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tags: deepsensor
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# First RAM steps with DeepSensor

## Listing people who may be interested in (or are already) using/contributing to DeepSensor

- Alejando Coca-Castro
- Turing Fellow who can contribute more on the community-building/demonstrator side of things than code/research side of things
- Oliver Strickson and REG RSEs
- TBC: potential force-multiplier RSEs
- Magnus Ross
- reached out to Scott with interest about contributing to DeepSensor and we've been having a conversation to figure out a way he can do that. Currently, I've just suggested finding better open-source data for the demonstrators and contributing a use case notebook to the DeepSensor Gallery
- Rohit Singh Rathaur (RohitRathore1 on GitHub)
- Zeel Patel (https://twitter.com/patel_zeel_)
- Keen to both use and contribute to DeepSensor
- Gave the nice user testimonial Tom shared in NERC Webinar
- Tom has had a 30 min meeting with him (Agu 18th) and he had good feedback
- Nils Lehmann
- Emailed Tom. Interested in using DeepSensor for sea level rise prediction. Would be keen to contribute to the code.
- Risa Ueno
- Using DeepSensor for weather prediction
- Jonas Scholz
- Cambridge Master's student that Tom supervised summer 2023 who was the most active early DeepSensor user and contributed
- Writing an ML conference workshop paper on his work (using DeepSensor)
- Taking a year in unrelated industry but will apply to PhDs
- Paolo Pelucchi
- Uni of Valencia PhD researcher, did a secondment at Turing summer 2023 and worked with Tom on active learning research for aerosols over Europe
- Helped catch some bugs and gave good feedback as an early user
- Will continue this research project
- Anna Vaughan
- Close collaborator from the environmental NP research group Tom works with. She prefer to write her own code but supports the DeepSensor project and asked Tom to write her current NP forecasting research code by extending DeepSensor with her custom models (but Tom doesn't have much spare time for this).
- Dani Jones
- One of Tom's supervisors from BAS who oversaw the project that led to DeepSensor
- They are moving to the States but keen to use DeepSensor for some Great Lakes research applications, maybe starting winter 2023 or spring 2024 ish.
- Martin Rogers
- BAS AI Lab ML Researcher who is interested in using ConvNPs for filling gaps/combining satellite data (feature not yet implemented)
- Kenza Tazi
- BAS/Cambridge AI4ER PhD researcher interested in a mini research project using DeepSensor
- Sam Jackson
- Was on the Turing 'EnvSensors' project, not working directly with Tom, but is interested to try satellite data functionality. Could be interested in contributing or testing (not yet asked).
- Peter Yatyshin
- Turing Fellow working with Scott Hosking, offered to proofread DeepSensor code
- Dr James Clark
- Marine Ecosystem Modeller at Plymouth Marine Lab https://www.pml.ac.uk/People/Dr-James-Clark
- Reached out to Tom in early 2023 about potentially using DeepSensor to control fleets of AUVs around Plymouth. Was too early for real-world deployment at the time but may be worth reaching out again in 2024.
- Andrew Fleming?

## Potential collaborations
- [ ] Satellite imagery collaboration with Scivision community. How can we start that collaboration (see issue [22](https://github.com/tom-andersson/deepsensor/issues/22) and [23](https://github.com/tom-andersson/deepsensor/issues/23)).
- Comment in README, users with questions about satellite imagery can be directed to example in the Gallery -- and then onwards to the Scivision.

## Tasks to get started
- [ ] Create structure for the DeepSensor community calls
- We could record them for the help of both follow-up and engagement from non-present community members
- Follow-up after the calls with evaluation of what people think/feel about it
- Add note that #thoughtswelcome tag on GitHub allows for you to start getting involved, in general discussions that don't have to be on nitty-gritty details or code contributions.
- [ ] Submit codebase for peer review
- [ ] It would be good to formalise the various ways of contributing to the project so that it doesn't require so much of our time to facilitate this
- [ ] Create a `CONTRIBUTING.md` file
- [ ] Detail the steps for contributing to the project, from cloning the repository to submitting a pull request (PR).
- [ ] Include a checklist for contributors to follow before submitting a PR.
- [ ] Mention the types of contributions you are looking for (e.g., code, documentation, bug reports, etc.).
- [ ] Create an `ISSUE_TEMPLATE.md` and `PULL_REQUEST_TEMPLATE.md`: Templates to standardise the information that contributors need to provide when submitting an issue or a PR.
- [ ] Define and Document Code Review Practices: Specify what maintainers are looking for in a review and what contributors can expect from the process.
- [ ] Leverage GitHub’s [CODEOWNERS](https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners) feature: Assign automatic code review assignments to relevant maintainers or trusted contributors
- [ ] Create Onboarding Documentation for New Contributors: A simple, welcoming guide for newcomers can include setup instructions, common project conventions, and links to “good first issues”.
- [ ] Use Project Boards or Issues for Roadmap Planning: Clearly lay out the future plans for the project so contributors can understand priorities.

- [ ] Set up basic community management:
- [ ] Create a CODE_OF_CONDUCT.md file: Establish community behavior standards.
- [ ] Define recognition and rewards
- [ ] Publicly thank people for their contributions, in the project’s README, on social media, or in release notes.
- [ ] Define levels of contributions that lead to increased responsibilities in the project, like becoming a maintainer.
- [ ] Make stickers!?

- [ ] More advanced community management:
- [ ] Set up and engage with a Slack channel on the ai4environment team.
- [ ] Encourage members to try DeepSensor and ask for their feedback. Make it clear that you value and act on user input.
- [ ] Post opportunities for community members to contribute to DeepSensor, whether through coding, documentation, or other means. Clearly list the skills needed and the steps to get started.
- [ ] Host a webinar/Q&A session
- [ ] Post tutorials/videos of how-to in the Slack community