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Although OpenEdition Lab’s projects have focused on enriching and interrelating content with a view to creating a reading recommendation system ((Since 2011, OpenEdition Lab, in collaboration with the LSIS, has been carrying out three research projects on annotating bibliographic references with Bilbo and on the interrelation of documents depending on their type, such as on OpenEdition Review of Books, or depending on the links shared via citations using Grapher)), it appears we know very little about the ways in which our readers use and cite our content.The Internet giants keep their usage data to themselves, as these represent monetizable personal data. Sensitive and profitable, these data are out of the scientific community’s reach. Yet if the economy of attention is truly at the heart of the digital economy, we must build a shared expertise of digital usages. To move beyond this impasse it seems necessary to analyse the ways in which scientific output is read.Funded by ISTEX, the Usages alpha project aims to study the way readers use the OpenEdition platforms. It will do so by exploiting site traffic logs and user statistics in order to create a detector of the different types of usage in evidence on OpenEdition ((Marin Dacos will be presenting this work on the unexpected reader detector at DH 2017 on 8 August 2017, alongside Joël Gombin from the cooperative Datactivist and  Pierre-Carl Langlais, a researcher in information and communication science.)). The studies aim to identify reader usages by establishing an interpretative framework, a methodology and a set of models.On 8 August, as an introduction to the plenary sessions at the Digital Humanities 2017 conference in Montreal, Marin Dacos will give a keynote speech presenting the “unexpected reader detector” project. For more information, see: https://oep.hypotheses.org/1887