> For the complete documentation index, see [llms.txt](https://irosyadi.gitbook.io/irosyadi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://irosyadi.gitbook.io/irosyadi/data-engineering/awesome-data-set.md).

# Awesome Data Set

## List of Dataset

* [Tensorflow Open Dataset](https://www.tensorflow.org/datasets/catalog/overview)
* [Lionbridge](https://lionbridge.ai/datasets/)
* [Kaggle](https://www.kaggle.com/datasets)
* [Google Dataset Search](https://datasetsearch.research.google.com/)
* <https://github.com/awesomedata/awesome-public-datasets>
* <https://www.kdnuggets.com/datasets/index.html>
* <https://goldberg.berkeley.edu/jester-data/>
* <https://grouplens.org/>
* <https://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/>
* [https://web.mit.edu/emeyers/www/face\\\_databases.html](https://web.mit.edu/emeyers/www/face%5C_databases.html)
* <https://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/>
* <https://yann.lecun.com/exdb/mnist/>
* <https://www.drivendata.org/>
* <https://www.kaggle.com/datasets>
* <https://archive.ics.uci.edu/ml/index.php>
* <https://research.google.com/youtube8m/>
* <https://cloud.google.com/bigquery/public-data/>
* <https://registry.opendata.aws/>
* <https://github.com/fivethirtyeight/data>
* <https://www.rbi.org.in/Scripts/Statistics.aspx>
* <https://data.worldbank.org/>
* <https://data.gov.in/>
* <https://www.data.gov/>
* [Datasets for Data Mining, Data Science, and Machine Learning](https://www.kdnuggets.com/datasets/index.html)
* [Harvard Dataverse](https://dataverse.harvard.edu/)

## Dataset Tool

* [Datasette](https://datasette.io/) An open-source multi-tool for exploring and publishing data

## Specific Dataset

* [TED Talk Data Set](https://www.kaggle.com/thegupta/ted-talk) [Scraper](https://github.com/The-Gupta/TED-Scraper/blob/master/Scraper.ipynb)
* [MediaPipe Objectron by Google](https://ai.googleblog.com/2020/03/real-time-3d-object-detection-on-mobile.html) The model was designed for real-time 3D object detection for mobile devices. This model was trained on a fully annotated, real-world 3D dataset and could predict objects' 3D bounding boxes. [GitHub](https://github.com/google-research-datasets/Objectron/)
* [Public EEG Datasets](https://github.com/meagmohit/EEG-Datasets)