Editorial: Asteroid modeling: Processing and combining diverse datasets

For decades now, we have seen a continuous exponential growth of data in asteroid science resulting 8 from targeted observations as well in data originating from surveys dedicated to other astronomical 9 objects but containing serendipitous asteroid measurements. These measurements are diverse from 10 sparse to dense in time, differ in type (occultations vs. photometry) and are often burdened with 11 different systematic and random errors. Combing and processing those distinct-in-nature data thus 12 becomes a conceptual and computational challenge. Furthermore, comparison of results based on 13 various datasets and methods may also prove problematic. 14 Owing to the rise of big data multiple advances continue to be made in asteroid science in the data 54 processing part, usage of modern machine learning methods, and development of novel methods 55 allowing for incorporation of diverse data types. These approaches often must be tailored to the 56 specific problems at hand. In particular, asteroid science often relies on serendipitous measurements 57 originating from surveys and space missions with other primary targets, thus requiring more 58 ingenuity and creativity in the data processing part. This is reflected in the current Research Topic. 59