OA book usage data exchange

Abstract

Conceived in 2015 at the Scholarly Communications Institute, the Open Access Book Usage Data Trust (OAEBUDT) has grown into an international effort to develop and pilot a global data trust for OA monograph usage data. It progressed through three stages:• Project 1 (2018-2019): This project documented the challenges and opportunities facing the aggregation, analysis, and communication of OA book usage.• Project 2 (2020-2022): This project documented the data supply chain (Clarke and Ricci, 2021) and use cases related to OA book usage data (Drummond and Hawkins, 2021), developed infrastructure for usage data aggregation and provision, and identified community-based governance models to support a diverse, global data trust for OA monographs usage data.• Project 3 (2022-forward): In partnership with OPERAS and OpenAIRE, this project is developing the Governance Building Blocks for its Industrial Data Space (IDS) infrastructure, with an eye towards how such infrastructure can be extensible to usage and impact data for other scholarly outputs.Since 2015, with support from the Mellon Foundation, stakeholders have worked through the global OAEBUDT effort to foster the secure, multi-party exchange, aggregation and benchmarking of book usage-related data. This work aims to increase trust in usage metrics, improve data quality, and reduce reporting and compliance resource-burdens related to OA usage data. Prior work, such as facilitated stakeholder interviews and virtual design workshops, revealed diverse use-cases and staff roles interested in OA book usage data across libraries, publishers, and publishing platforms and services (Drummond and Hawkins, 2021). It also indicated a shared system-wide need for simplifying usage data curation, aggregation and management given the time and costs individual institutions allocate when combining COUNTER-compliant reporting alongside other usage data dashboards, and non-COUNTER compliant web- analytics. This reinforced the hypothesis that a “Data Trust” model could benefit those working with usage data by facilitating the aggregation and processing of data upstream according to shared, transparent community norms, thereby generating economies of scale (O’Leary and Hawkins, 2019), for example, reducing costs and resources used when aggregating and curating such data.Creating a data exchange ecosystem to unlock public/private usage data sharing would help minimise the time and resources used, plus provide more transparency to all the organisations involved in the data exchange process. While the term “usage data” is often used today to refer to web analytic reports that tally page visits and file downloads, the OABUDT works towards a future where linked usage data analytics regularly inform book publishing and scholarly communications operations. Clear ethical norms, privacy andsecurity guardrails are required to unlock such data exchange for granular analytics.After researching and documenting the challenges, use cases and supply chain for OA book usage data from 2015 to 2021, the current effort is focused on developing governance building blocks for a sustainable infrastructure. These blocks are modelled on a certifiable European IDS that will help ensure that organisations wanting to participate in data sharing meet the highest security standards. In April 2023, an OA Book Usage Data Exchange Use Guidelines and Principles meeting gathered publishers, libraries, standards, and service representatives to draft principles and identify trust mechanisms for such an IDS infrastructure.The aim is to ensure that usage data exchange would be both trusted and support ethical, non-harmful data use.The principles and trust requirements drafted in this meeting are now being shared with the global open science community for consultation to ensure that they meet the needs of diverse OA book stakeholders and the research community at large. This poster will provide background to the OAEBUDT and a preview of emerging participation and ethical data exchange and use guidelines for the data trust.