The Global Centre for Risk and Innovation (GCRI)
The science, technology, engineering and mathematics Nexus Reports rests on. Each method has a canonical page in the STEM Commons, hosted by GCRI, and a page here on how it is applied in this field.
Reports · 5 methods in use
| Probability and statistics | 4 | Cross-validation | Q2 | Current |
| Uncertainty quantification… | 4 | Expert review | Q4 | Current |
| Data engineering,… | 12 | Hindcast | Q3 | Review due |
Nexus Reports · Method registryMethod registry, in outline: domain structure with placeholder names and illustrative values, not a record.
The Nexus Reports STEM foundation is the set of canonical methods its findings depend on, from probability and statistics to data engineering, interoperability and stewardship. Each method has a canonical STEM Commons page and a page here on its use in the research and evidence field.
Fig.The methods this platform draws on and what each one feeds. Design, not a record.
Where people and networks concentrate, the exposure through which shocks travel between systems, shown for East Africa as an example geography for this subject.
Editorial work rests on probability and statistics for figures, uncertainty quantification for how ranges are shown and data engineering for tracing every figure to its source.
When a finding names the methods it depends on, a reader can see its assumptions and limits, and an expert in that method can challenge it.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| STEM Commons | The shared reference of science, technology, engineering and mathematics methods for the Nexus network, hosted by GCRI, with one canonical page per method and a named steward platform. |
| Steward platform | The platform that maintains the canonical description of a method in the STEM Commons and reviews how other platforms apply it. |
| Probability and statistics | Probability and statistics provide the mathematical framework for describing uncertainty and learning from data. |
| Uncertainty quantification and model validation | Uncertainty quantification and model validation characterise how uncertainty in inputs, parameters and model structure affects predictions. |
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.