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Data Sharing in Zooarchaeology Challenges and Promises Sarah Whitcher Kansa The Alexandria Archive Institute Unless otherwise indicated, this work is licensed.

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Presentation on theme: "Data Sharing in Zooarchaeology Challenges and Promises Sarah Whitcher Kansa The Alexandria Archive Institute Unless otherwise indicated, this work is licensed."— Presentation transcript:

1 Data Sharing in Zooarchaeology Challenges and Promises Sarah Whitcher Kansa The Alexandria Archive Institute Unless otherwise indicated, this work is licensed under a Creative Commons Attribution 3.0 License

2 What is data publishing? Why is it good for you? Why are we publishing data as part of this project?

3 Started in 2007 Publishes archaeological data (open access / open data) Archiving by California Digital Library Prioritizes access & reuse Started in 2007 Publishes archaeological data (open access / open data) Archiving by California Digital Library Prioritizes access & reuse

4 Data reuse is hard Needs documentation (esp. methods), ideally with data creator “Standards” applied, recorded in different ways Expand on this initial study Data reuse is hard Needs documentation (esp. methods), ideally with data creator “Standards” applied, recorded in different ways Expand on this initial study

5 “Traditional” Data Sharing MethodProsCons PublicationsControl (over content shared) Rewards (promotion, etc.) Difficult to reuse Incomplete data Slow dissemination Conference Papers Control (over content shared) Public statement of work Quick dissemination No control over reuse Citation problems Control (over content shared and who it’s shared with) Low time-commitment to prepare for sharing Direct contact with author (for questions, trouble-shooting) One-to-one, hidden communication channel Lacks documentation Can’t pass it on for reuse How to cite? No means to validate

6 Web-Based Data Editing & Publishing ProsCons Only one-time effort at outset Control (over content shared) Public statement of work – even early on Direct contact with author (trouble-shoot) Adds critical documentation (methods) Linked to print publications Clear citation (also impact) Others can validate Rewards (increasing) More research opportunities (collab., linked data, etc.) Responsible stewardship of your data! Requires TIME (at outset) No control over reuse Exposure

7 “Data sharing as publishing” model … but need concrete examples of reuse! - NEH projects - EOL project “Data sharing as publishing” model … but need concrete examples of reuse! - NEH projects - EOL project

8 EOL Computable Data Challenge (Ben Arbuckle, Sarah W. Kansa, Eric Kansa)

9 Anatolia Zooarchaeology Case Study: Aims Collaborative research paper(s) –Drawing on integrated datasets –Linked to published data –Example of research potential of data publishing –Eventually fill in the gaps (spatial and temporal) Data publications –Lasting outcome, not just one-time integration –Edited, verified data –Linked data for future research opportunities

10 EOL Computable Data Challenge 1.14 different sites zooarchaeologists 3.Decoding, cleanup, metadata documentation 4.220,000+ specimens entities linked to 143 EOL concepts 6.Collaborative analysis 7.Parsed out to you because so large EOL Computable Data Challenge 1.14 different sites zooarchaeologists 3.Decoding, cleanup, metadata documentation 4.220,000+ specimens entities linked to 143 EOL concepts 6.Collaborative analysis 7.Parsed out to you because so large

11 Data are challenging! 1.Decoding takes 10x longer 2.More work needed modeling research methods (esp. sampling) 3.Requires lots of back-and-forth with data authors. 4.Tension between modeling needs and familiarity with tools (Excel). Data are challenging! 1.Decoding takes 10x longer 2.More work needed modeling research methods (esp. sampling) 3.Requires lots of back-and-forth with data authors. 4.Tension between modeling needs and familiarity with tools (Excel). Archiving is not enough! NEED data editing!

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31 “Distal epiphysis unfused” dist. unfused d. uf. 30 uf. dist., f. prox. Distal epiph. unfused Distal end unf.

32 “Distal epiphysis unfused”

33 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

34 Open Context Entity Reconciliation Many project- specific terms related to global terminologies Authors / Editors relate project-specific terminologies to global terminologies Project Specific PropertyEOL Link (Global Terminology) Cervidaehttp://eol.org/pages/7685/http://eol.org/pages/7685/ (Cervidae) Cervid (Cervidae) Cervinaehttp://eol.org/pages/ /http://eol.org/pages/ / (Cervinae) Cervus / Damahttp://eol.org/pages/ /http://eol.org/pages/ / (Cervinae) Cervus sp.http://eol.org/pages/34545/http://eol.org/pages/34545/ (Cervus) Red deerhttp://eol.org/pages/328649/http://eol.org/pages/328649/ (Cervus elaphus) Cervus elaphushttp://eol.org/pages/328649/http://eol.org/pages/328649/ (Cervus elaphus) C. elaphushttp://eol.org/pages/328649/http://eol.org/pages/328649/ (Cervus elaphus) Editorial work-flow helps annotate data for interoperability

35 “Ovis aries” Code: 14 Domestic sheep Code: 70 Code: 16 Ovis aries Code: 15 Sheep O. aries Schaf

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39 - Foundation for future work, much of which we can’t even imagine. - Disambiguates terms at the outset, allowing for future informed uses of the data. - Growing movement that allows data to be part of the web (not just on the web). Why is linked data important for this project?

40 Questions for this project (and in collaboration with DIPIR): - How was the data reuse experience for you? - Your thoughts on data publication - Feedback on EOL concepts

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