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<p>The VCF (Variant Call Format) is a format for text files, which is generally stored in a compressed manner to make the data retrieval of variants fast. The data which is redundant is not stored, only the variations are stored. VCF files are used to store all variant types which includes single nucleotide polymorphism (SNP) in a specific position of the genome, short insertions and deletions (INDEL) and structural variants (SV). VCF-validator includes various checks to ensure that the VCF file is consistent. It is based on a formal grammar and performs lexical, syntactic, and semantic analysis of the VCF file. It also includes a tool called VCF-debugulator which fixes errors such as the presence of duplicate variants automatically. SNPs and INDELs are fully supported in VCF-validator, but the support for SVs is still limited. The aim of this project is to improve the support for structural variants in the validator and the debugulator.</p>

<p>The main objective of McGill initiative in Computational Medicine (MiCM) is to deliver inter-disciplinary research programs and empower the use of Big Data in health research and health care delivery. One of the ways MiCM aims to achieve this objective is to strengthen collaborative research by sharing and transferring knowledge within the community.</p> <p>In order to realize the potential of Computational Medicine at McGill University, there is a need to better connect researchers in life sciences and clinical domains with researchers and students in the data sciences (e.g., statistics, bioinformatics, medical informatics, computer science, epidemiology). The former has interesting datasets and questions, while the latter can apply or develop quantitative methods to look for solutions to these questions.</p> <p>To facilitate this type of matchmaking, this project works on a database-driven, lightweight web application, with the purpose of matching McGill research data projects, with masters and doctoral students seeking looking for interesting projects to analyze.</p>

Music Blocks is a visual programming environment that combines programming and music creation, but users often struggle to debug their projects due to unclear error messages and a lack of guided assistance. This challenge limits the platform’s educational potential. To address this, the project proposes an AI-powered debugger that will analyze user projects, detect errors, and provide real-time, context-aware solutions. By leveraging open-source LLMs like LLaMA 3 and Retrieval-Augmented Generation (RAG), the debugger will understand Music Blocks-specific issues and offer structured guidance, helping users learn while troubleshooting. The solution involves developing a JSON-to-text converter to interpret project structures, training an LLM on curated Music Blocks datasets, and implementing RAG to fetch relevant documentation and examples. A user-friendly debugger widget will be integrated into the Music Blocks interface, enabling seamless interaction with features like error detection, step-by-step explanations, and creative suggestions. The backend will use FastAPI for efficient API handling and AWS for scalable deployment, ensuring the system remains responsive and accessible to users worldwide. The debugger will empower users to independently troubleshoot projects, enhance learning, and make Music Blocks more accessible. By combining AI with guided problem-solving, it aligns with Sugar Labs’ mission to foster creativity and education through open-source tools.

<p>MusicBrainz for Android was first created in 2010-11 as a part of GSoC. Since 2015, no updates have been made to the project. The app is currently broken as the it does not adhere to android ecosystem. I feel a mobile app is a necessity for an organization like MusicBrainz. A mobile app will help increase user engagement and open up MusicBrainz to a plethora of new users.</p>

<p>Transitioning the matrix connector in opsdroid to matrix-nio API to add support for E2EE and facilitate further development</p>

This project aims to make Sugar activities more accessible to a wider audience by extending them beyond the Sugar environment. It involves maintaining existing flatpak applications and porting 12 new activities to flatpak. The selection of activities for porting is based on their usability, popularity, and impact on users.

The objective of this project is to improve the functionality of cBioPortal by introducing support for categorical and binary data analysis and visualization in the Generic Assay format. The project involves implementing new services and controllers in the backend to provide the needed GenericAssayData for the frontend. The frontend will support features to allow users to perform Fisher's exact test for binary data and chi-square test for categorical data, and to view the results in a clear and easy-to-understand bar chart and table format. In addition, end-to-end tests will be added to ensure the feature works as expected. Dliverables include wrapped and tested visualization functionalities in comparison page and comparison tab in results.

<p>Using ReactJS and Javascript for computation and visualisation of vehicle range to ease range anxiety among electric vehicle drivers.</p>

<h4>Jérémie Amsellem</h4> <p>Full proposal is available at : <a href="https://wiki.sugarlabs.org/index.php?title=Summer_of_Code/2016/SugarizerOS" target="_blank">https://wiki.sugarlabs.org/index.php?title=Summer_of_Code/2016/SugarizerOS</a></p>

<p>There has been a recent surge in the development of open-source computational methods for simulating human evolution and analyzing human genome data. These provide many new opportunities for genomic research, but the integration of these different resources is currently poor. In particular, turning models of human history into evolutionary models is notoriously time-consuming and bug-prone, and it requires knowledge of the specifics of each simulation tool. The major goals of this project are to develop a library of widely used historical models that integrate across multiple simulation tools, and to develop more robust and user-friendly model specification tools to automate the workflow of genomic and evolutionary analyses.</p>

Sugar's Journal records everything a child creates, but reflection one of its three core design pillars amounts to an empty text box that most children never touch. There is no prompt, no conversation, no mechanism to surface what a child learned from what they made. This project adds that mechanism by embedding a conversational reflection panel directly in the Journal, powered by the existing Sugar-AI backend. When a child opens a Journal entry, A reflection buddy asks a single artifact grounded question rooted in constructionist pedagogy. The conversation is adaptive, capped, and never forced. A gentle non-blocking nudge on activity exit reminds children that Reflection Buddy is ready without interrupting their workflow. What a child says they want to try next is saved and surfaced at the start of their next session, closing the loop from Reflect back to Imagine. Offline deployments are covered by Reflection Sparks, a curated bank of static prompts. Deliverables: 1. 2 FastAPI endpoints integrated into Sugar-AI (/reflect/chat and /reflect/summary) 2. A GTK3 reflection panel embedded in the Journal view 3. Metadata persistence via D-Bus 4. A feed-forward banner 5. An offline fallback 6. Full test coverage, working across all 40+ Sugar activities without activity-specific configuration.

Design and build a real-time peer mentorship matching room using WebRTC and Node.js WebSockets.

<p>Vert.x has already the capability to send metrics to Hawkular. However, students in thie proposal have to implement the functionality needed so that resources in vert.x can report themselves into the inventory of Hawkular, so that the Hawkular UI and other clients can find them with additional traits like operations, names of metrics etc. This also implies being able to run operations from Hawkular inside vert.x.</p>

Modernize the Prometheus alerts visualizer layout with HSL colors, active search filtering, and silences detail overlays.

<h3>Goal</h3> <p>This project aims at fully implementing end-to-end encryption in matrix-python-sdk, following the official implementation guide and making use of the Python bindings already present in libolm.</p> <h4>Rationale</h4> <p>Currently, even though end-to-end encryption has landed in Matrix for over a year, only the three SDK upon which is built Riot on different platforms (JS, Android and iOS) ship with encryption. On desktop, this situation prevents writing clients, bots and bridges that can interact with encrypted rooms in any other language than javascript. Tackling this issue would benefit the matrix ecosystem by seeing more featureful projects emerge in a variety of languages (Python in this case). It also supports the Matrix long-term goal of having E2E encryption turned on by default in private rooms.</p>

The goal of this proposal is to address the issue of how difficult it can be to package and reuse computational workflows and analyses in genomics and health research since diverse data and workflow standards don't always work together seamlessly. By creating a Python library and related command-line tool that enable bidirectional conversion between RO-Crates (with pertinent computational workload profiles) and GA4GH WES/TES payloads, the RO-GA4GH Bridge project seeks to address this issue.

<p>I am applying to work on Ruma's automated checks issue <a href="https://github.com/ruma/ruma/issues/221" target="_blank">https://github.com/ruma/ruma/issues/221</a> to build a special-purpose tool to check code style rules and formatting specific to Ruma. I will use <code>rust-analyzer</code>'s <a href="https://github.com/rust-analyzer/rust-analyzer/tree/master/crates/syntax" target="_blank">syntax</a> crate to parse Rust files into a typed AST that is checked against rules. I plan to use this AST to be able to format code inside macros since this is a big part of Ruma. I have started a proof of concept tool <a href="https://github.com/DevinR528/ruma-check" target="_blank">ruma-check</a> that I will complete during GSoC.</p>

Music Blocks and Turtle Blocks lack a structured version control system, making it difficult for users to manage project history, collaborate, and contribute effectively. This project aims to integrate Git-based version control, allowing users to track changes, fork projects, submit pull requests, and collaborate seamlessly. To achieve this, I will develop a backend API that interacts with GitHub or GitLab, enabling repository creation, commit tracking, forking, and merging. A user-friendly frontend UI will be implemented to provide an intuitive experience for project management. Deliverables: -A backend API supporting Git operations (repository creation, commits, forks, PRs, merges) -A frontend "Git Menu" for managing project versions, viewing history, and contributing. -A "Projects" page displaying repositories with commit history and branches. -Robust testing and documentation to ensure maintainability. -Initial user testing with community contributors and educators. The goal is to use GitHub as a back-end and database and use Git's powerful features to make learning more effective for students.

OrcaAL is an active learning tool built by past GSoC students for identifying orca calls. It achieves this by leveraging both expert and machine annotations to train better models in a semi-supervised way. Past efforts have been made mostly to develop a functional proof-of-concept and improve the performance of the tool. As such, this project aims to improve the underlying infrastructure supporting the tool, as well as enhance future developers' experience when expanding upon OrcaAL's capabilities.

Sugar has 250+ activities which have scope for improvements. This project will involve working on at least 12 activities to improve them, which involves: Porting activities to python3 Adding features to improve the game designs Improving overall UI and bug fixes

<p>The aim of this project is to use the camera and processing power of modern day cell phones to develop an intuitive and user-friendly application for the concentration estimation of various bio-markers in a blood-sample. Continuing on from last year’s work, this year’s task will be to improve upon the algorithm and implement the algorithm in iOS. The algorithm of the Android application will also be modified to the improved version.</p>

This project aims to develop a feature that enables users to import their listening history from various services, including ListenBrainz exports, Spotify, etc. The proposed solution involves creating a backend API endpoint to handle file uploads, building normalizers to parse and validate data from different services, and converting the data to the JSONL format required by ListenBrainz. The solution also includes a frontend to handle file uploads and show the progress to the user. This project will help contribute to the completeness and accuracy of ListenBrainz data.

<p>Currently Listenbrainz uses MSIDs (Messybrainz-ID) for retrieving useful user stats (e.g. <a href="https://beta.listenbrainz.org/user/iliekcomputers/artists" target="_blank">user listens</a>). Now ListenBrainz also plans to generate data which could be used by MusicBrainz to show useful information like artist popularity. MusicBrainz has MBIDs (MusicBrainz-ID) associated with each artist, recording, and release. In order to provide MusicBrainz with the ability to access information based on MBIDs, we have to associate recording_mbids, artist_mbids and release_mbids to the listens where we can. For most of the listens we don’t have artist_mbids and relesae_mbids associated with them. But have recording_mbids associated with them. So, now I plan to associate MBIDs to MSIDs.</p>