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Risk Academy, led by the Nexus Consortium · Course catalogue · Risk Science and Methods

Geospatial and Risk Data Science

Risk data governance, provenance and licensing

Risk data governance, provenance and licensing, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

Risk data governance, provenance and licensing: foundations and practice

This area covers how risk data are governed so that evidence can be trusted and reused: data quality, provenance and lineage, licensing of data and models, metadata standards, data rights and consent, personal data protection, data-sharing agreements and open data.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
In authoring
Prerequisites
An L1 course in the area, or an entry review

Why now

Changes to lawful processing and the regulator's structure affect how personal data in risk datasets is governed. Department for Science, Innovation and Technology (UK), Data Use and Access Act 2025: plans for commencement, 12 October 2026

Learning outcomes

  1. Define provenance and the other key terms of the discipline and use them correctly in a written case
  2. Explain data quality and provenance and lineage with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against licensing of data and models, stating evidence, date and limits

Modules

  1. Data quality
    • Data quality
    • Provenance and lineage
    • Licensing of data and models
  2. Metadata standards
    • Metadata standards
    • Data rights and consent
    • Personal data protection
  3. Data-sharing agreements
    • Data-sharing agreements
    • Open data
  4. Lab: the discipline's method
    • 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.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Nexus Standards

Teaches

  • NS-3 The Global Risks Index (NXS-GRX) record UNIs (grammar, legacy crosswalk, allocation authority)
  • NS-3B NS-3B
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-P6 Data responsibility, rights and licensing
  • NRS-C8 Evidence, uncertainty and provenance
  • NRS-I1 Risk intelligence products and modalities
  • NRS-M22 Method fabric: Risk data

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C9 Risk models, methods and validation

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

Open now in this discipline

References

  1. Department for Science, Innovation and Technology (UK), Data Use and Access Act 2025: plans for commencement, 12 October 2026
  2. Wang and Strong, Beyond accuracy: what data quality means to data consumers, Journal of Management Information Systems, 1996
  3. Wilkinson et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 2016
  4. Sweeney, k-anonymity: a model for protecting privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002
02Live map

Connected activity in India

Night-time lights and population density, the footprint of electrified, connected activity, over India.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 2

Connected activity

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.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONETSources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • Optional layers (3 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

Frontier course · L3 advanced, programme courses and standards

Risk data governance, provenance and licensing: frontier methods

The methods, data, tools and studies that changed risk data governance, provenance and licensing between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
Risk data governance, provenance and licensing: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Changes to lawful processing and the regulator's structure affect how personal data in risk datasets is governed. Department for Science, Innovation and Technology (UK), Data Use and Access Act 2025: plans for commencement, 12 October 2026

Learning outcomes

  1. Explain what changed in risk data governance, provenance and licensing since 2024 and cite the studies, data or rules behind each change
  2. Reproduce UK Data (Use and Access) Act provisions entered into force in stages on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. UK Data (Use and Access) Act provisions entered into force in stages
    • The Act received Royal Assent on 19 June 2025; most data protection changes took effect on 5 February 2026 and the Information Commissioner's Office was replaced by an Information Commission on 30 September 2026.
    • Changes to lawful processing and the regulator's structure affect how personal data in risk datasets is governed.
    • Department for Science, Innovation and Technology (UK), 12 October 2026
  2. EU Data Act became applicable, including unfair contract term rules
    • The Data Act applies from 12 September 2025, giving users access to data from connected products, restricting unfair data-sharing terms imposed on businesses and setting rules for switching cloud providers.
    • Rights over device data and limits on unfair terms change how risk data can be obtained and shared under contract.
    • European Commission, 12 September 2025
  3. EU template requires public summaries of AI training content
    • The Commission published an explanatory notice and template setting a common minimum baseline for the public summary of training content that providers of general-purpose AI models must publish under the AI Act.
    • Training data summaries give users of models some provenance information needed to judge licensing and data quality risks.
    • European Commission, 24 July 2025
  4. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist

Labs with Academy tools

Nexus Standards

Teaches

  • NS-3 NXS-GRX record UNIs (grammar, legacy crosswalk, allocation authority)
  • NS-3B NS-3B
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-P6 Data responsibility, rights and licensing
  • NRS-C8 Evidence, uncertainty and provenance
  • NRS-I1 Risk intelligence products and modalities
  • NRS-M22 Method fabric: Risk data

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C9 Risk models, methods and validation

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

Open now in this discipline

References

  1. Department for Science, Innovation and Technology (UK), Data Use and Access Act 2025: plans for commencement, 12 October 2026
  2. European Commission, Data Act, 12 September 2025
  3. European Commission, Explanatory Notice and Template for the Public Summary of Training Content for general-purpose AI models, 24 July 2025
  4. Wang and Strong, Beyond accuracy: what data quality means to data consumers, Journal of Management Information Systems, 1996
  5. Wilkinson et al., The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data, 2016
  6. Sweeney, k-anonymity: a model for protecting privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the course?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.

Key terms

Term

Provenance

The record of where data came from and how they were produced.

Term

Lineage

The path of data through processing steps from source to output.

Term

Metadata

Data that describe other data, such as source, coverage and licence.

Term

Data licence

The terms under which data may be used, shared and adapted.

ReportsContribute

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.

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