Convolutional neural networks have been the focus of re-search aiming to solve image denoising problems, but theirperformance remains unsatisfactory for most applications.These networks are trained with synthetic noise distribu-tions that do not accurately reflect the noise captured byimage sensors. Some datasets of clean-noisy image pairshave been introduced but they are usually meant for bench-marking or specific applications. We introduce the NaturalImage Noise Dataset (NIND), a dataset of DSLR-like im-ages with varying levels of ISO noise which is large enoughto train models for blind denoising over a wide range ofnoise. We demonstrate a denoising model trained with theNIND and show that it significantly outperforms BM3D onISO noise from unseen images, even when generalizing toimages from a different type of camera. The Natural ImageNoise Dataset is published on Wikimedia Commons suchthat it remains open for curation and contributions. We ex-pect that this dataset will prove useful for future image de-noising applications.