New project planning prompts and questions

[abkfenris]

When you are proposing a project in the project planning discussions, here is a format & questions that you can use to get things started. While these questions and format is optional, they may be useful for thinking through the project setup.

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Title

Brief title describing the proposed project.

Summary

A one sentence summary of what this project aims to achieve. The Title+Summary combination would be super useful to grab people's attention when you pitch the idea in the #ohw22-project channel. We found it very useful to dream big but be somewhat narrowly focused in a given project, being mindful about the compressed project work time during OHW. (BUT, we have had projects that continued to develop beyond a single OHW! So you're absolutely welcome to plan for that.)

Personnel

List all leads and participants on the project. (it's ok to start with a partial list, then update as more people show interest)

Specific tasks

List the specific tasks you want to accomplish and/or if you have links to GitHub

Data sets and infrastructure support

List any external data sets, particularly large ones, that you need for the project, as well as other infrastructure support such as libraries that needs to be included in the Hub -- if the project will be worked on using Hub resources (vs local machine). Feel free to ping @help-infrastructure if you want to discuss these with the infrastructure team.

The problem

What oceanographic data science problem are you going to explore? Try to focus on the data science/methodology problem first, followed by the location-specific oceanographic example afterwards. The project is likely more engaging for fellow hackers if it is centered around common data science problems. For example, the project could be to develop a workflow in Python to analyze CTD time series from multiple data sources, instead of emphasizing and focusing only on temperature variations in one very specific part of the ocean.

Application example

Here is where you follow up with a location-specific example of where the data science methodology applies and list example datasets (size, format, how to access) that could be used for this exploration.

Existing methods

How would you or others traditionally try to address this problem?

Proposed methods/tools

Building from what you learn at Oceanhackweek, what new approaches would you like to try to implement?

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Copy and paste-able version

## Title

Brief title describing the proposed project.

## Summary

A one sentence summary of what this project aims to achieve. The Title+Summary combination would be super useful to grab people's attention when you pitch the idea in the #ohw22-project channel. We found it very useful to dream big but be somewhat narrowly focused in a given project, being mindful about the compressed project work time during OHW. (BUT, we have had projects that continued to develop beyond a single OHW! So you're absolutely welcome to plan for that.)

## Personnel

List all leads and participants on the project.  (it's ok to start with a partial list, then update as more people show interest)

## Specific tasks

List the specific tasks you want to accomplish and/or if you have links to GitHub 

## Data sets and infrastructure support

List any external data sets, particularly large ones, that you need for the project, as well as other infrastructure support such as libraries that needs to be included in the Hub -- if the project will be worked on using Hub resources (vs local machine). Feel free to ping @help-infrastructure if you want to discuss these with the infrastructure team.

## The problem

What oceanographic data science problem are you going to explore? Try to focus on the data science/methodology problem first, followed by the location-specific oceanographic example afterwards. The project is likely more engaging for fellow hackers if it is centered around common data science problems. For example, the project could be to develop a workflow in Python to analyze CTD time series from multiple data sources, instead of emphasizing and focusing only on temperature variations in one very specific part of the ocean.

## Application example

Here is where you follow up with a location-specific example of where the data science methodology applies and list example datasets (size, format, how to access) that could be used for this exploration.

## Existing methods

How would you or others traditionally try to address this problem?

## Proposed methods/tools

Building from what you learn at Oceanhackweek, what new approaches would you like to try to implement?