GCRI · Data and Compute
Reproducible pipelines
A result can be trusted when others can rerun the same analysis on the same data and get the same answer, and when they can trace each number to its source. Reproducible pipelines combine versioned data and code, recorded environments, workflow systems and provenance. This page sets out why analyses fail to reproduce, how reproducibility is engineered and checked, and what evidence serves the researchers, publishers and agencies that depend on it.
How the work runs.
Data platform and pipelines
Reproducible pipelines · 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
Fig.Data platform and pipelines: example names and illustrative values, not a record or a live system.
Reproducible pipelines: how the risk develops.
Pipelines, models and experiment provenance. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Environments drift
Software libraries, operating systems and hardware change. An analysis that ran last year may give different results, or none, without a recorded environment.
Data versions change
Datasets are corrected and updated. Unless the exact version used is recorded, results cannot be traced or rerun.
Manual steps break chains
Steps done by hand in spreadsheets or interactive sessions are not recorded and cannot be repeated exactly.
Provenance links results to sources
Recording which inputs, code and parameters produced each output lets errors be traced and corrections propagate.
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
Reproducible pipelines let each domain's results be checked.
Pipeline methods are designed for the Methods and Technical series. FAIR principles for research software (2022) and PROV-O (2013) guide practice; lineage would follow NS-3b of the proposed Nexus Standards.
- Reproducibility audits.
- Pipeline templates with tests.
Model reproducible pipelines 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
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 |
|---|---|---|---|---|
| Published analyses with code and data available | percent of outputs | per audit | Publication audits | OBSERVED |
| Analyses rerun successfully by a third party | percent of attempted reruns | per audit | Reproduction studies | OBSERVED |
| Datasets with persistent identifiers | percent of datasets | annual | Repository metadata | OBSERVED |
| Pipelines with recorded environments | percent of production pipelines | per assessment | Pipeline repositories | DECLARED |
| Corrections issued after reproduction checks | count | annual | Correction logs | OBSERVED |
| Software archived with identifiers | count | annual | Software archive records | OBSERVED |
How reproducible pipelines 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 reproducible pipelines 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
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 |
|---|---|---|
| DataCite and Crossref metadata | Persistent identifiers for data, software and publications | Open, CC0 metadata |
| Software Heritage archive | Archived source code with intrinsic identifiers | Open, archive terms |
| Zenodo and institutional repositories | Deposited data and code with identifiers | Open, licence per record |
| W3C PROV standard | A model for representing provenance | Open standard, W3C terms |
| Reproducibility studies in published literature | Measured reproduction rates in fields | Licence per publication |
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 |
|---|---|---|
| Research teams and analysts | How to structure pipelines so results can be rerun | Checklists; pipeline templates |
| Publishers and report editors | What reproducibility evidence to require | Audit methods |
| Funders and agencies | Whether to fund archiving and reproducibility support | Reproduction rate evidence |
Outputs this topic produces
- Reproducibility checklists for analyses and reports
- Pipeline templates with versioned data, code and environments
- Provenance recording designs
- Audit methods for reproduction of published results
What experts ask about reproducibility.
- Can this result be regenerated from recorded inputs, or does it depend on undocumented steps?
- Which exact version of the data, code and model produced it?
- If an error is found upstream, which downstream results are affected?
- Will the pipeline still run in years, after software and hardware have changed?
- Can a reviewer without the original team understand what each step does and why?
- Are random seeds, model weights and configuration choices recorded with each result?
Why results become irreproducible.
Irreproducibility is rarely deliberate. It accumulates through small, reasonable shortcuts made under deadline pressure, each harmless alone. The cost appears later, when a result is questioned, a correction is needed or a team member leaves.
Software drift
Libraries and dependencies change, so the same code produces different results or stops running.
Moving inputs
Datasets are updated in place without versioning, so the original input cannot be recovered.
Manual steps
Undocumented manual cleaning or parameter choices cannot be repeated by others.
How pipelines are made reproducible.
- 01
Version control
Tracking code, configuration and, where possible, data versions. Limits: large datasets need specialised tools.
- 02
Containerised environments
Packaging software dependencies so they can be rebuilt. Limits: containers themselves depend on underlying systems that change.
- 03
Workflow systems and provenance
Tools that run pipelines and record each step, studied under data engineering, interoperability and stewardship. Limits: adoption requires effort and discipline.
- 04
Numerical reproducibility
On parallel and accelerated hardware, floating-point results can differ slightly between runs, a matter for computing specialists. Limits: exact bitwise reproducibility is sometimes impractical, so tolerances must be stated.
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
- 03Optimisation and operations researchJob scheduling policies · Carbon-aware workload shifting · Capacity planningApplied 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 the difference between reproducibility and replicability?
What is a persistent identifier?
What is data provenance?
Is reproducibility the same as correctness?
Why can results differ on different hardware?
Does the platform review other teams' pipelines?
Contribute evidence from this page.
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