Standards and Tools in Neuroscience: a report on the Open Source Brain Workshop 2019

2019-10-01T10:52:42+01:00

While scientific work and output was traditionally limited to relatively small expert communities, the landscape is rapidly changing. Modern Science is far too complex to be carried out in isolation, and the need to increase the uptake of scientific output in society now seems far more pressing. As a result, scientific communities are pushing to make Science more Open.

The Neuroscience research community has also made this commitment. The philosophy of Free/Open Science, however, must be backed by Free/Open standards and tools. The Open Source Brain project is one of many initiatives that focus on developing Free/Open tools and standards for Neuroscience. A recent publication summarises their work.

While initially targeting computational Neuroscience, following the renewal of their funding from the Wellcome Trust, the Open Source Brain project are expanding their deliverables to support experimental data as well. With this in mind, they organised a workshop in September to discuss two key themes:

Accessible sharing of cellular Neuroscience data: by supporting the Neurodata Without Borders (NWB) format.
Modelling the cortex across scales: by further expanding the NeuroML model description language.

Since I was fortunate enough to attend this workshop, in this talk, I will summarise the main points that were discussed here. Time permitting, I will hope to begin a discussion in our group on how we can ensure that we follow and contribute to these standards and tools to make our research work and its outputs "default to Free/Open".

I will conclude with a (another?) short marketing pitch for our NeuroFedora project which shares these goals.

References/URLs

Open Science: https://en.wikipedia.org/wiki/Open_science
Free/Open Source Software: https://www.fsf.org/blogs/community/user-liberation-watch-and-share-our-new-video/
Open letter committing to the use of Open Source for Neuroscience: http://opensourceforneuroscience.org/
Open Source Brain: http://www.opensourcebrain.org
Neurodata Without Borders (NWB): https://nwb.org
NeuroML: https://www.neuroml.org
NeuroFedora: https://neuro.fedoraproject.org

Date: 04/10/2019
Time: 16:00
Location: D449

Introduction to Functional Data Analysis

2019-05-16T14:53:41+01:00

Yi Sun's journal club session, where she will present the paper "Applied Functional Data Analysis Methods and Case Studies (Ramsay, J. O. and Silverman B.W, 2002)"

The field of Functional Data Analysis (FDA) has seen rapid development over the last two decades. FDA refers to a collection of methods for analysing data over a curve, surface or continuum. It is very much involved with computational statistics. FDA has been applied to quite broadly in medicine, business and engineering.

In this talk, Yi will introduce the basic idea of FDA using a case study presented in the paper: zooming in on human growth.

“Human growth is not at all the simple process that one might imagine at first sight… Collecting records is time-consuming and expensive, because children have to be measured accurately and tracked for a long period of their lives.

[We] consider how to make this sort of record into a useful functional datum to incorporate into further analyses. A smooth curve drawn through the points is commonly called a growth curve, but growth is actually the rate of increase of the height of the child. In children this is necessarily positive… [We] develop a monotone smoothing method that takes this sort of consideration into account and yields a functional datum that picks out important stages in a child’s growth."

Date: 10/05/2019
Time: 16:00
Location: E251

UH Biocomputation Group - data analysis

Standards and Tools in Neuroscience: a report on the Open Source Brain Workshop 2019

References/URLs

Introduction to Functional Data Analysis