Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data

Abstract

The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

“…Nanosafety research has triggered innovations in data management, ontologies, mapping, and curation methods. The process of defining requirements for annotation of datasets with rich metadata, as described in another paper in this special issue [ 58 ], is underway. The objective of this case study is to describe typical use cases, from curation and management to search and retrieval, and to abide by the constraints that the FAIR principles impose on structural, machine readable unique identifiers.…”

“…Several methods have been developed to monitor NM transformations in different media [ 34 ]. Templates for experimental workflows have been produced that capture relevant physicochemical properties, characterize transformations, document the exposure-relevant form, manage data traceability, and the minimal set of metadata required for different forms of the NM (i.e., tracking of its extrinsic properties modulated with the surrounding medium) [ 58 ]. Capture of (bio) chemical NM transformations by time and fate in different media is certainly beyond the capacity of current 1 D text representations.…”

Manually curated transcriptomics data collection for toxicogenomic assessment of engineered nanomaterials

“…To achieve this, data digitization in the NanoPharos database is being optimized to be machine readable to allow the seamless data comparison, transformation and, where possible, combination, providing the user with bigger and more complete data sets. On top of that, the NanoPharos database goes beyond the technical character of the FAIR data principles and is implementing the scientific FAIR data principles (SFAIR) as defined recently by Papadiamantis et al 31 , providing users with the necessary scientific context and background information for them to be able to reuse the data with the highest possible confidence. Furthermore, NanoPharos is readily accessible via Representational State Transfer (REST) application programming interface (API) and is able to interact with external databases (e.g.…”

Recent Advances in Immunosafety and Nanoinformatics of Two-Dimensional Materials Applied to Nano-imaging

“…Allied with quality data infrastructure and processing, computational methods are sizeable to deal with complexity of nano-bio interface to assess and model the toxicity of nanomaterials in a variety of environments (163,(191)(192)(193)(194). To support safe-by-design approaches, international efforts have been made to provide data integration and sharing, modeling tools, standard protocols, and ontologies, to ensure Findable, Accessible, Interoperate, and Reusable (FAIR) data (195,196). For example, European projects, such as NanosolveIT and NanoCommons, and more recently CompSafeNano are initiatives facing on this direction (164,165,197,198).…”