This repository contains descriptions of, and links to, all projects created by participants at Neurohackademy 2018. If you're adding a new project, please use the template below.

• Project template: Template to use when creating new projects
• Brain Age
• NIPYPE pals
• ConvOuch
• study_forrest
• nilearn_task_networks
• BIDSonym
• MNI Atlas Reader
• better-google
• Parcellation Fragmenter
• bidsQC
• O-Factor

When adding a new project to the listing, please copy and paste the template below.

Name: [project name]
Project url(s): [link to GitHub repo or other resources]
Contributors: [list of people involved], NAME
Description of project: [a few sentences describing the project]
How to get involved: [optional explanation of how one can get involved in the project]

Name: Brain Age
Project url(s): https://github.com/danielmlow/brain_age
Contributors: Julie Schneider, Ali Cohen, Danielle Sullivan, Daniel Low, Shina Seabury, Rachel Mak-McCully, Michalis Kassinopoulos, Prokopis Prokopiou
Description of project: How old is your brain? The current project implements Support Vector Regression (SVR) to predict #brain-age from structural MRI and resting-state fMRI seperately in neurotypical and clinical populations.
How to get involved: Contact Daniel Low

Name: The Nipype Pals Part 1: PALS Project url(s): https://github.com/npnl/pals Contributors: @kaoriito @joselise Description of project: We dockerized PALS with a GUI, refactored PALS with nipype (in progress), incorporated ni-learn for QC images. How to get involved: Check out our issues (more will be added soon!): https://github.com/npnl/pals/issues

Name: The Nipype Pals Part 2: The Virtual Brain Project url(s): https://github.com/amandakeasson/tvb_nipype Contributors: @axiezai @yishuwei [@amandakeasson](https ://github.com/amandakeasson) Description of project: We made a nipype workflow for The Virtual Brain How to get involved: Check out our issues (more will be added soon!): https://github.com/amandakeasson/tvb_nipype/is sues

Name: ConvOuch
Project url(s): https://github.com/jarodroland/ConvOuch
Contributors: Jarod Roland, Zheng Ma
Description of project: Train a simple convolutional neural network to classify brain CT images has having an intracranial hemorrhage or not. Determine and select usable scans and organize them in the BIDS format
How to get involved: Try the model code with your own data!

Name: study_forrest
Project url(s): http://studyforrest.org/ Contributors: Aya Ben-yakov, Jeff Brooks, Jin Cheong, Joao Guassi Moreira, Zheng Ma Description of project: Broadly, examine how neural representations of characters and emotions are shared between observers, change with experience, and track with other aspects of physiology.

Name: nilearn_task_networks
Project url(s): https://github.com/kfinc/nilearn_task_networks
Contributors: Josh Gray, Janelle Liu, Kamalaker Dadi, Karolina Finc, Ilkay Isik
Description of project: Building a pipeline and tutorial for task fMRI analysis from nistats to nilearn

Name: BIDSonym
Project url(s): https://github.com/PeerHerholz/BIDSonym, https://hub.docker.com/r/peerherholz/bidsonym/, https://osf.io/x4dku/
Contributors: Rita Ludwig, Peer Herholz
Description of project: a BIDS app for de-identification of neuroimaging data

Name: MNI Atlas Reader
Project url(s): https://github.com/miykael/mni_atlas_reader
Contributors: Dan Gale, Ross Markello , Michael Notter
Description of project: This package provides a Python interface for generating coordinate tables and region labels from fMRI statistical images.
How to get involved: Check out our issues on github or create a new one. We're thrilled to welcome new contributors!

Name: better-google
Project url(s): https://github.com/tjlagrow/better-google , https://github.com/shreyaspadhy/easyhcp
Contributors: Saige Ruthaford, Shreyas Padhy, Anita Keshavan, Xavier Guell Paradis, Theodore J. LaGrow, Patrick Beukema
Description of project: U-nets on T1 to T2, and HCP interactive tool (waaaay-beta)\

Name: Parcellation Fragmenter
Project url(s): https://github.com/miykael/parcellation_fragmenter
Contributors: Kristian Eschenburg, Claudio Toro, Amanda Sidwell and Michael Notter
Description of project: Fragments a surface mesh into N-equally sized annotation parcels. Besides looking beautiful, such surface parcellation could be used as feature extract for machine learning or functional connectivity approaches.
How to get involved: Check out our issues on github or create a new one. We're thrilled to welcome new contributors!

Name: bidsQC
Project url(s): https://github.com/kdestasio/bidsQC
https://github.com/kdestasio/pybids
Contributors: Krista DeStasio, Kristian Eschenburg, James Kent, Lee Tirrell, Kirstie Whitaker, Tal Yarkoni
Description of project: Add a validate_sequences() module to pybids that creates user-friendly summary data frames to check whether a BIDS data set:

1. Contains any files with duplicate content
2. Is missing files that are expected to exist
3. Has more files than are expected for a given subject \

How to get involved: Try bidsQC on your data set and let us know how it works!

Name: hacktrack
Project url(s): https://github.com/rmarkello/hacktrack
Contributors: Ross Markello, Dan Gale, and Michael Notter
Description of project: A meta-project for examining Neurohackademy by the (wildly oversimplified and non-representative) numbers How to get involved: If you want to explore the data check out the Binder link in the main repository! Play around with the data and if you're inspired enough to generate a pretty plot, raise an Issue on the repository and let us know!

Name: O-Factor
Project url: https://github.com/srcole/o-factor
Contributors: Daniel Reznik, Emily Wood, Federico Adolfi, Kristina Rapuano, Mikella Green, Rosa Li, Rotem Botvinik-Nezer, Scott Cole
Description of project: Open-access articles were scraped from the PubMed Central API. Their full-text was searched for keywords (and phrases) indicative of code and data sharing. The prevalence of code and data sharing (and preregistration) was computed as a measure of the "openness" of the research published in that journal (it's "O-Factor").
How to get involved: Check out our issues on github or create a new one. We're thrilled to welcome new contributors!