Kaggle Datasets
Kaggle Datasets were added only a few years back. Currently, there are more than 200,000 datasets available on the platform, contributed by the users. There were, of course, datasets in the past, associated with the competitions. With the new Datasets section, Kagglers can get medals and ranking based on the recognition of other users on the platform, in the form of upvotes for datasets contributed.
Everybody can contribute datasets and the process to add a dataset is quite simple. You first need to identify an interesting subject and a data source. This can be an external dataset that you are mirroring on Kaggle, provided that the right license is in place, or the data is collected by yourself. Datasets can also be authored collectively. There will be a main author, the one that initiates the dataset, but they can add other contributors with view or edit roles. There are a few compulsory steps to define a dataset on Kaggle.
First, you will have to upload one or multiple files and give a name to the dataset. Alternatively, you can set the dataset to be provided from a public link, which should point to a file or a public repository on GitHub. Another way to provision a dataset is from a Kaggle Notebook; in this case, the output of the notebook will be the content of the dataset. The dataset can also be created from a Google Cloud Storage resource. Before creating a dataset, you have the option to set it as public, and you can also check your current private quota. Each Kaggler has a limited private quota (which has been increasing slightly over time; currently, it is over 100 GB). If you decide to keep the dataset private, you will have to fit all your private datasets in this quota. If a dataset is kept private, you can decide at any time to delete it if you do not need it anymore. After the dataset is initialized, you can start improving it by adding additional information.
When creating a dataset, you have the option to add a subtitle, a description (with a minimum number of characters required), and information about each file in the dataset. For tabular datasets, you can also add titles and explanations for each column. Then, you can add tags to make the dataset easier to find through searching and clearly specify the topic, data type, and possible business or research domains, for those interested. You can also change the image associated with the dataset. It is advisable to use a public domain or personal picture. Adding metadata about authors, generating DOI (Digital Object Identifier) citations, and specifying provenance and expected update frequency are all helpful in boosting the visibility of your dataset. It will also improve the likelihood that your contribution will be correctly cited and used in other works. License information is also important, and you can select from a large list of frequently used licenses. With each element added in the description and metadata about the contributed dataset, you also increase the usability score, calculated automatically by Kaggle. It is not always possible to reach a 10/10 usability score (especially when you have a dataset with tens of thousands of files) but it is always preferable to try to improve the information associated with the dataset.
Once you publish your dataset, this will become visible in the Datasets section of the platform, and, depending on the usability and the quality perceived by the content moderators from Kaggle, you might get a special status of Featured dataset. Featured datasets get more visibility in searches and are included in the top section of recommended datasets when you select the Datasets section. Besides the Featured datasets, presented under a Trending datasets lane, you will see lanes with themes like Sport, Health, Software, Food, and Travel, as well as Recently Viewed Datasets.
The datasets can include all kinds of file formats. The most frequently used format is CSV. It is a very popular format outside Kaggle too and it is the best format choice for tabular data. When a file is in CSV format, Kaggle will display it, and you can choose to see the content in detail, by columns, or in a compact form. Other possible data formats used are JSON, SQLite, and archives. Although a ZIP archive is not a data format per se, it has full support on Kaggle and you can directly read the content of the archive, without unpacking it. Datasets also include modality-specific formats, various image formats (JPEG, PNG, and so on), audio signals formats (WAV, OGG, and MP3), and video formats. Domain-specific formats, like DICOM for medical imaging, are widely used. BigQuery, a dataset format specific to Google Cloud, is also used for datasets on Kaggle, and there is full support for accessing the content.
If you contribute to datasets, you can get ranking points and medals as well. The system is based on upvotes by other users, upvotes from yourself or from Novice Kagglers, or old upvotes not being included in the calculation for granting ranking points or medals. You can get to the Datasets Expert tier if you acquire three bronze medals, to Master if you get one gold medal and four silver medals, and to Datasets Grandmaster with five gold medals and five silver medals. Acquiring medals in Datasets is not easy, since upvotes in Datasets are not easily granted by users, and you will need 5 upvotes to get a bronze medal, 20 upvotes for a silver medal, and 50 upvotes for a gold medal. Once you get the medals, as these are based on votes, you can lose your medals over time, and even your status as Expert, Master, or Grandmaster can be lost if the users that upvoted you remove their upvote or if they are banned from the platform. This happens sometimes, and not so infrequently as you might think. So, if you want to secure your position, the best approach is to always create high-quality content; this will bring you more upvotes and medals than the minimum required.
