GCRI · Artificial Intelligence and Autonomous Systems
Artificial Intelligence and Autonomous Systems
Trustworthy artificial intelligence, robotics-enabled autonomy, computational methods and social effects. Artificial Intelligence and Autonomous Systems is an established platform of The Global Centre for Risk and Innovation.
- STEM methods
- 7
- Canonical methods this field applies
- Topics
- 8
- Subject pages in the field
- Counterparts
- 3
- Platforms in shared fields across the network
- Report series
- 2
- Reports series it contributes to
The workspace for Artificial Intelligence and Autonomous Systems.
Model registry
Model registry · Release gate · Example interface, not a record
Models
| M-463 triage assistant | Public service | High | 21 of 24 suites | Blocked |
| M-896 flood nowcast | Science | Medium | 11 of 12 | Released |
| M-308 document search | Internal | Low | 7 of 8 | Released |
| M-641 agent planner | Agentic | High | 9 of 16 | Evaluating |
Model card
- Intended use
- triage support, human decides
- Out of scope
- eligibility decisions
- Known limits
- lower accuracy on rare cases
- Owner
- Model owner
Incident log
- D-14INCHallucinated citation in summary
- D-7MITRetrieval grounding added
- D-4INCPrompt injection via uploaded file
- D-1MITTool permission narrowed
Artificial Intelligence and Autonomous Systems · Model registry. Model registry: example names and illustrative values, not a record.
Evaluation science for AI and autonomy.
The platform’s methods are model assessment, reliability analysis, AI evaluation and the design of human oversight. It is designed to produce a library of AI safety methods, sector use-case studies and evaluation protocols that others can inspect and reuse.
Who it serves
AI researchers, applied scientists, safety engineers and public-sector technologists, with the operators and reviewers who deploy and scrutinise AI.
How it works
Benchmark design, statistical evaluation, red-teaming, reliability engineering and studies of human-AI interaction, each with stated limits.
What it is designed to publish
An AI safety method library, sector use-case studies and evaluation protocols, through reviewed publication.
The platform does technical AI science. It does not regulate AI, certify or approve systems, or write policy; governance belongs with The Global Risks Forum (GRF) and financial-sector model risk with The Global Risks Alliance (GRA).
A threshold model of collective behaviour.
Each agent acts once enough of its contacts have; small changes in the mean threshold or the early movers decide whether the behaviour stays local or cascades through the population. Teaching model with stated illustrative values, not a prediction.
Controls
- Mean threshold (share of neighbours)
- 18%
- Spread of thresholds
- 8 points
- Early movers
- 2.0% of people
- Contacts per person
- 8
Readouts
- Share who acted in the end
- 100%
- Global cascade
- Yes
- Steps to settle
- 11
Numbers, methods and sources
| Step | Share who have acted (%) |
|---|---|
| 0 | 1.0 |
| 1 | 3.5 |
| 2 | 10.5 |
| 3 | 22.0 |
| 4 | 36.8 |
| 5 | 55.3 |
| 6 | 70.3 |
| 7 | 82.3 |
| 8 | 92.8 |
| 9 | 99.0 |
| 10 | 100.0 |
| 11 | 100.0 |
Methods
- Threshold models of collective behaviour Granovetter (1978), American Journal of Sociology 83
- Global cascades on random networks Watts (2002), PNAS 99
- Complex contagions Centola and Macy (2007), American Journal of Sociology 113
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Artificial Intelligence and Autonomous Systems in Foundry
- Tested at Artificial Intelligence and Autonomous Systems in Universe
- Codified in Digital, Cyber and AI in Standards
- Published in Artificial Intelligence and Autonomous Systems in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
The questions this field works on.
Model evaluation
Validity, calibration and generalisation.
TopicAgentic systems
Tool access, delegated authority and safe execution.
TopicAI cyber security
Adversarial methods and model exposure.
TopicAI in science
Discovery and reproducibility.
TopicAI and public services
Public accountability, access and rights.
TopicAutonomous physical systems
Robots, sensors and controls.
TopicCompute and energy
Compute concentration, efficiency and demand.
TopicLabour and social effects
Work, education, public trust and inclusion.
How Artificial Intelligence and Autonomous Systems is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in Artificial Intelligence and Autonomous Systems: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Model, equations and assumptions
The model structure used for Artificial Intelligence and Autonomous Systems, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)hazard, exposure, vulnerability and coping capacityEAL = ∑k pk · Lkover events k with annual probability pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Assumption | Why it is made | How it is tested |
|---|---|---|
| Hazard and exposure are independent within an event | Keeps the loss integral tractable | Compare with a copula fit on joint records |
| Couplings are linear near the operating point | Lets shocks propagate through A | Perturb beyond the linear range and compare |
| Vulnerability curves transfer between places | Local curves are rarely available | Validate against observed losses where they exist |
- Inputs
- versioned datasets with provenance
- Outputs
- distributions, never single numbers
- Code
- released with the model card and tests
- Reproduction
- same data, code, environment and seed
What the work is read against.
The platform does not set standards. It reads its evidence against the frameworks that competent bodies have adopted, so a finding can be placed where a decision-maker already works.
| Framework or standard | Body | What it sets |
|---|---|---|
| AI Risk Management Framework and Generative AI Profile | US National Institute of Standards and Technology (NIST) | Functions and practices to govern, map, measure and manage AI risks |
| Artificial Intelligence Act | European Union | Risk-based obligations for AI systems placed on the EU market, including high-risk and general-purpose models |
| ISO/IEC 42001 and ISO/IEC 23894 | ISO and IEC (JTC 1/SC 42) | An AI management system standard and guidance on AI risk management |
| OECD Recommendation on Artificial Intelligence | OECD Council | Principles for trustworthy AI and recommendations to governments, adhered to beyond OECD members |
| Recommendation on the Ethics of Artificial Intelligence | UNESCO | Values, principles and policy areas for ethical AI adopted by member states |
| Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law | Council of Europe | A treaty on AI activities across their life cycle consistent with human rights |
| International AI Safety Report | Independent experts, commissioned by governments after the AI Safety Summit series | A scientific synthesis of capabilities and risks of general-purpose AI |
| OWASP Top 10 for Large Language Model Applications | OWASP Foundation | Common security risks in applications built on large language models |
| ISO 10218 Robots and robotic devices: safety requirements for industrial robots | International Organization for Standardization | Safety requirements for industrial robots and their integration |
What Artificial Intelligence and Autonomous Systems does.
AI risk is the potential for AI systems and autonomous machines to cause harm through errors, misuse, insecurity or their effects on society. Artificial Intelligence and Autonomous Systems covers model evaluation, agents, AI security, AI in science and public services, autonomous physical systems, compute and energy, and labour. Developers, deployers and regulators decide.
Its community
AI researchers; applied scientists; safety engineers; public-sector technologists.
Its outputs
AI safety method library; sector use-case studies; evaluation protocols.
What AI and Autonomous Systems studies, and what it never vouches for.
Artificial Intelligence and Autonomous Systems, studies how AI models and autonomous machines are evaluated, secured, deployed and governed, and what they change for public services, science, energy use and work. It vouches for no model, rates no provider and interprets no law for a client; developers, deployers, regulators and the people affected decide.
Evaluation results shown here are illustrations unless marked as released. A benchmark score is evidence about a test, not about every use.
AI enters every domain as a tool and a dependency.
- WaterHow this field couples to itMachine-learning hydrology improves prediction in ungauged basins and must be validated out of sample.
- EnergyHow this field couples to itAI data centres add electricity and cooling demand on specific grid nodes.
- FoodHow this field couples to itCrop monitoring and advisory tools reach farmers through models with uneven local accuracy.
- HealthHow this field couples to itClinical AI must be evaluated on the populations it will serve.
- BiodiversityHow this field couples to itSpecies identification models depend on training data biased toward well-studied places.
Where AI evidence goes.
AI evaluations are designed for the Exponential Technology series and tested systems for the Labs Evidence series. Models studied would carry model cards under NS-12 of the proposed Nexus Standards.
- Evaluators can contribute test designs and held-out datasets.
- Public bodies can share AI deployment reviews.
- Anyone can report an AI incident they observed in a public service.
Capable systems, uncertain behaviour.
Modern AI systems learn their behaviour from data rather than following rules written by engineers. That is the source of their power and of the central difficulty in trusting them.
Validity and robustness
Whether measured performance holds outside the test set, under distribution shift and against adversaries.
Shared dependencies
Concentration in models, compute and data creates common points of failure across sectors.
Effects on people
Errors and biases fall unevenly, and public trust depends on accountability that people can see.
A model that performs well on a benchmark may fail on inputs that differ subtly from its training data, may be confidently wrong, and may behave differently after an update. Large language models can produce fluent but false statements. Systems that can call tools, browse, write code or move in the physical world turn these errors into actions. Adversaries can manipulate inputs, poison training data or extract information the model should not reveal.
Failures cascade because AI is increasingly embedded in other systems: public services that decide eligibility, scientific pipelines that generate hypotheses, grid and logistics operations, and the security tools that defend networks. The same few foundation models, cloud platforms and chip supply chains sit beneath many applications, so a single weakness can be widely shared. Meanwhile the electricity and water used by large data centres connect AI to energy and climate systems.
The science problem is how to measure what these systems can and cannot do, under which conditions, with what uncertainty, and how to keep humans meaningfully in control of consequential decisions.
Grounded in STEM, connected across the Consortium.
The platform is the steward of two STEM Commons nodes, artificial intelligence and machine learning and AI evaluation and assurance engineering. It also applies high-performance, cloud and edge computing, probability and statistics, cybersecurity and cryptography, social and behavioural science and robotics and automation; see the STEM foundation.
- AI and Digital GovernanceInstitutionThe Global Risks Forum (GRF)What it addsGovernance of AI and digital systems: institutions, mandates and policy dialogue.
- Innovation NexusInstitutionThe Global Risks Forum (GRF)What it addsConvening on responsible innovation and the conditions for adoption.
- AI and Financial Model RiskInstitutionThe Global Risks Alliance (GRA)What it addsShared industry understanding of AI and model risk in financial services.
Field gateways: Artificial intelligence and Data and compute. Related science platforms at GCRI include Cybersecurity and Digital Resilience and Data and Compute.
The areas of artificial intelligence this platform takes on.
Artificial Intelligence and Autonomous Systems studies how AI models and autonomous machines are evaluated, secured, deployed and governed, and what they change for science, public services, energy use and work. Its pages set out the evaluation methods, incident evidence, security practice, standards and social research behind each question, with every value labelled by its evidence and every limit stated. It prepares methods and evidence; developers, deployers, regulators and the people affected decide.
Model evaluation
Measuring what models can do, where they fail and how far results generalise beyond the test.
Agentic systems
AI systems that plan and take actions with tools, and the oversight their autonomy requires.
AI and cybersecurity
Attacks on AI systems, AI used in attacks and defence, and the security of the AI supply chain.
AI in science
Machine learning for prediction, simulation and discovery, and the reproducibility questions it raises.
AI and public services
Algorithms in benefits, health, justice and administration, with accountability to the people affected.
Autonomous physical systems
Robots, vehicles and drones that sense and act in the physical world, and their safety.
Compute and energy
The chips, data centres, electricity and water that training and running AI require.
Labour and social effects
How AI changes tasks, jobs, skills, information and the distribution of benefits and harms.
Built on named science, technology, engineering and mathematics.
Every claim on Artificial Intelligence and Autonomous Systems rests on methods with a canonical page in the STEM Commons, so readers can see what a finding depends on and where its limits lie.
See the STEM foundation of Artificial Intelligence and Autonomous Systems
How work moves in this field.
A question becomes evidence, a governed build, an organised community, a controlled test, a reviewed publication and, finally, a decision taken by the competent authority under its own rules.
- Question Universe National, regional or institutional priority. Question in Artificial Intelligence and Autonomous Systems
- Evidence Foundry STEM foundation of the platform. Evidence in Artificial Intelligence and Autonomous Systems
- Build Foundry Foundry. Build in Artificial Intelligence and Autonomous Systems
- Organise Universe Nexus ecosystem. Organise in Artificial Intelligence and Autonomous Systems
- Test Universe Universe (independent site). Test in Artificial Intelligence and Autonomous Systems
- Publish Reports Reports. Publish in Artificial Intelligence and Autonomous Systems
- Decide Competent authority Competent authorities and institutions, through NXS-RLS (participants, agreements and handoffs). Decide in Artificial Intelligence and Autonomous Systems
- Renew Universe Corrections and the next annual agenda. Renew in Artificial Intelligence and Autonomous Systems
Each step of the cycle in the pillar it runs in; the decision stays with the competent authority, outside the programme.
- Labs
- Foundry
- Universe
- Nexus Standards
- Reports
- Risk Academy
- Competent authority (outside the programme)
How this works across the six pillars.
Artificial Intelligence and Autonomous Systems works through six pillars of one annual programme, led by the Nexus Consortium, with GCRI, GRF and GRA each contributing in its role: explore, experiment and prototype in Labs, build, simulate and prepare in Foundry, convene and test in Universe, codify, correct and apply in Nexus Standards, publish in every format in Reports, teach in the open in Risk Academy.
handoff in playrecorded handoffno direct handoff
Programme yearAll phases
Route
The whole programme
Every pillar hands its work to another; together they close the year’s feedback loops.
L · F · U · S · R · A
- Pillars
- 6 of 6
- Handoffs
- 18 of 18
- Loops
- 17 closed
Handoffs in play
- F01Universe → Foundry · Admitted mission becomes a cycle project with a decision caseReadiness preparation
- F02Foundry → Universe · Core Build Report and go or no-go evidenceControlled build
- F03Foundry → Reports · Sealed observations and indicator series behind a profileCloseout
- F04Universe → Reports · After-action and scenario findings for chapters 10 and 13After-action review
and 14 more
- LabsWhere questions become experiments and prototypes: new methods, data and tools are tried in the open, with their limits on record, before the Foundry builds them, under the Nexus Consortium.Labs
- FoundryWhere methods, data and models become reproducible builds, each run with its inputs, version and limits on record.Artificial Intelligence and Autonomous Systems in FoundryFoundry
- UniverseWhere a build is tested under controlled conditions with independent evaluators, so a method’s skill is measured, not assumed.Artificial Intelligence and Autonomous Systems in Universeuniverse.nexus
- Nexus StandardsWhere methods, identifiers and data schemas are codified in the open and corrected when a test finds a fault.Digital, Cyber and AI in Standardsnexustandards.com
- ReportsWhere findings are published with their method, uncertainty and evidence class, and corrected as new versions.Artificial Intelligence and Autonomous Systems in ReportsReports
- Risk AcademyWhere methods and systems are taught on real data and real places, by working the case, on Risk Academy.Faculty of Technology and Digital Systems in Academyrisksacademy.com
The same field, seen three ways.
GCRI studies a field as science, GRF governs it and GRA builds shared financial understanding of it. Artificial Intelligence and Autonomous Systems links to its counterparts so that work on one side is visible on the others.
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
- NIST AI Risk Management Framework 1.0 (2023) and Generative AI Profile (2024)
- Regulation (EU) 2024/1689, the Artificial Intelligence Act
- International AI Safety Report (January 2025)
- ISO/IEC 23894:2023 Artificial intelligence, guidance on risk management (ISO)
- Nexus Standards, the proposed NS series (nexustandards.com)
Pages in Artificial Intelligence and Autonomous Systems.
Mandate and publisher
PageField overview
PageResearch and evidence
PageInnovation and practical technology
PageProgrammes and projects
PageData and technical methods
PageSpecialist community
PageLearning and professional development
PageEvents and technical dialogues
PageResearch and publications
PageParticipation paths
PageContact the specialist team
PageTrust, rights and status
PageSTEM foundation of Artificial Intelligence and Autonomous Systems
PageHow work moves in this field
PageBuild in Foundry
PageTest in Universe
PagePublish in Reports
PageThe same field across the Consortium
PageTopics
Questions and answers.
Who publishes Artificial Intelligence and Autonomous Systems?
Is Artificial Intelligence and Autonomous Systems active?
How can I contribute?
Where are findings published?
Which values on this platform are real?
How do I report an error?
Who publishes the platform?
Will the platform certify or approve AI systems?
Contribute evidence from this page.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
- ZenodoOpen depositDeposit it and submit to the communityPublish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.
- ReportsGCRISee every way to contributeThe contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.
Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.
How Artificial Intelligence and Autonomous Systems works.
Choose a view and press Play to follow the platform’s process, its programme year, the standards it applies and how its findings are published. The outline shows how the work is designed to run, not the status of any decision.
Artificial Intelligence and Autonomous Systems
How Artificial Intelligence and Autonomous Systems works
Platforms · Programme year · Standards · Reports
A platform joins a field to named methods, labelled evidence and the outputs it publishes, without becoming the authority it serves.
01 Scope
- 01Field and familiesStart · The platform leadThe home states the field, its families and who it serves.
- 02Topic dossiersRecord · Platform authorsTopics explain the subject with labelled evidence.
02 Evidence
- 03Evidence labelsRecord · GCRIEach value is labelled: observed, published statistics, estimated, modelled, scenario or unknown.
- 04MethodsStep · Method ownersMethods come from the STEM Commons, each with one canonical page.
03 Work
- 05Scenarios on the systemsModel · Foundry pillarScenarios run on the Nexus systems, labelled as scenario.
- 06Standards that applyRecord · Nexus StandardsThe platform names the standards that apply. A validator report is evidence for review.
04 Publish
- 07EditionsRecord · ReportsFindings are published through Reports with their limits.
- 08Authority decidesOutcome · The competent authorityDecisions and execution stay with the government, operator or licensed institution.
Artificial Intelligence and Autonomous Systems home · Platforms · Shared methodsAn outline of how the work is designed to run; it does not report the status of any decision or record.
Take part in Artificial Intelligence and Autonomous Systems.
Start from who you are. Answer an open call. Or form and lead a competence cell on the Artificial Intelligence and Autonomous Systems platform.
National institutions
Ministries, statistics offices and agencies: a national desk and chapter, and a seat in the working groups. National chapters
Sectors and professions
Operators, regulators, banks, insurers and professional bodies whose work touches Artificial Intelligence and Autonomous Systems. Participate
Learning
Courses and practice, with a record of every course completed. Free to study. Open learning
Standards
Draft, review and adopt the methods and data standards with the working groups. Working groups
Communities and research
Universities, civil society and individual experts: projects, data and review. Write to the team
| Open call | Seeks | Closes |
|---|---|---|
| Founding call for experts to the working groups CALL-FOUNDING-EXPERTS | expert, editor, chair | 2026-12-15 |
| Open nomination for the three independent expert seats of the Global Standards Council CALL-GSC-IND | council-member | 2026-12-01 |
| Nominations of interim regional members (applies only if the transitional provision is decided) CALL-GSC-REG | council-member | 2026-12-15 |
| Review of the wave-1 risk core outlines (C1, C2, C3, C4, C8, C10, C11) CALL-REVIEW-WAVE1 | contributor | 2027-01-31 |
| Experts for the core standards of risk science: uncertainty and evidence grading, scales, structured expert judgement, tolerability, extremes and compound events, catastrophic and existential risk, ethics and equity, units and measurement (NRS-C17 to NRS-C24) CALL-CORE-SCIENCE | expert, editor, chair | 2027-01-31 |
| Independent reviewers for the families of the hazard atlas, three from at least two regions for each family (NHS2-R19), priority families first CALL-ATLAS-REVIEW | expert | 2027-03-31 |
| Members of the interim Frontier Safety Review Panel and red-team reviewers (biosafety and biosecurity, AI safety and security, critical-infrastructure security); applications treated in confidence CALL-FRONTIER-SAFETY-REVIEW | expert | 2026-11-30 |
| Experts in ontology, metrology, software verification and conformity assessment for the requirement register, the NHS-2 machine representation, the conformance test suites and the conformity assessment scheme CALL-CONFORMANCE-ONTOLOGY | expert, editor | 2027-01-31 |
Calls past their closing date are hidden automatically. Every call, proposal and route: open areas and calls.
Gather the cell
Bring together three to seven people with complementary skills: data, field practice, policy and communication.
Register it
Write to the team with the cell’s name, members, area and region. You receive a reference and a contact. Write to the team
Choose a charter
Pick one focus: research and evidence, data and maps, standards review, or community preparedness.
Learn together
Follow shared study paths at Risk Academy, with practice and a record of every course completed. Hazards and resilience
Lead and report
Hold regular sessions. Publish what the cell learns through Reports. Bring findings to the working groups. Working groups
What a national team or a local community can do on The Global Risks Index (NXS-GRX) today.
Your country’s profile
Open risk profiles for 195 countries, with sources and licences on every value. Open the Index
Correct or reply
Correct a value or reply for your country. The change is logged with its effect. Open the Index
Offer data
Offer a dataset. It enters through the licence gate, with your credit. Open the Index
Every figure on this page is indexed: hazards, climate, conflict, the world.
The Index is the Consortium’s indexing system for risk data. Every indicator, hazard, series and country profile carries its Nexus Standards identifier, its source and its licence, so what this page shows can be traced, corrected and reused.
- 01 · Sources205 registered sourcesEvery value keeps its source and licence; withheld values are counted.Data and licences
- 02 · Nexus StandardsNIS-1 and NHS-1451 indicators, 905 hazards; every record carries a UNI.Nexus Standards
- 03 · The Index54 series, 195 country profilesComputed on the standards; no value is published until an edition is released.The Global Risks Index
- 04 · This pageArtificial Intelligence and Autonomous SystemsMaps, figures and feeds read the Index in the context of hazards, climate, conflict.Public API
Series in this context hazards, climate, conflict
- GLBGlobal SystemsThe state of interdependent global risk: the twenty domain indices aggregated to the world, the five core systems (water, energy, food, health, biodiversity) and their couplings, the global tail register and the gap…NXS-GRX-AR-000002Hazards · Climate · Conflict
- THDisaster Risk Intelligence (DRI)Signals, observations, event streams, geospatial layers and scenario packs turned into structured risk intelligence for preparedness and planning.NXS-GRX-AR-000111Hazards · Climate · Conflict
- GSGlobal CitiesUrban exposure, heat, flooding, informal settlement, service dependency and the economic weight of cities at risk.NXS-GRX-AR-000068Hazards · Infrastructure · Climate
- GSGlobal ClimateChronic and acute physical climate hazard, transition exposure, adaptation gaps and scenario pathways to 2030, 2050 and 2100.NXS-GRX-AR-000065Climate · Hazards · Finance
Country profiles the world
Every country profile carries the source and licence of each value. Correct a value or reply
Hazard domains NHS-1
- Atmosphere and weatherNXS-GRX-AR-000135
- Solid EarthNXS-GRX-AR-000138
- Systemic and compound eventsNXS-GRX-AR-000148
Geodata and API open
Open layers on an equal-area hexagon grid, shown as densities per km² or as symbols: People · GHSL 2025People · WorldPop 2025Built-up areaPortsAirportsRoadsRailwaysRivers and lake centrelines
/api/v1/public/grix · https://nexustandards.com/uni/{uni}
Developers · Data · Made with Natural Earth, Public domain; GHSL and WorldPop, CC BY 4.0
Snapshot of the Index registries pinned 2026-10-23 · 22c6eed. Research and risk-analytics information: not a rating, an official classification, a warning, a benchmark or advice.
Bring a method, a dataset or a question.
Information and enquiries are open today. 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.
Legal documents.
Maps, data and figures on this site are shown for context and general information. They are not advice, forecasts or warnings; the limits of the information are set out in the Disclaimers and Data Notices.