nih-fmrif Goto Github PK

hcp_cca_analysis-1

The purpose of this study is to replicate the results of Smith et al. (https://www.fmrib.ox.ac.uk/datasets/HCP-CCA/) and extend this analysis to the larger HCP_1200 dataset

hcp_smith_et_al

heudiconv_helpers

logging_ls_times

A notebook to describe a quick assessment of file access times.

machinelearning

public code releases from the NIMH Machine Learning Team

mcrscpy_datasci_job

me-ica

Core code for ME-ICA command line interface

mi_test

test ways to calculate mutual information between a discrete and continuous variable

ml

mlc_job_ad

Advertisement for positions within the Machine Learning Core at the NIMH IRP

mlt-external

mlt_body_part_segmentation

mri-artifacts-lecture

Lecture on commonly observed artifacts in MRI

mriqc

The package provides a series of image processing workflows to extract and compute a series of NR (no-reference), IQMs (image quality metrics) to be used in QAPs (quality assessment protocols) for MRI (magnetic resonance imaging).

mriqcwebapi

mriqc_db_endpoint

ndar_access

This is a workbook documenting how to download and access data from the NDAR collection.

neurodocker

Generate custom Docker images and minimize existing containers

nginx_server

nhw_ipynb

Advanced Jupyter/IPython features class for Neurohackweek 2017

nielson_abcd_2018

Code for Detecting and harmonizing scanner differences in the ABCD study - annual release 2.0

nih-fmrif.github.io

This github org account is used by the Center for Mutlimodal Neuroimaging and the Functional MRI Facility at the NIMH Intramural Research Program in Bethesda, Maryland

nih_irp_pubdb

An application to catalog and measure NIH IRP publications.

nih_protocol_search

nimh_ds_env

NIHM OHBM Backend

nimh_fulltexts

Downloading NIMH fulltext papers from PMC with the goal of finding data statements

nimh_irp_clustering

nimh_ohbm

NIMH OHBM front end

nimh_repro_2017_03

A workshop on open and reproducible neuroscience held in March, 2017. These materials will cover 1) Using Python and Github to produce & share reproducible processing pipelines, 2) Structuring and reporting your data using community-recognized standards, 3) Accessing and contributing to shared repositories, 4) Using pre-print servers

nimh_repro_2017_08

Repository for the NIMH Workshop on Open and Reproducible Neuroscience on Aug 1 & 2, 2017