Quantifying trophic interactions is a critical path to understanding and forecasting ecosystem functioning, but fitting trophic models to field data remains challenging. It requires flexible statistical tools to combine different sources of information from the literature and fieldwork samples. In an article published in Ecological Applications, we present EcoDiet, a hierarchical Bayesian modeling framework to simultaneously estimate food-web topology and diet composition of all consumers in the food web, by combining (i) a priori knowledge from the literature on both food-web topology and diet proportions; (ii) stomach content analyses (SCA), with frequencies of prey occurrence used as the primary source of data to update the prior knowledge on the topological food-web structure; (iii) and biotracers data, notably stable isotope analyses (SIA), through a mixing model. Inferences are derived in a Bayesian probabilistic rationale that quantifies the uncertainty around both the topological structure of the food web and the diet proportions. The present SEANOE dataset makes available the trophic and literature data to which EcoDiet was applied in this publication to assess its performance and demonstrate its ability to deal with real case-studies. On the one hand, it includes the diet matrix and the associated topology matrix describing a simplified but realistic food web, and the SCA and SIA data simulated from these matrices. On the other hand, it displays real in situ SCA and SIA data for most trophic groups of the Celtic Sea food web (collected during EVHOE surveys, EATME project) and a priori topology and diet matrices issued of a bibliographic search (with associated scores characterizing the reliability of the literature information). This SEANOE dataset allows to reproduce the runs of the EcoDiet model conducted as part of the Ecological Applications article.