A platform to display the carbon neutralization information for researchers, decision-makers, and other participants in the community.

Overview

Welcome to Carbon Insight

Carbon Insight is a platform aiming to display the carbon neutralization roadmap for researchers, decision-makers, and other participants in the community. Our mission is to visualize the world's most cutting-edge research on carbon emission, carbon sink, and carbon flux to generate insights of carbon and society. We strive to accelerate climate studies and global climate actions with computational innovations.

With the global consensus of the 1.5°C goal of the Paris Agreement, the world has a goal to achieve carbon neutralization by 2050. This ambitious goal requires collaboration from all fields. To tackle the climate crisis together, we must first understand where carbon comes from and where it goes.

With Carbon Insight, you can work with the world's most updated carbon-related data and generate insights as you wish.

For example, in our first release, you can leverage the dataset provided by Carbon Monitor, to have a daily anthropogenic CO2 emission estimation by country and sector since January 2019.

Carbon Insight also lets you observe and track correlations between global carbon emissions and socioeconomic factors such as COVID-19 and GDP.

We aim to achieve the following goals:

  • Using data visualization to support scientific research, allowing researchers to identify problems and ideas that are not easily seen in conventional ways
  • Acting as a tool that allows all users to explore carbon neutralization pathways under different scenarios and with technology innovations
  • Illustrating data and science of carbon neutralization for the non-professionals to raise public awareness towards climate change

How to use

Using Carbon Monitor, a dataset providing daily estimations of CO2 emissions by country/sector, as an example, we demonstrate two ways to do analysis with carbon-related data:

  • interactable Power BI reports, and
  • code examples

If you want a straightforward view of a global emission map by country, you can download our Power BI reports and filter results based on your interest.

(New to Power BI? Check the instructions on how to download the Power BI app and how to explore with dashboards, reports, and apps in Power BI.)

If you have some basic coding knowledge and want to get your hands dirty customizing your own analysis or combining different datasets to scale your research, go to our Jupyter Notebook Tutorials and walk through the code examples we provide on how to acquire, process and visualize carbon-related data.

Release Note

2022/01/06 release:

Contributors

Carbon Insight started with a research collaboration between MSRA and Zhu Liu's team from Department of Earth System Science, Tsinghua University. We share a vision of demonstrating efforts towards carbon neutralization through visualization, benchmarking, and insightful analysis with both global consistency and local detail. Our collaboration goes wider to more areas of carbon footprint monitoring and deeper to using advanced machine learning algorithms to assist the modeling of carbon flux.

We're a fully open project and welcome contributors or collaborators from the whole community, if you wish to contribute to the project or raise suggestions, contact us at [email protected].

Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection".

A2S-USOD Code for our work "Activation to Saliency: Forming High-Quality Labels for Unsupervised Salient Object Detection". Code will be released upon

15 Dec 16, 2022
PyTorch implementation for "HyperSPNs: Compact and Expressive Probabilistic Circuits", NeurIPS 2021

HyperSPN This repository contains code for the paper: HyperSPNs: Compact and Expressive Probabilistic Circuits "HyperSPNs: Compact and Expressive Prob

8 Nov 08, 2022
A gesture recognition system powered by OpenPose, k-nearest neighbours, and local outlier factor.

OpenHands OpenHands is a gesture recognition system powered by OpenPose, k-nearest neighbours, and local outlier factor. Currently the system can iden

Paul Treanor 12 Jan 10, 2022
Light-weight network, depth estimation, knowledge distillation, real-time depth estimation, auxiliary data.

light-weight-depth-estimation Boosting Light-Weight Depth Estimation Via Knowledge Distillation, https://arxiv.org/abs/2105.06143 Junjie Hu, Chenyou F

Junjie Hu 13 Dec 10, 2022
Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR

UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-

Microsoft 282 Jan 09, 2023
SANet: A Slice-Aware Network for Pulmonary Nodule Detection

SANet: A Slice-Aware Network for Pulmonary Nodule Detection This paper (SANet) has been accepted and early accessed in IEEE TPAMI 2021. This code and

Jie Mei 39 Dec 17, 2022
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference

RawVSR This repo contains the official codes for our paper: Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference Xiaoh

Xiaohong Liu 23 Oct 08, 2022
This is an official implementation for "DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation"

DeciWatch: A Simple Baseline for 10× Efficient 2D and 3D Pose Estimation This repo is the official implementation of "DeciWatch: A Simple Baseline for

117 Dec 24, 2022
Train CPPNs as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images.

cppn-gan-vae tensorflow Train Compositional Pattern Producing Network as a Generative Model, using Generative Adversarial Networks and Variational Aut

hardmaru 343 Dec 29, 2022
FMA: A Dataset For Music Analysis

FMA: A Dataset For Music Analysis Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson. International Society for Music Information

Michaël Defferrard 1.8k Dec 29, 2022
Differentiable architecture search for convolutional and recurrent networks

Differentiable Architecture Search Code accompanying the paper DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang. arX

Hanxiao Liu 3.7k Jan 09, 2023
Code for the submitted paper Surrogate-based cross-correlation for particle image velocimetry

Surrogate-based cross-correlation (SBCC) This repository contains code for the submitted paper Surrogate-based cross-correlation for particle image ve

5 Jun 30, 2022
CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes

CHERRY is a python library for predicting the interactions between viral and prokaryotic genomes. CHERRY is based on a deep learning model, which consists of a graph convolutional encoder and a link

Kenneth Shang 12 Dec 15, 2022
Official code for paper "Optimization for Oriented Object Detection via Representation Invariance Loss".

Optimization for Oriented Object Detection via Representation Invariance Loss By Qi Ming, Zhiqiang Zhou, Lingjuan Miao, Xue Yang, and Yunpeng Dong. Th

ming71 56 Nov 28, 2022
A Python module for parallel optimization of expensive black-box functions

blackbox: A Python module for parallel optimization of expensive black-box functions What is this? A minimalistic and easy-to-use Python module that e

Paul Knysh 426 Dec 08, 2022
《Dual-Resolution Correspondence Network》(NeurIPS 2020)

Dual-Resolution Correspondence Network Dual-Resolution Correspondence Network, NeurIPS 2020 Dependency All dependencies are included in asset/dualrcne

Active Vision Laboratory 45 Nov 21, 2022
A Simplied Framework of GAN Inversion

Framework of GAN Inversion Introcuction You can implement your own inversion idea using our repo. We offer a full range of tuning settings (in hparams

Kangneng Zhou 13 Sep 27, 2022
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

CPT This repository contains code and checkpoints for CPT. CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Gener

fastNLP 341 Dec 29, 2022
Code for "The Intrinsic Dimension of Images and Its Impact on Learning" - ICLR 2021 Spotlight

dimensions Estimating the instrinsic dimensionality of image datasets Code for: The Intrinsic Dimensionaity of Images and Its Impact On Learning - Phi

Phil Pope 41 Dec 10, 2022
Open source implementation of AceNAS: Learning to Rank Ace Neural Architectures with Weak Supervision of Weight Sharing

AceNAS This repo is the experiment code of AceNAS, and is not considered as an official release. We are working on integrating AceNAS as a built-in st

Yuge Zhang 6 Sep 07, 2022