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Session 1: Warm-up, general course information & setup

Python for psychologists - Winter term 2023

Course originally created by:

Peer Herholz (he/him)

Research affiliate - NeuroDataScience-ORIGAMI lab at MNI, MIT, McGill & BRAMS

Member - BIDS, ReproNim, Brainhack, UNIQUE, CNeuroMod

@peerherholz

19/10/2023

Instructor:

Aylin Kallmayer (she/her)

Phd student – Scene Grammar Lab Frankfurt

@aylinsgl

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Get to know each other
Provide general information about this course
General Introduction to the course
Discuss arrangements regarding examination/homework
Troubleshoot installation; setup
Ask & Answer questions

Objectives for this session

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First of all - Code of Conduct

https://aylinsgl.github.io/Python_For_Psychologists_23-24/CoC.html

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This course is only possible due to the efforts of the great Peer Herholz (https://peerherholz.github.io/), who created and compiled most of the information you’ll be presented with for PsyMSC04

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Freelance/contract Researcher; Research affiliate at the McGovern Institute, MIT; the MNI, McGill University

Postdoc at Goethe-University Frankfurt

Postdoc at McGill University

Ph.D. at Philipps-University Marburg/BRAMS

M.Sc. at Philipps-University Marburg

M.A. at Philipps-University Marburg

B.A. at University of Leipzig

2021 - now

2021 - 2022

2019 - 2021

2015 - 2019

2012 - 2015

2009 - 2012

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Was adapted by…

2022 - now

2019 - 2021

2015 - 2019

Research Assistant/Ph.D. Goethe-University Frankfurt

M.Sc. Psychologie Philipps University Marburg

B.Sc. Psychologie Philipps University Marburg

Previous work:

Neural correlates of Depression
Cognitive control/error processing/Optimal control theory
EEG/fMRI/VR/Motion-tracking

Research Interests:

Open-science, methods
Auditory neuroscience, cognitive neuroscience of language, artificial intelligence in cognitive neuroscience

Michael Ernst

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2021 - now

2018 - 2021

2015 - 2018

PhD Psychology Goethe University

MSc Cognitive and neuroscience with computer science minor Goethe University

BSc Psychology Goethe University

Previous work:

Supervised and unsupervised deep learning in vision science
Task dependent hierarchical representations
Models of high-level vision
EEG, eye-tracking, psychophysics, computational modelling

Research Interests:

Vision science, cognitive science, philosophy of mind
Artificial intelligence
Reproducibility

Aylin Kallmayer

Instructor 2023-2024

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Introduction round - who are you?

your name
your operating system
Your major & minor
your research/programming experience
why did you choose this class?
is there anything in particular you’re hoping to learn?

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Why even learn programming?

How can programming be integrated into a research workflow?

What parts of the research workflow can benefit from it?

How should code be run, tested & documented?

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If we DON’T care about these things…

https://www.economist.com/leaders/2013/10/21/how-science-goes-wrong

“Modern scientists are doing too much trusting and not enough verifying - to the detriment of the whole of science, and of humanity.”

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Make science reproducible again!

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Research outputs should be FAIR

https://ogsl.ca/wp-content/uploads/Fair-rectangle-en.png

Taken from https://www.force11.org/fairprinciples

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Transparency motivations

showing & sharing work, public trust,

auditability

Participatory motivations

direct democracy, feedback & support

Personal motivations

learn coding, community, skills, fun

Why open source? Why python?

Python Is Accessible

human readable, great for beginners, lots of materials

Python Is Reliable and Efficient

many use-cases in different computing environments, comparably fast

Python has amazing Libraries

versatile and broad applications in basically all research/applied fields

Python has big data, data science & ML/DL

many libraries, widely used, optimized

Python has a healthy, active and supportive community

documentation, guides, tutorials, forums

Python has future options

in high demand in academia & industry

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modules

content �&

aspects

FAIR

outcome

running experiments

data analyzes

introduction

experimentation

analyzing data

software setup

the shell

computing envs

data types

control flow operations

functions

computing environment

data acquisition

experiment skeletons

presenting stimuli

collect responses

quality control

output sorting

online experiments

experiment script

data structures

data wrangling

descriptive stats

inferential stats

visualization

documentation

executable formats

reproducible analyses

walkthroughs

basics of

programming

We would (ideally) divide our programming endeavor into 3 modules/blocks, incorporate respective FAIR/open-science resources/tools & base it on real-world data.

Bringing everything together

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The goal is to have you all be able to program at an introductory level.

It’s generally accepted that it takes

people 10 years to move from novice to expert programmer. But, there are lots of steps in between! We’re working to move you further away from novice (& in the direction of expert) than you are right now.

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Pillars of this course

this course has a website (Jupyter Book) that provides all information, outline, materials, etc.

https://aylinsgl.github.io/Python_For_Psychologists_23-24
your “go-to starting point”, always check things there

This course further has a discord-server for communication (you can of course always write an e-mail or schedule a personal meeting)

great for working together synchronously or asynchronously
text and voice/video chats, screen sharing
Invite-link

https://discord.gg/u6yxBpJt

Pfp_Goethe_2023-24

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final exam

Bash basics

Python basics

executable & reproducible research report

course work/participation

participation/readings

homework assignments

The course - tasks & credits

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Questions?