FLOW: Participant Survey Data from Freshwater Citizen Science Project¶
Category: Water Quality · Size: 407 kB · Format: PDF, XLSX License: CC-BY-4.0 · Zenodo record · Data sheet on the CSDH
Participant survey data from the FLOW project (2021) monitoring and protecting freshwater streams, analysing how citizen science fosters knowledge and collective action.
The data is mounted read-only at /srv/data/flow-freshwater/.
Save anything you produce in your personal folder (~/).
What's in the dataset¶
In [1]:
from pathlib import Path
DATA = Path('/srv/data/flow-freshwater')
for f in sorted(DATA.rglob('*')):
if f.is_file():
print(f"{f.relative_to(DATA)} ({f.stat().st_size/1e6:,.1f} MB)")
Data_FLOW_survey.xlsx (0.1 MB) Dateset_Explanation.pdf (0.3 MB)
Load the data¶
The dataset comes as Excel; pandas reads it directly.
In [2]:
import pandas as pd
xlsx = sorted(DATA.rglob('*.xlsx'))
print('Using:', xlsx[0].name)
df = pd.read_excel(xlsx[0])
df.head()
Using: Data_FLOW_survey.xlsx
Out[2]:
| No | ID | group | complete | age | gender | attitude.1 | attitude.2 | attitude.3 | awareness.1 | ... | plan_behavior2.3 | personal_efficacy.1 | personal_efficacy.2 | personal_efficacy.3 | personal_norms.1 | personal_norms.2 | personal_norms.3 | skills.1 | skills.2 | skills.3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1A5KDSL7X1 | Treatment | 2 | 18.0 | male | 3.666667 | 3.500000 | NaN | 4.50 | ... | NaN | 4.0 | 4.0 | NaN | 2.25 | 2.00 | NaN | 3.000000 | 3.500000 | NaN |
| 1 | 2 | 1FZN47ETFG | Treatment | 2 | 16.0 | female | 5.000000 | 4.000000 | NaN | 3.50 | ... | NaN | 4.0 | 4.0 | NaN | 4.00 | 2.75 | NaN | 1.833333 | 4.333333 | NaN |
| 2 | 3 | 1GB6QR7D7W | Treatment | 2 | 17.0 | female | 4.500000 | 4.500000 | NaN | 4.25 | ... | NaN | 4.0 | 5.0 | NaN | 3.50 | 3.75 | NaN | 2.000000 | 3.666667 | NaN |
| 3 | 4 | 1GLGSGANPU | Treatment | 3 | 47.0 | female | 5.000000 | 5.000000 | 4.833333 | 4.75 | ... | 3.5 | 5.0 | 5.0 | 5.0 | 5.00 | 5.00 | 4.75 | 4.833333 | 5.000000 | 5.0 |
| 4 | 5 | 1M6DC33S2D | Treatment | 2 | 19.0 | male | 4.166667 | 4.666667 | NaN | 4.25 | ... | NaN | 3.0 | 5.0 | NaN | 3.50 | 3.25 | NaN | 1.666667 | 3.500000 | NaN |
5 rows × 53 columns
First look¶
Shape, types and basic statistics.
In [3]:
df.info()
df.describe(include='all').T.head(20)
<class 'pandas.core.frame.DataFrame'> RangeIndex: 555 entries, 0 to 554 Data columns (total 53 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 No 555 non-null int64 1 ID 555 non-null object 2 group 555 non-null object 3 complete 555 non-null int64 4 age 546 non-null float64 5 gender 543 non-null object 6 attitude.1 553 non-null float64 7 attitude.2 550 non-null float64 8 attitude.3 81 non-null float64 9 awareness.1 550 non-null float64 10 awareness.2 548 non-null float64 11 awareness.3 81 non-null float64 12 behavioral_control.1 536 non-null float64 13 behavioral_control.2 533 non-null float64 14 behavioral_control.3 81 non-null float64 15 collective_efficacy.1 533 non-null float64 16 collective_efficacy.2 527 non-null float64 17 collective_efficacy.3 81 non-null float64 18 group_id.1 164 non-null float64 19 group_id.2 206 non-null float64 20 group_id.3 81 non-null float64 21 group_norms.1 147 non-null float64 22 group_norms.2 190 non-null float64 23 intrinsic_motivation 213 non-null float64 24 knowledge_score.1 546 non-null float64 25 knowledge_score.2 544 non-null float64 26 knowledge_score.3 80 non-null float64 27 nature_rel.1 555 non-null float64 28 nature_rel.2 555 non-null float64 29 nature_rel.3 81 non-null float64 30 interest.1 547 non-null float64 31 interest.2 545 non-null float64 32 past_behavior1.1 551 non-null float64 33 past_behavior1.2 548 non-null float64 34 past_behavior1.3 81 non-null float64 35 past_behavior2.1 542 non-null float64 36 past_behavior2.2 544 non-null float64 37 past_behavior2.3 81 non-null float64 38 plan_behavior1.1 550 non-null float64 39 plan_behavior1.2 551 non-null float64 40 plan_behavior1.3 81 non-null float64 41 plan_behavior2.1 537 non-null float64 42 plan_behavior2.2 539 non-null float64 43 plan_behavior2.3 81 non-null float64 44 personal_efficacy.1 537 non-null float64 45 personal_efficacy.2 529 non-null float64 46 personal_efficacy.3 81 non-null float64 47 personal_norms.1 548 non-null float64 48 personal_norms.2 545 non-null float64 49 personal_norms.3 81 non-null float64 50 skills.1 547 non-null float64 51 skills.2 545 non-null float64 52 skills.3 81 non-null float64 dtypes: float64(48), int64(2), object(3) memory usage: 229.9+ KB
Out[3]:
| count | unique | top | freq | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| No | 555.0 | NaN | NaN | NaN | 278.0 | 160.358972 | 1.0 | 139.5 | 278.0 | 416.5 | 555.0 |
| ID | 555 | 555 | 1A5KDSL7X1 | 1 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| group | 555 | 3 | Control2 | 250 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| complete | 555.0 | NaN | NaN | NaN | 2.145946 | 0.353371 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 |
| age | 546.0 | NaN | NaN | NaN | 42.175824 | 19.516971 | 13.0 | 25.0 | 41.0 | 59.0 | 84.0 |
| gender | 543 | 2 | female | 281 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| attitude.1 | 553.0 | NaN | NaN | NaN | 4.230199 | 0.636721 | 1.0 | 4.0 | 4.333333 | 4.666667 | 5.0 |
| attitude.2 | 550.0 | NaN | NaN | NaN | 4.254242 | 0.665762 | 1.5 | 4.0 | 4.333333 | 4.833333 | 5.0 |
| attitude.3 | 81.0 | NaN | NaN | NaN | 4.469136 | 0.500077 | 3.0 | 4.0 | 4.666667 | 4.833333 | 5.0 |
| awareness.1 | 550.0 | NaN | NaN | NaN | 4.276364 | 0.614444 | 2.0 | 4.0 | 4.5 | 4.75 | 5.0 |
| awareness.2 | 548.0 | NaN | NaN | NaN | 4.412865 | 0.60181 | 1.75 | 4.0 | 4.5 | 5.0 | 5.0 |
| awareness.3 | 81.0 | NaN | NaN | NaN | 4.657407 | 0.329404 | 3.5 | 4.5 | 4.75 | 5.0 | 5.0 |
| behavioral_control.1 | 536.0 | NaN | NaN | NaN | 3.6875 | 0.87745 | 1.0 | 3.0 | 3.5 | 4.5 | 5.0 |
| behavioral_control.2 | 533.0 | NaN | NaN | NaN | 3.704503 | 0.82911 | 1.0 | 3.0 | 3.5 | 4.5 | 5.0 |
| behavioral_control.3 | 81.0 | NaN | NaN | NaN | 3.679012 | 0.841534 | 1.5 | 3.0 | 3.5 | 4.5 | 5.0 |
| collective_efficacy.1 | 533.0 | NaN | NaN | NaN | 3.953096 | 0.965405 | 1.0 | 3.0 | 4.0 | 5.0 | 5.0 |
| collective_efficacy.2 | 527.0 | NaN | NaN | NaN | 3.99241 | 0.941209 | 1.0 | 3.0 | 4.0 | 5.0 | 5.0 |
| collective_efficacy.3 | 81.0 | NaN | NaN | NaN | 4.148148 | 0.807947 | 2.0 | 4.0 | 4.0 | 5.0 | 5.0 |
| group_id.1 | 164.0 | NaN | NaN | NaN | 3.896341 | 0.950421 | 1.0 | 3.0 | 4.0 | 5.0 | 5.0 |
| group_id.2 | 206.0 | NaN | NaN | NaN | 4.082524 | 0.84852 | 1.0 | 4.0 | 4.0 | 5.0 | 5.0 |
A first chart¶
Histogram of the first numeric column — swap it for the variable you care about.
In [4]:
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.ipynbnotebook). - Questions and results: on the platform forum.
Attribution: data from FLOW: Participant Survey Data from Freshwater Citizen Science Project, license CC-BY-4.0. Notebook from the Citizen Science Data Hub (CSDH) — Fundación Ibercivis.
In [5]:
# ⚠️ 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 -- flow-freshwater.ipynb && echo "Restored. Now: File → Reload Notebook from Disk"