The Environmental AI Book

[acocac]

Project Lead: Alejandro Coca-Castro @acocac

Mentor: Delphine Larivière @Delphine-L

Welcome to OLS-4! This issue will be used to track your project and progress during the program. Please use this checklist over the next few weeks as you start Open Life Science program 🎉.

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Week 1 (week starting 13 September 2021): Meet your mentor!

• Meet mentor for 30 minutes
• Create an account on GitHub
• Check if you have access to the HackMD notes set up for your meetings with your mentor
• Prepare to meet your mentor(s) by completing a short homework provided in your shared notes
• Complete your own copy of the open leadership self-assessment and share it to your mentor
  If you're a group, each teammate should complete this assessment individually. This is here to help you set your own personal goals during the program. No need to share your results, but be ready to share your thoughts with your mentor.
• Make sure you know when and how you'll be meeting with your mentor.

Before Week 2 (week starting 20 September 2021): Cohort Call (Welcome to Open Life Science!)

• Attend call or catch up via YouTube

• Create an issue on the OLS-4 GitHub repository for your OLS work and share the link to your mentor.

• Draft a brief vision statement using your goals

  This lesson from the Open Leadership Training Series (OLTS) might be helpful

• Leave a comment on this issue with your draft vision statement & be ready to share this on the call

• Check the Syllabus for notes and connection info for all the cohort calls.

Before Week 3 (week starting 27 September 2021): Meet your mentor!

• Meet mentor
• Look up two other projects and comment on their issues with feedback on their vision statement
• Complete this compare and contrast assignment about current and desired community interactions and value exchanges
• Complete your Open Canvas (instructions, canvas)
• Share a link to your Open Canvas in your GitHub issue
• Start your Roadmap
• Comment on your issue with your draft Roadmap
• Suggest a cohort name at the bottom of the shared notes and vote on your favorite with a +1

Before Week 4: Cohort Call (Tooling and roadmapping for Open projects)

• Attend call or catch up via YouTube
• Look up two other projects and comment on their issues with feedback on their open canvas.

Week 5 and later

• Meet mentor
• Create a GitHub repository for your project
• Add the link to your repository in your issue
• Use your canvas to start writing a README.md file, or landing page, for your project
• Link to your README in a comment on this issue
• Add an open license to your repository as a file called LICENSE.md
• Add a Code of Conduct to your repository as a file called CODE_OF_CONDUCT.md
• Invite new contributors to into your work!

This issue is here to help you keep track of work as you start Open Life Science program. Please refer to the OLS-4 Syllabus for more detailed weekly notes and assignments past week 4.

Week 6

• Attend call or catch up via YouTube

Week 7

• Meet mentor

Week 8

• Attend call or catch up via YouTube

Week 9

• Meet mentor

Week 10

• Attend call or catch up via YouTube

Week 11

• Meet mentor

Week 12

• Attend call or catch up via YouTube

Week 13

• Meet mentor

Week 14

• Attend call or catch up via YouTube

Week 15

• Meet mentor

[acocac]

W1 & W2 📘

• Self-assessment notes
• Vision statement notes

Vision statement:
I’m working with environmental, other natural and data scientists to create a community-driven resource, named the Environmental AI book, compiling state-of-art research in the application of AI and Data Science for monitoring and modelling a wide diversity of settings of the natural and urban environments. We expect to evolve as an inclusive, diverse and active community dedicated to making collaborative, reusable and transparent research in Environmental science.

[wykhuh]

AI and Data Science is an ever evolving field that can be applied to many different fields. How will your group keep track of all the new developments? How often will be the book be updated?

One of the issues with AI is making the results of AI algorithms transparent and reproducible. Will your group deal with this topic?

[acocac]

@wykhuh, thank for your great comments. Here are some thoughts for each of them:

• How will your group keep track of all the new developments?

  • The group will keep track of the new developments by providing community activities such as collaboration cafes, co-working sessions, among others.

• How often will be the book be updated?

  • What do you mean by being update? to update the technology or content? if you mean the content, we expect to target a given amount (TBC) of interactive notebook per month. Note we're still in a testing stage. From a set of preliminary community-driven use-cases, we have learnt how to improve and accelerate the production and revision of new content.

• Will your group deal with this topic?

  • We're taking transparency and reproducibility very seriously in the Environmental AI book. It's indeed one of the main features of the project. For instance, we use technologies such as ReviewNB which allows users to keep track of the discussion between the use-cases authors and reviewers (see for example this discussion from a recent pull request PR#3. In addition, we're integrating the interactive notebooks with the Pangeo Binder. The binder ensures the end-users can run the notebooks without dealing with the dependencies installation in their local systems.

Hope the above answers correctly addressed your comments.

[nadinespy]

Hi @wykhuh, I was wondering what you exactly mean by "compiling" - are you thinking of a mere enumeration of AI and Datascience applications, with rather short descriptions, or do you plan to go into each of these in more detail? How comprehensively do you want to outline the different methods?

Also, another question I had was whether you could specify a bit more who can contribute & collaborate in that project.

[acocac]

@nadinespy thanks for your questions.

By compiling I mean to provide demonstrators of the most of AI & DS applications for Environmental Science. Is still compiling too confusing? May I replace it by demonstrating?

Regarding the contribution/collaboration roles, the e-book will serve as a reference to connect the following players in Environmental science:

• Researchers with some background in environmental science interested in data-driven methods.
• Researchers with some background in computer science interested in environmental studies.
• Anyone else interested in reproducibility, inclusive, shareable and collaborative AI and data science for environmental applications.

Would you suggest wrapping the above roles in a short sentence and add it to the vision statement?

[acocac]

Link to the GitHub repository for the Environmental Data Science book, formerly know as the Environmental AI book ⛰ 🌳 🏙️ ❄️ 🔥 🌊 project: https://github.com/alan-turing-institute/environmental-ds-book/

[acocac]

W3 - Open Canvas 📋
Link