RRepoGEO

REPOGEO REPORT · LITE

iphysresearch/DataSciComp

Default branch master · commit f9bbbdca · scanned 6/24/2026, 12:47:43 PM

GitHub: 1,676 stars · 299 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface iphysresearch/DataSciComp, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify archived status and redirect in README's opening

    Why:

    CURRENT
    This Repo was permanently **archived** in 2019/10/01. **BUT this is not the end！**<br>With almost 3 months together in joint development, the project has been reborn once more! <br>We convert our website from static to dynamic such that everyone can submit a challenge/competition on their own interests. And more functions are coming. Please look forward to it! Click <a href="https://www.datascicamp.com">HERE</a> for our new website! You can also move to <a href="https://github.com/datascicamp/DataSciCamp">Welcome page</a> for more infomations.
    COPY-PASTE FIX
    This repository, `iphysresearch/DataSciComp`, was permanently **archived** on 2019/10/01. The project has since been reborn and is actively maintained at a new location. For the active project, including a dynamic collection of data science challenges and competition deadlines, please visit our new website: [https://www.datascicamp.com](https://www.datascicamp.com) or the new GitHub repository: [https://github.com/datascicamp/DataSciCamp](https://github.com/datascicamp/DataSciCamp).
  • highabout#2
    Update repository description to reflect archived status and new location

    Why:

    CURRENT
    A collection of popular Data Science Challenges/Competitions || Countdown timers to keep track of the entry deadlines.
    COPY-PASTE FIX
    [ARCHIVED] This repository (iphysresearch/DataSciComp) was archived on 2019/10/01. The active project, a dynamic collection of data science challenges and deadlines, has moved to https://github.com/datascicamp/DataSciCamp and https://www.datascicamp.com.
  • mediumtopics#3
    Add topics to clarify archived status and content type

    Why:

    CURRENT
    challenge, competition, data-challenge, data-science, data-science-competitions, project
    COPY-PASTE FIX
    archived-project, data-science-challenges-list, competition-deadlines, data-science-competitions, project-redirect, legacy-repository

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface iphysresearch/DataSciComp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaggle
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle · recommended 2×
  2. DrivenData · recommended 2×
  3. Zindi · recommended 2×
  4. Analytics Vidhya · recommended 2×
  5. Devpost · recommended 2×
  • CATEGORY QUERY
    Where can I find a curated list of upcoming data science challenges and deadlines?
    you: not recommended
    AI recommended (in order):
    1. Kaggle
    2. DrivenData
    3. Zindi
    4. Analytics Vidhya
    5. Topcoder
    6. Devpost

    AI recommended 6 alternatives but never named iphysresearch/DataSciComp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help me track entry deadlines for various data science competitions?
    you: not recommended
    AI recommended (in order):
    1. Kaggle
    2. DrivenData
    3. Zindi
    4. Analytics Vidhya
    5. Devpost
    6. AIcrowd

    AI recommended 6 alternatives but never named iphysresearch/DataSciComp. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of iphysresearch/DataSciComp?
    pass
    AI did not name iphysresearch/DataSciComp — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts iphysresearch/DataSciComp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named iphysresearch/DataSciComp explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo iphysresearch/DataSciComp solve, and who is the primary audience?
    pass
    AI did not name iphysresearch/DataSciComp — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)
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iphysresearch/DataSciComp — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite