Big picture science

Big picture science

Scientific integrity

You have a responsibility to me, the institutions that support our work, and the broader scientific community to uphold the highest standards of scientific accuracy and integrity. By being in the lab you agree to adhere to professional ethical standards. There is never an excuse for fabricating or misrepresenting data. If you have any questions, or in the unlikely event that you have concerns about a research practice you have seen in the lab, please talk to me immediately.

It is also important that you prioritize the accuracy of your work while in the lab. Unintentional errors due to inattentiveness or rushing can be extremely damaging and produce results that turn out to be incorrect. Although you may feel pressure for a high quantity of research, it is critical that everything we do is of the highest quality. Please double-check your work frequently. In many cases multiple people will double-check a data set to ensure no mistakes have crept in along the way.

Open, accurate, and reproducible science

Open science

We are working towards putting all stimuli, data, and analyses online and linked to each research publication. The idea is not to simply tick a box of “open science”, but to make these resources readable and useable for reviewers and other researchers. In service of this:

•          Items need to be documented and described. At a minimum, each collection should have a README file at the top level that provides details about the item in question (such as a set of stimuli or an analysis).

•          Code should be tested, bug-free, and helpfully commented.

•          Links should be permanent (ideally a DOI).

In pursuit of this high level of organization and documentation, lab members will frequently be asked to review and error-check lab materials (including video files, text lists of stimuli, etc.). Lab members creating stimuli or conducting research projects should organize them from the outset in a way that is conducive to eventual sharing (GitHub, iPython notebooks, etc.).

Accurate science

A key part of accuracy is anticipating and avoiding “adverse events” (including near misses), and creating structures in the lab that facilitate a high level of reliability.

Examples of adverse events include:

•          Any of the lab computers malfunctioning (including freezing or crashing)

•          Not being able to find the installation information for a software program

•          Nearly running out of money to pay participants (this counts as a “near miss” which we also need to discuss)

As a lab member it is your responsibility to be aware of times when things don’t go as planned and bring these to the attention of the rest of the group. Even better, let’s all work together to find ways of preventing such occurrences in the future.