Environmental Data Science

Welcome!

This repository gathers information about Environmental Data Science, such as events, groups, books, papers, journals, courses, etc.

The idea is to have a place to find information about environmental data science and share it with others.

Feel free to contribute by suggesting contents (e.g., open an issue) or by making a pull request to add new content in this file.

This repository was created by Beatriz Milz in March 2021.

Content

Organizations

• Openscapes
  • Open educational resources for Openscapes Champions
  • NASA Openscapes

Events

Future events

• Environmental Data Science Summit - February 4 - 6, 2025 - Santa Barbara, California

Past events

• Environmental Data Science Summit - February 6 - 8, 2024 - Santa Barbara, California

• Environmental Data Science Summit - February 7 & 8, 2023 - Santa Barbara, California

• Environmental Data Science Summit - February 8-9, 2022 | Santa Barbara, California

• TAI4ES 2022 Summer School

Groups

• EcoDataScience - GitHub, Slack

Books

• Environmental Data Analysis - An Introduction with Examples in R, by Carsten Dormann

• Introduction to Environmental Data Science, by Jerry Davis (SFSU Institute for Geographic Information Science) - https://doi.org/10.1201/9781003317821

• Environmental Data Analysis - Methods and Applications, by Zhihua Zhang.

• Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, by Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein.

• Análises Ecológicas no R, by FR Da Silva, T Gonçalves-Souza, GB Paterno, DB Provete, MH Vancine (in portuguese)

Blogs and Newsletters

• Dynamic Ecology

• Theoretical Ecology

• Methods in Ecology & Evolution - Blog

Articles

About Data Science

• 50 years of Data Science - David Donoho

About Environmental Data Science

• Data Science of the Natural Environment: A Research Roadmap

• Data Analytics for Environmental Science and Engineering Research

• Machine Learning and Data Analytics for Environmental Science: A Review, Prospects and Challenges

• Evolution of machine learning in environmental science — A perspective

• Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science

• Machine learning and deep learning—A review for ecologists

• Welcoming More Participation in Open Data Science for the Oceans

Special issues

• Environmetrics - Special Issue: Environmental Data Science: Part 1 - February 2023

• Environmetrics - Special Issue: Environmental Data Science: Part 2 - March 2023

• Methods in Ecology and Evolution - Special Feature: Realising the Promise of Large Data and Complex Models

Journals

• Environmental Data Science - ISSN 2634-4602

• Environmental Science and Technology

• Frontiers in Environmental Science

• Environmetrics

• Methods in Ecology and Evolution

R Packages

• CRAN Task View: Analysis of Ecological and Environmental Data

Environmental Data Journalism

• New Data Tools and Tips for Investigating Climate Change

• Journalists Toolbox - Environment Resources

Data visualizations

• water data visualizations - USGS
  • USGS - Water Data For The Nation Blog

Workshops

• Environmental Data Visualization Literacy Workshop With R

Courses

Free courses

• Data Science for Ecologists and Environmental Scientists

• Data Carpentry Ecology Curriculum

Disciplinas de pós-graduação no Brasil

• PRPG CIAMB - UFG - Análise de Dados Ambientais - Material da disciplina

Academic courses

• University of California, Santa Barbara - Bren School of Environmental Science & Management - Master of Environmental Data Science

• Imperial College London - Msc Environmental Data Science and Machine Learning

• WEC-33806 - Data Science for Ecology - Wageningen University & Research (online course)

Content from courses at the Master of Environmental Data Science (MEDS) - Bren School

• Reference

• About MEDS

• All courses

• EDS 211 Team Science, Collaborative Analysis and Project Management

• EDS 212 Essential Math for Environmental Data Science

• EDS 213 Metadata Standards, Data Modeling and Data Semantics

• EDS 214 Analytical Workflows and Scientific Reproducibility

• EDS 215 Introduction to Data Storage and Management

• EDS 216 Meta-analysis and Systematic Reviews

• EDS 220 Remote Sensing and Environmental Data

• EDS 221 Scientific Programming Essentials

• EDS 222 Statistics for Environmental Data Science

• EDS 223 Spatial Analysis for Environmental Data Science

• EDS 230 Modeling Environmental Systems

• EDS 231 Text and Sentiment Analysis for Environmental Problems

• EDS 232 Machine Learning in Environmental Science

• EDS 240 Data Visualization and Communication

• EDS 241 Environmental Policy Evaluation

• EDS 242 Ethics and Bias in Environmental Data Science

Other

• Publications from AI2ES

• Better Science in Less time

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss a new resource.

We follow this code of conduct for contributors.

Contributors: Beatriz Milz, Maurício Vancine, Juliano Van Melis