Links are critical to deep-time knowledge preservation

Cheap and simple data blob storage is widely available and relatively easy to access and use. However, easily applying the right kind of compute is still laborious with too many complex barriers, arcane commands, hard to understand costs, and psychological anxiety that interrupts flow state when prototyping, analyzing, and generally doing data intensive research and engineering.

URLs can safely and effectively store user created code or configuration (even credentials [1]) (access to things). URLs can also . This allows anyone with the link to not only view the created resources but also to edit.

We have a plan for the lifecycle of the organization.

Check out the workflow on the app

There are new bio AI models coming out every week. Last week is was bioemu:

https://www.microsoft.com/en-us/research/blog/exploring-the-structural-changes-driving-protein-function-with-bioemu-1/

How to keep up?

Deciding where how how to publish your data intensive scientific workflows can be difficult, with no clear solution.

Beyond jupyter notebooks​

We're excited to announce our second weekly science challenge, focusing on biological data visualization! This week, we're exploring gene expression data analysis, showcasing how Metapages can handle scientific workflows in your browser.

Metapages supports, aligns, and hopes to even extend the FAIR principles, with the caveat that metapages are about connecting code and compute to data, with our emphasis on code and compute. So some of these principles are not quite applicable and we discuss how they could be extended here.

The POSI principles are a set of goals to promote open scholarly infrastructure that guide the heart and direction of the metapage platform. Some aspects specific to our organization:

Regardless of what happens to individuals or organizations, your workflows will run​

Metapage workflows require three things to run:

1. A website serving the standalone metapage app
2. A website serving the metaframe container client
3. A metaframe container worker agent running locally connecting to 2.

It’s one thing to have access to data, but it’s another thing to have code and also a reliable, reproducible way to run the code that operates on the data. In other words, it’s not enough to just serve data, we also need principles around HOW we create compute environments that process the data.

This competition is closed! Click here for the winner​

Our data expiration policy takes a long term view: some data never expires while our organization lives, some data lives a very long time, and some data expires relatively rapidly.

Binder is a way to share Jupyter notebooks. That means all the advantages and disadvantages of Jupyter notebooks.

Bits In Bio Global Mixer Presentation