March 10, 2022 Dataset Open Access

Cameron Neylon; Bianca Kramer

Dublin Core Export

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  <dc:creator>Cameron Neylon</dc:creator>
  <dc:creator>Bianca Kramer</dc:creator>
  <dc:date>2022-03-10</dc:date>
  <dc:description>This repository contains data on 17,419 DOIs cited in the IPCC Working Group 2 contribution to the Sixth Assessment Report, and the code to link them to the dataset built at the Curtin Open Knowledge Initiative (COKI).

References were extracted from the report's PDFs (downloaded 2022-03-01) via Scholarcy and exported as RIS and BibTeX files. DOI strings were identified from RIS files by pattern matching and saved as CSV file. The list of DOIs for each chapter and cross chapter paper was processed using a custom Python script to generate a pandas DataFrame which was saved as CSV file and uploaded to Google Big Query.

We used the main object table of the Academic Observatory, which combines information from Crossref, Unpaywall, Microsoft Academic, Open Citations, the Research Organization Registry and Geonames to enrich the DOIs with bibliographic information, affiliations, and open access status. A custom query was used to join and format the data and the resulting table was visualised in a Google DataStudio dashboard.

This version of the repository also includes the set of DOIs from references in the IPCC Working Group 1 contribution to the Sixth Assessment Report as extracted by Alexis-Michel Mugabushaka and shared on Zenodo: https://doi.org/10.5281/zenodo.5475442 (CC-BY)

A brief descriptive analysis was provided as a blogpost on the COKI website.

The repository contains the following content:

Data:


	data/scholarcy/RIS/ - extracted references as RIS files
	data/scholarcy/BibTeX/ - extracted references as BibTeX files
	IPCC_AR6_WGII_dois.csv - list of DOIs
	data/10.5281_zenodo.5475442/ - references from IPCC AR6 WG1 report


Processing:


	preprocessing.R - preprocessing steps for identifying and cleaning DOIs
	process.py - Python script for transforming data and linking to COKI data through Google Big Query


Outcomes:


	Dataset on BigQuery - requires a google account for access and bigquery account for querying
	Data Studio Dashboard - interactive analysis of the generated data
	Zotero library of references extracted via Scholarcy
	PDF version of blogpost


Note on licenses: 
Data are made available under CC0 (with the exception of WG1 reference data, which have been shared under CC-BY 4.0)
Code is made available under Apache License 2.0</dc:description>
  <dc:description>Archived version of Release 2022-03-10 of GitHub repository: 
https://github.com/Curtin-Open-Knowledge-Initiative/ipcc-ar6</dc:description>
  <dc:identifier>https://zenodo.org/record/6344388</dc:identifier>
  <dc:identifier>10.5281/zenodo.6344388</dc:identifier>
  <dc:identifier>oai:zenodo.org:6344388</dc:identifier>
  <dc:relation>url:https://github.com/Curtin-Open-Knowledge-Initiative/ipcc-ar6/releases/tag/v0.8</dc:relation>
  <dc:relation>doi:10.5281/zenodo.5475442</dc:relation>
  <dc:relation>doi:10.5281/zenodo.6327206</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/coki</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/publicdomain/</dc:rights>
  <dc:subject>IPCC, Crossref, references, DOIs</dc:subject>
  <dc:title>Analysis of references in the IPCC AR6 WG2 Report of 2022</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>