The FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles describe requirements for sustainable research data. To implement the FAIR principles for artificial intelligence (AI) methods, it is necessary to record the connections between raw data, model architecture, hyperparameters, and the results so that this knowledge can be later used to track the thought process and to enable comparative and transfer studies. We propose and implemented a concept that automatizes research data management for artificial intelligence, minimizing the overhead for the individual researcher. An AI project, which had the goal to develop a neural network for automatically detecting breathing motion in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images of the lung, was managed using the proposed concept as a proof of concept. Three different model architectures, a regular Convolutional Neural Network (CNN), a two-branch CNN and a hybrid model consisting of a time-distributed CNN followed by a Long short-term Memory (LSTM) network were trained and compared. As a result of using the proposed concept we were able to record rich metadata and links between entities and automatically generate a knowledge graph for data provenance of the AI work packages of this project
