nexustandards.com/data · 4 taught
Data and Evidence
NRS-C4 Risk data model, identifiers and exchange; NRS-C8 Evidence, uncertainty and provenance
Illustrative: a chain of blocks each linked by hash to the one before, with a Merkle tree under the newest
Risk Academy, led by the Nexus Consortium · Risk Science and Methods · Department of Geospatial and Risk Data Science
Quality, provenance, lineage, licensing, rights and open data
Risk evidence is only as good as the record of where it came from.
Search any place or open the world; the layers and generators follow risk data governance, provenance and licensing on the planet map, the globe and in 3D.
Drag to move, right drag or Shift with a drag to turn and tilt, Ctrl with the wheel to zoom. Keyboard: arrows move, Shift with arrows turn and tilt, + and – zoom, 0 resets, V steps through the views, Escape clears.
Flat plan of the same data: relief, the three reference epicentres with magnitude and depth, and the numbers below; the sampler's figure shows one precomputed field with its parameters. The 3D twin opens on devices that can draw it, once its data and code have been checked.
The boundaries, names and designations shown do not imply any judgement on the legal status of any territory, or any endorsement or acceptance of such boundaries. Countries: Natural Earth (public domain).
Night-time lights and population density, the footprint of electrified, connected activity, over India.
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Layers 2
Connected activity
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Connected activity, framed on India.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Layers and dates
For data stewards, analysts, legal and compliance staff and product teams who need risk data that can be trusted, traced, shared and reused within the law and the licence.
Each method is listed with what it assumes and where it fails.
Bodies and standards are named so learners can find and apply their published work; the Academy teaches their use and links to the publisher, and no affiliation is implied.
A dataset is fit for a risk use only when enough of its records are present, recent enough for the question, traceable to a source, released under terms that allow the use and safe to publish; each of those is a measured quantity, the first three multiply into a fitness share, and the last two are gates that no score can open.
Sources: Wang and Strong, Beyond accuracy: what data quality means to data consumers, Journal of Management Information Systems (1996); Wilkinson et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data (2016); Sweeney, k-anonymity: a model for protecting privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (2002); World Wide Web Consortium, PROV-O: The PROV Ontology (2013); Carroll et al., The CARE Principles for Indigenous Data Governance, Data Science Journal (2020).
risksacademy.comrisk-sciencediscipline
The Nexus Standards the discipline teaches toward, grouped by master standard, with the disciplines that teach each.
nexustandards.com/data · 4 taught
NRS-C4 Risk data model, identifiers and exchange; NRS-C8 Evidence, uncertainty and provenance
nexustandards.com/foundations · 1 taught
NRS-P6 Data responsibility, rights and licensing
Nexus Standards at the AcademyThe Academy proposes how each discipline teaches toward the Nexus Standards; the mapping is open for public review.
Research and preprints as they are published, by discipline, with the faculty's other tools.
The hub lists the newest research and preprints for Risk data governance, provenance and licensing, each with its source and date. Open the research hub
From the Academy's risk taxonomy: the discipline's own entries and those of its primary courses, with how many courses teach each.
Recent developments in risk data governance, provenance and licensing, each with its source; sources checked on 16 October 2026.
Courses for this discipline are in development; until they open, study it through the connected courses and open lessons below.
Courses with this primary disciplineIn preparation
Each course has its primary discipline elsewhere and teaches this field as part of its scope.
| Lab | What you do in it | Courses using it |
|---|---|---|
| Geo quiz | Check spatial reasoning on where hazards and systems meet | 2 |
| Map lab | Read hazard, exposure and boundaries on real maps | 1 |
| GeoAI lab | Apply Earth observation and machine learning to open imagery | 1 |
Press Play to run this process from this page's own data.
Field platforms of the ecosystem where the subject is studied and applied.
Foundry system pages describe what each system and its rooms are designed to do; the courses teach the methods these systems apply.
Start from who you are.
Start with Wilkinson, then Carroll, then Wang.
Key terms
The record of where data came from and how they were produced.
The path of data through processing steps from source to output.
Data that describe other data, such as source, coverage and licence.
The terms under which data may be used, shared and adapted.
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.
The catalogue lists each course with its outcomes, labs, hours and level.