The InVID FIVR-200K dataset has been developed in the context of the InVID project with the aim of simulating the problem of Fine-grained Incident Video Retrieval (FIVR). FIVR is the problem where: given a query video, the objective is to retrieve all associated videos, considering several types of associations that range from duplicate videos to videos from the same incident. To address the benchmarking needs of such problem, the large-scale video dataset FIVR-200K has been constructed. It comprises 225,960 YouTube videos collected based on 4,687 major news events crawled from Wikipedia, and 100 video queries selected based on an automatic selection process. For the annotation of the dataset, an annotation protocol has been devised with respect to four types of video associations, i.e., Near-Duplicate Videos (ND), Duplicate Scene Videos (DS), Complementary Scene Videos (CS), and Incident Scene Videos (IS). To this end, FIVR-200K dataset contains the list of the collected Youtube ids, the crawled events from Wikipedia and the video annotations, which include the set of videos for each associations type for each query in the dataset.