Feature Proposal: Time to First Result (TTFR) for Dataset Discovery #26
Description
⏱️ Feature Proposal: Time to First Result (TTFR) for Dataset Discovery
Summary
I propose adding a Time to First Result (TTFR) estimate for each dataset returned by the chatbot.
TTFR estimates how long it typically takes a user to go from discovering a dataset to producing a first meaningful result (e.g. a basic visualization, summary statistics, or a first baseline analysis).
This feature does not predict final research outcomes.
It provides a practical planning signal that helps users choose datasets they can realistically work with.
Motivation
Currently, the application excels at discovering relevant datasets, but users still face a common and costly problem:
Downloading datasets that turn out to be too large, too complex, or too time-consuming for their skills or timeline.
This especially affects:
- students and early researchers,
- interdisciplinary users,
- users working under time constraints (coursework, proposals, demos).
Adding TTFR directly addresses this gap by answering:
“How long before I can get something working with this dataset?”
What “Time to First Result” Means (Scope)
Time to First Result (TTFR) is defined as:
The estimated time required for a reasonably competent user to go from dataset access → first useful output.
Examples of “first result”:
- a basic visualization,
- summary statistics,
- one successful pipeline run,
- a reconstructed image or connectivity plot.
TTFR does not mean:
- publication-ready analysis,
- fully optimized models,
- final scientific conclusions.
This keeps expectations realistic and avoids over-promising.
How TTFR Is Estimated (High-Level)
TTFR is calculated as a range, not a single number, by decomposing the workflow into three phases:
1. Access & Setup
- dataset access friction (open vs login vs approval),
- documentation clarity,
- format standardization.
2. Preprocessing
- data modality (e.g. MRI vs microscopy vs simulated),
- multimodal complexity,
- dataset size/resolution (estimated via buckets).
3. First Output
- effort to generate a basic visualization or baseline analysis.
Final output example:
⏱ Time to First Result: ~4–7 days
Breakdown:
• Access & setup: ~1 day
• Preprocessing: ~2–4 days
• First output: ~1–2 days
How Required Signals Are Obtained
Signals are derived in two stages:
Stage 1: Inference from Existing Metadata (MVP)
From the dataset information already shown:
- modality keywords (MRI, PET, MEG, microscopy, simulated),
- number of modalities (single vs multimodal),
- source-level defaults (e.g. OpenNeuro → BIDS, CIL → images),
- documentation proxies (presence of authors, license, description length).
This alone is sufficient for a usable first version.
Stage 2: Optional Source Metadata Enrichment (Future)
Where available:
- dataset size from source APIs/pages,
- file format confirmation (BIDS, NWB, NIfTI),
- access restrictions (open vs approval).
If enrichment fails, the system safely falls back to Stage 1 estimates.
Why This Feature Is Valuable
- Helps users choose feasible datasets early.
- Reduces wasted time and compute.
- Adds decision support without cluttering the UI.
Importantly, TTFR is transparent and explainable:
- ranges instead of exact numbers,
- visible assumptions,
- expandable breakdown per dataset.
UI / UX Considerations
Recommended minimal UI:
-
Show a single line on each dataset card:
⏱ TTFR: 4–7 days -
Expandable details (optional):
- phase breakdown,
- assumptions (e.g. “assumes intermediate familiarity”).
This feature complements the existing dataset discovery flow by adding practical, user-centric guidance with minimal overhead.
I’d be happy to:
- prototype the estimator logic,
- help define metadata normalization rules,
- or contribute an initial implementation if this proposal aligns with the project goals.
Thanks for considering!