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Insightful Data

Empowering Climate Action

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Upcoming Events

  • Data Science for Climate Impact
    Data Science for Climate Impact
    Fri, Sep 06
    University of San Francisco
    Sep 06, 2024, 12:30 PM – 2:00 PM PDT
    University of San Francisco, 101 Howard St, San Francisco, CA 94105, USA
    Sep 06, 2024, 12:30 PM – 2:00 PM PDT
    University of San Francisco, 101 Howard St, San Francisco, CA 94105, USA
    Attend a panel led by climate advocates and AI advocates. Learn how data science can empower advocacy efforts and mitigate risks through early warning systems and predicting future climate trends.
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  • Climate Game Night
    Climate Game Night
    Sun, Aug 11
    Virtual Event
    Aug 11, 2024, 11:40 AM
    Virtual Event
    Aug 11, 2024, 11:40 AM
    Virtual Event
    Join us for an evening of Climate Change Simulation Game. Become a stakeholder, take responsibility, overcome challenges, and build ways to tackle climate impact.
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Recent Updates

About

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The Path
Embarking on a journey of using data science for climate impact in 2020, we have delved into numerous aspects that have shaped our expertise. We aim to change the climate through a journey of affinity of the minds, federation of the will, merger of the skills, embracing of the technology and integration of diversity.

Current Studies

Unforeseen Wildfire Impacts

April 2025

Delving into the realm of Unforeseen Wildfire Impacts, we are employing innovative techniques to dissect the intricate workings of my experimental model. Collaborations with other research facilities are on the horizon to expand the scope of this investigation.

Reassessing Climate Change

July  2025

The enigma of Reassessing Climate Change continues to intrigue, necessitating a deeper exploration of its underlying mechanisms. Enhanced data collection and precision measurements are pivotal in unraveling the mysteries surrounding this pivotal research domain.

Phenology Insights

September  2025

Pioneering advancements in Phenology studies, we aim to craft a sophisticated tool for deciphering the dynamics within our model system. Its heightened sensitivity enables the detection of nuanced changes in response to experimental variables.

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