority file.
It took ANAC five hours and forty-five minutes to classify the 2,673 (2,841 minus 168) names, or about eight seconds per name. The database-bound process of retrieving the candidate set of MARC records given a family name consumed most of this time.

107. ANAC Matching very dependent on contextual data
Machine matching much faster than manual (8 sec. vs. 7 min.)
Performance reasonable even with dirty metadata.
Machine matching could enhance manual work
Combination of machine processing and human intervention produced best results
Approach could be tweaked by comparing names to multiple authority files or domain specific databases
ANAC not a generalizable tool, but there are others

108. Date Normalization
How to make “ca. 1880” a machine-readable date but not a 19-2 baseball score
California Digital Library
Created for American West project, so sidestepped issue of B.C.E. date normalization
Uses <date> element
If no <date>, searches for date-like strings in <title>, then <description>
Currently normalizes to YYYY only, not MM or DD (will add later)

109. <date>: Encoding Variances ca
(ca). 1920)
by CAD
Unknown
ca. June 19, 1901.
(ca). June 19, 1901)
[2001 or 2002.]
1853.
c1875.
c1908 November 19
c1905
1929 June 6
[between 1904 and 1908]
[ca. 1967]
1918 ?
[1919 ?]
191-?
1870 December, c1871
1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929

110. Normalization Process
Extract dates
Standardize approximate dates, e.g. ca = 1902~
CDL uses +/- 5 years e.g. 1902~ =
Normalize Dates
Populate date.found or date.guess
Create era, decade and year tokens

111. Recognizing Unknowns
Recognizes wide range of expression of date unknownness in <date> element, e.g. unknown, unkn, unavail, n.d., nd, undated, no date, not indicated
Looks for date-like strings to normalize in <title>, then <description> when <date> element contains one of these expressions
If no date, look for Civil War, Renaissance, dates of reigns of sovereigns, etc.

112. Known Issues
Distinguishing “c” for circa from “c” for copyright (currently interprets as later)
Getting tripped up by baseball scores in non-<date> elements
Getting tripped up by 4-digit item identifiers

113. Thumbnail generation and creating actionable URLs
Thumbnail: “A miniature representation of a page or image that is used to identify a file by its content”—PC Magazine
Need coordination between metadata harvesting and thumbnail grabbing
Digital libraries need digital objects
<identifier> element in DC is a problem: hard to identify link to actual object

114. Thumbnail generation and creating actionable URLs
“Users should be able to download, manipulate, morph, annotate, cross-search, and repurpose digital library content”
Find best possible link, find best possible image, build thumbnail: registry of links; data providers retain the responsibility of maintaining the authoritative version of their resource
Need to find a way to express intellectual property rights related to manipulation of objects
Try to get providers to supply better metadata, but in mean time use what we’ve got

115. Characteristics of quality metadata:
Completeness. -- choosing an element set allowing the resources in question to be described as completely as is economically feasible, and -- applying that element set as completely as possible.
Accuracy. -- the metadata being correct and factual, and conforming to syntax of the element set in use.
Provenance. Here provenance refers to the provision of information about the expertise of the person(s) creating the original metadata, and its transformation history.
Conformance to expectations. Metadata elements, use of controlled vocabularies, and robustness should match the expectations of a particular community.
Logical consistency and coherence. -- element usage matching standard definitions, and consistent application of these elements.
Timeliness. Currency--metadata keeping up with changes to the resource it describes. Lag -- a resource’s availability preceding the availability of its metadata.
Accessibility. Proper association of metadata with the resource it describes and readability by target users contribute to this characteristic.

116. Additional characteristics that make quality metadata more useful in a shared environment:
Proper context. … each record contain the context necessary for understanding the resource the record describes, without relying on outside information.
Content coherence. … need to contain enough information such that the record makes sense standing on its own, yet exclude information that only makes sense in a local environment.
Use of standard vocabularies. The use of standard vocabularies enables the better integration of metadata records from one source with records from other sources.
Consistency. All decisions made about application of elements, syntax of metadata values, and usage of controlled vocabularies, should be consistent within an identifiable set of metadata records so those using this metadata can apply any necessary transformation steps without having to process inconsistencies within such a set.
Technical conformance. Metadata should conform to the specified XML schemas and should be properly encoded.

117. How Can We Ensure A Better Quality?
Make Policies on:
minimum quality requirements,
quality measurement instruments,
quality enforcement policies,
quality enhancement actions, and
the training of metadata creators.
Training!
A 2-hour training may eliminate hundreds of errors
IT team should talk with content team
A test of crosswalk for OAI harvest may prevent thousands of mis-matched or missed values
Use Tools:
Provide instructions on best practices
Use template for inputting records, with suggested syntax, vocabularies, and build-in values
Use validators
Implement duplicate checking algorithm

118. Final Thoughts
Creating shareable metadata requires thinking outside of our local box
Creating shareable metadata will require more work on our part
Creating shareable metadata will require our vendors to support (more) standards
Creating shareable metadata is no longer an option, it’s a requirement

119. Before we share… Check our metadata Appropriate view? Consistent?
Context provided?
Does the aggregator have what they need?
Documented?
Can a stranger tell you what the record describes?

120. More thoughts
Automated metadata enhancement techniques promise to play an essential role in building and aggregating digital library collections
But they are not a “magic bullet” – must be used together with other techniques
User-contributed metadata
Content-based retrieval
Item-level attention by specialists
Many collections could benefit
Legacy collections described in MARC
Special collections largely undescribed, especially at the item level
Should technical services expand metadata activities
Catalogers and their skills essential to this process

121. The Way Forward?
Service providers should be more demanding (i.e. require that data providers adhere to certain standards and use certain vocabularies, require “pre-washed” metadata.
Data providers should consistently use appropriate standard schemas in their local systems.
Service providers should consider “adding value” via services like vocabulary mapping, query expansion, vocabulary-assisted searching, user-added metadata, post-harvest subsetting, metadata enhancement, etc

122. Lessons Learned
Metadata (descriptive, technical, rights, administrative, preservation) is one of your biggest investments.
Do it once, do it right (consistent schemas, controlled vocabularies), and you can re-purpose metadata in a wide variety of ways.
Good descriptive metadata records can be core—records don’t need to be “full” to be “good.”
Creation of consistent, standards-based descriptive metadata (a.k.a. cataloging!) is time- and labor-intensive, but it’s worth it.