Streamlit ML app - Geographic AI for Soil Assessment (GAIA) by Oluwasegun Daramola

Streamlit ML app - Geographic AI for Soil Assessment (GAIA)

Oluwasegun Daramola

Data Scientist

Web Designer

Web Developer

Linux

Python

gaia - Geographic AI for Soil Assessment

App Link

streamlit

streamlit-option-menu

pandas

numpy

scikit-learn==1.2.2

joblib

catboost

plotly

The Land Use / Cover Area frame statistical Survey Soil (LUCAS Soil) dataset which is a European Union effort to survey European topsoil characteristics and Microbiome sequence data from the LUCAS Soil Survey for prokaryotes, fungi, and microeukaryotes. Avalaible here:

https://doi.plutof.ut.ee/doi/10.15156/BIO/2263453

Set Up Qiime2 Environment Set up your Qiime2 environment by following the instructions from Qiime2 Documentation

Create Directories for Analysis Create two new directories: one for bacteria analysis and another for fungi analysis.

Organize Relevant Files Move all relevant files to their respective directories. Ensure that reference files from the respective databases (SILVA 138 for bacteria and UNITE QIIME release version 9.0 for fungi) are saved in their respective folders.

Download Files In each directory, use the following command to download the files specified in the _download_links.txt:

Create Manifest Files Use the provided manifest.sh files to generate the manifest files required for Qiime2 import.

Run Analysis Scripts Navigate to each directory and execute the analysis.sh files. Before running, review the content of these files to ensure that all necessary files are accounted for and modify the scripts as needed to suit your specific analysis requirements.

Info

LUCAS Interactive Maps

Global Soil Mycobiome consortium (GSMc) Dataset

https://private-user-images.githubusercontent.com/102333264/290100675-51c409c4-05c8-4bda-a1fc-1a5b72f6ed96.gif?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjU4Mjk0MjgsIm5iZiI6MTcyNTgyOTEyOCwicGF0aCI6Ii8xMDIzMzMyNjQvMjkwMTAwNjc1LTUxYzQwOWM0LTA1YzgtNGJkYS1hMWZjLTFhNWI3MmY2ZWQ5Ni5naWY_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwOTA4JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDkwOFQyMDU4NDhaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1hZWRjYzYzMTVmMTVmYjhmOGQ0MDM3ODQ3Y2NlOTM0MzVjM2UwNzU4NTk2NDBkMzM0YmQyZDhkYWQ1MDUzZDY5JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.s39o0SEyi2WoFPmFH3xeVKM9g1vgB8Bgj6MH2bu027c

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Welcome to the Geographic AI for Soil Assessment gaia interface, your ultimate companion in Predicting and visualizing soil microbial biodiversity.

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