Miscellaneous Notes |
This page is aimed at keeping some of my academic notes and sharing them with those who may be of interest.
The conference organized by the Advanced Computational Neuroscience Network (ACNN) was held at the University of Michigan this year. There are a couple of travel scholarships available for students and postdocs.
The opening keynote speaker, Professor Russ Poldrack, introduced the role of Brain Imaging Data Structure (BIDS) in neuroimaging and data sharing. We can find more information on Github (https://github.com/bids-standard). A collection of software, pipeline, and platform were mentioned in the talk: Docker, MRIQC, fmriprep, UCLA Consortium for Neuropsychiatric Phenomics (CNP), OpenNEURO, brainlife, and CBRAIN. Note that OpenNEURO is a free and open platform for sharing MRI, MEG, EEG, iEEG, and ECoG data.
Reproducibility. Professor Tristan Glatard talked about big data infrastructures for Neuroinformatics: Performance, Reproducibility, and interoperability. I highlight several important points here.
Pipelines. I list some pipelines that were introduced in the conference: the Statistics Online Computational Resource, ACNN Pipeline, CompSci, the NIC platform, and BrainLife. Also, for researchers focusing on working neuroimaging (MRI) data, SchizConnect may be of interest to some of you.
This workflow was introduced in the course STAT-S681 Networks taught by Professor Stanley Wasserman at Indiana University in 2017. For setting up a network model, we essentially follow a rather straightforward procedure: (Using the package ergm in R.)
The materials are based on the couse Reproducible Results & the Workflow for Data Analysis taught by Professor J. Scott Long in fall 2017.
A workflow for data analysis is a set of coordinated procedures to work efficiently to create analyses that are accurate and reproducible. Workflow includes the entire process of research:
I would recommend the text The Workflow of Data Analysis Using Stata (Scott Long, 2008) for streamlining your workflow. This text introduces methods for analyzing data efficiently, effectively, and accurately. The examples in the book are based on using the statistical software Stata, but the workflow is essentially same regardless of which software you are using.