YouTube datasets for data and AI teams

The problem#

Collecting YouTube data at scale is slow and fragile, and most available datasets are a one-off scrape: no history, no statement of coverage, and no way to tell a re-count from a new record. Training and research work needs to know exactly what is in the data and what is missing.

What Viewershift answers#

We hold channel and video records collected continuously, with repeated view-count and subscriber readings, caption and transcript tracks in many languages, audio-track and dubbing data, chapters, tags and viewing heatmaps. The dataset catalog lists each dataset with its fields, its coverage stated plainly, and how it can be delivered. Samples are prepared by hand for each request, so they match what you plan to use.