1. Power of PID kernel information
Beth Plale
Indiana University
Bloomington, Indiana USA
SEAIP 2016

2. Conceptual model PID Kernel Metadata Handle (identifier)
black box data object
Handle (identifier)
Properties
Size
Checksum
Timestamps
Version
Pointer to data object
...
PID Kernel Metadata
Set of attributes is one Profile of PID Kernel Metadata. Optimally there are a minimal number of profiles: e.g., one per community/discipline

3. What a profile is
Base assumption: There is minimal set of information associated with each PID
Names for this metadata (in flux): kernel, casing, gateway metadata
Kernel metadata should be useful to
Maintainer of Data Object,
Discovery clients, and
Data reuse research
Each user community may design their own profiles.
No single size fits all – but full benefits realized when set of profiles is minimal
Size
Fixity Key
Timestamps
Is-derivation-of
...
No single size fits all – PID profiles are registered and referenced, they may differ between communities, but some elements recur
SEAIP 2016

4. Go on to imagine an Internet-scale data client that is handed a list of a billion IDs. Case 1: How does the client quickly sift through the list to find the research data? Case 2: When client winnows list down to research data, how does it then quicky discard the fakes?

5. What is killer app for this? Universal Provenance
Provenance relates a Data Object to others through data provenance expressed as relationships:
Is-derivation-of
Is-revision-of
Is-new-version-of
Is-part-of
Is-supplement-to | Is-supplemented-by
Has-metadata | Is-metadata-for
Making provenance relationships required

6. Two pronged activity Consensus around small number of profiles
Carried out in RDA
PID Testbed for adoption evaluation. Drawn from Research Data Alliance (RDA) Recommendations:
Persistent Identifier Types API and
RDA Data Type Registry

7. Prong I: Consensus around small number of profiles: activity in RDA
Science
Digital Humanities
Stuart Chalk, UNF
Alex Thompson, iDigBio
Tobias Weigel
Gabriel Zhou, IU
Cindy Chandler, WHO
Data Provider
Bridget Almas, Tufts
Daan Broeder, MPI
Beth Plale, HathiTrust
Stuart Chalk, UNF
Alex Thompson, iDigBio
Sharief Youssef, NIST
James Duncan, UVM
Data Consumer

8. Prong I: Consensus around profiles
Discuss in separate sub-groups over Winter ‘16:
Sciences
Data provider
Data consumer
Digital Humanities
Apply for WG status
Each group comes to consensus on set of attributes
At RDA P9 (Barcelona), begin integration of the views
Summer ‘17 : integration of views
Fall ’17 : write report

9. Prong II: PID Testbed for Evaluation
PID testbed for broader RDA audience:
Status: Proposal pending at NSF
Beth Plale, Indiana Univ; Larry Lannom, CNRI; Bridget Almas, Tufts
PID testbed for Pacific Rim Applications and Grid Middleware Assembly (PRAGMA)
13 year old assembly of Pacific Rim organizations and groups
PRAGMA Testbed
Datasets
HathiTrust Digital Library – Beth Plale, Indiana Univ
iDigBio – Jose Fortes, UFlorida
Sensor Weather data – Jay Combinido, ASTI, Philippines
Ecological data – Krisanadej Jaroensuttasinee, Walailak Univ, Thailand

10. I encourage you to reach out to me plale@indiana.edu
SEAIP 2016