hasaneyldrm/exercises-dataset

1,324-exercise fitness dataset — animation GIFs, 180×180 thumbnails, muscle-group & equipment data, and step-by-step instructions in 6 languages. The exercise data layer behind the LogPress app.

63
Hotness score
36
Reliability score
16,205
Stars
4 months
Age
0
Published reviews
0
Questions

Scores

Hotness and reliability

The two headline scores, measured daily, with 30, 90, and 180 day views.

Hotness score

Jul 19, 2026

Current63

Previous60

Weekly average

100500
Full metrics details

Derived only from GitHub GraphQL starredAt events after daily star totals are reconciled.

125 observed daily rows. Missing days are not fabricated.

Hotness formula

40% Hot today + 40% Hot this week + 20% Breakout.

  • 40% Hot today: 53
  • 40% Hot this week: 72
  • 20% Breakout: 23
  • Stars gained: 1d: 189
  • Stars gained: 7d: 3619
  • Stars gained: 14d: 6047
  • Stars gained: 30d: 15950
  • Stars gained: 90d: 16205

Per-day formula: 0.40 × Hot today + 0.40 × Hot week + 0.20 × Breakout.

  • Same-day multiplier = stars 1d ÷ max(1, stars 7d ÷ 7)
  • Weekly multiplier = stars 7d ÷ max(1, stars 30d ÷ 30 × 7)
  • Fortnight multiplier = stars 14d ÷ max(1, stars 90d ÷ 90 × 14)
  • Hot today = clamp(70 × log-scale(stars 1d, 1000) + 30 × breakout-scale(same-day, 4))
  • Hot week = clamp(75 × log-scale(stars 7d, 5000) + 25 × breakout-scale(weekly, 3))
  • Breakout = clamp(35 × breakout-scale(same-day, 4) + 40 × breakout-scale(weekly, 3) + 25 × breakout-scale(fortnight, 2.5))
DateStarsStars 1dStars 7dStars 14dStars 30dStars 90dSame-day multiplierWeekly multiplierFortnight multiplierHot todayHot weekBreakoutStored score

Reliability score

Jul 19, 2026

Current36

Previous35

Weekly average

100500
Full metrics details

Derived from the displayed continuity, closure, shipping, liveness, and support-burden components.

125 observed daily rows. Missing days are not fabricated.

Reliability formula

30% Continuity + 30% Closure + 20% Shipping + 10% Liveness + 10% Support burden.

  • 30% Continuity: 34
  • 30% Closure: 20
  • 20% Shipping: 0
  • 10% Liveness: 98
  • 10% Support burden: 98

Per-day formula: 0.30 × Continuity + 0.30 × Closure + 0.20 × Shipping + 0.10 × Liveness + 0.10 × Support burden.

  • Continuity = 35% active ratio 90d + 20% active ratio 30d + 15% PR efficiency 90d + 10% PR efficiency 30d + 10% liveness + 10% observed-history coverage
  • Closure = 55% PR merge efficiency 90d + 45% issue close efficiency 90d
  • Shipping = 100 × (20% × release ratio 30d + 30% × release ratio 90d + 50% × release ratio 180d); ratios are releases ÷ 2, 6, and 12, capped at 1
  • Support burden = 100 − 2 × open issues per 1,000 stars
  • Liveness = 100 × exp(−pushed days ago ÷ 120)

Stars, issues, pull requests, and releases are daily observations. Pushed-days and license are repository snapshot-derived inputs and are not independent historical GitHub events.

DateIssues openedIssues closedPRs openedPRs mergedReleasesStarsOpen issuesPushed days agoContinuityClosureShippingLivenessSupport burdenLicenseObserved daysMissing daysNeeds healingStored score

Reliability breakdown

What drives the reliability score

Component scores measured daily from GitHub activity (2026-07-19).

Continuity30% of headline34

Activity on 17 of 90 tracked days in the last 90 and 14 of 30 in the last 30.

Closure30% of headline20

Merged 9 of 46 PRs opened and closed 3 of 15 issues opened over the last 90 tracked days — PR flow carries 55% of this component, issue flow 45%.

Shipping20% of headline0

0 releases in the last 180 days, 0 in the last 90 and 0 in the last 30 — a steady cadence scores highest.

Liveness10% of headline98

Last push 4 days ago — freshness decays as pushes age (roughly halves every 83 days without a push).

Support burden10% of headline98

13 open issues against 16,205 stars — about 1 open issues per 1,000 stars. Lighter backlogs score higher.

Adoption confidence50

Modeled — how confidently teams are adopting this repo. Stargazers

Maintenance quality36

Modeled from PR/issue responsiveness and upkeep signals. Activity pulse

Risk score39

Modeled — lower is better; adoption and continuity risk. Repository

Stays active (30d)38%

Modeled chance the repo stays active over 30 days. Activity pulse

Stays active (90d)43%

Modeled chance the repo stays active over 90 days. Activity pulse

Release rhythm (180d)0

Regularity of releases over the last 180 days. Releases

Maintainer bus risk (90d)65%

Modeled — lower is better; concentration of commits in few maintainers. Contributors graph

Measured history

Project metrics

Weekly GitHub totals with 30, 90, and 180 day views.

Issues opened

Jul 19, 2026

Current6

Previous1

Weekly total

630
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

124 observed daily rows. Missing days are not fabricated.

DateValue

Issues closed

Jul 19, 2026

Current0

Previous2

Weekly total

210
Full metrics details

GitHub Search issue totals queried for exact UTC days; rolling values are sums of proven daily counts.

124 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests opened

Jul 19, 2026

Current12

Previous12

Weekly total

19100
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

124 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests closed

Jul 19, 2026

Current4

Previous17

Weekly total

1790
Full metrics details

GitHub Search pull-request totals queried for exact UTC days; rolling values are sums of proven daily counts.

124 observed daily rows. Missing days are not fabricated.

DateValue

Pull requests merged

Jul 19, 2026

Current3

Previous2

Weekly total

320
Full metrics details

GitHub API observation. Historical values render only when the source provides a real observation for that day.

124 observed daily rows. Missing days are not fabricated.

DateValue

Issue close ratio (daily)

Jul 17, 2026

Current0.00

Previous0.00

Daily ratio — a gap means nothing was opened that day

How this works: each day's value is issues closed that day ÷ issues opened that day. Days when nothing was opened render as gaps — never a rolling average or a made-up zero. Values above 1 mean the project closed more issues than arrived that day.

2.001.000.00
Full metrics details

GitHub Search issue totals queried for exact UTC days; rolling values are sums of proven daily counts.

10 observed daily rows. Missing days are not fabricated.

Per-day formula: issues closed that day ÷ issues opened that day; blank when nothing was opened (a gap, not a zero).

DateIssues openedIssues closedStored score

Pull request close ratio (daily)

Jul 17, 2026

Current0.00

Previous1.50

Daily ratio — a gap means nothing was opened that day

How this works: each day's value is pull requests closed that day ÷ pull requests opened that day. Days when nothing was opened render as gaps — never a rolling average or a made-up zero. Values above 1 mean the project closed more pull requests than arrived that day.

1.500.750.00
Full metrics details

GitHub Search pull-request totals queried for exact UTC days; rolling values are sums of proven daily counts.

13 observed daily rows. Missing days are not fabricated.

Per-day formula: pull requests closed that day ÷ pull requests opened that day; blank when nothing was opened (a gap, not a zero).

DatePRs openedPRs closedStored score

Join the conversation

Reviews · Questions · Posts

Share what you know about exercises-dataset — write a review from your real experience, ask an implementation question, or publish a post about how you use it.

Share your experience

Write or update your review

Explain what worked, what broke down, and what another team should know before adopting exercises-dataset.

Write your review now — it saves locally and publishes automatically after you sign in.

Rating
Positive attributes
Negative attributes

Project Q&A

Questions and answers

Browse implementation threads tied directly to hasaneyldrm/exercises-dataset. Each question links through to the full answer page.

Q&A No threads yet

Be the first to ask how teams run exercises-dataset in production. Every question you post becomes a durable, searchable answer page other developers can find.

Ask the first question

Related posts

Posts tagged with the same topics

These posts come from the same topic surface as this repo, so readers can move from project evaluation into practical writeups and migration notes without leaving context.

Posts No topic-linked posts yet

Share how your team uses exercises-dataset — a migration note, an architecture writeup, or a comparison. Your post reaches everyone browsing these same topics.

Write the first post
Get a weekly email with the hottest new projects in the Training Datasets and Multimodal Datasets world.
No Spam. Unsubscribe easily at any time.

Copyright 2018-2026 Awesome Open Source.  All rights reserved.