Here is a collection of resources that I have found useful.
Prof. Gary King has a great lecture series on Quantitative Social Science Methods hosted on Youtube.
MIT’s open course on statistics, provides a introductory level overview on topics such as MLE, regression, bayesian statistics, and principle component analysis. Link
Some resources for how to do simulation-based power analysis in R (using the “simr” package)
https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12504
https://humburg.github.io/Power-Analysis/simr_power_analysis.html
For fMRI:
https://brainpower.readthedocs.io/en/latest/index.html
About 100 talks from neuromatch conference! Everything is tagged and linked and closed captioned via machine learning.
A series of tutorial videos on fMRI analyses (featuring univariate analyses, MVPA, RSA, classification) taught Prof. Rebecca Saxe of MIT.
Course Overview: “The goal of the class is to introduce: (1) how the scanner generates data, (2) how psychological states can be probed in the scanner, and (3) how this data can be processed and analyzed. Students will be expected to analyze brain imaging data using the opensource Python programming language. We will be using several packages such as numpy, matplotlib, nibabel, nilearn, fmriprep, and nltools. This course will be useful for students working in neuroimaging labs, completing a neuroimaging thesis, or interested in pursuing graduate training in fields related to cognitive neuroscience.”
For example, they have a chapter on PPI analysis using nilearn pipelines here.
Github repo of collections of parcellations and multivariate signature patterns
Seitzman 2021 Parcellation ("with improved representation of the subcortex and cerebellum)
Online book (with python code examples) from the Dartbrains Neuroimaging group Link
Here are some resources on fMRI data visualization
Dr. Martin Monti, from UCLA, has made lectures from his course names “Computational Methodology for Neuroimaging” available online here.
Friendly Universe is a Qualtrics survey template to anonymously measure the structure of individuals’ social networks. Friendly Universe measures the nodes and edges of the network, as well as the group (i.e. family, friends), and the emotional closeness of each node. This tool is easy to use and can be custromized through Qualtrics and Javascript.
An online tool that uploads portrait photos, automatically extracts the avatar, and generates a profile picture.
I built this site using Rmarkdown. I was specifically looking for tools to build a personal website with the following considerations:
Simple: All I needed was to display some information on a static website. I was not planning on writing blogs.
Automated: such as being able to pull information from my google scholar page.
Easy to use.
I ended up building this site using Rstudio, inspired by this tutorial.
Syllabus (with A LOT of useful resources) on data management from SIPS 2021. syllabus.docx
4. Social network analysis
Here are some useful resources on social network analysis:
Book: Egocentric data analysis
Lecture videos from Duke’s 2021 Social Networks and Health Workshop: Link
Some social network datasets to play with: 1, 2, 3
Network visualization using Python Github repo