1. PID centric fabric constructed piece by piece
Beth Plale, Tobias Weigel
Drawn in part from RDA PID Training, Garching, 2016/08/31

2. Objective RDA Data Fabric examines fabric composition
Composing from RDA Recommendations (largely but not exclusively)
PID related recommendations are particularly powerful
RDA Persistent Identifier Types API Recommendation and
RDA Data Type Registry Recommendation

5. Objectives of this Session
PIT API WG stopped short of recommending minimal metadata associated with PIDs
There is no universal set of PID minimal metadata that will be agreed upon by all
Organize 2 to 3 small groups of RDA members, where group is bound by some shared interest, who will meet between now and Barcelona (Spr 16) and define a minimal set that works for them

6. What is killer app for this? Bringing universality to provenance
Provenance relates Data Objects to one another through
Revision: change to object through time
Derivation: attribution of one data object to those data objects that influenced its creation
Replication: relating identical objects to one another
Metadata DO to the DO itself
Part of: data objects part of same collection ….
W3C PROV standard for provenance is well defined

7. What is killer app for this? Universality of provenance
How:
Provenance record pointed to as part of minimal metadata record associated with a PID
Provenance definition available in DTR
Gives: well defined map of relationship of one data object to others
Why important?: data provenance is siloed. This approach breaks down the silos.
Has potential for universality of data provenance; a goal that has been elusive since data provenance inception in 2005

8. Minimal provenance (as JSON-LD)
{ "prov": " }, "prov:wasDerivedFrom": “IDENTIFIER", "prov:revisionOf": “IDENTIFIER", "prov:primarySourceOf": “IDENTIFIER", "prov:quatationOf": “IDENTIFIER", "prov:specializationOf": “IDENTIFER", "prov:alternateOf": “IDENTIFIER", "prov:hadMember": “IDENTIFER", "prov:memberOf": “IDENTIFIER" }

9. Provenance definition in Data Type Registry

10. Approach: core profile and community extensions
Size
Fixity key
Data Provenance
Policy
...
Size
Fixity key
Timestamp – Creation
Timestamp – Last Mod
Data Provenance
Policy
Owner
...
Size
Granularity
Data Provenance
Policy
...
Climate sciences
Material sciences
Linguistics
Core profile

11. What is a profile and how does it relate to PID records?
Base assumption: There is minimal core set of information associated with each PID
Minimal set should be useful not only to maintainer of Data Object, but should facilitate DO‘s discovery and use
Each user community may design their own profiles.
No single size fits all – but recurring elements should be reused
Size
Fixity Key
Timestamps
Data Provenance
...
No single size fits all – PID profiles are registered and referenced, they may differ between communities, but some elements recur

12. Example profile registered in Data Type Registry
Property name
Target type
Mandatory?
DO Location
URL
Yes
Policy
Policy specification*
Time stamp (last modified)
Date/Time
Data provenance
PID
Deletion flag
Boolean No
Deletion reason
String
Would Policy be an attribute you think important to your community‘s profile? Why?

13. Take pulse of room
How many of you are data providers?
How many are interested in tool building to consume data under the minimal PID-DTR model?
Questions
End of public portion of meeting. Remainder of meeting targeted to those who are interested in working on this topic

14. Parcel interested parties into groups; begin discussion within group
Next steps
Define criteria by which we self organize into small groups to work Sep – Mar 2016
My interest is as data provider
Framework for analysis of HathiTrust 14M digitized books from university libraries (Plale, director)
data consumer:
PRAGMA: Pacific Rim partners in facilitating shared computing and data sharing (Plale, steering board)
Parcel interested parties into groups; begin discussion within group