Dataset Open Access

DOIBoost Dataset Dump

La Bruzzo, Sandro; Manghi, Paolo; Mannocci, Andrea

Research in information science and scholarly communication strongly relies on the availability of openly accessible datasets of metadata and, where possible, their relative payloads. To this end, CrossRef plays a pivotal role by providing free access to its entire metadata collection, and allowing other initiatives to link and enrich its information. Therefore, a number of key pieces of information result scattered across diverse datasets and resources freely available online. As a result of this fragmentation, researchers in this domain end up struggling with daily integration problems producing a plethora of ad-hoc datasets, therefore incurring in a waste of time, resources, and infringing open science best practices. 

The latest DOIBoost release is a metadata collection that enriches CrossRef (October 2019 release: 108,048,986 publication records) with inputs from Microsoft Academic Graph (October 2019 release: 76,171,072 publication records), ORCID (October 2019 release: 12,642,131 publication records), and Unpaywall (August 2019 release: 26,589,869 publication records) for the purpose of supporting high-quality and robust research experiments. As a result of DOIBoost, CrossRef records have been "boosted" as follows:

  • 47,254,618 CrossRef records have been enriched with an abstract from MAG;
  • 33,279,428 CrossRef records have been enriched with an affiliation from MAG and/or ORCID;
  • 509,588 CrossRef records have been enriched with an ORCID identifier from ORCID.

This entry consists of two files: doiboost_dump-2019-11-27.tar (contains a set of partXYZ.gz files, each one containing the JSON files relative to the enriched CrossRef records), a schemaAndSample.zip, and termsOfUse.doc (contains details on the terms of use of DOIBoost).

Note that this records comes with two relationships to other results of this experiment: 

  1. link to the data paper: for more information on how the dataset is (and can be) generated;
  2. link to the software: to repeat the experiment

When citing this dataset please cite this record in Zenodo and the relative article: La Bruzzo S., Manghi P., Mannocci A. (2019) OpenAIRE's DOIBoost - Boosting CrossRef for Research. In: Manghi P., Candela L., Silvello G. (eds) Digital Libraries: Supporting Open Science. IRCDL 2019. Communications in Computer and Information Science, vol 988. Springer, doi:10.1007/978-3-030-11226-4_11
Files (54.1 GB)
Name Size
doiboost_dump-2019-11-27.tar
md5:ce681a06289c1ec6c6b66ef08dd3c7df
54.1 GB Download
schemaAndSample.zip
md5:1fa427d04764bc60d6dd77b6071c685e
3.9 kB Download
termsOfUse_dataset.docx
md5:d53028310151bed623389fea7fc47baf
72.4 kB Download
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