Update project-owners.md

site/contents/materials/workshop/project-owners.md

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 # Working with project owners
 
-## Who can be a project owner?  
+The project owners are the heart of your workshop and will be the key to making it a success. 
+They're doing something really unusual and brave by opening up their work to ethical discussion - please make them feel appreciated.
+
+## Who can be a project owner? 
 
 Anyone using statistics or data science in their work! 
 We intend for the Data Hazards to be useful with all types of projects, including those still in their abstract phases. 
+It's most useful to think about hazards at the start of the project (when there's still many decisions left to make), but we can also think about them as a post-mortem to influence future work.
+
+### Identifying project owners
+
+Proeject owners can be:
+- Academic staff, e.g. post-docs, PIs at any stage of a research project.
+- Research students at any stage in their research
+- Start-up founders
+- Data Scientists or Software Engineers
+- Company employees working in relation to a specific data science project within a company 
+
+<!--
+Write this bit:
+## Benefits for project owners
+-->
+
+## What do the project owners need to do?
 
-## What do the project owners need to do?  
+Project owners need to:
+- turn up to the workshop
+- give a 5 minute presentation about their project
+- answer questions for 1 minute about their project
+- listen and be willing to learn new perspectives from the audience
+- reflect on what they've heard
+- identify ways of mitigating risks in their project going forwards
+<!-- TODO: put here more reusable info for project owners-->
 
-The main thing they need to prepare in advance is a description of their project, which is key to the workshop format. 
+The main thing they need to prepare in advance of a workshop is a 5-minute description of their project, which is key to the workshop format. 
 Here are some things we recommend their 5-minute talk includes:  
 - The overall objective of the project
 - Fairly detailed description of the variables in the dataset they are using (and what is not included)