Denoising diffusion MRI data has gained significant interest over the last years, due to the inherently low-SNR in the dMRI signal. Despite the existence of a number of denoising algorithms, open questions exist on how, and even whether, to denoise data. A consistent set of evaluations that comprehensively characterise newly-developed approaches and their impact on downstream applications is lacking for providing insight to these questions. Here we propose EDDEN, a framework for Evaluating DMRI DENoising approaches, consisting of a set of unique data and assessments. We demonstrate its use using 3 exemplar denoising methods (NLM, MPPCA and NORDIC).