Know Your Experiments: Interpreting Categories of Experimental Data and Their Coverage

Data management in scientific domains is more important than ever due to the increasing availability of experimental data. Automatically integrating and managing the information would significantly speed up their reuse and, in particular, the development of predictive models for a given domain. However, the diversity, ambiguity, and complexity of experimental data make it hard in practice. In this work, we propose a general approach to overcome these challenges, combining a human-in-the-loop process with a new methodology to understand automatically the semantics of experimental data, which can also be used as a data cleaning procedure. In addition, we focus on assessing the domain coverage of an experimental database using only categorical characteristics of the domain, which is essential for model validation or to understand if and where there is a need to perform additional experiments.