Preprint Open Access

OpenAIRE's DOIBoost - Boosting CrossRef for Research

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


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        "affiliation": "Institute of Information Science and Technology - CNR", 
        "name": "La Bruzzo, Sandro", 
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        "affiliation": "Institute of Information Science and Technology - CNR", 
        "name": "Manghi, Paolo", 
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        "affiliation": "Knowledge Media Institute - Open University", 
        "name": "Mannocci, Andrea", 
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    "description": "<p>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 metadata integration problems, in order to produce custom datasets of 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 (Nov 2018) with inputs from Microsoft Academic Graph (May 2018), ORCID (Oct 2018), and Unpaywall (Jun 2018) for the purpose of supporting high-quality and robust research experiments, saving times to researchers and enabling their comparison. To this aim, 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.</p>", 
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        "program": "H2020", 
        "title": "OpenAIRE Advancing Open Scholarship"
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      "data paper", 
      "dataset", 
      "ORCID", 
      "Microsoft Academic Graph", 
      "Unpaywall", 
      "CrossRef", 
      "reproducible science", 
      "metadata", 
      "aggregation"
    ], 
    "language": "eng", 
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    "notes": "This is the pre-print of a data paper accepted for publications at IRCDL 2019 conference: http://ircdl2019.isti.cnr.it. \n\nPlease cite as: \n\nLa 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", 
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