Welcome to the Climate Change AI wiki! You’re joining a community working across continents, at the intersection of different fields, on a global problem. The wiki is a place to learn about, share, and distill ideas related to climate change and AI, and we hope that will grow into an accessible and comprehensive resource for the community.
Wiki contributors should help cultivate an environment for productive resource development by following these guidelines.
Intellectual Diversity and Humility: The wiki facilitates exchange between people with different backgrounds and areas of expertise. Please keep contributions focused on ideas and content relevant to the CCAI mission, and treat others with respect in all interactions. When editing pages, remember that others might not share your jargon, may be unfamiliar with what you think is common knowledge, and may not speak English as a first language. Be professional: don’t make brash claims, do your homework, and avoid making unnecessary demands on others’ time.
Constructive Discussion: Contribute content that can support learning, solutions, and progress that could have real world impact. Strive to develop pages that advance the discourse on climate change and AI. To that end, don’t use this wiki as a political soapbox, for example, but do try to identify actionable ideas or great work and make them visible to the community.
Scientific Integrity: The wik is designed to support open exchange of ideas, and everything will be publicly visible on the web. Don’t misrepresent your or others’ work, provide evidence to support all claims, and be sure to properly attribute ideas if sharing them elsewhere.
We will enforce the following ground rules. If you see pages or edits that violate these, please flag them for a moderator to review.
- Be Respectful: Personal attacks have no place on the the wiki or its talk pages. Rude, abusive, or offensive behavior will not be tolerated.
- Don’t Discriminate: Making broad statements about groups of people and other types of exclusionary behavior will not be accepted.
- Don’t spam: Any form of advertising will be taken down.
- No Misinformation: This is no space for conspiracy theories or climate denial, and we consider those distractions from the CCAI mission.
- No Illegal Activities: Any edits that seem to promote illegal activity will be taken down.
- Respect Ownership: Do not contribute content that belongs to someone else without their permission, do not publicly share private individuals’ information, and don’t violate intellectual property laws.
These are some useful tips and things to keep in mind to encourage healthy discussion, both here and in general.
- When in doubt, assume the best (give people the benefit of the doubt).
- Practice giving clear explanations: This wiki is an opportunity to practice giving creative and clear explanations of what can often be technical concepts. Imagine yourself as the reader.
- Practice asking good questions: Notice when you don’t understand or are curious about something that's not discussed on any pages you can find, and ask about developing the topic on a talk pages!
- Moderators have final authority on all decisions about content on the wiki. They are all volunteers, and work hard to apply the community guidelines consistently.
- Please help foster a positive environment by flagging pages or edits that violate these guidelines. Don’t engage those with problematic behavior – just flag the offending material.
- Moderators will warn users who engage in behavior violating these guidelines, but failure to heed several warnings will result in the users being blocked from the wiki.
- You may appeal a moderator decision by posting a request to unblock on your User Talk page. All appeals will be reviewed, but moderators hold no obligation to reply.
There are many ways that you can contribute to the Climate Change AI wiki.
- On many pages, you will find descriptions of relevant resources for learning about or contributing to a problem. These lists will often be incomplete, and we invite you to extend them. For example, do add relevant journals or conferences that you notice are not currently listed.
- When adding a resource, make sure to add a link and a brief description. This will ensure that readers will be able to get an overview of many resources at a glance, but can easily seek out further details if they find the description interesting.
- When adding a resource, make sure to maintain consistency in formatting. If you notice that all previously added resources were in boldface font, make sure to stick to that.
Improve content and clarity
Community wikis become more refined through many small changes contributed by many people. Do not hesitate to add a new piece of information or suggest a rewording that improves clarity.
- If you notice that an idea is touched upon but not fully developed, a sentence or two of additional elaboration can improve the richness of the wiki’s content.
- Tighten verbose text or add concrete examples.
- If you notice that something worth discussing — perhaps a description is too technical or imprecise — then make sure to begin a discussion on the page’s Talk page. The talk page can be found by prefixing « Talk: » to the page name. For example, the talk page for Electricity systems is at Talk:Electricity Systems.
Identify and develop "Machine Learning Application Areas"
Within a general problem domain (e.g., electricity systems), there are often a few more specific subproblems of interest (e.g., supply and demand forecasting). These are typically linked from "Machine Learning Application Areas" sections on individual pages across the wiki. We invite you to both add new application areas and develop the associated pages.
When creating a new application area page, consider adding the following sections,
- Relevant readings: What books or papers would you recommend that someone new to the field read, to become oriented in the topic?
- (Online) Course materials: Are there existing structured learning programs for learning about the application area?
- Community: What societies, journals, or conferences should a new contributor to the application area become familiar with?
- Libraries and Tools: Are there software packages that can facilitate work in this application area?
- Data: Are there common types of data that are used in this application area? Are there example sources that are publicly available?
- Future directions: What are the open problems in this area?