If you are affiliated with any academic institution worldwide, I’ll pay you to help redpill the world by surfacing aggregated mortality rate data from public health registries

The data we need to expose the harms caused by vaccines is accessible in public health registries in Denmark, the UK, and other countries, but ONLY to qualified academic researchers who are sponsored by their institutions.

This excludes me, but it does include a broad range of people who are:

• Employed by, or formally affiliated with, an approved institution or

• Enrolled as a student on an approved research project

These are routinely approved:

I will pay you for your time to obtain access to the data we need and export it so it is publicly available without any risk of a privacy violation or objection!

The data exported is just the mortality rate for that week for each cohort. That’s it. Just the value of deaths/alive. It cannot be reverse engineered to reveal any PII.

You will need institutional approval to get access to the data.

All you will be doing is publicly surfacing aggregated data that can be crunched by other researchers to determine vaccine safety and efficacy and help eliminate misinformation. Everything you will do is 100% legal and ethical. It will help save lives.

Plus, this research not “anti-vax.” It is non-judgmental. It is simply exposing mortality rate data. That’s it.

So ideally, I’m looking for someone who has done this before and knows how the sandboxes work (see below).

So if you are a researcher (student, post-doc, staff member, or faculty member, etc.) and would like to help:

1. please email me at this email set up specifically for this project:

   

2. include your hourly rate

Accessing Danish registry data from Python is fully supported on both major Danish data platforms.

Here’s what that looks like in practice.

You usually see data as:

Typical stack inside the platform:

You’re not SSH-ing into a DB like a DBA.

You’ll do things like:

import pandas as pd

# Example: read a registry table (details vary by platform)
df_vax = pd.read_sql(
    "SELECT person_id, dose_date, dose_number FROM vaccination_table",
    con
)

df_death = pd.read_sql(
    "SELECT person_id, date_of_death FROM death_register",
    con
)

df = df_vax.merge(df_death, on="person_id", how="left")

Or, if tables are already exposed as files:

import pyreadstat

df, meta = pyreadstat.read_sas7bdat("vaccination.sas7bdat")

From there:

• weekly hazards

• cumulative hazards

• KCOR curves

…are all pure Python.

National Danish registries = millions of rows.

The normal pattern is:

1. Do big joins once (SQL or SQL-backed pandas)

2. Materialize a denormalized cohort table

3. Do all modeling in Python

This scales fine and is how most registry papers are done.

• No pip install from the internet

• Pre-nstalled libraries only (or whitelisted installs)

• No external APIs

• No data leaving Python without export approval

None of this interferes with KCOR-style analysis.

• ✔ Python access is standard

• ✔ pandas + SQL works normally

• ✔ You can do full end-to-end KCOR in Python

• ✔ SQL is optional, not mandatory

ChatGPT offered to:

• sketch a Python-only KCOR pipeline that matches Danish constraints

• show how to structure outputs so they’re auto-approved for export

• or compare Python vs R tradeoffs inside DST/SDS

With your help, we can set the data free and expose the truth.

All I need is the mortality rate per cohort on a weekly basis. It’s so simple. A one-time data export. It’s ethical. Legal. And non-judgemental.

Use the contact info above if you qualify and are interested.

I regret I didn’t think of this before.

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