Research in information science and scholarly communication strongly relies on the availability of openly accessible datasets of scholarly entities metadata and, where possible, their relative payloads. Since such metadata information is scattered across diverse, freely accessible, online resources (e.g. Crossref, ORCID), researchers in this domain are doomed to struggle with (meta)data integration problems, in order to produce custom datasets of often undocumented and rather obscure provenance. This practice leads to waste of time, duplication of efforts, and typically infringes open science best practices of transparency and reproducibility of science. In this article, we describe how to generate DOIBoost, a metadata collection that enriches Crossref with inputs from Microsoft Academic Graph, ORCID, and Unpaywall for the purpose of supporting high-quality and robust research experiments, saving times to researchers and enabling their comparison. To this end, we describe the dataset value and its schema, analyse its actual content, and share the software Toolkit and experimental workflow required to reproduce it. The DOIBoost dataset and Software Toolkit are made openly available via Zenodo.org. DOIBoost will become an input source to the OpenAIRE information graph.
- Scholarly communication
- Open science
- Data science
- Data integration
- Microsoft Academic Graph
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Crossref APIs, https://www.crossref.org/services/metadata-delivery/rest-api.
Microsoft Academic Graph, https://aka.ms/msracad.
OpenAIRE EXPLORE, http://explore.openaire.eu.
GRID database, https://www.grid.ac.
The field “access-rights” can assume the values OPEN, EMBARGO, RESTRICTED, CLOSED, UNKNOWN.
Apache Oozie, http://oozie.apache.org.
Affero General Public License, https://en.wikipedia.org/wiki/Affero_General_Public_License.
Crossref REST API - GitHub, https://github.com/Crossref/rest-api-doc.
MAG Schema, https://microsoftdocs.github.io/MAG/Mag-ADLS-Schema.
Unpaywall data format, https://unpaywall.org/data-format.
Levenshtein Distance, https://en.wikipedia.org/wiki/Levenshtein_distance.
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La Bruzzo, S., Manghi, P., Mannocci, A.: DOIBoost Dataset Dump (Version 1.0) [Data set]. Zenodo (2018). http://doi.org/10.5281/zenodo.1438356
La Bruzzo, S.: DOIBoost Software Toolkit (Version 1.0). Zenodo, 1 October 2018. http://doi.org/10.5281/zenodo.1441058
This work could be delivered thanks to the Open Science policies enacted by Microsoft, Unpaywall, ORCID, and Crossref, which are allowing researchers to openly collect their metadata records for the purpose of research under CC-0 and CC-BY licenses. The MAG dataset is available with ODC-BY license thanks to the Azure4research sponsorship signed between Microsoft Research and KMi. This work was partially funded by the EU projects OpenAIRE2020 (H2020-EINFRA-2014-1, grant agreement: 643410) and OpenAIRE-Advance H2020 project (grant number: 777541; call: H2020-EINFRA-2017) .
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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, Cham. https://doi.org/10.1007/978-3-030-11226-4_11
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