Ledonline Journals provide article-level usage metrics through the Open Journal Systems (OJS) usage statistics framework. This document describes how usage data are collected, processed, filtered, and interpreted across the Ledonline Journals publishing platform.
Usage data are collected automatically through the OJS server-side logging system. Each request to journal content is recorded at the time of access and stored in system logs.
Data are processed once per day through scheduled automated batch operations executed overnight (Central European Time). This process ensures consistent and reproducible daily aggregation of usage statistics.
Reported metrics are derived from processed log data after automated filtering and represent aggregated usage events rather than raw or unprocessed access requests.
The platform reports article-level usage indicators based on processed server logs.
Before aggregation, all data undergo automated processing that includes:
As a result, usage metrics represent cleaned and aggregated interaction events, not individual users or sessions.
All metrics must be interpreted as aggregated usage events and not as measures of unique users, unique readers, or unique downloads.
Article page views represent the number of validated requests to the article landing page, including metadata and abstract display where applicable.
This metric includes both:
Automated processing removes non-human traffic and repeated requests occurring within short time windows, in order to reduce distortion from automated or accidental interactions.
PDF full-text accesses represent validated requests to view or download the full-text PDF version of an article.
The same filtering and aggregation pipeline applied to all metrics is used here, including bot exclusion and suppression of repeated rapid requests.
Each recorded value corresponds to an aggregated access event, not to an individual download action or a unique reader.
Compared to article page views, PDF accesses provide a more direct indicator of reading intent, as they are less influenced by navigation and browsing behavior.
For journals where the metric “External Referral Visits (referrer detected)” is enabled, this indicator represents the number of article access events for which an external HTTP referrer was successfully recorded in the OJS usage logs.
This metric is partial by design, as referrer information depends on browser behavior, privacy configurations, HTTPS security policies, and intermediate redirect chains that may remove or suppress referrer data.
As a result, this indicator should be interpreted as a lower-bound estimate of externally originated traffic rather than a complete measurement of all external referrals.
The metric is included to provide additional contextual information on content discoverability outside the journal platform, complementing page view and PDF access statistics.
The usage statistics system applies automated filtering and normalization procedures designed to ensure data reliability and comparability over time.
These procedures reduce the impact of:
The methodology is consistent with widely adopted practices in scholarly publishing analytics and is broadly aligned with COUNTER-inspired principles for usage normalization, without constituting formal COUNTER certification.
Usage metrics reflect patterns of interaction with published content, but they are influenced by the technical characteristics of web-based systems.
Factors such as caching mechanisms, proxy servers, institutional gateways, and client-side behavior may affect recorded usage events.
Therefore:
No metric should be interpreted as a direct measure of individual readership.
Usage metrics are provided as contextual indicators of dissemination and engagement with journal content.
They are not intended to be used as standalone measures of research quality, scientific merit, or scholarly impact.
Ledonline Journals adopt a responsible metrics approach aligned with international best practices in research evaluation, including principles promoted by COARA, DOAJ, and broader initiatives in scholarly communication transparency.
Usage indicators should therefore be interpreted as descriptive analytics rather than evaluative metrics.