Sources
Original records, publications, feeds, documents or observations from which evidence begins.
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Morganable Data Lab Public Evidence Infrastructure
Africa Governed Data Systems
Evidence needs infrastructure.
Morganable Data Lab develops governed datasets, reproducible methods and evidence infrastructure designed to strengthen journalism, research, monitoring and intelligence across Africa.
01 Source
02 Provenance
03 Schema
04 Validation
05 Version
06 Evidence
01 The Data Mandate
A number without provenance is not enough.
Evidence is only as dependable as the systems beneath it.
Morganable Data Lab exists to make structured evidence more reliable, intelligible and reusable. Its work begins before a chart is drawn or an indicator is published.
Sources must be identified. Definitions must be explicit. Transformations must be documented. Validation must occur. Versions must remain distinguishable. Where appropriate, methods should be reproducible by another competent person.
The objective is not simply to accumulate data. It is to create evidence systems that journalism, research, monitoring and intelligence can use without losing sight of where the underlying information came from or how it changed.
01 Acquire
Identify and obtain relevant source material through appropriate and documented channels.
02 Structure
Convert heterogeneous source material into governed, intelligible and comparable data structures.
03 Validate
Test data against defined rules before treating it as institutional evidence.
04 Document
Preserve definitions, source context, assumptions, transformations and known limitations.
05 Version
Preserve historical states so later revisions do not silently erase what existed before.
06 Reproduce
Structure methods so important outputs can be tested, reconstructed and challenged where practicable.
Evidence Pipeline
From raw source to institutional evidence.
Data should not move invisibly from acquisition to publication. Each important transition should have a defined purpose, documented assumptions and an appropriate control.
01 Source Discovery
Identify authoritative, relevant or otherwise necessary sources capable of supporting the intended use.
CONTROL QUESTION:
Where does the evidence originate?
Where does the evidence originate?
02 Acquisition
Obtain source material through documented channels while respecting applicable rights, access conditions and restrictions.
CONTROL QUESTION:
Can it be reliably and appropriately used?
Can it be reliably and appropriately used?
03 Provenance
Preserve source identity, acquisition context and evidentiary ancestry required for later scrutiny.
CONTROL QUESTION:
Can we trace where this came from?
Can we trace where this came from?
04 Schema
Map information into defined structures so that fields, relationships, units and concepts remain intelligible.
CONTROL QUESTION:
What exactly does each field mean?
What exactly does each field mean?
05 Validation
Apply quality rules, consistency checks and defined controls before accepting data into downstream evidence systems.
CONTROL QUESTION:
Has this passed the required checks?
Has this passed the required checks?
06 Transformation
Clean, derive, aggregate or otherwise transform data through methods that remain documented and reviewable.
CONTROL QUESTION:
What changed between source and output?
What changed between source and output?
07 Versioning
Preserve identifiable dataset states so revisions can be distinguished from evidence available at an earlier time.
CONTROL QUESTION:
Which version produced this result?
Which version produced this result?
08 Institutional Use
Release appropriate evidence into journalism, research, monitoring, intelligence or public data products.
CONTROL QUESTION:
Is this evidence fit for its intended use?
Is this evidence fit for its intended use?
Data Architecture
More than datasets.
Durable evidence depends on a connected architecture of sources, structures, transformations, metadata and historical states.
Schemas
Defined structures governing fields, units, relationships and machine-readable meaning.
Datasets
Governed analytical objects prepared for defined research, monitoring or public-interest purposes.
Pipelines
Repeatable processes for acquiring, cleaning, transforming and validating structured evidence.
Metadata
Definitions, units, dates, scope, assumptions and context necessary to interpret data responsibly.
Provenance
The evidence trail connecting outputs to underlying sources and transformations.
Versions
Preserved historical states enabling revisions and changes to be traced over time.
Outputs
Public or institutional evidence products suitable for journalism, research, monitoring or intelligence.
Quality Gates
Data does not become evidence merely because it exists.
Important datasets and outputs should satisfy defined controls appropriate to their source, intended use, methodology and publication risk.
Source Gate
Is the source sufficiently identifiable, credible and appropriate for the intended evidentiary use?
Schema Gate
Are fields, concepts, units and relationships structurally defined well enough to prevent semantic ambiguity?
Validation Gate
Has the data satisfied relevant integrity, consistency, range and transformation checks?
Provenance Gate
Can a reviewer understand where the evidence came from and what happened to it?
Replication Gate
Where appropriate, can another competent person reconstruct the important analytical result?
Release Gate
Is the evidence suitable for the audience, claim and use for which it is about to be released?
Research + Data
Research asks the question. Data engineering makes the evidence dependable.
Morganable Research Institute and Morganable Data Lab are closely connected but institutionally distinct. Research defines the substantive enquiry and interpretation; the Data Lab governs structured evidence where data-intensive work is involved.
Morganable Research Institute
Question. Concept. Method. Interpretation.
MRI determines what is being investigated, why it matters, what conceptual framework applies and how findings should be interpreted.
- Research questions
- Conceptual design
- Methodological strategy
- Substantive interpretation
Evidence Junction Shared
Evidence
Environment Research ↔ Data
Evidence
Environment Research ↔ Data
Morganable Data Lab
Sources. Structure. Validation. Reproducibility.
The Data Lab establishes governed data systems required to support reliable quantitative or structured evidence.
- Source provenance
- Dataset engineering
- Validation controls
- Versioned evidence
Institutional Infrastructure
One evidence capability. Multiple public-interest uses.
The Data Lab can support different Morganable capabilities without collapsing their distinct institutional functions.
Common Evidence Infrastructure Morganable Data Lab
Newspaper
Verification, historical comparison, data investigations and evidence-led journalism.
Research Institute
Structured datasets, reproducible methods and comparative evidence for sustained research.
Intelligence
Governed evidence inputs for monitoring, assessment and decision-relevant analysis.
Observatories
Repeatable measurement, stable definitions, vintages and longitudinal evidence systems.
Ask Morganable
Structured evidence capable of supporting transparent, evidence-led public knowledge access.
Dataset Stewardship
Datasets have lifecycles.
Institutional evidence should evolve through identifiable states rather than being silently overwritten whenever new information becomes available.
01 Created
02 Validated
03 Versioned
04 Updated
05 Superseded
06 Preserved
The public page describes the stewardship principle without exposing security-sensitive internal registry, access-control, datastore or backup architecture.
Open Evidence
Maximum appropriate transparency.
Reproducibility and public scrutiny are strengthened when evidence, methods and documentation can be examined.
Transparency must nevertheless respect legitimate rights, licences, privacy obligations and source restrictions.
Reproducibility Principle
Open where appropriate. Transparent wherever practicable.
Depending on rights, sensitivity and methodological needs, Morganable may make different parts of an evidence package publicly available.
Public datasets
Derived data
Schemas
Methodology
Code
Documentation
Validation outputs
Replication materials
Where licensing, confidentiality or legitimate restrictions prevent redistribution, Morganable should still describe methods and evidentiary limitations as transparently as practicable.
Explore
Enter the evidence environment.
01
Data
Public datasets, indicators and data-led evidence available through Morganable's data discovery environment.
Explore Data →
02Research
Studies and research outputs drawing on structured evidence, methods and comparative analysis.
Explore Research →
03Observatories
Continuous monitoring systems built around repeatable measurement and preserved evidence over time.
Explore Observatories →
04
Engineering Evidence
Public technical documentation, schemas and reproducibility materials can be surfaced here as the engineering evidence environment matures.
Public engineering gateway — developing
Morganable Data Lab
From source to evidence. Built to survive scrutiny.
Provenance · Validation · Versioning · Reproducibility · Africa
Morganable
Independent Digital-First Newspaper
Morganable is an independent digital-first newspaper owned by Morganable Media Group, publishing journalism across news, business, entrepreneurship, spotlights, entertainment, sports, lifestyle and opinion for readers in Nigeria, Africa and the wider world.
