MERL Studio turns indicator definitions into a single source of truth, flowing into your logframes, data forms, and quality checks automatically. Built for monitoring, evaluation, research, and learning practitioners who deserve better tools.
An indicator usually gets rebuilt at every stage. You define it in planning, retype it into the logframe, build it again in your data tool, then try to reconcile at analysis when the copies don't match.
Here it's defined once, and that definition carries through every stage that follows. Follow one from your theory of change down to analysis: it's the same definition the whole way, never re-entered.
Your theory of change sets out the change you're aiming for and how you expect to get there, so it's where the pipeline starts. Map it the way your methodology works rather than forced into one fixed shape, and pin each assumption to the outcome that rests on it.
Pick the indicators that would actually show whether your programme is working, and define each one properly, with its type, baseline, targets, disaggregation, and the rules that keep it clean. This is the single definition the rest of the pipeline inherits. Nothing downstream needs to have it re-entered.
Same definition, now sitting under its result, in whatever shape your donor wants. Nothing retyped, and performance against the target moves as data comes in. The logframe stops being a document you redo at report time.
This is the handover from planning to doing. The indicator turns into a question with its rules already attached. What you collect can't drift from what you designed, because it's the same definition. Export it to Kobo, ODK or SurveyCTO if that's where you work.
Collect however the work demands; field teams gathering data from participants, or a survey sent to people outside the programme. Either way, the answers land in the workspace against the indicator they belong to, ready to use. No re-keying, no spreadsheets going round by email.
The validation from the indicator runs the moment data arrives, and again in the background. A wrong unit or an impossible number gets flagged then, not six months later mid-analysis. The methodology you committed to in planning is doing the checking.
Because it was defined once and checked on the way in, the data comes out disaggregated and consistent; the cleaning step mostly isn’t there. And it lines back up against the theory of change you started with, so you're not just reporting numbers: you can show the change behind them, and trace your contribution to it.
The pipeline runs between two layers.
At the design end, the Data Framework defines the entity types, attributes, and shared model that everything else in the pipeline references.
At the execution end, the Field Portal is where field teams collect data against participants and activities, filling that shared model with real data from the ground.
The pipeline is the core loop. A programme needs more around it: the evidence behind your design choices, the assumptions and risks you're carrying, and the delivery you schedule in the field.
A citable evidence base built into the workspace. Add a source once by URL, DOI, or BibTeX import, and cite it from an indicator, a logframe outcome, or a theory of change assertion. Six months on, a new team member can see what evidence justified a design decision, instead of it living in someone's memory or a buried Word doc.
Every assumption and risk you record for your theory of change, or against a logframe result, collects in one register. Write it once, reference it in both places, and see the whole set in a single view: what's active, what's high-impact and still unmitigated, what's already materialised. The assumptions column of a logframe stops being dead text you retype each reporting round.
Plan what your programme does in the field and when: sessions, visits, distributions. Schedule them on a recurring cadence, generate the daily tasks they produce, and push those to field workers as a checklist. Completion flows back on its own, with no separate tracking sheet to keep in sync.
First month free on every workspace. Upgrade only when you need to invite others.
For independent practitioners evaluating the platform or running one small project.
For consultants managing multiple engagements and small-to-mid NGOs.