Water Pollution Data Research
Budget: ₹750 – ₹1,250 INR
I’m launching a focused pollution-control study on local waterways and need a researcher who can turn scattered information into a clear, evidence-based picture of what is really happening beneath the surface. The core of the job is data: finding it, validating it, analysing it and turning it into insights decision-makers can act on.
You will help me locate and compile raw water-quality figures—field samples, public monitoring stations, satellite or drone readings, academic papers, anything reputable—then clean and merge those datasets in a way that lets us spot patterns of contamination over time. Solid skills in Excel, Python or R for data wrangling, plus basic GIS mapping to visualise hotspots, will be essential.
Once the numbers are in shape, I’ll rely on you to build concise visuals (charts, maps, dashboards) and a short technical summary that flags the most critical pollution sources, trends and data gaps. Keep the write-up jargon-free yet credible; it should be readable by environmental officers and community leaders alike.
Deliverables
• Compiled raw dataset (CSV or similar) with clear source notes
• Documented cleaning/merging scripts or workflow
• 3–5 key visualisations (static or interactive) highlighting pollution hotspots and trends
• A 6–8 page report summarising methodology, findings, limitations and next-step recommendations
Acceptance criteria
Data sources must be publicly traceable or accompanied by field notes; code must run end-to-end on a fresh machine; visuals must tie directly to figures cited in the report.
If this aligns with your expertise in environmental data collection and analysis, let’s discuss timelines and milestones.
You will help me locate and compile raw water-quality figures—field samples, public monitoring stations, satellite or drone readings, academic papers, anything reputable—then clean and merge those datasets in a way that lets us spot patterns of contamination over time. Solid skills in Excel, Python or R for data wrangling, plus basic GIS mapping to visualise hotspots, will be essential.
Once the numbers are in shape, I’ll rely on you to build concise visuals (charts, maps, dashboards) and a short technical summary that flags the most critical pollution sources, trends and data gaps. Keep the write-up jargon-free yet credible; it should be readable by environmental officers and community leaders alike.
Deliverables
• Compiled raw dataset (CSV or similar) with clear source notes
• Documented cleaning/merging scripts or workflow
• 3–5 key visualisations (static or interactive) highlighting pollution hotspots and trends
• A 6–8 page report summarising methodology, findings, limitations and next-step recommendations
Acceptance criteria
Data sources must be publicly traceable or accompanied by field notes; code must run end-to-end on a fresh machine; visuals must tie directly to figures cited in the report.
If this aligns with your expertise in environmental data collection and analysis, let’s discuss timelines and milestones.