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ECCC Project, 100 Years of Open Access Meteorological Data

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This page is a translated version of the page Projet ECCC, 100 ans de données météorologiques en accès libre and the translation is 100% complete.
100 years of open access weather data
Enhancing, disseminating and reusing our meteorological heritage
Wikimedia Canada
Map of the 8,756 weather stations of the Meteorological Service of Canada that once existed.
Wikimedia Canada's ECCC project consists of importing, in Wikimedia Commons, a century of meteorological data collected by 8,756 stations across Canada. The objectives of the project include reusing our meteorological heritage, influencing the rest of the world to import similar data in open access in Wikimedia Commons, and solving some of the climate change issues that affect us.

Background

In 2019, Environment and Climate Change Canada (ECCC)[1] is offering Wikimedia Canada funding until March 31, 2021 for its project entitled: "ECCC Weather Observations in Wikimedia Commons".

The inclusion of government and scientific data in Commons is a world first.

Participants


Project stages and objectives

01

How do we want to use our weather heritage?

 
02

How can we influence the rest of the world to import similar open access data into Wikimedia Commons?

 
03

How can this data help us to solve some of the climate change issues that affect us?

Wikimedia Canada wants to bring together the general public, the weather community, the open data community, and science educators in the development, use, and dissemination of a century of Canadian weather data.
  • Phase 1 (2019-2020) : import weather data into Commons from 8,756 Canadian observation stations over the past 100 years.
  • Phase 2 (2020-2021) : to enhance the utilization these data, in particular, is to use and disseminate them in Wikimedia projects, including Wikipedia.

Examples of utilizing meteorological heritage

SPARQL queries in Wikidata

It is possible to combine station metadata with each other and with all other data in Wikidata through SPARQL queries.

For example:

Heat map


Imported data types

  1. "Temp. Max. Average (°C)"
  2. "Temp. Min. Average (°C)"
  3. "Frequency of precipitation (%)"
  4. "Highest temperature (°C)"
  5. "Highest temperature in a year"
  6. "Highest temperature period"
  7. "Data quality at the highest temperature"
  8. "Lowest temperature (°C)"
  9. "Lowest temperature year"
  10. "Lowest temperature period"
  11. "Data quality at the highest temperature"
  12. "Highest precip. (mm)"
  13. "Greatest precipitation year."
  14. "Greatest period of precipitation."
  15. "Greatest Precip. Data Quality"
  16. "Highest rain (mm)"
  17. "Highest precipitation year"
  18. "Highest period of rain"
  19. "Better quality precipitation data."
  20. "Largest snowfall (cm)"
  21. "Year with most snowfall"
  22. "Greatest period of snowfall"
  23. "Better quality of snowfall data"
  24. "Highest snow on ground (cm)"
  25. "Highest annual snow on ground amount"
  26. "Period with the greatest snow on ground"
  27. "The best snow on ground data quality"

Notes and references

See also

ECCC project, 100 years of meteorological data in free access.

Medias:

Presentations:

Tools:

Government of Canada:

WMO