Innovative Data Management in Advanced Characterization: Implications for Materials Design

Abstract

This paper describes a novel methodology of data management in materials characterisation, which has as starting point the creation and usage of Data Management Plan (DMP) for scientific data in the field of materials science and engineering, followed by the development and exploitation of ontologies for the harnessing of data created through experimental techniques. The case study that is discussed here is nanoindentation, a widely used method for the determination and/or modelling of mechanical properties on a small scale.The same methodology can be applicable to a large number of techniques that produce big amount of raw data, while at the same time it can be invaluable tool for big data analysis and for the creation of an open innovation environment, where data can be accessed freely and efficiently.Aspects covered include the taxonomy and curation of data, the creation of ontology and classification about characterization techniques, the harnessing of data in open innovation environments via database construction along with the retrieval of information via algorithms. The issues of harmonization and standardization of such novel approaches are also critically discussed.Finally, the possible implications for nanomaterial design and the potential industrial impact of the new approach are described and a critical outlook is given.

Self-Assembling Peptides and Carbon Nanomaterials Join Forces for Innovative Biomedical Applications

“…A general framework for a reliable risk assessment approach to develop nanotechnology responsibly is a common objective for societal welfare [ 91 ]. Innovative ways for more efficient data management [ 92 ] and inclusion of modern in silico methods, such as machine learning to make the most out of large datasets, hold the key to tackle this type of unresolved issues [ 93 ].…”

Data Shepherding in Nanotechnology. The Exposure Field Campaign Template

“…The European Union has released FAIR data management guidelines for Horizon 2020 projects [ 21 ] and any Horizon 2020 project that produces, assembles, or processes research data should provide the Data Management Plan (DMP) as an essential deliverable. As an overall practice, data management is connected with the entire lifecycle of data implementation, including the primary steps of data creation, capture, variations and final storage [ 22 ]. DMPs facilitate the above aspects, as they play a major role in data FAIRification.…”