Data Shepherding in Nanotechnology: An Antimicrobial Functionality Data Capture Template

Abstract

In this paper, we exhibit how to construct a template for capturing antimicrobial capacity data of nanomaterials or nanoenabled products. The template promotes the principles of making data scientifically findable, accessible, interoperable and reusable (FAIR), encouraging scientists to reuse it. The template construction roadmap entails the following steps: (1) recognize appropriate stakeholders, (2) allocate surveys to collect a general explanation of the data that will be created, (3) comprehend each stakeholder’s requirements, (4) cooperating and using straightforward communication with the participants for the selection of the minimum data requirement reporting and (5) template layout and ontological annotation. We provide an annotated template for capturing antimicrobial data, increasing their interoperability while populating it with real measurements as an example. By applying the roadmap or by utilizing the template portrayed herein, in the case of a safe-by-design nanoproject (Anticipating Safety Issues at the Design of Nano Product Development (ASINA)), data creators of antimicrobial assessments can store the data using the FAIR approach. Furthermore, data shepherds and scientists can skip the lengthy template generation process and speed up the community’s progress on the FAIR route.

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“…A harmonized and standardized framework on how to report NMs characteristics and experimental conditions, as well as making those measurements more comparable, is required to improve the forthcoming reporting data. Furxhi, et al [120] exhibited how to construct and utilize a template for capturing antimicrobial capacity data of NMs or nano-enabled products, promoting the principles of making data scientifically findable, accessible, interoperable, and reusable (FAIR), while encouraging scientists to reuse it. Researchers are expected to follow homogenized frameworks and data logging templates when reporting experimental attributes and outcomes in order to capture data in a format that will allow their re-usability, while increasing data quality.…”

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“…The data shepherd of ASINA has initiated the design and implementation of a data FAIRication process with multiple stakeholders who are unaccustomed with the notion of FAIR process, that is currently under development. 22,23 The process of data gathering and ensuring data quality allows the computational decision tool (ASINA Expert System, ASINA-ES) to utilise most of the data, existing or generated within the project.…”