About the Quality of Data and Services in Natural Sciences

Managing data related to natural sciences poses new and challenging problems as it is impossible represent reality on a one-to-one scale, imprecision has be taken into account, both in memorization its processing. Machine learning been key enabler the context of information extraction from data. However, data-driven results are strongly affected by volume, sparsity different types available sources. Therefore, becomes pivotal associate services about their quality, order effectively interpret results. Different levels granularity multiple modalities captured same processes could coexist, due technological constraints or other intrinsic limiting factors. In addition, might also result application requirements, outcomes at precision needs provided. Affinities quality issues domains such chemistry, biology, geoinformatics discussed paper.