Recent medial discussion on “fake-news” underlines the importance of evidence-baseddecision-making. To gather, analyze and interpret “facts” is, however, in our information-densedigital times, not always easy. Activities such as information seeking, knowledge building andevaluation in scholarly practice are often performed using bibliometric/informetric methods.The increased interest in bibliometrics also opens for new questions on how data sources arebeing used and what kind of challenges and/or possibilities that warrant further investigation. In this session for interaction and engagement, we invite participants to explore both means ofanalyzing already available data sources using machine-learning technology, as well as toinclude new sets of data that could augment the different views of grasping research activitiesusing algorithms. Such data could be both content-intensive (as text), time-sensitive (as events), contextual (in terms of links between different properties) or multi-modal, meaning that othersources, such as imagery, sound, and video – even material objects may constitute possiblecontributions as data as impact.