In this dataset are 63 .csv files, each a cleaned datasheet of city tree inventories from a single US city. Refer to eLife manuscript for complete information on data acquisition, data cleaning, and preliminary analyses.

Refer to the file Column_Headers_Dryad for information about each column.

METHODS:

Data Acquisition Summary:
We limited our search to the 150 largest cities in the USA (by census population). 
To acquire raw data on street tree communities, we used a search protocol on both Google and Google Datasets Search (https://datasetsearch.research.google.com/). We first searched the city name plus each of the following: street trees, city trees, tree inventory, urban forest, and urban canopy (all combinations totaled 20 searches per city, 10 each in Google and Google Datasets Search). We then read the first page of google results and the top 20 results from Google Datasets Search. If the same named city in the wrong state appeared in the results, we redid the 20 searches adding the state name. If no data were found, we contacted a relevant state official via email or phone with an inquiry about their street tree inventory. Datasheets were received and transformed to .csv format (if they were not already in that format). We received data on street trees from 64 cities. One city, El Paso, had data only in summary format and was therefore excluded from analyses.

Data Cleaning Summary:
First, we assembled and standardized a large dataset of N=5,132,890 city trees to enable the analysis of urban forests’ ecosystem structure. We acquired street tree inventories from 63 of the largest 150 US cities (those which had conducted inventories) and developed a standardization pipeline in R and Python. Each inventory was produced using different, city-specific methods: for example, some cities only reported a tree’s common name; some reported an address but no coordinates; some reported tree size in feet, some in meters; some scored tree health from 1-5 while others rated trees as “good” or “poor;” etc. Very few cities reported a tree’s native status. Therefore, we inspected metadata for all cities (and communicated with urban officials) to standardize column names, standardize metrics of tree health, and convert all units to metric. We converted all common names to scientific and manually corrected misspellings in all species names (see Data S9, and Materials and Methods, for full details). We manually coded all tree locations as being in a green space or in an urban environment to enable comparisons between location types. Finally, we referenced data from the Biota of North America Project on native status to classify each tree as native or not. The resulting dataset comprised 63 city datasheets each with 28 standardized columns.