Capacity of datasets from different sources to be combined, compared and reused without manual rework, thanks to shared formats, identifiers, metadata, semantics and access protocols across MELIAF functions. ​

Ability to follow reported figures, findings and claims back through models, analyses and datasets to original sources, enabling verification and strengthening credibility of MELIAF products. ​

Compatibility of mandates, decision rules, accountability arrangements and oversight processes that enables multiple institutions to jointly steward MELIAF data, standards and evaluations. ​

Bringing together data, tools, workflows and governance arrangements so MELIAF activities function as a connected system rather than fragmented, stand‑alone components. ​

Ability of diverse systems, processes, and actors to exchange, interpret, and use data, concepts, and insights so that monitoring, evaluation, learning, impact assessment and foresight activities operate as a coherent whole. ​

Foundational elements—standards, identifiers, schemas, APIs, governance rules and capacities—required to make MELIAF data, processes and tools connect, communicate and evolve together. ​

Relationship between level of interoperability achieved and the cumulative resources required, highlighting how early investment in standards and alignment can reduce marginal costs of connecting additional MELIAF systems. ​

Networked representation linking entities, indicators, documents, ToC elements and outcomes via typed relationships, enabling MELIAF users and algorithms to explore and infer connections. ​

Organization of data and documents into explicit, standardized formats that software can reliably parse, validate and process without manual intervention in MELIAF workflows. ​

Agreed set of fields, formats and vocabularies describing datasets, indicators, models and documents so MELIAF assets are findable, understandable, and reusable by humans and machines. ​

Baseline requirements for articulating ToCs, causal pathways, assumptions and evidence links so that MELIAF activities consistently express and test shared causal logic. ​

Core structure, definitions, disaggregations and metadata required for key performance indicators to ensure comparability, integrity and machine‑readability across MELIAF systems. ​

Formally specified set of concepts, relationships and rules describing a domain, providing a machine‑interpretable backbone for MELIAF data integration, reasoning and advanced queries. ​

Extent to which MELIAF workflows, timelines and decision procedures can interact and synchronize across initiatives, enabling joint planning, shared reviews and consistent reporting with minimal friction. ​

Ability of tools, dashboards, models, reports and evaluation products to plug into each other’s data structures, semantics and interfaces, allowing reuse and composition in MELIAF workflows. ​

Category describing a point along the results chain—such as input, activity, output, outcome or impact—used to classify indicators, data and findings in MELIAF systems.

Formal structural blueprint describing entities, fields, relationships and constraints in a dataset or database, providing a shared template for MELIAF data creation, storage and exchange. ​

Records that follow an agreed schema’s field definitions, types, relationships and constraints, enabling automated validation, integration and analysis across MELIAF systems. ​

Shared conceptual representation that maps data fields, indicators and entities to well‑defined meanings, enabling consistent interpretation and integration across MELIAF tools and datasets. ​

Commonly agreed narrative and diagram of how activities, outputs and outcomes are expected to contribute to long‑term impacts, serving as a reference for MELIAF planning and assessment. ​

Specific, time‑bound value set for an indicator, expressing expected performance and guiding planning, monitoring and accountability within MELIAF. ​

Consistency and non‑overlap of terms, categories and indicator labels so that MELIAF data, ToCs and reports use a shared conceptual vocabulary across scales and disciplines. ​

Explicit mapping between elements of a Theory of Change and projected benefits in quantitative models, ensuring consistency between narrative causal logic and forward‑looking analysis. ​