In today's technological landscape, we can gather extensive data on physical systems from a variety of sources including increasingly diverse and resolved sensors. Both past works and recent advancements in machine learning (ML), particularly the use of deep learning techniques , offer a promise towards analyzing the data and generating relations between them. However, these methods traditionally require organized data for their construction. This means that the label of each data point in each dimension (typically space, time, and parameters in a scientific context) must be known. While we hope, and often assume, this is the case (it seems trivial to mark where, when, and at what conditions a measurement is taken), we cannot always guarantee that the data comes readily organized in this manner.