<h1 id="2023-02-14"><a aria-hidden="true" class="anchor-heading icon-link" href="#2023-02-14"></a>2023-02-14</h1>
<h1 id="this-is-marcos-daily-open-notebook"><a aria-hidden="true" class="anchor-heading icon-link" href="#this-is-marcos-daily-open-notebook"></a>This is Marco's daily open-notebook.</h1>
Today is 2023.02.14
<h2 id="todo-today"><a aria-hidden="true" class="anchor-heading icon-link" href="#todo-today"></a>Todo today</h2>
<ul>
<li>still publish PDF online because images and DAGs don't work.</li>
<li>change DAG to split MS data and lotus</li>
</ul>
<h3 id=""><a aria-hidden="true" class="anchor-heading icon-link" href="#"></a></h3>
<h3 id="-1"><a aria-hidden="true" class="anchor-heading icon-link" href="#-1"></a></h3>
<h2 id="doing"><a aria-hidden="true" class="anchor-heading icon-link" href="#doing"></a>Doing</h2>
<ul>
<li>Trying to create multiple DAG and see which one is correct for our model.</li>
</ul>
<h2 id="paused"><a aria-hidden="true" class="anchor-heading icon-link" href="#paused"></a>Paused</h2>
<h2 id="done"><a aria-hidden="true" class="anchor-heading icon-link" href="#done"></a>Done</h2>
<ul>
<li>Used <code>pandoc</code> to convert latex file into markdown and then passed it into </li>
</ul>
Discussion with Pierre-Marie about the structure of MS and what could be used to implement the model. 
<img src="/commons-dws-public/assets/images/2023-02-14-15-17-40.jpg" alt="image">
We shouldn't forget that our model will likely not be on a 3D molecular level but a 2D since mass spec is not able to make the difference between enantiomers. 
<h2 id="notes"><a aria-hidden="true" class="anchor-heading icon-link" href="#notes"></a>Notes</h2>
Sirius and ISDB = info to molecular level
CANOPUS info to NPC (NP classifier class)
<ul>
<li><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>y</mi><mrow><mi>s</mi><mi>m</mi></mrow></msub></mrow><annotation encoding="application/x-tex">y_{sm}</annotation></semantics></math>ysm​ is the probability of having a molecule in a species</li>
<li><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>μ</mi><mi>m</mi></msub></mrow><annotation encoding="application/x-tex">\mu_{m}</annotation></semantics></math>μm​ is the average presence of one molecule across all species. </li>
<li><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>y</mi><mrow><mi>s</mi><mi>m</mi></mrow></msub></mrow><annotation encoding="application/x-tex">y_{sm}</annotation></semantics></math>ysm​ is then the average presence across all species plus an error <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>ϵ</mi></mrow><annotation encoding="application/x-tex">\epsilon</annotation></semantics></math>ϵ. And that is why we assume that error is normally distributed across the mean <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>μ</mi></mrow><annotation encoding="application/x-tex">\mu</annotation></semantics></math>μ.</li>
</ul>
<h2 id="todo-tomorrow"><a aria-hidden="true" class="anchor-heading icon-link" href="#todo-tomorrow"></a>Todo tomorrow</h2>
<h3 id="-2"><a aria-hidden="true" class="anchor-heading icon-link" href="#-2"></a></h3>
<h3 id="-3"><a aria-hidden="true" class="anchor-heading icon-link" href="#-3"></a></h3>
<h3 id="-4"><a aria-hidden="true" class="anchor-heading icon-link" href="#-4"></a></h3>
<h2 id="today-i-learned-that"><a aria-hidden="true" class="anchor-heading icon-link" href="#today-i-learned-that"></a>Today I learned that</h2>
<ul>
<li></li>
</ul>

2023-02-14

Concrete steps to access and contribute to the DBGI Dendron


![](/assets/images/allard-lab.jpg)

Welcome the the COMMONS Lab Open Dendron !

In the [COMMONS Lab](https://www.unifr.ch/bio/en/groups/allard/) we intent to follow the [Open Notebook Science](https://en.wikipedia.org/wiki/Open-notebook_science) approach to document our research.

For this we use the [Dendron](https://www.dendron.so/) system as a mean to efficiently capture notes and publish them.

## What is this note

This note is a succinct tutorial note aiming to get you started in the use of the COMMONS Lab Dendron.

## Tutorial

### Dendron. 

#### What is Dendron ?

> Dendron is an open-source, local-first, markdown-based, note-taking tool. Think of it as a cache for everything that you care about - if you've spent more then five minutes solving a problem, you should never spent any more time solving the same exact problem.
> 
> Dendron is a knowledge base built by and for developers and integrates natively with IDEs like VS Code and VSCodium.

#### Where to get more info on Dendron ?

- You can get more information in the Dendron system at the official website www.dendron.so
- All the Dendron documentation is hosted here https://wiki.dendron.so/. It is, obviously, a Dendron itself.

#### Concrete steps to access and contribute to the DBGI Dendron

1. Install [VSCode](https://code.visualstudio.com/download)
2. Install [Dendron](https://marketplace.visualstudio.com/items?itemName=dendron.dendron) from the VSCode marketplace 
3. Clone the COMMONS Dendron repo

    In your terminal
    ```
    git clone https://github.com/commons-research/commons-dws-public.git
    ```
4. In VSCode open (File/Open) the cloned repository.
5. Voila ! You should now be in the COMMONS Lab Open Dendron.

Feel free to explore and contribute.
A first, important step could be to configure VSCode so that you can easily add a daily note. See steps described at [[open-notebook.commons.setup]]


## Questions, comments, suggestions ?

Feel free to contribute here https://github.com/orgs/commons-research/discussions

