Towards a scientific data framework to support scientific model development

Abstract

Editor: Silvio Peroni (https://orcid.org/0000-0003-0530-4305) Solicited reviews: HannaĆwiek-Kupczyńska (https://orcid.org/0000-0001-9113-567X); Aliaksandr Birukou (https://orcid.org/0000-0002-4925-9131); Stian Soiland-Reyes (https://orcid.org/0000-0001-9842-9718)Abstract. The sharing of scientific and scholarly data has been increasingly promoted over the last decade, leading to open repositories in many different scientific domains. However, data sharing and open data are not final goals in themselves, the real benefit is in data reuse, which allows leveraging investments in research and enables large-scale data-driven research progress.This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0). 2451-8484 © 2019 -IOS Press and the authors. G. Scalia et al. / Towards a scientific data framework to support scientific model developmentFocusing on reuse, this paper discusses the design of an integrated framework to automatically take advantage of large amounts of scientific data extracted from the literature to support research, and in particular scientific model development. Scientific models reproduce and predict complex phenomena and their development is a rather challenging task, within which scientific experiments have a key role in their continuous validation. Starting from the combustion kinetics domain, this paper discusses a set of use cases and a first prototype for such a framework which leads to a set of new requirements and an architecture that can be generalized to other domains. The paper analyzes the needs, the challenges and the research directions for such a framework, in particular those related to data management, automatic scientific model validation, data aggregation and data analysis, to leverage large amounts of published scientific data for new knowledge extraction.

About the Quality of Data and Services in Natural Sciences

“…Context-aware data quality management supported by an effective metadata management has been recently discussed in [5]. General challenges related to the management of data and data-driven services in the context of scientific data frameworks have been discussed in [38]. In this context, metadata management has a key role.…”

“…The varying granularity, quality and number of collected features characterize the integration and the analysis of scientific experiments from multiple sources and collected over extended periods of times (for example, in the combustion kinetics domain [38]). In this case, technological limitations constrain the resolution and the confidence of the collected measurements.…”

“…Recently, a set of requirements to enhance the capabilities of data-driven scientific frameworks has been discussed in [38]. These include:…”

“…For its importance, integration is a prerequisite in most of the other activities [30]. One additional challenge often faced in this area is the lack of references or ontologies to drive the integration [38]. Instead, little or no prior information is often available, and complementary strengths of the different sources need to be exploited to automatically generate ontologies or achieve integration in the absence of curated references.…”

“…By making an "open-world" assumption, a system accounts for the existence of data samples external to it, which can be queried/produced (with an associate cost) and integrated into the system (e.g., in the training phase). In this setting, the best predicate for querying new information depends, in general, on the already available information, the cost associated to gather the new information and on a background knowledge [38]. The net result of this approach is a virtuous refinement cycle.…”