4 What does this record describe? identifier:publisher: Museum of Zoology, Fish Field Notesformat: jpegrights: These pages may be freely searched and displayed. Permission must be received for subsequent distribution in print or electronically.type: imagesubject: ; 1926; 0812; 18; Trib. to Sixteen Cr. Trib. Pine River, Manistee R.; JAM26-460; 05; 1926/05/18; R10W; S26; S27; T21Nlanguage: UNDsource: Michigan 1926 Metzelaar, ;description: Flora and Fauna of the Great Lakes Region
7 Some Emerging Trends in Metadata Creation “Schema-agnostic” metadataMetadata that is both shareable and re-purposeableHarvestable 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 recordsThe need for practical, economically feasible approaches to metadata creation
8 Building “Good digital collections” Interoperable – with the important goal of cross-collection searchingPersistent – reliably accessibleRe-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 aggregatedEnhancing metadata for existing collections is often difficult to undertake because of a lack of tools to automate the process
10 IssuesIMLS 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 IssuesUseful services depend on good metadata, but most metadata is not very goodHuman created metadata is expensiveAutomated crawling strategies are limited by:Accessibility barriers (rights issues, technical issues)Variable results with crawling technologies for non-textBest metadata does not rely solely on information contained within the resource itselfEx.: Controlled vocabularies, descriptions, links
12 What is shareable metadata? Is quality metadataPromotes 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 contextIs useful outside of its local contextIs 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 contextIs useful outside of its local contextPreferably is machine processable
14 Why share metadata anyway? Benefits to usersSingle search of a variety of digital resourcesAggregation of subject-specific resourcesHigher quality resourcesBenefits to institutionsIncreased user access to collection by allowing metadata to appear in other placesExposure to broader audience, new usersSurfacing rare, unknown, or scattered collections
15 Different shapes and sizes… Range of different aggregations:focused subject area v. comprehensivespecialized audience v. general audienceRange of different displays
16 Metadata as a view of the resource There is no monolithic, one-size-fits-all metadata recordMetadata for the same thing is different depending on use and audienceAffected by format, content, and contextDescriptive vs. administrative vs. technical, etc. data
17 Metadata is a view of a resource No monolithic, one-size-fits-all metadata recordThe view might be different depending on use and audience as well as format, content, and contextContent standard is a viewMetadata standard is a viewVocabulary used is a view
18 Choice of metadata format(s) as a view Many factors affect choice of metadata formatsMany different formats may all be appropriate for a single itemHigh-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 digitalHierarchical record with all versionsPhysical with link to digitalAll versions in flat recordContent but not carrier
20 Finding the right balance Metadata providers know the materialsDocument encoding schemes and controlled vocabulariesDocument practicesEnsure record validityAggregators have the processing powerFormat conversionReconcile known vocabulariesNormalize dataBatch metadata enhancement
21 6 Cs and lots of Ss of shareable metadata ContentConsistencyCoherenceContextCommunicationConformanceMetadata standards Vocabulary and encoding standardsDescriptive content standards Technical standards
22 Content Choose appropriate vocabularies Choose appropriate granularity Make it obvious what to displayMake it obvious what to indexExclude unnecessary “filler”Make it clear what links point to
23 Consistency Records in a set should all reflect the same practice Fields usedVocabulariesSyntax encoding schemesAllows aggregators to apply same enhancement logic to an entire group of records
24 CoherenceMetadata format chosen makes sense for materials and managing institutionNot just Dublin Core!Record should be self-explanatoryValues must appear in appropriate elementsRepeat 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 locallyCurrent safe assumptionsUsers discover material through shared recordUser then delivered to your environment for full contextContext driven by intended use
26 Communication Method for creating shared records Vocabularies and content standards used in shared recordsRecord updating practices and schedulesAccrual practices and schedulesExistence of analytical or supplementary materialsProvenance of materials
27 Conformance to Standards Metadata standards (and not just DC)Vocabulary and encoding standardsDescriptive 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 usersCreating shareable metadata will require more work on your partCreating shareable metadata will require our vendors to support (more) standardsCreating 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 standardRDA is closely aligned with MARC and MODSUseful to have a RDA – MODS examples particularly as MODS is shifting away from MARCAlthough 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 aggregatorsCIC Metadata PortalRecords and digital resources shared by consortium of institutions, provided forEducators, researchers, and general publicBenefits: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 forEducators, researchers, policy makers, and the general publicBenefits: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-PMHMetadata 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 harvestedService providersHarvest metadata from data providersProvide searching of harvested metadata from multiple sourcesCan 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) formatCan 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 metadataCan be general or domain-specificCan choose to collect metadata in formats other than DCGenerally link out to holding institutions for access to digital contentOAIster 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.
50 Finding the right balance Metadata providers know the materialsDocument encoding schemes and controlled vocabulariesDocument practicesEnsure record validityAggregators have the processing powerFormat conversionReconcile known vocabulariesNormalize dataBatch metadata enhancement
51 Why share metadata? Benefits to users Benefits to institutions One-stop searchingAggregation of subject-specific resourcesBenefits to institutionsIncreased exposure for collectionsBroader user baseBringing together of distributed collectionsDon’t expect users will know about your collection and remember to visit it.
52 Why share metadata with OAI? “Low barrier” protocolShares metadata only, not content, simplifying rights issuesSame effort on your part to share with one or a hundred service providers (basically)Wide adoption in the cultural heritage sectorQuickly eclipsed older methods such as Z39.50
53 Common Problems with Metadata in Aggregation ConsistencySufficiencyCompatibility
54 Consistency problems Appearance of data Application of format Granularity of recordsVocabulary usageResult:Service Provider must normalize data (if can determine what “normal” is)
55 Sufficiency problemsToo little info for understanding what resource is, especially outside of local contextResult:Users don’t know whether a resource is relevant or not
56 Compatibility problems Information in records isErroneousUnnecessaryIncompatibleResult:Interferes with harvesting and indexing
57 Common content mistakes No indication of vocabulary usedNamesLCNAF: Michelangelo Buonarroti,ULAN: Buonarroti, MichelangeloPlacesLCSH: Jakarta (Indonesia)TGN: JakartaSubjectsLCSH: Neo-impressionism (Art)AAT: PointillismShared record for a single page in a bookLink 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 institutionGeographic information lacking higher-level jurisdictionInclusion of administrative metadata
59 1. Duplication problemDifferent collections describing same source:
61 Duplication problems Duplicates can be identical records, can describe the same source, but with different metadata, andcan 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 recallCompleteness Search & DisplayNo 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 expectationsHigher user expectations are a good thing!Many expected functions will be easier with robust structured metadataGenre accessFaceted browsingLimiting scope by time, place, etc.
69 Libraries are having trouble meeting those expectations Non-textual resources are more difficult to searchLegacy metadata isn’t always structured in ways that allow high-level servicesLegacy metadata doesn’t always include enough information to allow high-level servicesCreating new metadata needed to provide high-level services is prohibitively expensive
70 Enter automated enhancement methods Much research has been doneLittle of it has been put into production systems in library metadata creation environmentsStill requires human interventionFear of human skills becoming devaluedMetadata aggregators, out of necessity, are among the first implementersAutomatic 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 presentIncorrect data: values not conforming to proper usageConfusing 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 RepositoryEnriched recordAggregation
73 AggregationIt 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.
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-harvestingTraining how to use OAI tools
80 Value space that should follow standardized rules: Examples from values associated with DATE element 1979T21:48.00Z200003C1999, 2000January, 1919May, 19191987, c2000?19991952 (issued)(1982)1930?]Between 1680 and 1896?dc.date: Recommended best practice for encoding the date valueis 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 elementenengen-GBen-USEnglishengfrenewKoreanDeutschGerman LOCLANGUAGE:: Germandc.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 MetadataDigital Library Federation / Aquifer Implementation Guidelines for Shareable MODS Records
84 Metadata Enhancement Clustering and classification Automated name authority controlDate normalizationThumbnail generation and creating actionable URLs
85 Clustering and classification UC-Irvine and MichiganEvaluate topic/subject-based metadata enhancementClustering: “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 recordsWorks by finding patterns of co-occurring wordsDetermines the mix of topics associated with each record
87 Clustering and classification Mix of scientific repositoriesAverage of 75 words per recordUsed words from <title>, <description>, <subject> for clusteringOnly kept words that occurred in more than 10 recordsResult: a final vocabulary of 90,000 wordsCluster words into topics: ended up with 500 topics
88 Clustering and classification 500 topics too many to look atNeeded to organize topics under broad topical categoriesCluster the clusters (automatic)Use pre-defined categoriesClassify group of keywords (manual + automatic)Create hierarchy by hand (manual)
89 clustering is learning the topics vocab-ularyClusterpreprocesstopicmodel(cluster/learn)topicsOAIrecordsvocab-ularyClassifypreprocesstopicmodel(classify)1. topics in records2. records in topicsOAIrecordsclassification 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 Surveyvocab-ulary<ID=oai:CiteSeerPSU:44072>reinforcement learning surveysurvey 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 surveypreprocess
91 Example Topics (1) Words in Topic Topic Label gene sequence genes sequences cdna region amino_acid clones encoding cloned coding dna genomic cloning clonegene sequencingsocial cultural political culture conflict identity society economic context gender contemporary politic world examines tradition sociology institution ethic discoursecultural identitygeneral_relativity gravity gravitational solution black_hole tensor einstein horizon spacetime equation field metric vacuum scalar matter energy relativityrelativityhouse garden houses dwelling housing homes terrace estate home building architecture residence homestead residences road cottage domestic fences lawn historicdomestic architecture
92 Example Topics (2) Words in Topic Usefulness large small size larger smaller sizes scale sized largestReasonable but unusablefoi para pacientes por foram dos doen resultados grupo das tratamento entreTopic about patient treatment, in Spanishbuilding street visible santa_ana view avenue public_library front orange cornerNot usable: mix of concept words and specific geographic location words
93 Topics Assigned to a Record Metadata RecordTopic 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%)
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 clustersWorked wellCan choose desired number of BTCsBy classifying groups of keywordsWorked well tooThen review and manually editInclude or exclude any subtopic
101 Clustering and classification: Further evaluation Need to test non-English and cultural heritage repositoriesNeed usability testing“On the horse” problem more prevalentWhen to re-cluster?
102 Automated Name Authority Control (ANAC) Johns Hopkins University: research only; never implemented29,000 Levy sheet music records13,764 unique names
103 ANACThe 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 abbreviatedMiddle names: often abbreviatedFamily nameModifiers: titles and suffixesDate: publication yearLocation: publication location (city)LC record:Given name: includes abbreviationsMiddle names: includes abbreviationsBirth: year of birthDeath: year of deathContext: miscellaneous data
104 ANACThe 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 ANACIn 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 ANACOverall, 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 workCombination of machine processing and human intervention produced best resultsApproach could be tweaked by comparing names to multiple authority files or domain specific databasesANAC not a generalizable tool, but there are others
108 Date NormalizationHow to make “ca. 1880” a machine-readable date but not a 19-2 baseball scoreCalifornia Digital LibraryCreated for American West project, so sidestepped issue of B.C.E. date normalizationUses <date> elementIf 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 CADUnknownca. June 19, 1901.(ca). June 19, 1901)[2001 or 2002.]1853.c1875.c1908 November 19c19051929 June 6[between 1904 and 1908][ca. 1967]1918 ?[1919 ?]191-?1870 December, c18711920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929
110 Normalization Process Extract datesStandardize approximate dates, e.g. ca = 1902~CDL uses +/- 5 years e.g. 1902~ =Normalize DatesPopulate date.found or date.guessCreate era, decade and year tokens
111 Recognizing UnknownsRecognizes wide range of expression of date unknownness in <date> element, e.g. unknown, unkn, unavail, n.d., nd, undated, no date, not indicatedLooks for date-like strings to normalize in <title>, then <description> when <date> element contains one of these expressionsIf no date, look for Civil War, Renaissance, dates of reigns of sovereigns, etc.
112 Known IssuesDistinguishing “c” for circa from “c” for copyright (currently interprets as later)Getting tripped up by baseball scores in non-<date> elementsGetting 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 MagazineNeed coordination between metadata harvesting and thumbnail grabbingDigital 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 resourceNeed to find a way to express intellectual property rights related to manipulation of objectsTry 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, andthe training of metadata creators.Training!A 2-hour training may eliminate hundreds of errorsIT team should talk with content teamA test of crosswalk for OAI harvest may prevent thousands of mis-matched or missed valuesUse Tools:Provide instructions on best practicesUse template for inputting records, with suggested syntax, vocabularies, and build-in valuesUse validatorsImplement duplicate checking algorithm
118 Final ThoughtsCreating shareable metadata requires thinking outside of our local boxCreating shareable metadata will require more work on our partCreating shareable metadata will require our vendors to support (more) standardsCreating 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 thoughtsAutomated metadata enhancement techniques promise to play an essential role in building and aggregating digital library collectionsBut they are not a “magic bullet” – must be used together with other techniquesUser-contributed metadataContent-based retrievalItem-level attention by specialistsMany collections could benefitLegacy collections described in MARCSpecial collections largely undescribed, especially at the item levelShould technical services expand metadata activitiesCatalogers 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 LearnedMetadata (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.