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Usage Statistics from repositories 

The most widely used and leading quantitative measurement of impact on the scholarly communication is the use of citation data. In recent years, with the evolvement of institutional and thematic repositories and the advancement of OA practices, usage statistics is emerging as an additional indication of the assessment in the scholarly landscape.

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OpenAIRE is harvesting usage data from repositories in an effort to produce metrics that will lead to an alternative way of assessing of FP7 OA publications. To ensure for maximum comparability of the statistics, the OpenAIRE Guidelines for Usage Statistics are in alignment with recent national initiatives in the UK (JISC PIRUS2), the Netherlands (SURF SURE) and Germany (OA-Statistik), all participating in the Knowledge Exchange Group on Usage statistics.

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A brief introduction is presented in the following sections, but you can also download the icon OpenAIRE Guidelines for Usage Statistics v1.0 to find detailed information on how to participate in this effort.



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  • 1 - What does usage statistics provides for stakeholder groups?

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    • For researchers
      • to indicate the relevance of a publication
      • to indicate the impact level of particular authors
    • For academic libraries
      • to indicate the level of acceptance and usage of the repository hosting publications
      • to support refinement and optimization of repository websites and metadata information by analysis of the use of devices, browsers, search engines and terms
    • For funding organisation
      • to indicate the impact of research results
      • to provide insights on current trends in research

  • 2 - Why are usage statistics important?

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    Usage statistical data has enormous potential as

    • it is available much earlier than citation analysis: recording is started immediately after publication - very rapid indicator of scholarly trends (no publication delays)
    • it can be recorded for all types of scholarly communications: papers, journals, preprints, blogs, datasets, chemical structures, software etc. (and not just 10,000 journals)
    • metrics for the item-level (i.e., publication) are more meaningful especially for documents on repositories
    • it can be applied in an aggregated form so that more accurate and timely conclusions are derived within a specific discipline or region
    • it will allow to compare usage data across repositories on a European level, when centrally aggregated

  • 3 - How can your repository participate?

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    Aggregation requires standardization on the recording of usage events, exclusion of robot accesses and data exchange mechanisms. The icon OpenAIRE Guidelines for Usage Statistics v1.0, based on OAI-PMH, specify a common format of usage events and transfer protocol (OAI-PMH) for a straightforward adoption by data providers.

    Legal  issues - Privacy Policy

    In alignment with the European Act of personal data protection (http://europa.eu/legislation_summaries/information_society/l14012_en.htm), the IP address, session-id and in some cases also the C-class Subnet must be pseudonymised before transferring the usage data to the aggregator service.

    Repository log file conversion and transfer tools

    Below is a list of tools that can help in conversion and transfer of collected usage data:

    • SURFshare-sure (Statistics on the Usage of Repositories) provides a software for the conversion of Apache2 log files of repositories into OpenURL Context Object Files and for the OAI-PMH transfer to a log Aggregator, like OpenAIRE's Usage Data Aggregator Service.
    • OA-Statistik Data Providers plugins for DSpace, WebDoc and OPUS repositories that convert repository usage data formats into the OpenURL Context format and expose them with OAI-PMH.

    Check out regularly for an update of the list of tools.

  • 4 - How is aggregating performed?

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    Once the usage data is  harvested in OpenAIRE, it is cleaned and harmonized to procude more accurate statistics. Some of these transformation processes include:
    • Removal of all malformed usage events .
    • Unique publication identifier generation (currently by cross referencing DRIVER's identifiers).
    • Filtering out of all robot initiated requests(COUNTER and custom black robot identification lists).
    • Multiple simultaneous streams consolidation.
    • Type of request (download full text or view metadata) deduction for uknown types of events.
    • Double click filtering according to the COUNTER rules.
    • Unique requester detection, which uses multiple input event fields to enhance precision.
    • Grouping of multiple events of each requester into sessions (inactivity rule of 30 mins).
    When sufficient usage data is available to OpenAIRE, additional types of analyses will be explored in search of usage impact metrics. Also, when combined with other types of data (e.g., author ids, citation, etc.) additional results may be deduced.