Explore Projects

The currently available open-review portal helps in reviewing the research articles, By facilitating official reviewers to comment on the research article. This may be a time-consuming process and as the number of submitted articles increases, it would be difficult for a small group of reviewers to handle them and review them. The Sci-commons portal allows every enthusiastic user to review a paper and comment on it and also rate submitted articles. This enhances the quality of research articles and also decreases the review time. This can also help in gaining new perspectives from normal users. During the reviewal process, most users want to stay anonymous in their reviews. Sci-commons help in achieving this by allocating random handles for users who submitted a review. This anonymity helps in making the review process ethical, fearless, and objective.

<p>While running simulation on cortical surfaces we need to calculate geodesic distance as opposed to euclidean distance due to the shape of the cortical surface. The virtual brain uses geodesic_library for this calculation. The library implements the <a href="https://pdfs.semanticscholar.org/f890/2dc723ac2a49ee52efbc58947c21f8d0970b.pdf" target="_blank">original paper</a> in C++. The original source code can be found in Google Code Archive: <a href="https://code.google.com/archive/p/geodesic" target="_blank">https://code.google.com/archive/p/geodesic</a>. tvb_geodesic repository implements a cython wrapper on top of the C++ code which then is released to Pypi (<a href="https://pypi.org/project/tvb-gdist/" target="_blank">tvb-gdist</a>) and conda-forge (<a href="https://anaconda.org/conda-forge/tvb-gdist" target="_blank">Tvb Gdist</a>).</p> <p>However, the code is now outdated and users have reported various <a href="http://github.com/the-virtual-brain/tvb-geodesic/issues/" target="_blank">issues</a>. In this project, we aim to update the code and fix those issues.</p>

This is a proposal to focus on general web development of LORIS modules and API, mentioning key deliverables such as familiarization with the LORIS codebase, improvements to the pre-existing code and issues, existing API and module bug fixes, Code or improve a feature and feature requests, add documentation about the changes and suggest changes, and refactor code after review. I have expanded upon those deliverables and specified them in detail

<p>Maxima is a computer algebra system, which has been growing for the past 40 years. However, given the growing use of Python in Neuroscience, under the need of a common platform for scientific computation and numerical capabilities, I propose:</p> <ol> <li>To work on making a Maxima-to-Python translator, which will be entirely accessible from a running Maxima instance. It will be a loadable Maxima add-on.</li> <li>To write tests for the translator, to ensure it is not buggy and is usable.</li> <li>To document the code for both - the end user, and developer.</li> </ol>

I am planning to develop a Latex to XML pipeline and adapt the final format to PMC requirements and integrate the pipeline with the current Scholastica platform and explore a standalone platform for NBDT Journal. Deliverables ● Creating a pipeline to convert latex papers into XML. ● Adapting the final format to PMC requirements. ● Integrating with the current Scholastica platform. ● Exploring and prototyping a standalone platform.

Artifacts are parts of a measured signal generated from sources other than those of interest. For electroencephalography (EEG) data, artifacts from a variety of physiological and environmental sources must be excluded or repaired to ensure that appropriate interpretations are applied during clinical evaluation and downstream analyses. We will develop a suite of web-based tools for facilitating artifact annotations for the Global EEG Norms project of the Global Brain Consortium (GBC). This project will enable clinical neurophysiologists to upload EEG data in standardized formats; annotate data with artifacts, such as electrical noise and movement artifacts, using a novel ontology that is interoperable with the Hierarchical Event Descriptors specification; and export this data to the Brain Imaging Data Structure standard. While the resulting libraries of this project will be integrated into a Progressive Web Application for clinical neurophysiologists, they will also be released standalone as tools to further facilitate the development of neuroscience tools on the Open Web. This could reduce the barriers to entry for neurophysiology research by enabling standalone data management and analysis applications to be released on modern browsers. In the long run, this may enable the rapid diagnosis of neurological disorders in lower to middle-income countries with unreliable internet connections.

The Active Segmentation Platform for ImageJ (ASP/IJ) aims to provide a general-purpose workbench that allows biologists and other domain experts to access state-of-the-art techniques in machine learning to achieve excellent image segmentation and classification. This project enhances the functionality and user experience of ASP/IJ by updating its user interfaces and developing a new visualization and reporting panel, exposing the Weka advanced visualization and analysis functions.

<p>This project is about packaging of the the virtual brain(tvb) to the scientific community, using the most used scientific software distributors such as packaging tvb to anaconda, Develop a native launcher for tvb distributions, Packaging and distribution for neurodebian , Develop a vagrantfile script for virtual machines, Develop a script( Dockerfile ) for building a docker image, And finally if time allow develop a script for building a Amazon Web Image(AMI).</p>

Ensuring healthy vision in infants requires early detection of potential problems. Current Infant Visual Function Measurement Systems built in previous GSOC 22 and GSOC 23 are efficient and robust solutions but are limited by difficulties with infant cooperation and shortcomings in eye-tracking technology. To address this, a comprehensive upgrade is proposed. A user-friendly and visually engaging interface will be built for researchers and clinicians. Additionally, a wider variety of captivating stimuli, both visual and auditory, will be incorporated to keep infants engaged during testing. The core functionality, eye-tracking, will be significantly improved by exploring advanced deep learning techniques. This will enable precise gaze estimation in all directions, including up and down, providing a more complete picture of an infant's visual attention. Furthermore, a novel test will be developed to identify potential vision issues associated with anisometropic amblyopia. Finally, the system will be designed for seamless integration with various external eye-tracking hardware devices, increasing its flexibility and applicability. This project strives to create a user-friendly and robust Infant Visual Function Measurement System, empowering researchers and clinicians with a powerful tool for early detection and intervention, ultimately leading to better visual development for young children.

This project aims to build HarmonyHub, a web platform for personalized and engaging learning experiences with variable-pitch instruments like wind instruments (e.g., Trumpet, Flute, Clarinet) and bowed string instruments (e.g., Violin, Viola). Most wind and string instruments are among the most difficult instruments to master. This project seeks to bridge the gap between traditional music education and modern technological capabilities. The website offers customized exercises based on a student's skill level and desired progression speed, with real-time feedback for improvement. Personalized features, such as adapting exercises to master different rhythms with precise control over intonation, loudness, pitch, etc., will be implemented. Students and teachers will be provided with features to input any score, and the website will analyze the score, play the sound, and demonstrate how to play it on the instrument with pictures for the students to understand and replicate. When the students play it, real-time feedback of their performance will be provided. All of this is done through proper API integrations of APIs available for this process.

<p>State of the art computational tools are in especially high demand in the field of Neuroscience. However, bottleneck exists in terms of how much data can be transferred between hard disk and memory for computation [1]. As research increasingly relies on processing huge volumes of data, this issue demands attention. One strategy which addresses this is memory compression of data in memory (DRAM). Algorithms that are effective do a good job of decompressing exactly as much of the data as are needed for the calculations, so that they still minimize the memory footprint of the program without significant speed drops.</p> <p>This proposal exists to address this bottleneck within the Neuromapp program created by members of the Blue Brain Project team. The project proposed is split into research and implementation of in-memory compression along the lines of compression library selection, interface algorithm development, and block data structure design. The resulting compression mini-app is intended to relieve this bottleneck, and provide accelerated calculation capacity for the suite of associated mini-apps that come with Neuromapp.</p>

Project Deliverables • Admin Section : Implement an admin dashboard to manage users, projects, and datasets. Add moderation tools for handling flagged content and project approvals. Implement role-based access control (RBAC) for better user management. • Forking Datasets/Projects : Enable users to fork existing projects and datasets, creating editable copies. Implement versioning & attribution tracking for forked projects. Ensure that forked projects maintain links to original versions. Provide users with a UI to track changes between original and forked projects. • Following/Joining Projects : Allow users to follow projects for updates and notifications. Implement a Join Project feature requiring owner approval for collaborations. • Project Dashboard Improvements : Improve UI/UX for easier navigation and better data organization. Add interactive components for better visualization of project statistics. Enable filtering and sorting options for users to manage multiple projects efficiently. • Notebook Management Enhancements : Enhance notebook versioning, metadata, and organization. Improve JupyterLite integration for a smoother editing experience. Implement a notebook history tracker, allowing users to revert to previous versions. • Publication List Enhancements : Improve publication listing, filtering, and categorization. Enable direct linking to project-related publications. • Versioning & Querying Enhancements : Implement dataset version control for reproducibility. Improve querying components to facilitate advanced data filtering. Enable users to compare different versions of datasets using a visual diff tool.

<p>This is a project is about identifying and parameterizing the precursors of neuronal cells within early-stage embryos. We will co-register the names with the cells due to their general location, and we collect quantitative data about static location after division and vectors that describe shape changes and overall positional changes in the embryo. For this, we need to develop image segmentation tools, which is the top priority of my proposal, to assist further development of this project. My approach, in nutshell, is to try out a variety of segmentation techniques to achieve the top priority of this project. The project will conclude with a development of a proper ImageJ plugin which will facilitate application of our approach on other databases.</p>

GUI: In this part, we aim to build a new GUI for HNN, using the HNN core functions and open source practices, in the form of an ipywidget. Mimicking the current functionality and tutorials in the existing GUI, it will allow users to interactively set the experiment settings and get visualization results presented on the fly. Unlike the traditional HNN GUI, all these functionalities could be used within JupyterLab, making it convenient and friendly to those who prefer to use HNN on servers. This part will also benefit users that need to frequently adjust simulation settings and get real-time results but have little programming experience. CSD: Currently HNN-core provides APIs to record local field potentials from simulations while an API to turn these signals into source density signals is missing. In this part we will implement a series of mainstream algorithms to calculate the CSD and a visualization API that calls them in the backend to visualize the CSD.

<p>LORIS, or Longitudinal Online Research and Imaging System, is a research data platform (github.com/aces/LORIS) for neuroscience studies. It is a web-based and open source framework and it assists with data collection and data sharing across sites. LORIS hosts frontend services that allow researchers to view, manipulate, and share data within the platform. It also has backend services, implemented with RESTful API, that allows for data sharing.</p> <p>Managing neuroscience data and databases is a very important issue so that research can move forward. In order for LORIS and other data management platforms to run smoothly, there needs to be a very well-managed set of tests in place with very good test coverage.</p> <p>The project idea aims to increase the test suite of LORIS to improve test coverage and further improve the LORIS platform. This will be accomplished by both improving on the already existing test suite and also adding to it. The goal will also be to improve the maintainability of the code implemented. Improving LORIS’s test coverage will do much to help the LORIS developers and users and will better the platform’s usability and effectiveness.</p>

The project will develop the first version of an app that uses Bluetooth to connect with heart-rate and respiration sensors, calculate metrics of synchronization and variability based on these measurements and implement a biofeedback protocol to control this variability. This will allow the study of autonomic nervous function, and will allow biofeedback protocols for mood and health intervention to be implemented via live tracking of breathing and heart-rate. I plan to use the available open source code which is in python and re-write the modules in dart using the pub dev packages.

While music educators strive to provide personalized learning paths, existing educational tools often lack the flexibility to adapt to diverse student needs. HarmonyHub bridges this gap by integrating Generative AI into a modular, web-based platform, empowering teachers to design customized exercises and dynamically adjust lessons based on individual progress.By leveraging AI-assisted personalization, HarmonyHub enhances the teacher’s ability to cater to different learning styles, ensuring a more engaging and effective music education experience for all students. 2. Key Features This project contributes to the open-source music education ecosystem by: Empowering teachers with AI-driven tools for personalized lesson planning. Providing a no-code interface to enable educators to create and modify exercises. Enhancing inclusivity by tailoring lessons based on students’ technical proficiency, learning speed, and backgrounds. Expanding the Harmony Hub ecosystem by integrating Generative AI for real-time content creation and assessment. Demonstrating how LLMs can be effectively applied to domains involving non-textual data, such as music.

The idea of the project is to develop IO routines adapted from HNN-core objects while maintaining backwards compatibility with HNN-GUI. The requirement is to save various objects in newer formats while having capability to read both new and old formats. The proposed new format for HNN-core outputs is the Cross-language, flexible and binary format hdf5. It consists of groups (folder) and datasets (individual files) to store data modularly.

This project aims to further develop an open-source intracranial EEG (iEEG) analysis platform by integrating clinically relevant seizure detection and seizure onset zone (SOZ) estimation algorithms, along with enhanced visualization tools. The goal is to support clinicians and researchers through a semi-automated, transparent, and interactive workflow for epilepsy analysis. By combining automated methods with expert input, the platform will facilitate reproducible and interpretable analyses in both clinical and research settings.

<p>Neuroimaging scans can be costly in both time and money to acquire. This creates a barrier for many underfunded researchers without access to the required equipment. Responsible data sharing can level the playing field, but the many different specifications of these acquired images cause portability issues between different labs and scientists. BIDS (Brain Imaging Data Structure) is a framework for organizing data that standardizes file organization and dataset description between different researchers.</p> <p>The most important part of BIDS are the users: the more people that use it, more data will be shared and the more powerful it will become. We want to make it easy to learn and more adopted. BIDS is platform independent and still an adapting, growing tool, meaning the greater the community the better it will be.</p> <p>The creation of the BIDS starter kit will focus on the following aspects: creating introductory documentation, making interactive tutorials, creating examples, and structuring the platform in a way where the tutorial can grow and adapt with the community. Where appropriate, we will leverage existing resources and link to tutorials that already exist.</p>

<p>The PyOpenWorm data management tool aids in creating, storing, and sharing information about Caenorhabditis elegans and about the evidence supporting it. This initiative will focus on providing a primary source of distribution for that information. This specification will integrate a Bit-Torrent client into the existing PyOpenWorm codebase enabling researchers to transfer data sources. This will provide OpenWorm Foundation and the larger neuroscience research community with a peer-to-peer file sharing framework with the ability to limit access to sensitive information and also protect against malicious changes. It will pave the way for easier sharing and collaboration, ultimately leading to a better understanding of C.elegans.</p>

Eye tracking has varieties of applications ranging across usability and user experience research, gaming, driving, and gaze-based interaction for accessibility to healthcare. The smartphone gaze could also provide a digital phenotype for screening or monitoring health conditions such as autism spectrum disorder, dyslexia, concussion, and more The project idea is to develop an eye tracker using deep learning in Python using TensorFlow/PyTorch. The work was to improve and update a neural network based, state of the art eye tracker

The project develops an improved Python-based CLI for the CBRAIN distributed computing platform, aiming on usability and modular design. It replaces complex command usage with interactive, prompt-based workflows, reducing reliance on memorization. The system leverages existing CBRAIN API routes and restructures the codebase into a clean, feature-based architecture. It supports user, project, file, task, and data provider management, along with advanced operations like authentication, batch processing, multi-session, data upload / download, file querying / selection and server control. Administrative tools for logs, quotas, and system monitoring are included. Overall, the project delivers a scalable, maintainable, and user-friendly CLI that enhances productivity.