While some people last Friday dressed in Halloween costumes or handed out candy to trick-or-treaters, a group of U.S. data scientists published a list of “dearly departed” datasets that have been axed, altered or had topics scrubbed since President Donald Trump returned to the White House earlier this year.
The timing of the release of the “Dearly Departed Datasets” with “All Hallows' Eve” may have been cheeky, but the purpose was serious: to put a spotlight on attacks by the Trump administration on federal datasets that don't align with its priorities, including data dealing with gender identity; diversity, equity and inclusion; and climate change.
Officials at the Federation of American Scientists and other data scientists who compiled the list divided the datasets into those that had been killed off, had variables deleted, had tools removed making public access more difficult and had found a second life outside the federal government.
The good news, the data scientists said, was that the number of datasets that were totally terminated number in the dozens, out of the hundreds of thousands of datasets produced by the federal government.
The bad news was that federal data sets were still at risk because of loss of staff and expertise by federal government workers who lost their jobs or voluntarily departed under Elon Musk’s cost-cutting blitz, and data that reflected poorly on the Republican administration's policies could still be in the cross-hairs, they said.
The “dearly departed” figures which were killed off include a Census Bureau dataset showing the relationship between income inequality and vulnerability to disasters; a health surveillance network which monitored drug-related visits to emergency rooms; and a survey of hiring and workhours at farms, according to the review.
The race and ethnicity column was eliminated from a dataset on the federal workforce. Figures on transgender inmates were removed from inmate statistics, and three gender identity questions were taken out of a crime victims' survey, the data scientists said.
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