Mentors: Anca Doloc-Mihu <adolocm@gmail.com> and Cengiz Gunay <cgunay@ggc.edu>
Skill level: Intermediate to Advanced (preferrable)
Required skills: Angular (Typescript), HTML/CSS/Bootstrap, ASP.Net Core (C#), PostgreSQL, JavaScript (ObservableHQ, D3.js, Vega, Plotly) and Python (Jupyter)
Time commitment: Part time (flexible hours: 175 or 350)
About: AnalySim is a data sharing and analysis platform that seeks to simplify the visualization of datasets. With Analysim, researchers can collaborate by hosting their data and publishing their analysis notebooks to the world, or browse through multiple user-generated projects. Analysim is currently being hosted on the NSF-funded ACCESS-CI project infrastructure, but it can also be deployed independently via Docker.
AnalySim aims to be a general data sharing and hosting resource for crowdsourced-analysis, but it provides additional support for a specific type of dataset: one where many parameter combinations need to be tested and measurements are recorded for each instance. These datasets are very useful in mathematical modeling of natural phenomena, such as in computational neuroscience. We provide easy sharing, analysis, visualization, and collaboration capabilities on these datasets. In this GSoC iteration, we are improving on features developed in the summer and winter of 2024.
Project is still in progress and a demo site is available at: https://analysim.tech
Source code is at: https://github.com/soft-eng-practicum/AnalySim
Aims: Working on a more consistent visual user interface style and improved user experience. Design an optimal dashboard layout along with other necessary pages for project notebooks, publications, and commenting. Improve user experience workflow for common actions, and improve usability of CSV data browser. Adding examples of datasets, notebooks, and projects.
Website: https://analysim.tech
Tech keywords: Angular (Typescript), HTML/CSS/Bootstrap, ASP.Net Core (C#), PostgreSQL, JavaScript (ObservableHQ, D3.js, Vega, Plotly) and Python (Jupyter)