# Turing RSS use case workshop

Day: Monday 17th April (10:00 am - 14:00 pm)

Attendants:
- Chris Burr
- Anne Lee
- Matt Forshaw: Skills advisor
- Vera Matser
- Brian Tarran: RSS Journalist on Data scientist. Expertise on data science case studies.
- Fatemeh Torabi: Sr Data scientist from RSS. Area of
- G Hiscoke:
- Sarah Nietopski
- Jennifer Ding

### Notes from Malvika
- The Turing Way: Training-specific case studies (Format: Curated example from research with links and resources to source code, data and other open work combined with prompts to enable engagement activities)
- Practitioners Hub: Assessment and impact based reporting (Format: We will use this impact story format to work with different collaborators, exploring the use of open, reproducible and collaborative research with examples from different sectors -- all case studies will be combined at the end in a Goldacre Review)
- DCE and DT will likely follow ASG white paper example (but not call it white paper :smile: ) -- curating examples and resources exemplifying the position of DT and DCE in economy, health, climate, security etc. Co-creation of case studies will be how we begin engaging with different stakeholders here/

Logged in document here: https://hackmd.io/@turingway/ttw-case-studies

### Turing Commons
https://hackmd.io/@turing-commons/H1QQXtcGh
Courses available

Case studies are free to downloads:
- printed hangout
- design for reflection, deliveration. Create spaces to thing about challenging topics
- Common repo with datasheet available
- Activities to facilitate workshops
- Resources available: illustrations

### Real World Data Science

Case study approach.
Research and conversations with data scientist identify a need for:
- worked examples and case studies showing practical applications of methods, and;
- real-world industry use studies

They made surveys and it was stressed the to show data science work in context, and to walk readers through the whole data science process, beginning to end.

Thei notes for contributors:
- Problem formulation and project goals,
- Background - putting both the organisation and data science project in the appropriate context
- Approach
- Implementation: what was done, how and why
- Impact and key takeaways - assess project outcomes against stated goals. What lessons wre learned along the way, and how might these uniform future projects?

They encourage contributors to build data and code into their case studies. They want readers to understand the problmes and decisions organisations face.

Contributions in development employ a mix of resources:
- Written case studies, providing the project narrative
- Jupyter notebooks to document analysis steps in detail
- Github repo, containing material and instructions necessary for replication

*Open Case Studies: **opencasestudies.org**

## Case studies slideck from Jen
- [Notes - Hackmd](https://hackmd.io/O1RwUTnmQLGPZmFWbD37ww?both)

- [Slide Deck](https://docs.google.com/presentation/d/16Ay3xqEcy-yYvh5LHerFKclzvmD1pY5mkNr46dJjhK4/edit#slide=id.g22dbbd969e6_0_29) : with exercises about:
- what is and isn't a "case studies"
- who are your case studies for? Why create them?
- Who are they created?
- other activities + links to other resources

What is a case study from different Turing projects:
- **RSS**: Reproducible notebook with narrative and documentation with wider context that informs the “why” and “how” of a project
- **EDS**: High quality notebooks to better demonstrate environmental models
- **TDS**: Notebook tutorial that tells a story with an interesting, open dataset
- **TC**: Hypothetical example that aligns an RRI practice with a domain-specific problem that is accompanied by text and activities to enable deliberation
TTW: Text-based collation of community projects with synopsis and links to resources (e.g. Github, articles, and notebooks)

**Chris shared the following chart and module and user journey (PDF)** :
This module introduces the Project Lifecycle model which depicts the process of designing, developing, and deploying an AI system.
https://alan-turing-institute.github.io/turing-commons/skills-tracks/rri/rri-101-index/

### The Turing Way Case study template:
https://github.com/alan-turing-institute/the-turing-way/blob/main/book/templates/case-study-template/case-study-template.md

### Proto Persona Development

Why do we create a persona?
- ensure user is central to case study design
- Common understanding of user needs
- Aim collaboration where different teams target the same users
- Aid decision making of what type of case study would fulfill users need

Multiple ways to create persona:
- focus on Behaviour, goals, needs challenges
How will we verify the persona?
- existing data? interviews?

For our **Practitioners Hub** understand levels of Github knowledge, interests (use Vera's material to develop the different Personas)

Working groups:
(1) Building a persona for leaders/commissioners
(2) Educators

Vera:
We would like to create best practices, guidelines, and how to invite contributors to expand the process of writting case studies.

Organise a joint **Book dash** (action for TTW?) to build those documents together. We could get the insight from different users

Differences between Data science personas writting case studies, type of questions.

### BridgeAI - Turing will be involved in:
- Design/create material for senior leadership that has no knowledge of data science
- Turing Data science experts providing support to SME companies, how to integrate new AI tools to their organisations

Next steps:
- meeting every 6 months