Environmental

Chemistry

Many different disciplines need to access and integrate chemical information, including oceanography, meteorology, astronomy, metabolomics, proteomics, bioinformatics, geology, biomedical informatics, and many others. Key facets of chemistry data practices across disciplines surfaced in the WorldFAIR Chemistry webinar series “What is a chemical?” and in the International Data Week 2023 session on “Beyond FAIR: reusing chemical data across disciplines with CARE, TRUST and openness”. Ascertaining chemical composition is a common need in characterising research samples in many fields and further integration with other chemical property data can enrich the scientific knowledge base and enable additional analyses and activities; for example, environmental monitoring and chemicals risk assessment. The desire by some national governments for more sustainable practices and zero pollution in the chemistry industry creates a need for an evolution in the way we assess chemical risk in order to protect health and the environment. Being able to reliably and comprehensively capture chemical substances and associated data will be key to effectively achieving these aims. Confirming the identity of chemical substances as described in the initial specification of ‘D3.3 Utility services for Chemistry Standards’ is an important part of tracking provenance and reusability of chemical data and central to the practice of environmental sciences.

Nanomaterials 

Application of the recommendations and guidelines presented in WorldFAIR deliverable ‘D4.1 Nanomaterials domain-specific FAIRification mapping’ and ‘D4.3 Nanomaterials human / machine-actionable provenance and persistence policies’ will ensure that nanomaterials and nanosafety research data will have greatly increased relevance and reusability for nanoinformatics modelling, for design of safe and sustainable materials and for use in regulatory risk assessment and environmental risk assessment. Implementation of InChI and the nanomaterials extension of InChI will increase data harmonisation and interoperability, and linking the nanomaterials InChI and the materials, samples and data provenance information (as described in D4.3) will increase confidence in meta-analyses and datasets utilised in modelling and risk assessment.  We note also the increased focus of the European Commission on advanced materials and Safe and sustainable by design (SSbD), both of which are derived directly from the nanosafety research community, and thus the WorldFAIR nanomaterials Case Study will thus have impact on a much larger community as these groups come together under the umbrella of a Materials Data Ecosystem and marketplace. Life Cycle Assessment (LCA), which is a central part of SSbD, is extensively dealing with matters with potential environmental impacts from the production, use and disposal of chemicals and materials, and the products into which these are embedded. As yet, LCA utilises only a small fraction of the available ecotoxicity data, and a key ambition of the new SSbD projects including PINK and INSIGHT is to develop the tools and workflows that allow existing LCA tools to integrate with a broader range of ecotoxicity data and ecotoxicity prediction models. This includes development of common data documentation approaches, data reporting standards, and especially mapping approaches to link the different semantic universes existing currently in the (nano)safety and the materials modelling communities and fill gaps especially for LCA in collaboration with PARC and others.

The WorldFAIR deliverable ‘D4.2 FAIRification of nanoinformatics tools and models recommendations’ on making modelling FAIR is being utilised in Horizon Europe project INSIGHT who will be developing a number of predictive models for chemical and nanomaterials environmental risk assessment and wish to make them FAIR in accordance with the WorldFAIR recommendations. The guidelines are also feeding into the PARC project’s work on the Safe and Sustainable by Design framework, and the work planned in the PINK and CHIASMA projects, which will develop models for environmental risk assessment and make them FAIR. 

The ongoing work on identifiers and data provenance, initiated in WorldFAIR D4.3 is being continued in the PINK project and will have a major impact on data trustworthiness and, thus, availability of (nano and advanced materials) data for reuse. The importance of harmonisation of approaches with chemistry via the Chemistry Case Study has been invaluable also, since materials and chemistry are not separable. For this reason the extension of InChI for nanomaterials builds on and reuses InChIs, and the starting points for material production are chemicals and these have to be clearly defined for material provenance (D4.3).

Biodiversity 

The Biodiversity work in WP9 facilitated the core restructuring of the GBIF data model which we anticipate will be an update of the TDWG community Darwin Core standard. The implications of this are broad and relevant well beyond the GBIF community and are beginning to have a positive impact on the community. The focus on expansion of a simple model to a more complex model based on the concept of an Event will provide opportunities for new data types to be linked to the biodiversity occurrence data served by GBIF. This is increasing the use of FAIR biodiversity data in policy. This Event is a common aspect of other WorldFAIR case studies and can be used in the CDIF.

The data model is being incorporated in many important international biodiversity projects.  The new ecological data is being tested by the largest citizen science data network – eBird as well as groups associated with the Oceanic Biodiversity Information Service (OBIS), and The US National Environmental Obertain Network (NEON).  This work has spurred data model advancement in the Agricultural biodiversity (WP10) and in biotic interactions in general (GloBI). The data model is being taken up by the GBIF community leading to richer biodiversity data. For example the Distributed System of Scientific Collections (DiSSCo), which is on the European Strategy Forum on Research Infrastructures (ESFRI) road map, is using the new data model in its FAIR digital object architecture.

Agricultural Biodiversity

Pollination is a major ecosystem service which underpins several sustainable development goals and its importance to maintaining life on Earth is widely recognised. Data and knowledge gaps on pollination and plant-pollinator interactions were identified by the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) and this was one of the most important motivations for this Case Study. Having established concrete guidelines and tools to promote plant-pollination interactions interoperability, as well as tools for FAIR assessments, this effort has promoted standards adoption and ensured FAIR data for understanding plant-pollinator interactions. As mentioned above, it is expected that the outputs generated by this Case Study will continue to be adopted and improved.

Ocean Science and Sustainable Development

Based on work carried out by this case study, for the first time, previously isolated ocean data systems are now able to seamlessly interface with each other’s metadata catalogues: they’ll know who has what kind of data, what it’s about, and where and when it’s relevant. This will be a foothold in deepening interoperability to priority data types, which overlap with several other WorldFAIR case studies. More importantly, the work here is shifting digital cultures towards an “interoperability first” model, promoting greater collaboration to face planetary crises.

Disaster Risk Reduction 

Making Disaster Risk Reduction (DRR) data FAIR will allow global and regional institutions, which often have the best access to resources, to more easily collaborate with National governments and local communities to understand DRR needs and implement solutions. In addition, by promoting the transformation of DRR data to machine-readable, semantically linked data there is the opportunity to use pre-trained Artificial Intelligence (AI) algorithms that can analyse large, complex datasets and fuse heterogenous data. This is already happening with national and international space agencies, the United Nations Office for Disaster Risk Reduction (UNDRR), World Meteorological Organization (WMO), and other global agencies. Our case study aims to enhance this collaboration. Recent advances in technology, particularly remote sensing data, which is income agnostic and provides global coverage, provide an opportunity to reduce data gaps for lower income countries. However, unless this data is provided in a FAIR and open fashion there is the potential for the gaps to widen as countries with more resources and technical capacity make better use of DRR data. Encouraging the use of FAIR and open DRR data should help close any historical or emerging DRR data inequalities