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add link of our Slack workspace
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larvorg committed Nov 9, 2020
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3. Download
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4. Slack workspace
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3. Download
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4. Slack workspace
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<h1 id="getting-started-with-meteonet">Getting started with MeteoNet</h1>
<h2 id="1-kaggle">1. Kaggle</h2>
<p>You are new to MeteoNet ? You can start by looking at the data on <a href="https://www.kaggle.com/katerpillar/meteonet">Kaggle</a> where you will find notebooks to help you explore, cross-check all data types and predict time series! You can contribute to challenges and/or propose yours! </p>
<p>You are new to MeteoNet? You can start by looking at the data on <a href="https://www.kaggle.com/katerpillar/meteonet">Kaggle</a> where you will find notebooks to help you explore, cross-check all data types and predict time series! You can contribute to challenges and/or propose yours! </p>
<p>No need to install anything, you just have to create a Kaggle account and you can start playing with MeteoNet!</p>
<h2 id="2-meteonet-toolbox">2. MeteoNet toolbox</h2>
<p>You can also look at our sample notebooks on the <a href="https://github.com/meteofrance/meteonet">GitHub Repository</a>!</p>
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<p>Now, you should be able to open each notebook and explore the sample data!</p>
<h2 id="3-download">3. Download</h2>
<p>After checking the notebooks, you should have all you need to start playing around with the real dataset. You can download it <a href="https://meteonet.umr-cnrm.fr/dataset/">here</a>.</p>
<h2 id="4-slack-workspace">4. Slack workspace</h2>
<p>If you have any question and/or suggestion (about the data, the toolbox, the use-cases, the kaggle page...), do not hesitate to go to our <a href="https://tinyurl.com/meteonet-slack">Slack workspace</a>! </p>



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## Kaggle

You are new to MeteoNet ? You can start by looking at the data on [Kaggle](https://www.kaggle.com/katerpillar/meteonet) where you will find notebooks to help you explore, cross-check all data types and predict time series! You can contribute to challenges and/or propose yours!
You are new to MeteoNet? You can start by looking at the data on [Kaggle](https://www.kaggle.com/katerpillar/meteonet) where you will find notebooks to help you explore, cross-check all data types and predict time series! You can contribute to challenges and/or propose yours!

No need to install anything, you just have to create a Kaggle account and you can start playing with MeteoNet!

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After checking the notebooks, you should have all you need to start playing around with the real dataset. You can download it [here](https://meteonet.umr-cnrm.fr/dataset/).

## Slack workspace

If you have any question and/or suggestion (about the data, the toolbox, the use-cases, the kaggle page...), do not hesitate to go to our [Slack workspace](https://tinyurl.com/meteonet-slack)!

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