The document discusses the challenges of data sharing in archaeology, focusing on the 'Thousand Flowers' project, which aims to publish open access, high-quality archaeological data. It highlights issues such as complexity, scalability, ethics, and the need for improved data documentation and usability practices. The project emphasizes the importance of linking data to broader networks, improving workflows, and evolving publication standards to better fit modern research needs.

1. Case-Study: Publishing to the
“Web of Data” in Archaeology
Quality and Workflows
Eric Kansa
UC Berkeley / OpenContext.org
Unless otherwise indicated, this work is licensed under a Creative Commons
Attribution 3.0 License <http://creativecommons.org/licenses/by/3.0/>

2. “Small Science” data sharing
is hard:
(1) Complexity
(2) Scalability
(3) Ethics, cultural property
claims, IP
(4) Incentives
(5) Preservation
Image Credit: “Grand Canyon NPS” via Flickr (CC-By)
http://www.flickr.com/photos/grand_canyon_nps/5975537378/

3. Thousand Flowers
●
Open Context: Open access,
open licensed data for
arhaeology
●
Archiving by California Digital
Library
●
Persistent Identifiers (DOIs,
ARKs)
●
Web services
●
NSF/NEH links for data
management plans

4. Thousand Flowers
Fills a Gap:
Most data sources are institutional.
Open Context publishes individual,
small group contributions

5. Thousand Flowers
Fills a Gap:
Challenge:
Most data sources are institutional. Diverse
Open Context publishes individual, contributions,
small group contributions needing lots of
work to clean-
up and “link” to
the Web of Data

6. •
3-year project Oct 2010 – Sep 2013
•
Funded with a National Leadership Grant from the
Institute for Museum and Library Services, LG-06-
10-0140-10, “Dissemination Information Packages
for Information Reuse”
•
Ixchel Faniel, PI & Elizabeth Yakel, Co-PI
http://www.dipir.org

7. DIPIR Collaboration

8. The Big DIPIR Questions
Research Questions
1. What are the significant
properties of data that
facilitate reuse by the
designated communities at the
three sites?
2. How can these significant
properties be expressed as
representation information to
ensure the preservation of
meaning and enable data
reuse?

9. Open Context Interviewees
•
22 Ph.D. or graduate students
interviewed
–
13 men
–
9 women
•
Novices / Experts
–
19 experts
–
3 novices
•
Interviewees who where
curators or professors also
with a curatorial role = 6

10. Raw Data is Unappetizing?

11. Data Documentation Practices
I use an Excel spreadsheet…which I … inherited from my research
advisers. …my dissertation advisor was still recording data for each
specimen on paper when I was in graduate school so that's what I
started …then quickly, I was like, "This is ridiculous.“… I just started
using an Excel spreadsheet that has sort of slowly gotten bigger and
bigger over time with more variables or columns…I've added …color
coding…I also use…a very sort of primitive numerical coding system,
again, that I inherited from my research advisers…So, this little book
that goes with me of codes which is sort of odd, but …we all know
that a 14 is a sheep.” (CCU13)

12. Data Documentation Practices
I use an Excel spreadsheet…which I … inherited from my research
advisers. …my dissertation advisor was still recording data for each
specimen on paper when I was in graduate school so that's what I
started …then quickly, I was like, "This is ridiculous.“… I just started
using an Excel spreadsheet that has sort of slowly gotten bigger and
bigger over time with more variables or columns…I've added …color
coding…I also use…a very sort of primitive numerical coding system,
again, that I inherited from my research advisers…So, this little book
that goes with me of codes which is sort of odd, but …we all know
that a 14 is a sheep.” (CCU13)
A long way to go before we
get usable, intelligible data

13. Sometimes data is better
served cooked.

14. Thousand Flowers
●
Clean-up and document
contributed data
●
Map to ArchaeoML (general
ontology)
●
Mint URIs to entities
(potsherds, projects, contexts,
people)
●
Link to important vocabularies /
collections (Pleiades,
Encyclopedia of Life)
●
Working on CIDOC-CRM
(RDF) representations (not
straightforward)

15. Open Context: Record

16. Open Context: Record
●
XHTML + RDFa (Dublin Core,
Open Annotation, etc.)
●
XML (ArchaeoML)
●
Atom
●
RDF (draft CIDOC)
●
Link to GitHub versioned file

17. Open Context: Record

18. Open Context: Record

19. Open Context: Visutalization of Data Linked to the EOL

20. My Precious Data
Image Credit: “Lord of the Rings” (2003, New
Line), All Rights Reserved Copyright

21. Data sharing as publication

22. Data Publishing

23. Publishing
Data Quality and Standards
Alignment
(1) Check consistency
(2) Edit functions
(3) Align to common standards
(“Linked Data” if applicable)
(4) Issue tracking, version
control

24. Publishing
Tools of the Trade
(1) Google Refine (check, edit,
consistancy)
(2) Mantis (issue-tracker,
coordinate edits, metadata
creation)

25. Publishing
Tools of the Trade
(1) Domain scientists (Editorial
Board) check data
(2) Iterative “coproduction”
between contributors and
editoris

26. Publishing
Project Metadata
Column Descriptions

27. Web of Data (2011)
Main Contributors:
●
Institutions (esp. government)
●
Thematic collections / projects

28. Publishing
Entity Reconciliation
(1) With Google Refine
(2) Implemented, EOL and
Pleiades (gazetteer)
(3) Use existing mappings to
improve future reconciliation

29. ●
CDL Archiving Service
●
EZID for persistent Identity: DOIs
(aggregate resources), ARKs
(granular resources) and Merritt
Repository
●
Helps build trust in community

30. CDL as Infrastructure
●
Platform / Services
disciplinary communities
can use for “Data
Publishing”
●
Different communities
work out
semantic/interoperability
needs, editorial policies, University of California (System)
incentives, etc. Repository,
All disciplines
(UC-funded library, grants)

31. CDL as Infrastructure Future data
Future data publisher
publisher
●
Platform / Services
disciplinary communities
can use for “Data
Publishing”
●
Different communities
work out
semantic/interoperability
needs, editorial policies, University of California (System)
incentives, etc. Repository,
All disciplines
(UC-funded library, grants)

32. eScholarship: UC’s OA Publishing Platform

33. Platform for traditional publishing

34. Also supports new genres

35. Summary
Outcomes of Publishing Data:
(1) Communicate and set
expectations about content and
quality
(2) Organize workflows to improve
data quality and usability
(3) Make “datasets” first class citizens
in world of scholarly
communications

36. Final Thoughts
Publication needs to evolve!
(1) Participating in Linked Data is
a great goal, but far removed
from most everyday practice
(2) Researchers need help.
(3) 19th century publication norms
poorly suited to 21st century
methods, research, public
goals