Skip to content
Open notes and resources related to my fellowship with mozilla.
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
onboarding_notes
LICENSE
README.md
open-ideas.md

README.md

mozilla-open-fellowship

Notes and open collaborative documents for my Mozilla Fellowship. Early Autumn 2018 - Late Summer 2019.

Projects

Lead / Solo

🔧 Tool
Back Your Scientific Stack
A proposal to emulate, and build upon, the work done by backyourstack.com, in order to inspire recognition and funding for the open source projects that a lot of scientists rely on. The core goal is to encourage funders to accept (and grantee's to apply for) donations to open source projects as part of their grant.

🔧 Tool
Feed.Me
A browser-extension to customise (social media) feeds client-side. The aim is give anyone the power to take control over the algorithms that dictate what they see on social media.

💾 Database
Meta Open Database
Our aim is to create a database that will provide, and link, information about the multitude of factors that determine whether a piece of tech is right for you. This project also acts as a backend to Back You Scientific Stack.

💻 Software
Jupyter Span / Graphical Notebooks
Computational notebooks that reflect the mental model of humans. We are developing an experimental new UI for Jupyter that reflects their use as an IDE for data science and seperates out this use case from report-generation.

📜 Paper
Continuous Research
Continuous delivery for science!

Core Contributor

📕 Book
The Turing Way - A Guide to Reproducible Data Science
The Turing Way is a lightly opinionated guide to reproducible data science. Our goal is to provide all the information that researchers need at the start of their projects to ensure that they are easy to reproduce at the end.

💻 Software
Spikeforest / MountainLabJS
A framework supporting continuous benchmarking of spike sorting software. Designed for use in any benchmarking application especially where big datasets and long-running algorithms are the norm. Includes wrapping of spike sorters in singularity containers and python classes (or JSON objects), job batching, comparison with ground truth, and processing using remote compute resources.

You can’t perform that action at this time.