Time series are measured and analyzed across the scientific disciplines, with new methods for their analysis being developed regularly. This is particularly true for quantifying patterns in brain dynamics, for which hundreds of methods have been developed and applied. How can we distinguish a real advance from a new method that actually reproduces the behavior of an existing method? The need for comprehensive and systematic comparison is paramount in methodological literatures like time-series analysis, but is incredibly difficult to achieve practically. We have recently developed an online portal for comparing time-series data, CompEngine (https://www.comp-engine.org/), that allows scientists to drag-and-drop their data onto our portal to get an answer to the question: âwhat sorts of data from across science are similar to the data that I measure?â However, there is still no way for scientists to compare their methods to alternative methods from other disciplines. Achieving this would enable scientists to better work together across disciplinary boundaries, towards a unification of methods for time series. Such an endeavor would be transformative in facilitating the concentration of scientific effort towards meaningful interdisciplinary progress, and could become a template for similar efforts applied to other data types.
In this project, we will continue to develop and refine a Django online platform for comparing time-series analysis methods. As CompEngine does for time-series data, this would allow a scientist to upload their code, compute the results on a dataset, and search a library of existing features for the most similar methods. The scientist could then be given a âuniquenessâ score, and be able to visualize their method in a broader scientific context. We have begun to develop this at GitHub - DynamicsAndNeuralSystems/Comp-Engine-Features. Interested students should have interest and/or experience in web development, python, django, databases, and time series.
#GSOC2021
Hey!
I am Aditya R Rudra, a second year undergraduate from National Institute of Technology Karnataka.
I read through this project idea and found it fascinating to work on.
Any guidance regarding how to start and make meaningful contributions will be appreciated.
RegardsâŚ
Hi Aditya, welcome to Neurostars, and thanks for your interest in our work! This project is run with @ocliff who might be quicker to give helpful responses
The first step would be to look at and get familiar with the code repository linked to above. You may also be interested in the progress made during GSoC last year
Ben
Hello Everyone,
I am Diptanshu Mittal pursuing my undergrad in Computer Science with Applied Mathematics.
I found the project really interesting and want to contribute to the project. I went through the GitHub repo and would love to discuss new features for the platform. @ben.fulcher@ocliff
Dear Diptanshu Mittal, welcome to Neurostars and thanks for your interest in this project!
Excellentâthanks for looking through the GitHub and suggesting some improvements.
Weâre excited to develop some new features for this portal, which we think could be a really nice model to help scientists evaluate their algorithms. I guess the next step is to follow the INCF timeline and prepare a proposal. @ocliff and I would be happy to review and provide feedback on your draft for implementing new features to this web portal.
Hi @ben.fulcher@ocliff, have the interns for this idea been selected? If not Iâd be interested to join. Iâve mentioned this in the Update documentation infrastructure project too that I have worked with Python as a part of projects in college (specifically, have built sites in Django) and would love to be a part of this.
Hi there @akshayprabhu thanks for your interest.
Students are selected from the quality of their project proposals, submitted by all applicants that nominate this project by the deadline. You should check the GSoC for the timeline of when these proposals are due.
Best,
Ben
