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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<title>Tools</title>
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<div class="home-content-wrap wf-section">
<div class="title-group animated-item pre-anim blur-in-anim">
<div class="label cc-light">Geoweaver</div>
<h2 class="heading">Open-source Productivity Tool for Managing FAIR ML Workflows</h2>
</div>
<div class="rich-text w-richtext animated-item pre-anim blur-in-anim">
<p>
Earth scientists spend numerous hours coding and exploring new technologies leading to ML-based scientific
discovery. However, many efforts involving ML workflows developed in universities and research institutes are
incomplete or not reproducible. Geoweaver (<a href="https://www.mdpi.com/2220-9964/9/2/119" target="_blank">Sun
et al., 2020</a>) is an open-source workflow management tool that saves the history of
ML model runs together with the source code and the output logs in a local database (two portable files under
home directory) that can be shared with others. <a href="https://esipfed.github.io/Geoweaver/"
target="_blank">Geoweaver</a> is a user-friendly tool that has been designed with the scientist's needs in
mind. The workflow automation, provenance recording, history exportation, are completely under the hood,
allowing scientists to focus on problem solving and information extraction without worrying about potential
loss of their experiment history or source code. Using Geoweaver is a long term investment and will make every
scientist' work fresh, preserved and understandable even years after the original experiments.
</p>
<div class="project-container">
<figure>
<img class="image rounded small" src="assets/img/geoweaver_2.png" loading="lazy"
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</figure>
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<div class="label cc-light">GStatSim</div>
<h2 class="heading">Open-source Tool for Interpolation and Simulation</h2>
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<div class="rich-text w-richtext">
<p class="animated-item pre-anim blur-in-anim">
GStatSim is a Python package specifically designed for geostatistical interpolation and simulation. It is
inspired by open source geostatistical resources such as GeostatsPy and SciKit-GStat. The functions are intended
to address the challenges of working with datasets with large crossover errors, non-linear trends, variability
in measurement density, and non-stationarity. These tools are part of our ongoing effort to develop and adapt
open-access geostatistical functions.
</p>
<div class="project-container animated-item pre-anim blur-in-anim">
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