1. Value Added Services for Repositories
Kazu Yamaji, NII Japan

2. Where Discovery Service Add Value International Cross Domain Level
Registry Service
DB+Logs
Add Value
Discovery
Service
Discovery
Service
National
Cross Domain
Level
DB+Logs
Add Value
Repository
Repository
Repository
Institution
Domain Level
DB+Logs
DB+Logs
DB+Logs
Add Value

3. Institution & Domain Level

4. National & Cross Domain Level Portal services of Japanese IRs
NII harvests almost all Japanese IRs
- JAIRO is a “gyroscope” of
IR content
- Use it to search all
IRs in Japan at once!
OAI-PMH
IRDB Content Analysis shows
how contents are growing and
gives detailed information on each IR.
CiNii is the largest database of
academic journal articles in Japan.
Metadata on journal articles
and departmental bulletins goes to CiNii and is linked to the full texts in the IRs.
junii2
junii2 is a “Dublin Core” application profile for
institutional repository with an OpenURL　
compliant schema.
It has been adopted by almost all IRs in Japan.

5. National & Cross Domain Level Linkage from CiNii metadata to fulltext in IR
Link to the IR

6. National & Cross Domain Level JAIRO : Japanese Institutional Repositories Online
To get
the detailed Stats of each IR
Narrow search by content type
Narrow search
by organization
Mainly utilized by librarians to compare their IR with others or at the inter-university paper copy and transfer services.

7. National & Cross Domain Level IRDB Content Analysis System

8. International & Cross Domain Level OpenDOAR

9. International & Cross Domain Level OpenAIRE

11. Required Technology Ex: Name Disambiguation
Type1: authorized ID -- authorized ID
Type3: authorized ID -- local ID
deduplication
of records
citation
linkage
Type2: authorized ID -- authorized ID
Type4: local ID -- local ID
heterogeneous
database
integration
citation
clustering

12. Required Technology Ex: Name Disambiguation
Automatic identification
Ternary classification (NOT, Uncertain, SURE)
Crowd sourcing
Data correction by researchers (Is this you?)
Curation by experts
Expensive (sometimes used for training and/or evaluating machine-learning based classifiers)