<h1 id="2023-04-06"><a aria-hidden="true" class="anchor-heading icon-link" href="#2023-04-06"></a>2023-04-06</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>
<p>Today is 2023.04.06</p>
<h2 id="notes"><a aria-hidden="true" class="anchor-heading icon-link" href="#notes"></a>Notes</h2>
<p>I think the model we have can work quite nicely with the Lotus database. I have generated some fake data and am trying to predict the parameters given to the model. It looks like it works pretty well. </p>
<p>Also we performed Gibbs sampling for trying to predict X given <span class="math math-inline"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>μ</mi></mrow><annotation encoding="application/x-tex">\mu</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal">μ</span></span></span></span></span> and <span class="math math-inline"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>ϵ</mi><mi>c</mi></msub></mrow><annotation encoding="application/x-tex">\epsilon_c</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">ϵ</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">c</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> it seems like it is able to find X fast and accurately !</p>
<h2 id="todo-today"><a aria-hidden="true" class="anchor-heading icon-link" href="#todo-today"></a>Todo today</h2>
<ul>
<li></li>
</ul>
<h2 id="doing"><a aria-hidden="true" class="anchor-heading icon-link" href="#doing"></a>Doing</h2>
<h2 id="done"><a aria-hidden="true" class="anchor-heading icon-link" href="#done"></a>Done</h2>
<ul>
<li>Finished implementing MCMC for predicting both gamma, delta and X. Works like a charm !</li>
</ul>
<h2 id="todo-tomorrow"><a aria-hidden="true" class="anchor-heading icon-link" href="#todo-tomorrow"></a>Todo tomorrow</h2>
<p>Holiday</p>

2023-04-06

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

