processing-scripts
processing-scripts > Google program for CPAC developers still open
Mar 25, 2015 02:03 PM | Ivan Roi
Google program for CPAC developers still open
Dear all,
The application period for Google Summer of Code (GSoC) projects with INCF (International NeuroInformatics Facility) will still be open for few days, and we encourage interested candidates to apply to this exciting opportunity.
Every year Google invites students to embark with an open-source institution of their choice for few months, and develop code in a project of the interest. These experiences make a real difference, boosting students' careers and endeavours in the process.
We would welcome interested students to direcly address any questions or enquiries, as well as intention to apply, to Dr Ivan J Roijals (ij.roijals@gmail.com).
Toolboxing complexity for connectomes: New code for big samples in C-PAC
C-PAC (Configurable Pipeline for the Analysis of Connectomes, http://fcp-indi.github.io/) is a new, developing tool designed to study connectivity in brain images, transformed into connectomes. It allows us to work with big datasets using computer clusters. Integrating some of the most successful imaging environments to date (FSL, AFNI, ANTS), and advancing several innovative methods in preprocessing (automatic quality control, motion correction, regional homogeneity), it provides researchers with the chance to test new network and dynamical properties. Complexity measures (graph and non-linear series) are currently among the best tools to study functional network properties and the design of the brain topological properties.
Aims
In contact with mentors, C-PAC developers and its forums, the successful student will develop very valuable and innovative tools that will help researchers to perform new analysis in connectomics:
- To add/expand a set of python scripts (within the scope of the developers' documentation; http://fcp-indi.github.io/docs/developer...), oriented to the analysis of non-linear series' within the time series workflow, thus giving a comprehensive set of analysis tools in this area to the researcher. Some of the proposed, such as sample and transfer entropies, multifractal analysis and surrogate testing, would help to test dynamical properties not available in current packages.
- Alternatively, this project is also aimed to produce code and integration of new network connectivity (generically within centrality and complexity structure), complementing the existing ones, in the frame of C-PAC's analysis/networks workflow and providing a more comprehensive set of tools for the study of connectomes.
- To put the test these tools to test and document its performance with the study of available databases (ie. 1000 connectomes datasets) and the properties that pinpoint in brain function. It will also be expected to help in the integration of workflows and components, possibly interacting with the C-PAC community.
Skills
- Essential: Hands-on experience in python, possibly in Nipype and sofware developing. Familiarity with cluster and grid computing and software carpentry / collaborative software (Github). Basic knowledge on scientific implementation. Basic knowledge on functional imaging processing. Knowledge in graph methods, time-series complexity analysis and information theory.
- Desirable: C++ and/or Fortran, Perl, Javascript, CSS. Working knowledge on imaging preprocessing and frequency analysis. Knowledge on imaging packages (FSL, AFNI).
Mentors
Ivan J Roijals (KI-INCF, ij.roijals@gmail.com). Co-mentors: Cameron Craddock (Child Mind Institute, cameron.craddock@gmail.com), Victor M Eguiluz (IFISC-UIB, victor@ifisc.uib-csic.es)
The application period for Google Summer of Code (GSoC) projects with INCF (International NeuroInformatics Facility) will still be open for few days, and we encourage interested candidates to apply to this exciting opportunity.
Every year Google invites students to embark with an open-source institution of their choice for few months, and develop code in a project of the interest. These experiences make a real difference, boosting students' careers and endeavours in the process.
We would welcome interested students to direcly address any questions or enquiries, as well as intention to apply, to Dr Ivan J Roijals (ij.roijals@gmail.com).
Toolboxing complexity for connectomes: New code for big samples in C-PAC
C-PAC (Configurable Pipeline for the Analysis of Connectomes, http://fcp-indi.github.io/) is a new, developing tool designed to study connectivity in brain images, transformed into connectomes. It allows us to work with big datasets using computer clusters. Integrating some of the most successful imaging environments to date (FSL, AFNI, ANTS), and advancing several innovative methods in preprocessing (automatic quality control, motion correction, regional homogeneity), it provides researchers with the chance to test new network and dynamical properties. Complexity measures (graph and non-linear series) are currently among the best tools to study functional network properties and the design of the brain topological properties.
Aims
In contact with mentors, C-PAC developers and its forums, the successful student will develop very valuable and innovative tools that will help researchers to perform new analysis in connectomics:
- To add/expand a set of python scripts (within the scope of the developers' documentation; http://fcp-indi.github.io/docs/developer...), oriented to the analysis of non-linear series' within the time series workflow, thus giving a comprehensive set of analysis tools in this area to the researcher. Some of the proposed, such as sample and transfer entropies, multifractal analysis and surrogate testing, would help to test dynamical properties not available in current packages.
- Alternatively, this project is also aimed to produce code and integration of new network connectivity (generically within centrality and complexity structure), complementing the existing ones, in the frame of C-PAC's analysis/networks workflow and providing a more comprehensive set of tools for the study of connectomes.
- To put the test these tools to test and document its performance with the study of available databases (ie. 1000 connectomes datasets) and the properties that pinpoint in brain function. It will also be expected to help in the integration of workflows and components, possibly interacting with the C-PAC community.
Skills
- Essential: Hands-on experience in python, possibly in Nipype and sofware developing. Familiarity with cluster and grid computing and software carpentry / collaborative software (Github). Basic knowledge on scientific implementation. Basic knowledge on functional imaging processing. Knowledge in graph methods, time-series complexity analysis and information theory.
- Desirable: C++ and/or Fortran, Perl, Javascript, CSS. Working knowledge on imaging preprocessing and frequency analysis. Knowledge on imaging packages (FSL, AFNI).
Mentors
Ivan J Roijals (KI-INCF, ij.roijals@gmail.com). Co-mentors: Cameron Craddock (Child Mind Institute, cameron.craddock@gmail.com), Victor M Eguiluz (IFISC-UIB, victor@ifisc.uib-csic.es)