This week I gave a talk about the Open Data ecosystem and how our open-source project https://NeuroDesk.org makes it very easy to access and publish open data. Here is the recording: Here are the slides:
Our latest work is out in eLife: https://elifesciences.org/articles/71186 We show how small pial arteries can be targeted effectively with high-resolution in vivo MR imaging. The data is openly available (https://doi.org/10.17605/OSF.IO/NR6GC) and may be used for building models of brain physiology. The pial arterial vasculature of the human brain is the Read more
I had the great opportunity to present in the AARnet Reseracher Talks webinar Winter Series 2021 and in this Research Showcase Webinar I highlighted interoperability examples between AARnet CloudStor with external tools and services like Zotero, the open science foundation (OSF) or high-performance computing clusters. I showed how CloudStor can Read more
I recently had the wonderful opportunity to present our deep learning work in magnetic resonance imaging at the ML4AU CoP showcase event to more than 65 participants. I showed one example on efficiently solving an ill-posed inverse problem for quantitative susceptibility mapping that was trained with purely synthetic data describing Read more
I was recently invited to join the panel for an @OSFramework webinar on Leveraging Open Ecosystems to Enhance Reproducible Workflows: The webinar covers OSF integration with @protocolsIO, R @aaronwolen, command line interfaces and jupyter notebooks – thank you for the great discussions!
Cloudstor is a wonderful storage service providing 1TB of owncloud storage for researchers in Australia. It’s like dropbox, but your data is in Australia, very fast to access from aarnet institutions and they provide a jupyter notebook service SWAN that can directly access this data 🙂 Wouldn’t it be nice Read more
The full publication can be found here: https://doi.org/10.1002/mrm.28590 The preprint is here: [1911.08118] Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks (arxiv.org) The data used to train the model is on OSF: And we also built a colab notebook (described Read more
For a paper we recently submitted (Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks – pre-print here: https://arxiv.org/abs/1911.08118 and published article here: https://doi.org/10.1002/mrm.28590) I was wondering if we can do more than just provide the source code. One problem I Read more