taz.de
US Government Data Under Threat: Experts Launch Data Rescue Efforts
Following Donald Trump's reelection, data on US government websites related to gender, sexual orientation, health, climate change, and diversity have been altered or removed, prompting statisticians and other experts to secretly archive and share these datasets to preserve their integrity.
- What are the immediate consequences of data manipulation and removal from US government websites on policy-making and public understanding?
- Since Donald Trump's reelection, US government websites have experienced data removal and alteration. This has prompted statisticians, demographers, and computer scientists to collaboratively archive and share datasets, ensuring future accessibility. Their actions highlight the importance of maintaining data integrity above partisan interests.
- How do staff reductions at statistical agencies affect the availability and reliability of government data, and what are the longer-term implications?
- The threat extends beyond data modification to include government-mandated staff cuts at statistical agencies, hindering data management. Researchers have documented substantial changes, including replacing "gender" with "sex" in health datasets. This raises concerns about data reliability and impacts analysis on crucial social issues.
- What systemic changes are needed to ensure the long-term integrity and accessibility of US government data, safeguarding against future political interference?
- The formation of groups like dataindex.us and Data Mirror demonstrates a proactive response to potential data loss and manipulation. The unofficial revival of a Census Bureau advisory committee, despite official non-participation, underscores the growing concern among experts. Future data integrity depends on sustained collaborative efforts and transparency.
Cognitive Concepts
Framing Bias
The framing of the article strongly emphasizes the threat to data integrity and the actions taken by independent researchers to preserve it. While this is important, a more balanced approach might also include perspectives from government officials explaining the reasons behind data changes or removals, provided such explanations exist and are credible. The headline itself could be more neutral, focusing on the preservation efforts rather than solely highlighting the alleged threats.
Language Bias
The article generally maintains a neutral tone, using precise language like "modified data sets" and "substantially changed." However, phrases such as "in aller Stille kontaktieren" (in hushed tones) and descriptions of actions taken as a "rescue" might subtly convey a sense of urgency and potential wrongdoing, which while perhaps justified, should be further contextualized to ensure impartial reporting.
Bias by Omission
The article focuses on the removal or alteration of data related to gender, sexual orientation, health, climate change, and diversity, but it doesn't explicitly discuss other areas where data might have been manipulated or suppressed. This omission could lead to an incomplete understanding of the broader scope of the issue. While space constraints might justify some omissions, exploring other potential areas of data manipulation would strengthen the article.
False Dichotomy
The article doesn't present a false dichotomy, but it could benefit from acknowledging potential counterarguments or alternative explanations for the observed data changes. For instance, it could mention potential technical glitches or unintentional errors, alongside the presented concerns about political interference.
Gender Bias
The article appropriately addresses the significance of data related to gender and sex, correctly distinguishing between the two terms. The discussion of changes to gender-related data is presented factually and without gender stereotypes. However, ensuring balanced representation in sources and perspectives might further improve gender neutrality.
Sustainable Development Goals
The removal or alteration of government data sets, particularly those related to health, gender, and diversity, can negatively impact the ability to track and address issues related to poverty and inequality. Reliable data is crucial for effective policy-making and resource allocation to alleviate poverty.