Making Kaggle the Home of #Open_Data | No Free Hunch
▻http://blog.kaggle.com/2016/08/17/making-kaggle-the-home-of-open-data
As a scientist, you can publish the data and code from your latest experiment. Doing so enables other scientists in your field to reproduce the results in your paper and build on top of them. It will allow others to more deeply engage with your work and give it a wider audience.
As a hobbyist, you can publish data you’re passionate about on Kaggle and grow a community around the dataset that shares the same interest.
As a package author, you can release a dataset and code that showcases your package with executable documentation examples. Data scientists find it faster and easier to learn from examples instead of extensive API documentation.
As a student, you can use Kaggle to create your class projects. This saves you from needing to setup a local analytics environment, and starts building your data science portfolio. We recently revamped Kaggle profiles and the progression system to emphasize code and discussion, making them even more helpful for data science hiring managers.
As a data vendor, you can release a sample of your dataset. This is the best way to broadcast the potential of your data to the world’s largest community of data scientists.
As a company or nonprofit, you can publish data that you want our community to explore. At Kaggle, we release most of the publicly scrapable data on the site in an easily digestible form. We learned a lot about our own business from the kernels our community has created.
As a government, you can release the data your agencies collect on Kaggle. Rather than launching your datasets into an empty room, you can release them into a vibrant ecosystem and see the kind of insights the Kaggle community finds in on your data.