1:17pm , Tuesday 14th August 2018

Data Journalism Awards 2018

March 21, 2018

Lisbon– The Data Journalism Awards are the first international awards recognising outstanding work in the field of data journalism worldwide.

The Awards celebrates outstanding work in the field of data journalism worldwide. The competition can be won by any media organisation, big or small. The awards are given to examples of data-driven articles or investigations, data-driven applications and storytelling projects that use data visualisation, to cover topics relevant to the general public and aiming at having an impact on society.

With support from the Google News Lab, the John S. and James L. Knight Foundation, and Microsoft, in partnership with Chartbeat.

Works produced by individuals or teams of staffers from media companies and non-profit organisations, as well as freelancers or individuals are all eligible for entry.

The prizes for the Data Journalism Awards 2018 competition are worth $1801 (US) each and will be awarded at a special ceremony and gala dinner during the eighth annual GEN Summit in Lisbon, Portugal on 31 May 2018.

There are over 12 categories for 2018:

  • Best data journalism team
  • Best individual portfolio
  • Chartbeat award for the best use of data in a breaking news story, within first 36 hours
  • Data journalism website of the year
  • Data visualisation of the year
  • Innovation in data journalism
  • Investigation of the year
  • News data app of the year
  • Open data award
  • Public choice
  • Small newsrooms
  • Student and young data journalist of the year

Entries to the 2018 competition can be submitted until 26 March 2018 at 23:59 GMT. All work must have been published or aired between 10 April 2017 and 26 March 2018.

The finalists shortlisted for awards will be announced in April 2018, and the winning projects will be revealed at the 2018 ceremony and gala dinner in Lisbon on 31 May 2018, during the GEN Summit 2018.

To apply: https://goo.gl/4d9bUh

For more information: https://goo.gl/hMDLvf

 

 


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