GCRI · Data and Compute
Public interest data infrastructure
Public-interest data infrastructure is the set of statistical systems, administrative registers, open data portals, geospatial and research repositories that governments, researchers and citizens rely on. When it is underfunded, fragmented or politicised, decisions are made blind or on the wrong numbers. This page sets out how public data systems are built and sustained, how their quality and openness are measured, and what evidence serves the institutions that run them.
Public interest data infrastructure on the record.
Public interest data infrastructure: how the risk develops.
Dataspaces and interoperable evidence. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Trust rests on independence
Statistics are trusted when producers are professionally independent, methods are published and release schedules are kept. Interference, even suspected, erodes that trust quickly.
Registers feed many uses
Civil registration, business, address and land registers underpin statistics and services. Gaps or errors in one register spread to everything built on it.
Maintenance is easy to cut
Data systems need continuous funding for surveys, staff and technology. Cuts show up years later as missing series, delayed releases and declining quality.
Openness multiplies value
Data published under open licences with good metadata are reused by others. Closed or poorly documented data are underused even when collected at great cost.
Connected activity in the contiguous United States.
Night lights, population density and true-colour imagery over the contiguous United States, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Connected activity, for this page
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 the contiguous United States.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 EONET · NASA HLS S30Sources 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 hexagonal 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 hexagonal 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 hexagonal 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 hexagonal 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 hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal 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 hexagonal 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 hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal 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.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). 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).
- 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 (5 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
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
- Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE · 13 Oct 2026
Public data infrastructure serves evidence in every domain.
- WaterCouplingHydrological data exchange depends on national services and WMO policy.What to watchData shared internationally
- HealthCouplingHealth information systems feed surveillance and planning.What to watchTimeliness of reporting
- FoodCouplingAgricultural statistics depend on surveys and censuses.What to watchSurvey frequency
Findings are designed for the Methods and Technical series. The OECD Recommendation on data (2021) and the Global Digital Compact (2024) frame public-interest data; datasets would carry NS-3b metadata under the proposed Nexus Standards.
- Data stewardship practices from statistics offices.
- Data gaps documented by domain.
Data platform and pipelines.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Data platform and pipelines
Public interest data infrastructure · Public data platform · Catalogue v9
Datasets
| Flood extents | Open | Complete | Yes | Published |
| Health facility list | Open | Complete | Yes | Published |
| Mobility traces | Restricted | Partial | n/a | Rights review |
| Household survey | Restricted | Complete | Yes | Access by request |
Pipeline checks
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Datasets published with an open licence | percent of catalogued datasets | annual | Open data portal catalogues | OBSERVED |
| Datasets with complete metadata | percent of catalogued datasets | annual | Catalogue metadata review | OBSERVED |
| Release calendar adherence | percent of releases on schedule | annual | Statistics office records | Published statistics |
| Completeness of birth and death registration | percent of events registered | annual | Civil registration and vital statistics | ESTIMATED |
| Censuses conducted in the round | status by country | per census round | UN census programme records | Published statistics |
| Funding for national statistical systems | currency units | annual | Budget and donor records | REPORTED |
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. Teaching model with stated illustrative values, not a prediction. The map on this page shows The contiguous United States; the model itself is not tied to a place.
Controls
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 6%
- Assets still working
- 31%
- Amplification of the initial damage
- 12.3 times
- Steps to settle
- 4
Numbers, methods and sources
| Step | Data centres working (%) | Networks working (%) | Power working (%) |
|---|---|---|---|
| 0 | 94 | 94 | 94 |
| 1 | 89 | 39 | 94 |
| 2 | 50 | 28 | 94 |
| 3 | 39 | 22 | 56 |
| 4 | 39 | 22 | 33 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Data and Compute in Foundry
- Tested at Data and Compute in Universe
- Codified in Data and Evidence in Standards
- Published in Data and Compute in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| UN Statistics Division data and the World Population and Housing Census Programme | Status of censuses and published statistics | Public, UN terms of use |
| PARIS21 reports on statistical capacity and financing | Funding and capacity of national statistical systems | Public, PARIS21 terms |
| re3data registry of research data repositories | Research data repositories and their policies | Open, licence to check |
| National open data portals with DCAT catalogues | Dataset listings and metadata | Open data, licence per portal |
| UNSD civil registration and vital statistics resources | Registration completeness and standards | Public, UN terms of use |
Model public interest data infrastructure as a system. Run the cascade.
Assembled from the Nexus Standards area profile Digital, telecom and platforms: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Sea-ice loss
- Coastal erosion (set off downstream)
- Biodiversity loss (set off downstream)
- Arctic winter sea-ice collapse (set off downstream)
- Polar vortex disruption (set off downstream)
Systems and couplings
- Digital, telecom and platforms
- Energy utilities and grids Coupling
- Media and information Coupling
- Finance, insurance and capital markets Coupling
- Defence and civil protection Coupling
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H86Hazard family: Telecom and data-centre failure (NHS-1 infra.digital)NRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-H93Hazard family: Identity, trust and cryptographic failure (NHS-1 cyber.identity)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-T8Software and supply-chain integrity riskNRS-X6Cyber and digital security risk
Observation
- ITU DataHub ICT indicators (listed for reference)
- World Development Indicators (listed for reference)
- WMO Alert Hub and Register of Alerting Authorities (listed for reference)
- Proportion of population covered by a mobile network, by technology
- Fixed broadband subscriptions per 100 inhabitants, by speed
- Individuals using the Internet (% of population)
- Number of people per 100,000 that are covered by early warning information through local governments or through national dissemination mechanisms
Example geography
- North America: 243 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
Who decides, and what the evidence serves.
Data and Compute prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| National statistics offices | Which systems and series to prioritise and modernise | Maturity reviews; register dependency maps |
| Ministries of finance and planning | How much to fund statistical and data systems | Funding evidence; costs of missing data |
| Open data and digital government teams | How to improve publication, licensing and metadata | Catalogue audits |
Outputs this topic produces
- Data infrastructure maturity reviews
- Metadata and licence audits of open data catalogues
- Register dependency maps for statistics and services
- Evidence notes on funding of statistical systems
How public interest data infrastructure 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.
Datasets and provenance
The data classes behind public interest data infrastructure and what is recorded about each before it is used.
| Data class | Provenance recorded | Resolution stated | Licence checked |
|---|---|---|---|
| Satellite imagery, optical and radar | sensor, processing level, acquisition date | pixel size and revisit | per scene |
| Reanalysis and climate model output | model, experiment, member, version | grid and time step | per release |
| In-situ sensors and gauges | station, instrument, calibration record | point and sampling rate | per network |
| Population and asset exposure | method, vintage, input sources | cell size and unit | per release |
| Boundaries and reference geography | producer, edition, date | scale | per edition |
- Lineage
- W3C PROV record from source to figure
- Principles
- FAIR: findable, accessible, interoperable, reusable
- Gaps
- shown as missing, never filled silently
What data infrastructure experts ask.
- Can data from different agencies, countries and sectors be combined without lengthy manual cleaning?
- Who controls access, under what rules, and how is use recorded?
- Is the infrastructure sustainable beyond a single project's funding?
- Does it work for institutions with limited technical capacity, or only for the best-resourced?
- Can it serve a crisis, when several agencies suddenly need to combine data they have never shared before?
- How are errors in shared data reported back to the holder and corrected for every user?
What helps and what blocks sharing.
Most barriers to sharing are not technical. Data holders worry about liability, privacy, loss of control and the effort of preparing data for others. Infrastructure that lowers these costs, and gives holders confidence about how their data will be used, does more for sharing than infrastructure that simply makes data downloadable.
Shared meaning
Common vocabularies and metadata let systems understand each other's data, not just exchange files.
Usage policies
Machine-readable terms of use and access control let holders share while keeping control.
Stewardship
Data needs custodians with mandates and budgets. Without them, infrastructure decays.
How the infrastructure is built and evaluated.
- 01
FAIR principles
Guidance that data should be findable, accessible, interoperable and reusable. Widely adopted, but principles alone do not specify implementation.
- 02
Federated architectures
Data stays with holders and is queried or shared under common rules, an approach studied under data engineering, interoperability and stewardship. Limits: coordination overhead and uneven implementation.
- 03
Provenance standards
Records of where data came from and how it was transformed. Limits: only valuable if consistently captured.
- 04
Maturity assessment
Structured reviews of data infrastructure capability. Limits: self-assessment tends to be optimistic.
How it is studied, and what the methods cannot say.
- 01Data engineering, interoperability and stewardshipFAIR data assessment · Interoperable catalogues · Data quality pipelinesApplied here
- 02High-performance, cloud and edge computingWorkload characterisation · Porting codes to new architectures · Hybrid cloud and HPC workflowsApplied here
- 03Cybersecurity and cryptographySecure research environment design · Federated identity securityApplied here
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.
Questions and answers.
What is public-interest data infrastructure?
What are the UN fundamental principles for national statistics?
Why does metadata matter?
Is open data the same as public interest data infrastructure?
What is a dataspace?
Does the platform host datasets?
Contribute evidence from this page.
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