REPOGEO REPORT · LITE
jonathanwvd/awesome-industrial-datasets
Default branch master · commit 9277f770 · scanned 6/26/2026, 2:33:19 PM
GitHub: 503 stars · 68 forks
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 jonathanwvd/awesome-industrial-datasets, 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.
- highhomepage#1Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIXhttps://jonathanwvd.github.io/awesome-industrial-datasets/
- highreadme#2Reposition the README's opening paragraph to emphasize 'curated list' nature
Why:
CURRENTWelcome to the Awesome Industrial Datasets repository! This project aims to simplify the access to high-quality industrial datasets across various sectors such as chemical, mechanical, oil and gas, and more.
COPY-PASTE FIXWelcome to the **Awesome Industrial Datasets** repository, a meticulously curated collection of public industrial datasets. This project aims to simplify access to high-quality datasets across various sectors such as chemical, mechanical, oil and gas, and more, serving as a central hub for researchers, engineers, and data scientists.
- mediumlicense#3Clarify the repository's license directly in the README
Why:
COPY-PASTE FIX## License This project is licensed under [describe the actual license(s) found in your LICENSE file, e.g., 'a custom license combining MIT and Apache-2.0 terms']. Please refer to the `LICENSE` file for full details.
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.
- UCI Machine Learning Repository · recommended 2×
- Kaggle Datasets · recommended 1×
- Google Dataset Search · recommended 1×
- AWS Open Data Registry · recommended 1×
- Microsoft Azure Open Datasets · recommended 1×
- CATEGORY QUERYWhere can I find diverse real-world industrial datasets for machine learning model training?you: not recommendedAI recommended (in order):
- Kaggle Datasets
- UCI Machine Learning Repository
- Google Dataset Search
- AWS Open Data Registry
- Microsoft Azure Open Datasets
- Data.gov
AI recommended 6 alternatives but never named jonathanwvd/awesome-industrial-datasets. This is the gap to close.
Show full AI answer
- CATEGORY QUERYLooking for public time-series datasets from manufacturing or oil and gas for research.you: not recommendedAI recommended (in order):
- GE Predictix
- PTC ThingWorx
- Kaggle
- UCI Machine Learning Repository
- SECOM Manufacturing
- Condition Monitoring of Hydraulic Systems
- Gas Sensor Array Drift
- Predictive Maintenance Dataset
- Turbine Data
- NREL (National Renewable Energy Laboratory)
- OpenEI
- Mendeley Data
- Figshare
- Open-O&G (Open Oil & Gas Data Initiative)
- Paderborn University Bearing Dataset
AI recommended 15 alternatives but never named jonathanwvd/awesome-industrial-datasets. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 jonathanwvd/awesome-industrial-datasets?passAI did not name jonathanwvd/awesome-industrial-datasets — 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 jonathanwvd/awesome-industrial-datasets in production, what risks or prerequisites should they evaluate first?passAI named jonathanwvd/awesome-industrial-datasets 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 jonathanwvd/awesome-industrial-datasets solve, and who is the primary audience?passAI did not name jonathanwvd/awesome-industrial-datasets — 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?
Embed your GEO score
Drop this badge into the README of jonathanwvd/awesome-industrial-datasets. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/jonathanwvd/awesome-industrial-datasets)<a href="https://repogeo.com/en/r/jonathanwvd/awesome-industrial-datasets"><img src="https://repogeo.com/badge/jonathanwvd/awesome-industrial-datasets.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
jonathanwvd/awesome-industrial-datasets — 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