MARCSI: Inventory of Marine Citizen Science Initiatives (Global)¶
Category: Marine Biodiversity · Size: 1.4 MB · Format: CSV, JSON License: CC-BY-SA-4.0 (ShareAlike: derivative notebooks CC-BY-SA-4.0) · Zenodo record · Data sheet on the CSDH
Global inventory of marine citizen science initiatives, documenting past and present projects worldwide and assessing the FAIR compliance of the data they produce.
The data is mounted read-only at /srv/data/marcsi-marine/.
Save anything you produce in your personal folder (~/).
What's in the dataset¶
In [1]:
from pathlib import Path
DATA = Path('/srv/data/marcsi-marine')
for f in sorted(DATA.rglob('*')):
if f.is_file():
print(f"{f.relative_to(DATA)} ({f.stat().st_size/1e6:,.1f} MB)")
MARCSI database_final.csv (1.4 MB) MARCSI database_final_csvw.json (0.0 MB)
Load the data¶
The dataset comes as CSV. In the real world CSV files aren't always uniform (separator , or ;, UTF-8 or Latin-1 encoding), so we use a loader that detects it. We limit to 100,000 rows to explore quickly — drop nrows when you want to work with everything.
In [2]:
import pandas as pd
csvs = sorted(DATA.rglob('*.csv')) + sorted(DATA.rglob('*.csv.gz')) + sorted(DATA.rglob('*.gz'))
print('Using:', csvs[0].name)
def load_csv(path, **kw):
"""Robust reader: detects the separator and tries utf-8 then latin-1."""
for enc in ('utf-8', 'latin-1'):
try:
return pd.read_csv(path, sep=None, engine='python', encoding=enc, **kw)
except UnicodeDecodeError:
continue
df = load_csv(csvs[0], nrows=100_000)
df.head()
Using: MARCSI database_final.csv
Out[2]:
| Id | Marine citizen science initiative title | Description / initiative summary | Initiative topic / keywords | Scientific topic (1) | Scientific topic (2) | Scientific topic (3) | Start date | End date | Aim / intention / purpose of citizen science initiative / project | ... | Type of data collected | Quantity of data collected | Indicators used | Open Access | Findable | Accessible | Interoperable | Reusable | Distribution | License | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Tangaroa Blue Foundation Australian Marine D... | Australian national project to quantify and re... | Marine Debris, Partnerships | Ecology (e.g. coastal ecology, the state of ce... | NaN | NaN | 2004 | Present | Tangaroa Blue Foundation is an Australian-wide... | ... | Marine Debris (plastic, glass, ceramic & const... | Not found | See Marine Debris app | Yes (raw data is available) | No | Yes | No | No | https://www.tangaroablue.org/database/ | NaN |
| 1 | 2 | Litter Intelligence | Litter Intelligence is Aotearoas first and on... | Pollution, plastic, marine litter | Pollution (e.g. marine litter or the effect of... | NaN | NaN | 2018 | Present | To collect and input litter data, provide insi... | ... | Litter - platics, glass and ceramics, paper ... | Litter density on Beach - 320 Average items pe... | litter density, plastic percentage | Yes (raw data is available) | No | Yes | No | Yes | https://litterintelligence.org/data/ | NZ - CC 3 NC |
| 2 | 3 | Citclops | The Citclops project aims to develop systems t... | water quality; water colour; | Environmental variables (e.g. water quality, t... | NaN | NaN | 2012 | 2015 | Citclops aims: \n1. To enable citizens partic... | ... | Colour and clarity of the water | See the apps | Ocean colour via Forel-Ule index | Yes (raw data is available) | No | Yes | No | No | http://www.citclops.eu/ | NaN |
| 3 | 4 | Marine Debris Tracker | Designed to help citizen scientists by contrib... | marine litter, plastic | Pollution (e.g. marine litter or the effect of... | NaN | NaN | 2010 | Present | The Marine Debris Tracker mobile app, is a joi... | ... | plastic, metal, glass, rubber, cloth, paper & ... | 6,881,410 debris items collected | Total Debris count, total collection events | Yes (raw data is available) | No | Yes | No | No | https://debristracker.org/data | NaN |
| 4 | 5 | Phytoplankton Monitoring Network (PMN) | Better understanding of harmful algal blooms t... | Phytoplankton, harmful algal blooms | Environmental variables (e.g. water quality, t... | NaN | NaN | 2001 | Present | The National Phytoplankton Monitoring Network ... | ... | date, time of sampling, water and air temperat... | See the on-line data map> https://www.ncei.noa... | Date, species, water conditions (water tempera... | Yes (raw data is available) | No | Yes | No | No | https://www.ncei.noaa.gov/maps/phytoplankton/v... | NaN |
5 rows × 29 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: 1267 entries, 0 to 1266 Data columns (total 29 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Id 1267 non-null int64 1 Marine citizen science initiative title 1267 non-null object 2 Description / initiative summary 1266 non-null object 3 Initiative topic / keywords 1256 non-null object 4 Scientific topic (1) 1267 non-null object 5 Scientific topic (2) 159 non-null object 6 Scientific topic (3) 12 non-null object 7 Start date 1213 non-null object 8 End date 1176 non-null object 9 Aim / intention / purpose of citizen science initiative / project 1258 non-null object 10 Marine focus area 1256 non-null object 11 Geographical scale 1257 non-null object 12 Geographic location - country/countries 1234 non-null object 13 Initiative documentation 1258 non-null object 14 Initiative host / coordinator 1075 non-null object 15 Initiative contact and email 1043 non-null object 16 Sponsor / funding 1022 non-null object 17 Status 1248 non-null object 18 Data collection methodology 1015 non-null object 19 Type of data collected 1015 non-null object 20 Quantity of data collected 1003 non-null object 21 Indicators used 1004 non-null object 22 Open Access 1267 non-null object 23 Findable 690 non-null object 24 Accessible 690 non-null object 25 Interoperable 690 non-null object 26 Reusable 690 non-null object 27 Distribution 459 non-null object 28 License 41 non-null object dtypes: int64(1), object(28) memory usage: 287.2+ KB
Out[3]:
| count | unique | top | freq | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Id | 1267.0 | NaN | NaN | NaN | 634.0 | 365.895705 | 1.0 | 317.5 | 634.0 | 950.5 | 1267.0 |
| Marine citizen science initiative title | 1267 | 1266 | Marine animals and coral reef monitoring along... | 2 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Description / initiative summary | 1266 | 1249 | The objective of the network is to improve kno... | 5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Initiative topic / keywords | 1256 | 847 | Species monitoring | 66 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Scientific topic (1) | 1267 | 9 | Single species (e.g. marine mammals, fish, bir... | 494 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Scientific topic (2) | 159 | 7 | Biodiversity (e.g. collecting information on m... | 42 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Scientific topic (3) | 12 | 6 | Pollution (e.g. marine litter or the effect of... | 4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Start date | 1213 | 125 | Not found | 271 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| End date | 1176 | 54 | Present | 580 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Aim / intention / purpose of citizen science initiative / project | 1258 | 1239 | Our aim is to secure the future of Critically ... | 5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Marine focus area | 1256 | 8 | On shore | 308 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Geographical scale | 1257 | 12 | National | 383 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Geographic location - country/countries | 1234 | 560 | Global | 151 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Initiative documentation | 1258 | 1083 | Not found | 148 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Initiative host / coordinator | 1075 | 732 | Not found | 123 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Initiative contact and email | 1043 | 655 | Not found | 289 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Sponsor / funding | 1022 | 353 | Not found | 590 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Status | 1248 | 7 | active | 703 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Data collection methodology | 1015 | 188 | Not found | 438 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| Type of data collected | 1015 | 202 | Not found | 395 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
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.')
Working with data larger than memory¶
Your session has a 4 GB RAM limit, but you can analyse files of 10 GB or more without loading them whole:
- Read only the columns you need:
pd.read_csv(f, usecols=[...])/pd.read_parquet(f, columns=[...]). - Process in chunks and keep only the result:
total = 0 for chunk in pd.read_csv(file, chunksize=1_000_000): total += len(chunk)
- Query with SQL without loading anything — DuckDB (already installed) reads
CSV and Parquet straight from disk and only brings the result into memory:
import duckdb duckdb.sql("SELECT column, count(*) FROM '/srv/data/.../file.parquet' GROUP BY column").df()
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 MARCSI: Inventory of Marine Citizen Science Initiatives (Global), license CC-BY-SA-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 -- marcsi-marine.ipynb && echo "Restored. Now: File → Reload Notebook from Disk"