Illustrative: packages on a board with routed traces carrying clocked signals, or a data hall of racks and their status lights

Risk Academy, led by the Nexus Consortium · Course catalogue · Technology and Digital Systems

Computing, Data and Quantum Technologies

Data infrastructure, compute and semiconductors

Data infrastructure, compute and semiconductors, 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

Data infrastructure, compute and semiconductors: foundations and practice

This area covers the data infrastructure and computing capacity that science and public services rely on: cloud and edge systems, high-performance computing, chips, the energy and water use of compute, reproducible pipelines and access to research infrastructure.

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

Provides scenario ranges for compute energy demand that feed grid adequacy, siting and cost risk analysis. Lawrence Berkeley National Laboratory, United States Data Center Energy Usage Report: 2025 Update, 18 June 2026

Learning outcomes

  1. Define cloud concentration and the other key terms of the discipline and use them correctly in a written case
  2. Explain cloud and edge systems and high-performance computing 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 chips and semiconductors, stating evidence, date and limits

Modules

  1. Cloud and edge systems
    • Cloud and edge systems
    • High-performance computing
    • Chips and semiconductors
  2. Energy and water use of compute
    • Energy and water use of compute
    • Public interest data infrastructure
    • Data rights and trust
  3. Reproducible pipelines
    • Reproducible pipelines
    • Research infrastructure access
  4. Lab: the discipline's method
    • A system that depends on k services in series is available only when all of them are, so its availability is the product of theirs; four nines on each of ten services gives only about three nines on the whole, and redundancy on a dependency recovers most of the loss at a cost the architecture decision must weigh.
    • 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

  • NRS-T2 Compute and accelerated infrastructure risk
  • NRS-T13 Data and digital public infrastructure risk
  • NRS-H86 Hazard family: Telecom and data-centre failure
  • 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-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C6 Systemic and cascading risk
  • NRS-C7 Early warning, triggers and anticipatory action

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. Lawrence Berkeley National Laboratory, United States Data Center Energy Usage Report: 2025 Update, 18 June 2026
  2. Dean and Barroso, The Tail at Scale, 2013
  3. Armbrust et al., A View of Cloud Computing, 2010
  4. Hennessy and Patterson, A New Golden Age for Computer Architecture, 2019
02Live map

Connected activity in the United Kingdom and Ireland

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

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 the United Kingdom and Ireland.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

Data infrastructure, compute and semiconductors: frontier methods

The methods, data, tools and studies that changed data infrastructure, compute and semiconductors between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
Data infrastructure, compute and semiconductors: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

Provides scenario ranges for compute energy demand that feed grid adequacy, siting and cost risk analysis. Lawrence Berkeley National Laboratory, United States Data Center Energy Usage Report: 2025 Update, 18 June 2026

Learning outcomes

  1. Explain what changed in data infrastructure, compute and semiconductors since 2024 and cite the studies, data or rules behind each change
  2. Reproduce US data centres could use about 12% of electricity by 2030 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. US data centres could use about 12% of electricity by 2030
    • Lawrence Berkeley National Laboratory's 2025 update estimates data centres could account for 11.8% of US electricity by 2030, within a range of 9.5% to 15.3%, revising its earlier 2028 projections.
    • Provides scenario ranges for compute energy demand that feed grid adequacy, siting and cost risk analysis.
    • Lawrence Berkeley National Laboratory, 18 June 2026
  2. IEA follow-up projects data centre electricity roughly doubling by 2030
    • The IEA reports data centre electricity demand grew 17% in 2025 and projects it roughly doubling to about 3% of global demand by 2030, with turbines, transformers, chips and grid connections constraining build-out.
    • Links compute growth to energy supply, grid and supply-chain risks that operators and planners must assess together.
    • International Energy Agency, 16 April 2026
  3. EU Data Act applies, including cloud switching rights
    • The EU Data Act became applicable, giving users access to data from connected products, prohibiting unfair data-sharing terms, enabling public bodies to request data in emergencies and easing switching between cloud providers.
    • Reduces lock-in and concentration risk in cloud services and changes contractual risk for data holders.
    • European Commission, 12 September 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

  • NRS-T2 Compute and accelerated infrastructure risk
  • NRS-T13 Data and digital public infrastructure risk
  • NRS-H86 Hazard family: Telecom and data-centre failure
  • 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-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C6 Systemic and cascading risk
  • NRS-C7 Early warning, triggers and anticipatory action

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. Lawrence Berkeley National Laboratory, United States Data Center Energy Usage Report: 2025 Update, 18 June 2026
  2. International Energy Agency, Key Questions on Energy and AI, 16 April 2026
  3. European Commission, Data Act, 12 September 2025
  4. Dean and Barroso, The Tail at Scale, 2013
  5. Armbrust et al., A View of Cloud Computing, 2010
  6. Hennessy and Patterson, A New Golden Age for Computer Architecture, 2019
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

Cloud concentration

Reliance of many users on a small number of cloud providers.

Term

High-performance computing

Aggregated computing power used for large simulations and data analysis.

Term

Power usage effectiveness

The ratio of a data centre's total energy use to the energy used by its computing equipment.

Term

Reproducible pipeline

A documented, versioned sequence of data and code steps that others can rerun.

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.

Risk Academy
Related

Around Data infrastructure, compute and semiconductors: courses

Risk Academy

Study data infrastructure, compute and semiconductors with us, or teach it

Every course is free and open. Institutions and experts can co-teach, host a cohort, contribute a lab or review a course before it opens.

Browse the course catalogue Partner with the Academy
Risk Academy

Courses · practice · credentials

Not for profit

Our articles, maps and twins are free to read and free to republish. Readers and partners keep it that way.

Support our work

Who funds us

Risk Academy is a not-for-profit institution. To ask who funds a piece of work, use the funding questions form.

Independence

Editors and the expert reviewers named on each article decide what we publish. Funders and partners do not review or approve articles before publication.

Corrections policy

Licence

Articles: CC BY 4.0. Figures, maps, twins and data: CC BY 4.0. Other uses: ask through the permissions form.

Privacy choices