The hottest Data Integrity Substack posts right now

And their main takeaways
Category
Top Technology Topics
Points And Figures β€’ 1172 implied HN points β€’ 11 Feb 25
  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.
Vinay Prasad's Observations and Thoughts β€’ 111 implied HN points β€’ 12 Feb 25
  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.
Your Local Epidemiologist β€’ 2389 implied HN points β€’ 04 Feb 25
  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.
Science Forever β€’ 159 implied HN points β€’ 28 Feb 24
  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.
The Good Science Project β€’ 14 implied HN points β€’ 04 Nov 24
  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.
Get a weekly roundup of the best Substack posts, by hacker news affinity:
Rounding the Earth Newsletter β€’ 9 implied HN points β€’ 25 Oct 24
  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.
Data Thoughts β€’ 39 implied HN points β€’ 21 Jan 23
  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.
Minimal Modeling β€’ 16 HN points β€’ 20 Dec 23
  1. NULL values in databases create compatibility issues and add complexity to conditional operations
  2. Sentinel values, like empty strings or placeholders, are similar to NULL values and can lead to incorrect results
  3. Creating sentinel-free schemas involves separating attributes into individual tables and explicitly defining reasons for missing data
Machine Economy Press β€’ 5 implied HN points β€’ 08 Feb 24
  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.
Joshua Gans' Newsletter β€’ 0 implied HN points β€’ 03 Dec 15
  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.
Musings on Markets β€’ 0 implied HN points β€’ 28 Nov 09
  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.