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agroecological-modelling icon agroecological-modelling

Short introductory lecture on modelling in agro-ecology. The lecture presents different types of models, their interests, and their limits. It explains the main steps of a modelling project and provides several principles to become a "good" modeller. The repository also includes a figure illustrating the risk of overfitting a model (very common). The R code used to produce this figure is provided as well.

bjguahao icon bjguahao

北京市预约挂号统一平台挂号小助手

blivedm icon blivedm

获取bilibili直播弹幕,使用websocket协议

cmaps icon cmaps

user defined colormaps in matplotlib.

dgvmtools icon dgvmtools

R package for processing, analysing and visualising ouput from Dynamic Global Vegetation Models (DGVMs)

dominance-analysis icon dominance-analysis

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

echarts icon echarts

Apache ECharts is a powerful, interactive charting and data visualization library for browser

flash_drought icon flash_drought

Example python scripts to detect flash drought events from gridded soil moisture data

geebook icon geebook

Earth Engine and Geemap - Geospatial Data Science with Python

ggplot2 icon ggplot2

An implementation of the Grammar of Graphics in R

grace icon grace

Code and Scripts for the GRACE mission

hms-video-editor-demo icon hms-video-editor-demo

HUAWEI Video Editor Kit provides full video editing functions like video import/export, editing and rendering. This sample code is used to describe how to integrate the Video Editor Kit SDK.

lpjml icon lpjml

Welcome to the central open-source repository of LPJmL at PIK. You are free to download the code under the AGPLv3 license, see LICENSE file. Have fun. Please note that there is absolutely no support outside agreed collaborations. We also don't provide any input data, which is derived from other sources.

lstm icon lstm

基于LSTM神经网络的时间序列预测

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