2. Harvesting Metadata Using OAI-PMH Roy Tennant California Digital Library

3. Outline The Open Archives Initiative OAI-PMH The Harvesting Process Harvesting Problems Steps to a Fruitful Harvest A Harvesting Service Model The OAI Future

4. Open Archives Initiative Aimed at making the large and growing number of repositories of freely available digital content interoperable Protocol for Metadata Harvesting (OAI-PMH) specifies how repositories can expose their metadata for others to harvest Over 800 repositories world-wide support the protocol OAIster.org has indexed nearly 6 million items from over 500 of those repositories

5. www.oaforum.org/tutorial/

6. OAI-PMH Data providers (DP) — those with the stuff Service providers (SP) — those who harvest metadata and provide aggregation and search services Software for both DPs and SPs readily available OAI-PMH verbs: Identify ListIdentifiers ListMetadataFormats ListSets ListRecords GetRecord

7. OAI Architecture Source: Open Archives Forum Tutorial

9. Identify Provides basic information about a repository

10. ListMetadataFormats Lists available metadata formats

11. ListIdentifiers Lists all identifiers (or only those of the optionally specified set) Must include metadataPrefix attribute

12. ListSets Lists available sets

13. Library of Congress ListSets response

14. ListRecords Lists all records (or only those of the optionally specified set) Must include metadataPrefix attribute

15. GetRecord Retrieves a specific record Must include metadataPrefix and identifier attributes

16. The Harvesting Process Identifying Sources Selecting Sets Harvesting Metadata Processing Indexing Interface

17. A Harvesting Service Model

18. gita.grainger.uiuc.edu/registry/

19. errol.oclc.org

20. Selecting Sets Review the response to the ListSets verb May be instructive to search the collection in the native interface, if possible Look for descriptive pages on the site being harvested

23. Harvesting Many harvesting applications are available, I will focus on: Public Knowledge Project (PKP) Harvester Virginia Tech Perl Harvester Library software vendors increasingly offer harvesting products (e.g., ExLibris’ MetaIndex)

25. +-----------------------------------------+ | Harvester Sample Configurator | +-----------------------------------------+ | Version 1.1 :: July 2002 | | Hussein Suleman | | Digital Library Research Laboratory | | www.dlib.vt.edu :: Virginia Tech | ------------------------------------------+ Defaults/previous values are in brackets - press to accept those enter "&delete" to erase a default value enter "&continue" to skip further questions and use all defaults press -c to escape at any time (new values will be lost) Press to continue [ARCHIVES] Add all the archives that should be harvested Current list of archives: No archives currently defined ! Select from: [A]dd [D]one Enter your choice [D] : a{return} [ARCHIVE IDENTIFIER] You need a unique name by which to refer to the archive you will harvest metadata from Examples: nsdl-380602, VTETD Archive identifier [] : nsdl-380602{return} Virginia Tech Perl Harvester

26. Let’s Harvest!

27. Indexing Pick your favorite database/indexing software: MySQL SWISH-E Whatever is lying around… May need to specifically set up a method to search across the entire record May need different fields for indexing than for display Will need to deal with element collision

28. Interface Software interface (API) for other applications: SRU/SRW? MXG? Arbitrary Web Services schema? User interface: What functions do you want your users to be able to perform? What kinds of displays do you want?

29. Harvesting Problems Sets Metadata Formats Metadata Artifacts Granularity Metadata Variances

30. Sets Records are harvested in clumps, called “sets” created by DPs No guidelines exist for defining sets Examples: Collection Organizational structure Format (but is a page image an image? See example)

31. Metadata Formats Only required format is simple Dublin Core, although any format can be made available in addition Few DPs surface richer metadata Simple DC is simply too simple! Example (artifact vs. surrogate dates)

32. Metadata Artifacts “unintended, unwanted aberrations” Sample causes: Idiosyncratic local practices Anachronisms HTML code Examples: Circa = string of dates for searching purposes [electronic resource]

33. Granularity Record Granularity: what is an “object”? A book, or each individual page? Examples: CDL, Univ. of Michigan Metadata Granularity: Multiple values in one field Example: Univ. of Washington

34. Metadata Variances Subject terminology differences Disparities in recording the same metadata Example: date variances Mapping oddities or mistakes Examples: 1) format into description, 2) description into subject

35. Steps to a Fruitful Harvest Needs Assessment (it’s the user, stupid) DP Identification and Communication Metadata Capture Metadata Analysis Metadata Subsetting Metadata Normalization Metadata Enrichment Indexing & Display Interface (it’s still the user, stupid)

36. Needs Assessment What are you trying to accomplish? What will your users want to be able to do? What metadata will you need, and what procedures will you need to set up to enable these activities? Which repositories have what you want? Is what they have (e.g., sets, metadata) usable as is, or ?

37. DP Identification & Communication Identification: Use UIUC directory of DPs to identify potential sources Communication: Not required to tell them you are harvesting, but may help establish a good relationship May want to request that they surface a richer metadata format and/or provide a different set

38. Metadata Capture Sample questions to answer: Individual sets, or all? Richer metadata formats available? How frequently to reharvest? Start from scratch each time or update? Many software options

39. Metadata Analysis Finding out what you have (and don’t have) Encoding practices Gap analysis (e.g., missing fields, etc.) Mistakes (e.g., mapping errors) Software can help Commercial software like Spotfire In-house or open source software tools

40. Source: 2002 Master’s Thesis, Jewel Hope Ward, UNC Chapel Hill Five elements are used 71% of the time

45. Metadata Subsetting DP sets are unlikely to serve all SP uses well SPs will need the ability to subset harvested metadata

46. Metadata Normalization Normalizing: to reduce to a standard or normal state Prototype date normalization service screen

47. Metadata Enrichment Adding fields and/or qualifiers may be useful or required, for example: Metadata provider information Geographic coverage Subject terms mapped to a different thesaurus Authority control record The enrichment process may be the same tool as the subsetting tool (i.e., find a cluster of records and perform an action)

48. Indexing & Display Selected fields may need to be mapped to specific indexing and display elements Particularly required if harvesting different metadata formats But also needs to be done with multiple, conflicting fields: 1863. [2001 or 2002.] SHS 1,679 http://content.lib.washington.edu/cgi-bin/htmlview.exe?CISOROOT=/loc&CISOPTR=58 http://content.lib.washington.edu/loc/image/1679.jpg

49. A Harvesting Service Model

50. The OAI Future Further protocol development Services layered on top of OAI-PMH Shared software tools Best practices for both DPs and SPs

51. oai-best.comm.nsdl.org