Crowdsourced Vegetation Monitoring: Global Plant Trait Mapping¶

Category: Botany · Size: 8.4 GB · Format: ZIP License: CC-BY-4.0 · Zenodo record · Data sheet on the CSDH

Source data and 1 km-resolution plant trait maps, integrating citizen science (GBIF) with professional data (sPlot) to map plant functional traits globally.

The data is mounted read-only at /srv/data/global-plant-traits/. Save anything you produce in your personal folder (~/).

⚠️ Large dataset (8.4 GB). Your session has 4 GB RAM and your home folder is shared — don't extract the whole archive. Read the entries you need straight from inside the ZIP (see below); if you must extract, take only specific files, not everything.

What's in the dataset¶

In [1]:
from pathlib import Path

DATA = Path('/srv/data/global-plant-traits')

for f in sorted(DATA.rglob('*')):
    if f.is_file():
        print(f"{f.relative_to(DATA)}  ({f.stat().st_size/1e6:,.1f} MB)")

SCI_CIT_sparse_maps_1km.zip  (7,660.3 MB)
SourceData.zip  (725.4 MB)

Explore the ZIP¶

The dataset comes compressed. We list its contents without extracting; if it contains CSVs, pandas can read them straight from inside the ZIP. Remember: /srv/data is read-only — if you need to extract, do it into your folder (~/).

In [2]:
import zipfile
import pandas as pd

zips = sorted(DATA.rglob('*.zip'))
z = zipfile.ZipFile(zips[0])
print('Using:', zips[0].name)
names = z.namelist()
print(f'{len(names)} files inside; first 20:')
for n in names[:20]:
    print('  ', n)

csv_inside = [n for n in names if n.lower().endswith('.csv')]
if csv_inside:
    df = pd.read_csv(z.open(csv_inside[0]), nrows=100_000, low_memory=False)
    display(df.head())

Using: SCI_CIT_sparse_maps_1km.zip
74 files inside; first 20:
   gbif/X1080.tif
   gbif/X13.tif
   gbif/X138.tif
   gbif/X14.tif
   gbif/X144.tif
   gbif/X145.tif
   gbif/X146.tif
   gbif/X15.tif
   gbif/X163.tif
   gbif/X169.tif
   gbif/X21.tif
   gbif/X223.tif
   gbif/X224.tif
   gbif/X237.tif
   gbif/X26.tif
   gbif/X27.tif
   gbif/X281.tif
   gbif/X282.tif
   gbif/X289.tif
   gbif/X297.tif

Your turn¶

This is just the starting point. Some ideas:

  • Check the dataset challenge on its CSDH data sheet.
  • Work on a copy: right-click the file → Duplicate (or Save Notebook As…). Your changes only live in your Hub space — they're never pushed to GitHub.
  • Edited this notebook and want the original back? Use the Restore cell below (or the restore.ipynb notebook).
  • Questions and results: on the platform forum.

Attribution: data from Crowdsourced Vegetation Monitoring: Global Plant Trait Mapping, license CC-BY-4.0. Notebook from the Citizen Science Data Hub (CSDH) — Fundación Ibercivis.

In [3]:
# ⚠️ RESTORE: this DISCARDS YOUR CHANGES to this notebook and resets it to the original.
# 1. Uncomment the line below (remove the #)   2. Run this cell
# 3. Then: menu File → Reload Notebook from Disk

# !git -C ~/citizen-science-data fetch -q origin && git -C ~/citizen-science-data checkout origin/main -- global-plant-traits.ipynb && echo "Restored. Now: File → Reload Notebook from Disk"