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- Our team values diversity. We have experienced time and again that race, creed, sex, etc. do NOT make better scientists. We are aware of the inherent sexism in academia and we strongly oppose it.
- We value academic honesty. We are diligent in matters of plagiarism, giving credit where it is due, and data integrity.
- We are aware of unconscious human biases, so we strive to practice blind analyses as much as possible.
- We do not accept any behaviour that would damage a respectful working environment, no form of harassment, bullying, etc. are allowed.
- We consider ethical implications of our research in making decisions.
- We believe in academic freedom and its utmost importance for our world in the 21st century.
- APS Statement on Ethics: https://www.aps.org/policy/statements/ethics.cfm
- How to engage public, ie. "facts are not enough to convince people; money can smear science"
- Tim Harford, The Problem With Facts
- Brianna Rego, The Polonium Brief, Isis, Vol. 100, No. 3 (September 2009), pp. 453-484.
- Jennings, Grandoni, Rust, How Exxon went from leader to skeptic on climate change research
- Importance of self-assessment, ie. "don't be a pompous ass; your education does not make you a better scientist; if you think you are very good, you are probably not"
- https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect
- Rozenblit, Keil, The misunderstood limits of folk science:an illusion of explanatory depth, Cognitive Science 26 (2002) 521–562
- Kahan, Peters, Dawson, Slovic, Motivated Numeracy and Enlightened Self-Government
- Don't believe everything you read, ie. "some so-called scientists fake data; scientists can be quite gullible; understanding statistics is critical"
- Martin Gardner, Lessons of a Landmark PK Hoax
- Marcus, Oransky, Why Fake Data When You Can Fake a Scientist?
- J.P.A. Ioannidis, Why Most Published Research Findings Are False
- Kai Kupferschmidt, Researcher at the center of an epic fraud remains an enigma to those who exposed him
- You hidden biases, be aware of their consequences, ie. "don't pretend that you are a fully objective measuring device, you are human"
- Payne, Niemi, Doris, How to Think about "Implicit Bias"
- More to come...
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Topic revision: r4 - 08 Mar 2020, ErkcanOzcan
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