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Open data for water, sanitation and hygiene

openwashdata is an active global community that applies the FAIR principles to data from the water, sanitation and hygiene (WASH) sector, and teaches the skills to do it.

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64
open data packages
99
datasets inside them
4
years of publishing, since 2023

Dataset of the month

gwinfraassess holds an assessment of 123 groundwater monitoring wells in Malawi by BASEflow Malawi in 2022 and 2023. Field teams recorded the condition of each well, its water level logger, the measuring equipment and what the well needs to keep working. Every well but one has an improvement plan, and the action named most often is a trained person to read the gauge (76 wells), ahead of a manual dipper (74 wells).

Open gwinfraassess

The most common fix is a person

Share of the 122 wells with an improvement plan that need each action, coloured by kind of action. Hover over a bar for the number of wells.

actions = transpose(actions_data)

// width of the chart column, so the text keeps its size on every screen
chartWidth = Generators.observe((notify) => {
  const el = document.querySelector(".owd-dotm-chart");
  const report = () => notify(Math.max(280, el.clientWidth));
  const observer = new ResizeObserver(report);
  observer.observe(el);
  report();
  return () => observer.disconnect();
})

// chart-1, chart-3, chart-2 and a neutral grey from openwashdata/brand
palette = ["#8a3a8a", "#CF9E40", "#b04208", "#b9b9b9"]

Plot.plot({
  width: chartWidth,
  height: 400,
  marginLeft: chartWidth < 420 ? 170 : 200,
  marginRight: 36,
  marginTop: 4,
  marginBottom: 36,
  style: {fontFamily: "Atkinson Hyperlegible Next, sans-serif", fontSize: 13, background: "transparent"},
  ariaLabel: "Bar chart of improvement actions recommended for groundwater monitoring wells in Malawi",
  ariaDescription: "62 percent of wells need a trained gauge reader, 61 percent a manual dipper, 46 percent a replaced level logger and 42 percent repaired civil works.",
  x: {label: "% of wells", labelAnchor: "center", domain: [0, 70], grid: true, ticks: 4},
  y: {label: null, domain: actions.map((d) => d.label)},
  color: {
    domain: ["People", "Equipment", "Structure", "Replace or retire"],
    range: palette,
    legend: true
  },
  marks: [
    Plot.barX(actions, {
      y: "label",
      x: "share",
      fill: "group",
      tip: true,
      title: (d) => `${d.label}\n${d.n} of ${n_plans} wells (${d.share}%)\n${d.group}`
    }),
    Plot.text(actions, {y: "label", x: "share", text: (d) => `${d.share}%`, textAnchor: "start", dx: 4, fill: "#1e1e1e"}),
    Plot.ruleX([0])
  ]
})

Learn

Data Science for openwashdata (ds4owd), our free online course, teaches WASH professionals data science in R. Two cohorts have graduated so far. A third cohort will start in September 2027. We are currently preparing the sign-up forms for the third iteration. In the meantime, you can browse the course website of the second cohort from 2025.

Course website of the 2025 cohort Projects of the 2026 graduates About the course

Read

Stories, tutorials and opinions from and for the community.

From a raw spreadsheet to a published data package in a day

Oct 20, 2026
Lars Schöbitz

Beyond publishing data: what we learned building an open science culture at BASEflow Malawi

Aug 18, 2026
Emmanuel Mhango

I thought my job was about data. It turned out to be about people.

Aug 11, 2026
Emmanuel Mhango
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Supported by the Open Research Data Program of the ETH Board and Global Health Engineering, ETH Zurich

 
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