EricKansa_PublishingandPushing_ParallelC4

8/12/2019 EricKansa_PublishingandPushing_ParallelC4

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Publishing and Pushing:Mixing Models for Communicating

Research Data in Archaeology

Sarah Whitcher KansaThe Alexandria Archive Institute

& Open Context

Unless otherwise indicated, this work is licensed under a Creative Commons

Attribution 3.0 License

Benjamin ArbuckleUniversity of North Carolina,

Chapel Hill

Eric C. Kansa (@ekansa)UC Berkeley D-Lab

& Open Context


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Introduction

Challenges in Reusing Data1. Background

2. Data publishing workflow

3. Data curation and dynamism


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Need more carrots!

1. Citation, credit,intellectually valued

2. Research outcomes(new insights from datareuse!)


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EOL Computable DataChallenge

(Ben Arbuckle, Sarah

W. Kansa, Eric Kansa)


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Large scale data sharing &integration for exploring the

origins of farming.Funded by EOL / NEH


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1. 300,000 bone specimens

2. Complex: dozens, up to 110descriptive fields

3. 34 contributors from 15

archaeological sites4. More than 4 person years

of effort to create the data !


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Relatively collaborative bunch,Ben Arbuckle cultivatedrelationships & built trust over

years prior to EOL funding.


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204: Dynamics of Data Reuse when Aggregating Data through Time and

Space: The Case of Archaeology and Zoology

Elizabeth Yakel; Ixchel Faniel; Rebecca Frank


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Introduction





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1. Referenced by US NationalScience Foundation andNational Endowment for the

Humanities for DataManagement

2. Data sharing aspublishing metaphor


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Raw Data: Idiosyncratic,sometimes highly coded,often inconsistent


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Raw Data Can Be Unappetizing


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Publishing Workflow

Improve / Enhance

1. Consistency

2. Context(intelligibility)


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Sometimes data is betterserved cooked


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- Documentation

- Review, editing- Annotation


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- Documentation



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- Documentation



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- Documentation



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- Documentation



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Ovis orientalis

Code: 14

Wild

sheep

Code: 70

Code: 16

Ovis orientalis

Code: 15

Sheep,

wild

O.

orientalis

Sheep

(wild)


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- Documentation



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Controlled vocabulary

Linked Data applications


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Sheep/goathttp://eol.org/pages/32609438/

1. Needed to mint new

concepts likesheep/goat

2. Vocabularies need tobe responsive formultidisciplinaryapplications
http://eol.org/pages/311906/http://eol.org/pages/311906/


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Linking to UBERON1. Needed a controlled vocabulary for

bone anatomy2. Better data modeling than common in

zooarchaeology, adds quality.


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Linking to UBERON1. Models links between anatomy,

developmental biology, and genetics2. Unexpected links between the

Humanities and Bioinformatics!


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7000 BC (many pigs, cattle)

7500 BC (sheep + goat dominate, few pigs, few cattle)

6500 BC (few pigs, mixing with wild animals?)

8000 BC (cattle, pigs,

sheep + goats)

Nota neat model of progress to adopt a more productiveeconomy. Very different, sometimes piecemeal adoption in

different regions.

Separate coastal and inland routes for the spread of domestic

animals, over a 1000-year time period.


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Easy to Align

1. Animal taxonomy

2. Bone anatomy

3. Sex determinations

4. Side of the animal

5. Fusion (bone growth, up toa point)


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Hard to Align (poor modeling, recording)

1. Tooth wear (age)

2. Fusion data3. Measurements

Despite common research methods!!


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Under the hood exposure

will lead to better datadocumentation practices?


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Nobody expected their datato see wider scrutiny either..


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Professional expectations for data reuse

1. Need better data modeling(than feasible with, cough,Excel)

2. Data validation,normalization

3. Requires training &incentives for researchersto care more about quality

of their data!


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Data are challenging!

1. Decoding takes 10x longer

2. Data management plans shouldalso cover data modeling, qualitycontrol (esp. validation)

3. More work needed modelingresearch methods (esp. sampling)

4. Editing, annotation requires lots ofback-and-forth with data authors

5. Data needs investment to beuseful!


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Introduction





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Investing in Data is a Continual Need

1. Data and code co-evolve. Newvisualizations, analysis may revealunseen problems in data.

2. Data and metadata change routinely(revised stratigraphy requires ongoingupdates to data in this analysis)

3. Problems, interpretive issues in data(and annotations) keep cropping up.

4. Is publ i sh inga bad metaphor implyinga static product?


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Data sharing as publication

Data sharing as open sourcerelease cycles?


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Data sharing as open sourcerelease cycles?


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AND

Data sharing as open sourcerelease cycles


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One does not simplywalk into Mordor

Academia and share

usable data

Image Credit: Copyright Newline Cinema

Fi l Th ht


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Final Thoughts

Data require intellectualinvestment, methodological andtheoretical innovation.

Institutional structures poorlyconfigured to support datapowered research

New professional roles needed,but who will pay for it?

Th k !


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Thank you!

IDCC reviewers(excellent, very helpful

comments!)

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EricKansa_PublishingandPushing_ParallelC4