Light field imaging technology has been attracting the attention of researchers and engineers due to the ability to capture enriched visual information, which expands the processing capabilities of conventional 2D imaging systems. Dense multiview, accurate depth maps and multiple focus planes are examples of different types of visual information enabled by light fields. This technology is also emerging in medical imaging research, like dermatology, allowing to find new features and improve classification algorithms, namely those based on machine learning approaches. Since only few years ago practical light field cameras appeared in the market, the availability of light field content is still a scarse resource for research and development of new image processing algorithms. As a contribution for the research community, this paper presents a publicly available light field image dataset of skin lesions, named SKINL2. This dataset contains 330 light fields (250 in SKINL2 v1.0 and 80 in SKINL2 v2.0), captured with a focused plenoptic camera and divided into eight clinical categories, according to the type of lesion. Each light field is comprised of 81 different views of the same lesion and a lenslet image. A dermoscopic image of each lesion is also included. This dataset has high potential for advancing medical imaging research and development of new classification algorithms based on light fields, as well as in clinically-oriented dermatology studies.