I've compiled some resources for a blog post, I'll just post the relevant content here:
This one is from the city of Zürich, Switzerland, where I live. I've seen a recent Twitter post about this dataset, so that may have planted the idea that dog names can be open data.
Data goes back to 2015, and each year is one CSV file. To get an idea of the dataset size, I choose the complete year of 2019. 7647 records. It may be hard to find trends in so few dog registrations. Additionally, the Paw Patrol trend is slowly making it here to Switzerland. Since it started in North America, I'll go to look there.
Only 16k total names between 2017 and 2019. That's not enough dogs when there are so many possible names. And starting in 2017, I may not get a good before snapshot.
A list of active/current Seattle pet licenses, including animal type (species), pet's name, breed and the owner's ZIP code.
This might be a good dataset because records go back to 2000 and are updated through 2019. I can get snapshots before and during the PAW Patrol era. But I counted dogs registered in 2019 and it was 11k. In 2018, 7k. Still not enough.
This could be it. Recently updated. 24.1 MB CSV file. 345k total rows going back more than 10 years. 79k dog registrations in 2019. Explore the data here.
the fine print:
Each record stands as a unique license period for the dog over the course of the yearlong time frame.
What does this mean for my data? It means that dog names are assigned at least once per year. If I count unique dog names over multiple years, I'll be over counting.
Each record represents a unique dog license that was active during the year, but not necessarily a unique record per dog, since a license that is renewed during the year results in a separate record of an active license period.
This means that dog-names within a given year may actually be duplicate as well. If this was a real project, in order to fully trust my data, I would first count how many names are repeated. To do this, because there is no column
dog ID which would uniquely identify a dog, I would have to create a surrogate key based on the columns such as
BreedName, and perhaps also the geographical data