Bird Identification Accuracy in eBird Citizen Science Data (Argentina)

Category: Ornithology · Size: 2.7 MB · Format: HTML, RAR, XLSX License: CC-BY-4.0 · Zenodo record · Data sheet on the CSDH

Supplementary materials to quantify the accuracy of photographic bird identifications in eBird Argentina, with per-species ranking and network analysis.

The data is mounted read-only at /srv/data/ebird-id-accuracy/. Save anything you produce in your personal folder (~/).

What's in the dataset

from pathlib import Path

DATA = Path('/srv/data/ebird-id-accuracy')

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

Appendix_S1.rar  (1.8 MB)
Appendix_S2.xlsx  (0.0 MB)
Appendix_S3.html  (0.9 MB)

Load the data

The dataset comes as Excel; pandas reads it directly.

import pandas as pd

xlsx = sorted(DATA.rglob('*.xlsx'))
print('Using:', xlsx[0].name)
df = pd.read_excel(xlsx[0])
df.head()

Using: Appendix_S2.xlsx

	rank	quality_group	scientific_name	hard_to_id	samples	true_positives	false_positives	false_negatives	precision	recall	min (precision, recall)
0	1	high-quality	Polioptila dumicola	False	773	773	0	0	100.0	100.0	100.0
1	2	high-quality	Turdus chiguanco	False	458	458	0	0	100.0	100.0	100.0
2	3	high-quality	Amblyramphus holosericeus	False	443	443	0	0	100.0	100.0	100.0
3	4	high-quality	Coryphistera alaudina	False	385	385	0	0	100.0	100.0	100.0
4	5	high-quality	Tachuris rubrigastra	False	287	287	0	0	100.0	100.0	100.0

First look

Shape, types and basic statistics.

df.info()
df.describe(include='all').T.head(20)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 377 entries, 0 to 376
Data columns (total 11 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   rank                     377 non-null    int64  
 1   quality_group            377 non-null    object 
 2   scientific_name          377 non-null    object 
 3   hard_to_id               377 non-null    bool   
 4   samples                  377 non-null    int64  
 5   true_positives           377 non-null    int64  
 6   false_positives          377 non-null    int64  
 7   false_negatives          377 non-null    int64  
 8   precision                377 non-null    float64
 9   recall                   377 non-null    float64
 10  min (precision, recall)  377 non-null    float64
dtypes: bool(1), float64(3), int64(5), object(2)
memory usage: 29.9+ KB

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
rank	377.0	NaN	NaN	NaN	189.0	108.974768	1.0	95.0	189.0	283.0	377.0
quality_group	377	3	high-quality	291	NaN	NaN	NaN	NaN	NaN	NaN	NaN
scientific_name	377	377	Polioptila dumicola	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
hard_to_id	377	2	False	292	NaN	NaN	NaN	NaN	NaN	NaN	NaN
samples	377.0	NaN	NaN	NaN	183.297082	277.64094	6.0	32.0	78.0	210.0	1878.0
true_positives	377.0	NaN	NaN	NaN	180.639257	276.008571	5.0	30.0	75.0	204.0	1874.0
false_positives	377.0	NaN	NaN	NaN	2.657825	5.104457	0.0	0.0	1.0	3.0	46.0
false_negatives	377.0	NaN	NaN	NaN	2.38992	4.489847	0.0	0.0	0.0	3.0	30.0
precision	377.0	NaN	NaN	NaN	97.498183	4.768119	64.285714	97.368421	99.5671	100.0	100.0
recall	377.0	NaN	NaN	NaN	97.976756	3.921286	63.636364	97.619048	100.0	100.0	100.0
min (precision, recall)	377.0	NaN	NaN	NaN	96.567405	5.439289	63.636364	95.614035	98.908297	100.0	100.0

A first chart

Histogram of the first numeric column — swap it for the variable you care about.

import matplotlib.pyplot as plt

num = df.select_dtypes('number')
if num.shape[1]:
    col = num.columns[0]
    num[col].plot.hist(bins=50, figsize=(8, 4), title=col)
    plt.tight_layout()
else:
    print('No direct numeric columns: explore df on your own.')

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 Bird Identification Accuracy in eBird Citizen Science Data (Argentina), license CC-BY-4.0. Notebook from the Citizen Science Data Hub (CSDH) — Fundación Ibercivis.

# ⚠️ 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 -- ebird-id-accuracy.ipynb && echo "Restored. Now: File → Reload Notebook from Disk"