<h1 id="2024-06-18"><a aria-hidden="true" class="anchor-heading icon-link" href="#2024-06-18"></a>2024-06-18</h1>
<h1 id="pamreins-daily-open-notebook-commons-lab"><a aria-hidden="true" class="anchor-heading icon-link" href="#pamreins-daily-open-notebook-commons-lab"></a>PAMREIN's daily Open Notebook (COMMONS Lab)</h1>
<h2 id="todo---check-github"><a aria-hidden="true" class="anchor-heading icon-link" href="#todo---check-github"></a>Todo - <a href="https://github.com/orgs/commons-research/projects/2/views/1">Check Github</a></h2>
<p>-[]</p>
<h2 id="meetings"><a aria-hidden="true" class="anchor-heading icon-link" href="#meetings"></a>Meetings</h2>
<h2 id="daily-report-what-did-i-learn"><a aria-hidden="true" class="anchor-heading icon-link" href="#daily-report-what-did-i-learn"></a>Daily report (What did I learn?)</h2>
<p>Again I have problems with storage. I can have about 900GB memory per task. If I read in all the <em>.parquet files, it is not enough.
Even all the </em>.parquet files are only about 180GB, the compressed format will explode through the read in.<br>
I am looking for parallising this step, which can be maybe possible with "use_legacy_dataset=False" (<a href="https://stackoverflow.com/questions/74236493/why-reading-a-parquet-dataset-requires-much-more-memory-than-the-size-of-the-dat">https://stackoverflow.com/questions/74236493/why-reading-a-parquet-dataset-requires-much-more-memory-than-the-size-of-the-dat</a>). </p>
<h2 id="future-perspective"><a aria-hidden="true" class="anchor-heading icon-link" href="#future-perspective"></a>Future perspective</h2>
<h2 id="keywords"><a aria-hidden="true" class="anchor-heading icon-link" href="#keywords"></a>Keywords</h2>
<p><a href="/commons-dws-public/notes/3ca85tg6eqf78kgql1mj3vl">Abbreviations</a></p>

PAMREIN's daily Open Notebook (COMMONS Lab)

2024-06-18

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



2024-06-18

PAMREIN's daily Open Notebook (COMMONS Lab)

Todo - Check Github

Meetings

Daily report (What did I learn?)

Future perspective

Keywords