EOL Computational Data Challenge: Zooarchaeology Metrics Dataset Version 2 [Structured Data from Open Context]
Creators: name: David Orton
name: Nerissa Russell
name: Katheryn Twiss
name: Louise Martin
name: Sheelagh Frame
name: Benjamin S. Arbuckle
name: Hijlke Buitenhuis
name: Nerissa Russell
name: Stuart Campbell
name: Elizabeth Carter
name: Levent Atici
name: Arzu Demirergi
name: Kamilla Pawlowska
name: Canan Çakırlar
name: Claire Christensen
name: Arek Marciniak
name: Alfred Galik
name: Liz Henton
name: Lionel Gourichon
name: Daniel Helmer
name: Stephanie Meese
name: Adam Watson
name: Rebecca Daly
name: Serkan Yeni
name: Denise Carruthers
name: Rhian Mayon-White
name: Ian Cameron
name: Nobs Symmons
name: Banu Aydinuloglu
name: Amanda Erwin
name: Sarah Whitcher Kansa
name: Vesna Dimitrijevic
name: Chris Hills
name: Dusan Boric
name: Leola Leblanc
name: Adrienne Powell
Publisher: name: Zenodo
Year: 2023
Subjects: Open Context
Data Publication
Structured Data
GeoJSON
JSON-LD
CSV

description: This deposit archives structured-data files associated with the EOL Computational Data Challenge: Zooarchaeology Metrics Dataset Version 2 Data Table resource published by Open Context.
In general, Open Context is a dynamic and frequently updated database that integrates research data from a growing number of sources. However, Open Context also stores some static and unchanging, externally stored "Data Table" files. Unless otherwise indicated, the content of Data Table files (such as the resource deposited here) will not change even after updates to the Open Context database.
Deposition File Overview
This archival deposit includes two files, in two formats: 

CSV: The eol-computational-data-challenge-zooarchaeology-metric-2.csv contains the tabular data content for this resource in a CSV format. 
JSON: The eol-computational-data-challenge-zooarchaeology-metric-2.json provides additional metadata about the tabular data content for this resource in a JSON-LD format. 
Open Context is an open-source Python application built with the Django framework (see source code and Docker deployment code). To manage a wide variety of archaeological and related data, Open Context organizes information using a very abstract, graph-based schema. Open Context implements this schema using a Postgres relational database and the Django "Object Relational Model" (ORM). 
Because Open Context internally maintains data using a very abstract schema, data dumps directly from this schema would be difficult to understand and use. Therefore, Open Context makes select datasets (like this) available in more easily understood tabular expressions. A considerable amount of application logic in Open Context generated the more expressive tabular representation of data. This tabular data output from Open Context is meant to be considered as a static and unchanging "snapshot" of data selected for output at a specific time. 
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Open Context publishes research materials important to many different communities all with different histories, cultures, and expectations. We expect uses of these data to respect civil, legal, and ethical standards. To learn more about these expectations, please review:

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