CrowdDoing nutrition psychology data science of medicinal foods effectiveness volunteer

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CrowdDoing nutrition psychology data science deep learning of medicinal foods effectiveness volunteer help us to review data science pathways for medicinal foods evidence assessment. A poster about these opportunities can be seen here:
https://drive.google.com/file/d/19AU7IyozDSjdwi5XZoQ3FxPRzhSJNtis/view?usp=sharing
outcome effectiveness from peer to peer randomized RCTs medicinal foods development arc. CrowdDoing medicinal foods data science team aim to leverage public data to analyze co-variates with stress, anxiety, sleep and medicinal foods. We are also working towards a HIPPA compliant phase 2 that will let people test medicinal foods for themselves.

You can see an overview of our medicinal foods application here (https://docs.google.com/presentation/d/12aEqwiRctIx3QcvK6HNFy4uJ5TgRffxQ7xPLEINNyJw/edit?usp=sharing). You can see a data science collection roadmap for how we can match million of recipe reviews to co-variates like stress and anxiety here: https://drive.google.com/file/d/1gF951Q7-XHFblTG-znjW3xXA8XAkPslB/view?usp=sharing. You can see a brake-down of human capital requirements for this project here: https://docs.google.com/document/d/1Kf3mIHQ9_YilBk9kBCvCFXaTj2AbA07SFAX_hv55Cik/edit?usp=sharing.

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