The hottest Data Integrity Substack posts right now

And their main takeaways
Category
Top Technology Topics

Top Five Climate Science Scandals 2025

The Honest Broker Newsletter • 2846 implied HN points • 29 Dec 25
🌞 Climate & Environment Data Integrity
  1. Bad or fake datasets and low-quality models have been used in climate research and major assessments. Those errors need prompt correction and retraction to restore scientific trust.
  2. Major climate assessments and agencies are highly politicized and swing with each administration, which undermines credibility. Depoliticizing these institutions would help rebuild public trust.
  3. Financial “climate risk” products and the continued reliance on extreme, implausible emissions scenarios are distorting research and policy. Climate science should use more realistic scenarios and clearer links between risks and evidence.

Data and communication are gold

Your Local Epidemiologist • 2389 implied HN points • 04 Feb 25
🏥 Health Politics Data Integrity
  1. Public health data is crucial for keeping people safe and informed. When this data is lost or manipulated, it poses a serious risk to health and safety.
  2. Changes in government orders are causing confusion and delays in data reporting, affecting how health agencies communicate important health information.
  3. Local health departments and universities are still working to share necessary health updates while federal agencies face challenges. Staying informed through local sources is key during this instability.

Fake Data

Points And Figures • 1172 implied HN points • 11 Feb 25
🇺🇸 U.S. Politics Data Integrity
  1. There's a belief that government data, like unemployment numbers, may not be accurate and could be manipulated for a specific narrative. This raises doubt about how trustworthy these figures really are.
  2. The independence of government employees from elected officials is questioned, suggesting that the current system might not align with constitutional principles. This brings up concerns about who is truly accountable in the decision-making process.
  3. Concerns are raised about the reliability of various types of government data, including health and agricultural statistics. This makes people wonder if they can trust any information provided by the government.

Miracles In The (In)famous Pfizer Trial

QTR’s Fringe Finance • 25 implied HN points • 02 Feb 26
🏥 Health Politics Data Integrity
  1. Reported vaccine effectiveness jumped sharply within about five to seven days after the second dose, which seems biologically implausible and makes that rapid change suspicious.
  2. The trial protocol let investigators treat symptoms in the first week after vaccination as side effects without PCR testing, so many potential COVID cases in the vaccine arm could have been missed and efficacy overstated.
  3. Vaccine recipients reported fewer non-COVID symptoms outside the immediate reactogenicity window, suggesting differential outcome ascertainment and bias that reduce confidence in the trial’s results.

Thanks to STAT for putting me on the STATUS list

Science Forever • 159 implied HN points • 28 Feb 24
🔬 Science Data Integrity
  1. Holden Thorp was named by STAT News to the STATUS list of top 50 leaders in the life sciences for his work in research integrity.
  2. Thorp has challenged the stigma around corrections and retractions in scientific publishing, advocating for increasing public trust in the scientific enterprise.
  3. Recognition also goes to the team at Science, including Valda Vinson, Lauren Kmec, Meagan Phelan, and Lisa Chong, for their contributions to research policies and Thorp's work.
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DMED and CDC COVID Data Scandals

Who is Robert Malone • 16 implied HN points • 03 Jan 26
🏥 Health Politics Data Integrity
  1. Health data systems were retroactively altered to mask apparent spikes in illnesses, which eroded trust in military and public health records.
  2. Pressure to preserve institutional narratives, unchecked access to editable databases, and moral rationalizations combined into a self‑reinforcing 'fraud engine' that enabled systemic data manipulation until external scrutiny intervened.
  3. Preventing recurrence requires concrete reforms—immutable cryptographic data versioning, separation of analytics from communications, strong whistleblower protections, and real‑time access for independent review.

The reproducibility crisis and what it means for the NIH

Vinay Prasad's Observations and Thoughts • 111 implied HN points • 12 Feb 25
🔬 Science Data Integrity
  1. Many scientific experiments don't give the same results when repeated. It's like trying to bake a cake and it flops even though the recipe seemed good.
  2. Scientists often face pressure to produce results, which can lead to mistakes or even dishonesty in their work. They tend to focus more on getting results than on finding the truth.
  3. There's a big need for change in how science operates. Even if some discoveries are useful, there's a concern about whether research is really delivering reliable results.

Health New Zealand admits that they did not look at their own safety data. The vaccine is safe. Why bother to look at our data?

Steve Kirsch's newsletter • 7 implied HN points • 09 Dec 25
🏥 Health Politics Data Integrity
  1. Health New Zealand admits they haven't examined their own COVID vaccine safety data. This raises questions about how they can say the vaccine is safe without reviewing it.
  2. They declined a request for a public discussion on the data, which some see as a lack of transparency in addressing concerns.
  3. Health New Zealand also stated they will not conduct autopsies for anyone believed to have been harmed by the vaccine, which adds to doubts about their commitment to safety investigations.

Latest Ioannidis paper claiming millions of lives saved is nothing more than "Turtles All The Way Down"

Steve Kirsch's newsletter • 8 implied HN points • 28 Jul 25
🏥 Health Politics Data Integrity
  1. The new Ioannidis paper relies on assumptions and models instead of real data. This means its conclusions about lives saved are not based on solid evidence.
  2. The paper does not check its findings against real-world data or outcomes, which is a big problem. Without this verification, we cannot trust its claims.
  3. Transparency is crucial in science. The lack of access to raw data means no one can truly verify the numbers, making the findings questionable at best.

Data Quality

Data Thoughts • 39 implied HN points • 21 Jan 23
🕹 Technology Data Integrity
  1. Data quality is all about how useful the data is for the specific task at hand. What is considered high quality in one situation might not be in another.
  2. There are several key aspects of data quality, including accuracy, completeness, consistency, and uniqueness. Each of these factors helps to determine how reliable the data is.
  3. Improving data quality involves preventing errors, detecting them when they occur, and repairing them. It's about making sure the data is accurate and useful over time.

How to Improve Both Scientific Innovation and Reproducibility at Once

The Good Science Project • 14 implied HN points • 04 Nov 24
🔬 Science Data Integrity
  1. Science struggles with two main issues: not being able to reproduce results and not being as innovative as before. Many studies can't be repeated successfully, which raises concerns about their reliability.
  2. To boost both reproducibility and innovation, it's important to encourage sharing of failed experiments and null results. This would help scientists take risks and avoid only publishing positive outcomes.
  3. Creating 'Red Teams' in science can challenge current beliefs and assumptions. These groups would actively work to test and potentially disprove existing theories, fostering better scientific inquiry.

We interrupt this service for an important message

Klement on Investing • 3 implied HN points • 02 Aug 25
💰 Finance Data Integrity
  1. It's important for investors to have accurate data about the economy. Wrong data can lead to bad investment decisions and uncertainty.
  2. Recent actions, like firing a labor statistics chief over disappointing data, can threaten the integrity of economic information. This has historical examples that show negative outcomes.
  3. Advocating for the reinstatement of trusted data sources is crucial to maintain the reliability of economic measurements for everyone concerned.

Was the DMED Psyop a British Intelligence Operation?

Rounding the Earth Newsletter • 9 implied HN points • 25 Oct 24
🇺🇸 U.S. Politics Data Integrity
  1. The DMED project involves military health data, and there are claims of serious data issues that were ignored. This lack of action raises suspicions about its integrity.
  2. There seems to be a connection between figures in the movement against COVID vaccines and intelligence agencies, which adds layers of complexity to the conversation about vaccine safety.
  3. Some of the leading individuals in the Medical Freedom Movement have backgrounds that hint at broader agendas, creating doubt about their true intentions.

Apple's Pkl New Coding Language for Configuration

Machine Economy Press • 5 implied HN points • 08 Feb 24
🕹 Technology Data Integrity
  1. Apple released an open-source configuration coding language called Pkl to enhance safety and scalability of configurations.
  2. Pkl combines static and general-purpose language features for simplicity, expressiveness, and modularity in configuration coding.
  3. Apple's release of Pkl reflects their increasing involvement in open-source software and aims to provide a versatile solution for configuring infrastructure, applications, and environments.

Integrity and body cameras

Joshua Gans' Newsletter • 0 implied HN points • 03 Dec 15
🇺🇸 U.S. Politics Data Integrity
  1. Body cameras can serve as a tool for police accountability, but for them to be truly effective, the camera data should be managed by a neutral third party to ensure integrity and prevent misuse.
  2. Having an independent third party manage body camera footage can enhance transparency, credibility, and protection for police officers by reducing suspicions of tampering or misuse.
  3. Simply implementing technology like body cameras is not enough; ensuring the integrity of the data produced by the technology is crucial for its effectiveness and impact.

The CRU Scandals: A Reflection on Academia

Musings on Markets • 0 implied HN points • 28 Nov 09
🚌 Education Data Integrity
  1. Academic research often prioritizes getting published over exploring interesting questions. Researchers might choose to work on safe topics that are easier to publish instead of tackling big, challenging ideas.
  2. Bias can affect research outcomes. Researchers bring their own perspectives and preconceptions, which can influence what they study and how they interpret data.
  3. The educational background and connections of a researcher can greatly impact their chances of getting published. Those from elite institutions or who have influential mentors often have better success in the publishing world.