Story card template

[karenng-civicsoftware]

---

name: Story card template
about: Track the progress of a story card
title:
labels: Storycard
assignees:

Story Card Request

Project:
Card Title:
Card Document: (link here)

Milestones

Setup

• Create Card Document from this template and link above

Type of data processing / analysis this story card uses

• descriptive - simple data (re)-representation, doing summary statistics belongs to this category, we do this for all data sets
• explanatory - testing hypotheses and / or comparing data points
• predictive - any regression, model fitting, classification or clustering tasks
• prescriptive - when you want to recommend any action to be taken (we do this rarely, if at all)

Data documentation and proposed analysis

• Document metadata
• Decide whether to load to database or S3 with proper metadata documentation
• Review metadata and proposed data analysis

Set up data processing development environment

• Clone repo from data science git repo template
• Set up access to GitHub repo for all team members
• Set up a container from a suitable version of the Dockerfile template
• Prototyping and testing analysis proposals
• Review additional proposed data analysis identified through prototyping
• Write code for reproducible data processing steps with proper version control & data lineage
• Data science results produced and documented
• Data science peer reviewed

Build APIs

• Database deployed to CIVIC Cloud

• Initial backend API repo created via cookiecutter, using templatized names

• API developer confers with Data Visualization/Frontend teams regarding story card MVP

• API developer confers with Data Scientists regarding all needed calculations, filters and queries, validation

• perhaps using OpenAPI as a contract/organization first, can help understand the needs/requirements link to OpenAPI and
  Why use OpenAPI

• Basic API in container

• Basic API deployed to CIVIC Cloud

• TBD process for API design - standardization?

• API endpoint with all needed calculations, filters and queries

Data visualization:

• Concept clearly articulated through card title, visualization title/subtitle, card question(s)/action(s), and card context
• Titles and context use consistent language (e.g., census tract v. neighborhood) and match grain of data used in the visualization
• Visualization and component choices inline with data visualization best practices
• All components needed available in Storybook
• Components available in Storybook demonstrate all needed features
• Follows data visualization and interface guidelines available in Storybook

Design

• TBD Wireframes?
• TBD Design review?

Written content / additional links

• Write content
• Review content