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App Rank Anchors
Community-federated public app-store calibration anchors for the AppScope open app-intelligence stack.
Each row is a public fact — a segment + rank + observed download flow —
derived from the public Google Play realInstalls delta over a time window
paired with an app's chart rank in that window. Pooling these anchors across
self-hosting contributors lets the Garg–Telang download estimator calibrate
absolute scale (scale_b) per (platform, category, country) segment, and
graduates estimates from LOW to MEDIUM confidence as coverage grows.
Row schema
| field | type | meaning |
|---|---|---|
platform |
str | ios or android |
category |
str | store category (or all) |
country |
str | ISO country code |
list_type |
str | top-free / top-paid / top-grossing |
rank |
int | chart rank (1-based) |
observed_downloads |
int | observed download flow over the window |
window_days |
int | length of the observation window |
min_installs |
int? | public Play install bucket (Android) |
real_installs |
int? | public Play cumulative installs (Android) |
price_usd |
float | app price (0 for free) |
is_free |
int | 1 if free |
rating_count |
int? | public rating count |
captured_on |
date | capture date |
What is NOT here (by design)
No app identity (app_id is intentionally omitted), no personal data, no ad
data, and no creator data. AppScope's contribution tool whitelists rows to the
schema above and aborts (assert_public_only) if any ad/creator/identity field
appears. See the project's DATA_POLICY.md.
License
Released under CC-BY-4.0. Estimates derived from this data are modeled, not measured; no accuracy is warranted.
Contributing
Self-host AppScope, then run python -m appscope.federation.contribute --contributor <you> (requires an HF_TOKEN). Pull everyone's anchors back with
python -m appscope.federation.refresh_dataset.
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