Global Investigative Journalism Network (https://archive.gijn.org/tag/data-analysis/page/3/)
Being struck by lightning is often used as an example of heavenly retribution because it is so unlikely. Fatalities due to lightning are statistical outliers, since most people struck by lightning survive. So what is the best way to avoid becoming one of these outliers? The following is a step-by-step set of instructions for unpacking a dataset – and being careful about the conclusions we draw.
The past few years have seen an explosion of digital tools that can be used to enhance journalism research and reporting. In this new monthly feature GIJN’s IT Coordinator Alastair Otter takes a look at some of the best and latest tools and techniques for enhancing investigative and data-driven journalism.
As our governments and businesses become increasingly flush with information, more and bigger data are becoming available from across the globe. Increasingly, investigative reporters need to know how to obtain, clean, and analyze “structured information” in this digital world.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from March 12 to 18 finds disturbing news from @NASAEarth about low Arctic sea ice and temperature anomalies in the North Pole, @seeingtheory ‘s redesigned educational website on probability and statistics and top ten ways to clean your data by @Microsoft.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from December 31, 2018 to Jan 6, 2019 finds experts sharing their thoughts on machine learning in journalism with @storybench, @funkeinterativ and @webk1d’s useful tool to merge datasets, @pewresearch’s overview of female under-representation in online image searches, and an analysis by @EDNNews on corpses bodies sent back to El Salvador.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from January 28 to February 3 finds @sciam highlighting a curious case of mapping cholera, @nbarrowman arguing that raw data is not as perfectly objective as imagined, @bbc tracking Trump’s performance, and @VismeApp compiling a list of the best data visualizations on climate change.
What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from May 13 to 19 finds a preview snippet on sensible charts from @albertocairo’s upcoming book “How Charts Lie,” @ladatamx’s report on homicides in Mexico, @RepublikMagazin’s analysis on the changing ideologies of political parties in the European Union, and a recap of the Data Journalism UK conference by @paulbradshaw.
What’s the data-driven journalism crowd tweeting? Here are top links for Sept 5-10: America’s shrinking middle class (@FT); Latino voters & Trump (@UniNoticias); iPhone7 rave (@newyorker); DocumentCloud (@SchoolOfData); Chicago’s sensor net (@arrayofthings); Visual Vocab (@MartinStabe); and more.
What’s the data-driven journalism crowd tweeting? Here are top links for Oct 24-30: Clinton/Trump facial analysis (@benheubl ); 10K edits to Clinton/Trump Wikipedia pages (@chrisalcantara); Amnesty 36-year dataviz (@jwyg); Latinos in office (@UnivisionData); open source tools (@M_Mandalka); & more.
What are the hot data journalism tweets? Here are top links for Dec. 5-11: pie chart abuse (@johngrimwade); transit data tool (@ddjournalism); big data stories (@BigDataGirl); US diversity map (@PostGraphics); ddj survey (@Bahareh360); Swiss sprawl (@srfdata); dataviz types (@pol_ferrando); more.