Towards an infrastructure for open-source (online) training in data science and AI

[mishkanemes]

Project Lead: mihaelanemes
Mentor: jezcope

Welcome to OLS-3! 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 8 February 2021): Meet your mentor!

• 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 the HackMD 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 15 February 2021): Cohort Call (Welcome to Open Life Science!)

• Create an issue on the OLS-3 GitHub repository for your OLS work and share the link to your mentor.

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

• Draft a brief vision statement using your goals

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

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

Before Week 3 (week starting 22 February 2021): Meet your 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 (week starting 1 March 2021): Cohort Call (Tooling and roadmapping for Open projects)

• Look up two other projects and comment on their issues with feedback on their open canvas.

Week 5 and later

• 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-3 Syllabus for more detailed weekly notes and assignments past week 4.

[mishkanemes]

I am working towards building an online open-access tool to encourage and offer a platform for UK-based researchers to contribute to the national data science and AI training agenda.

[mishkanemes]

Project canvas

https://docs.google.com/presentation/d/1jt_sLApiJ1ubuSRN_0_6xCHylFrd81T80YpjLdHvaAg/edit?usp=sharing

[martinagvilas]

Sounds like an awesome idea!

Regarding the vision of the project:

I am working towards building an online open-access tool to encourage and offer a platform for UK-based researchers to contribute to the national data science and AI training agenda.

I was wondering what change the project is hoping to make in the long term. Creating a more collaboratively developed AI training agenda? Improving the training material? Maybe it would be a nice idea to specify this long term goal it in the vision statement 🚀

[mxrtinez]

Hi, sounds great the project. First, the link to the Canvas does not allow to see it. Second a question from the vision, is the training focused on tools or methodologies?

[mishkanemes]

up-goer five text redrafted vision

I am working towards building a space for people to have a say in the training direction.

[afzal442]

Your project's idea sounds amazing.

Hello @mihaelanemes, I wonder if people now mean researchers all over the world after changing your vision statement. Could you plz tell what's that have a say directs? And secondly plz tell do your objectives lie in creating workshops to give more freedom to share the research works openly? Thanks.

[mishkanemes]

Sounds like an awesome idea!

Regarding the vision of the project:

I am working towards building an online open-access tool to encourage and offer a platform for UK-based researchers to contribute to the national data science and AI training agenda.

I was wondering what change the project is hoping to make in the long term. Creating a more collaboratively developed AI training agenda? Improving the training material? Maybe it would be a nice idea to specify this long term goal it in the vision statement 🚀

That's a great point - the long term goal is enabling a more inclusive environment, and improving the training provision nationally and beyond. Worth adding it in the vision, thanks for the prompt!

[mishkanemes]

Hi, sounds great the project. First, the link to the Canvas does not allow to see it. Second a question from the vision, is the training focused on tools or methodologies?

Thanks @mxrtinez for flagging the broken link, I just fixed it so hopefully you can now see it. As of the training, it's mainly focused on data science & AI methods (eg how does RL work) as opposed to the best tools for doing RL :)

[mishkanemes]

Your project's idea sounds amazing.

Hello @mihaelanemes, I wonder if people now mean researchers all over the world after changing your vision statement. Could you plz tell what's that have a say directs? And secondly plz tell do your objectives lie in creating workshops to give more freedom to share the research works openly? Thanks.

hi @afzal442 people is restricted to UK researchers for now but could well include worldwide researchers after the pilot. Have a say was a reductionist way of saying contribute to training

The project objectives are to 1) get more people involved in the training agenda 2) be inclusive and transparent 3) improve the training provision

[mishkanemes]

Compare and contrast assignment

What kinds of community interactions exist in my organization’s internal culture and external community?

• We don’t have a central mission and vision which means things happen but without a specific purpose. Also, we tend to look inwards and as a national research body, it is important to be inclusive and look outwards as much as possible.

What kinds of value exchanges go with these interactions? Do I think they are equitable?

• We don’t value those who want to set a direction for the work and rather allow serendipitous interactions and activities to happen. If we had people contributing to the training agenda, we would need to recognise their work and make sure we offer them opportunities when they demonstrate interest.

What kinds of community interactions would I like to have in my organization’s internal culture and external community?

• We should do things more purposefully and with a larger vision in mind all the time, and we should enable and encourage contributions from anyone in the data science and AI community interested in teaching and training.

What kind of equitable value exchanges would I like my organization to offer as part of these interactions?

• We should be more open and transparent by having updated public web pages with the work we do. We should also encourage and recognise the efforts of the community members in developing, sharing and/or delivering training for the institute.

https://docs.google.com/document/d/1gOaOFRcL_zXuXcGxGGuf5Y1RWovwh7d1TScJ_Y7ZcWQ/edit?usp=sharing

[mishkanemes]

GitHub repo https://github.com/mihaelanemes/ols3-ati-training

README https://github.com/mihaelanemes/ols3-ati-training/blob/main/README.md