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