OPERAS Metrics Service: Visualising usage of Open Access monographs
Abstract
This presentation introduces the OPERAS Metrics Service, a platform that visualizes usage data for Open Access monographs. The service collects and normalizes information from distributor and publisher platforms, stores it in a centrally managed Metrics Core Database, and delivers it to publisher websites through a customizable display widget. The talk explores key technical challenges—such as data fragmentation, identifier inconsistencies, varied counting methods, and data quality issues—as well as ethical concerns including equity, context blindness, and transparency. It shows how OPERAS Metrics addresses these challenges by offering an open-source solution with transparent data processing and user-friendly display widget. The presentation highlights the ways in which OPERAS Metrics supports the needs of publishers, researchers, distribution platforms, research institutions, universities, and funding agencies seeking reliable and reproducible statistics in the context of Open Science.
Full text
OPERAS Metrics Service Maxim Kupreyev, Technical Manager @ OPERAS RI Göttingen, October 6, 2025 Visualising usage of Open Access monographs
"Metrics" is quantitative data used to describe the performance, usage and influence of scholarly works. It is aimed at: •Publishers (e.g. University Presses) monitor the performance of publications and evaluate editorial policies and strategies. •Authors / researchers track the visibility and reach of their work •Distribution platforms assess the performance of their services •Research Institutions / Universities use it for the responsible assessment processes •Funding Agencies justify allocation of resources and investments. •Libraries evaluate open access usage and guide collection development Collecting metrics for monographs
Main technical challenges •Data fragmentation: books are disseminated across multiple platforms •Identification: same book may use different URI types depending on the context (eISBN, DOI, URL) •Counting methods: diversity of measuring parameters and their definitions (views, reads, sessions, downloads, etc.) •Quality: data is inconsistently reported (e.g., name ambiguity, duplicate records) Main ethical challenges •Equity issues: researchers from underrepresented regions, languages, or institutions are disadvantaged in metric-based assessments. •Context blindness: metrics ignore qualitative aspects (e.g., novelty, societal relevance, educational value). •Transparency: proprietary databases (Clarivate, Elsevier) don’t fully reveal their algorithms or data sources, limiting reproducibility. With Operas Metrics Service we try to address these challenges Collecting metrics for monographs
OPERAS Metrics Service: data workflow Distributor platform (DOAB, Google Books, UPLOpen., etc.) Publisher platform (University Press, etc.) Metrics Core Database (hosted by OPERAS) Metrics display widget (on customer website) 1. Data collection based on DOIs a. Distributor -> Publisher b. Publisher's own website 2. Data processing and normalization 3. Data enrichment 4. Data storage 5. Data display on publisher website
Step 1: data collection based on DOIs Distributor platform (e.g. DOAB) Publisher platform (e.g. University Press) Supported platforms Google Books Open Book Publishers World reader Open Edition OAPEN JSTOR The Classics Library IRUS-UK SUB Göttingen EKT (Greece) Ubiquity Press Figshare UPLOpen Crossref (Cited by) Unglue.it OpenAIRE Components ●Drivers: are collecting data ○Using APIs of the distributor platforms ○Manually via CSV upload ○Locally (Access Logs Driver, Matomo) ●Plugins: are processing and normalizing data ●Storing the customer-related data on a local DB Drivers / Plugins
Step 2: writing data to the Metrics Core DB Publisher platform (e.g. University Press) Metrics Core Database (hosted by OPERAS) Identifier Translation Service: ●Maps publications to URIs, using DOI, eISBN, URL, etc. ●Allows converting from one identifier to another. ●Storing data in the central Operas Metrics Database. ●Enriching it with the alternative metrics (altmetrics): ○Crossref Relationships API combining results from: ■Hypothes.is ■Wordpress ■Wikipedia Drivers / Plugins
Step 3: displaying data on publisher website Metrics Core Database (hosted by OPERAS) Publisher platform (e.g. University Press) Display widget ●Metrics types (measures) ○Views and Reads ○Sessions ○Downloads ○Citations ○Shares ○Users (World Reader) ●Altmetrics types (measures) ○Annotations (on Hypothes.is) ○References (on Wikipedia or Wordpress blogs) ●Sending data to the user interface ●Displaying data in a widget
OPERAS Metrics Service Visualising usage of Open Access monographs: https://www.ubiquitypress. com/books/e/10.5334/bbj
Advantages of Operas Metrics 1. Diverse Data Collection: usage metrics collected from various sources (e.g. chapter reads in Google books, downloads in Open Edition or JSTOR …). 2. Centrally-Managed Database: metrics on open access books stored in a central database that can be accessed by anyone (Read API is free) 3. Open Source: based on open-source principles, offering an alternative to proprietary usage metrics services and emphasising community Operas Metrics Service