added ging jehli and nipraxis

README.md

@@ -42,6 +42,7 @@ Time to put that knowledge into action. These guides exclusively cover MRI/fMRI
 - [BrainIAK](https://brainiak.org/tutorials/) Tutorials for more advanced fMRI analysis including machine learning and real-time fMRI.
 - [Explorations of fMRI methods and theory](https://github.com/huffman-spatial-cognition-lab/exploration_of_fMRI_methods_and_theory) A course containing Python notebooks and instructions for running explorations of activation analysis and multivariate pattern analysis (MVPA) to demonstrate the interplay between fMRI methods and theory. Created for an undergraduate cognitive neuroscience course at Colby College by Derek Huffman. See the accompanying preprint [here](https://osf.io/preprints/psyarxiv/8kvfu).
 - [Data analysis for Neuroimaging (DAFNI)](https://schluppeck.github.io/dafni/) Denis Schluppeck's materials for the MSc Cognitive Neuroscience course at the University of Nottingham, covering SPM, git, FSL and MATLAB.
+- [Practice and theory of brain imaging](https://textbook.nipraxis.org/intro.html) A comprehensive course on neuroimaging in Python, with modules on reproducibility in programming/neuroimaging. Created by the Nipraxis team (Matthew Brett, Chris Markiewicz, Oscar Estaban, Zvi Baratz, Peter Rush).
 ### Meta-analysis of fMRI data
 - [NiMARE](https://nimare.readthedocs.io/en/stable/index.html) NiMARE is a Python package for performing meta-analyses, and derivative analyses using meta-analytic data, of the neuroimaging literature, providing a standard syntax for performing a wide range of analyses and for interacting with databases of coordinates and images from fMRI studies.
 - [SDM Project](https://www.sdmproject.com/) Seed-based d Mapping (formerly "Signed Differential Mapping") is a statistical technique for meta-analyzing studies on differences in brain activity or structure which used neuroimaging techniques such as fMRI, VBM, DTI or PET.
@@ -77,6 +78,7 @@ Some platforms commonly used for the recruitment and deployment of behavioural e
 - [Bayesian Models of Learning and Integration of Neuroimaging Data](https://training.incf.org/lesson/modelling-cognition-using-bayesian-inference) A four-session course teaching how Bayesian statistics may be used to build cognitive models of processes like learning or perception and theoretical and practical instruction on dynamic causal modeling as applied to fMRI and EEG data. Hosted on the INCF, ran by the Krembil Institute for Neuroinformatics.
 - [Bayesian Statistics and Bayesian Cognitive Modeling, Part 2](https://github.com/lei-zhang/BayesCog_Part2) The second part (two-days) of a five-day workshop covering Bayesian statistics and cognitive modeling. This second part focuses on more complex Bayesian models including hierarchical models, as well as Bayesian regression. Created by Lei Zhang, University of Birmingham.
 - [Computational Cognitive Neuroscience, Fourth Edition](https://github.com/CompCogNeuro/ed4) A freely available textbook, providing 'a complete, self-contained introduction to the field of Computational Cognitive Neuroscience, where computer models of the brain are used to understand a wide range of cognitive functions, including perception, attention, motor control, learning, memory, language, and executive function.' By Randall C. O’Reilly, Yuko Munakata, Michael J. Frank and Thomas E. Hazy.
+- [Modeling Hub](https://www.gingjehli.com/research-blog) A weekly series of tutorials and blogs covering 'various aspects of modeling from the foundational principles of hierarchical/multilevel modeling (spanning both Bayesian and Frequentist approaches) to the nitty-gritty of advanced modeling techniques, including applications with sequential sampling modeling, neural network modeling, and other machine learning techniques'. Created by Nadia Ging-Jehli (Brown). You can find the tutorial code on her [GitHub](https://github.com/gingjehli).
 ### Workshops/Groups/Summer Schools
 - [Computational Psychiatry Course Zurich](https://www.translationalneuromodeling.org/cpcourse/) A leading 6-day course organized by the Translational Neuromodeling Unit (TNU), University of Zurich & ETH Zurich designed to provide MSc and PhD students, scientists clinicians and anyone interested in Computational Psychiatry with the necessary toolkit to master challenges in computational psychiatry research. Whilst the workshop is delivered yearly in person, resources for past courses are made available.
 - [London-New York Computational Psychiatry course](https://www.cpcourse.org/) CPC++ is 'a hybrid course/conference format to equip the next generation of scientists and clinicians who study mental disorders with the tools to understand, to build, to analyse and to critically evaluate computational models of mental function and dysfunction'.