Bring your own project (and problems) session

This session

Take a few moments to reflect on our own current or intended practices with data
Identify opportunities and challenges, especially with respect to reproducibility

Form discussion groups.
Take notes via the HackMD collaborative notebook (links coming)
Very helpful if a few people take the notes and other’s contribute to the discussion
- Rotate this responsibility
- Please take care to listen and be respectful of everyone’s opinions
Notes will be shared via the workshop GitHub repository

Data and Meta Data
- What types/forms of data do you each work with? Write as many of these down as you can in a list. (e.g., photos)
- What would be useful to know about how each of these types of data were collected? (think: Camera brand, field of view)
How can we balance the ideals of data collection with the practical challenges and constraints often faced in real-world research settings?
- List some data or data collection practices that you think you (or others) could document better and would be of high value
- What form would that documentation take?

Storage
- Where do you tend to store raw data you’ve collected? (e.g., personal computer hard disk)
- What are some of the challenges you face when storing your raw data? (e.g., limited capacity)
Organization
- How do you tend to organize raw data as you are collecting it (or after you collect it), and where did that organizational practice come from?
- What are some issues that an organizational strategy has addressed or causes for you (or others)?

Data Cleaning and Transformation
- What does it mean to you to “clean” data, and where do you put this data?
- How do you incorporate and document manual steps?
Analyses
- What are some challenges you’ve faced, or foresee facing, when it comes to documenting how you’ve analyzed data?
- To what extent does a tool, such as Git and GitHub, address these challenges?
- What are some pros/cons of sharing your analysis code?

Motivations
- What motivates you to share archival data with others?
- What are the challenges you might face when attempting to share archival data?
Strategy
- How do you, or would you, go about sharing a dataset? What are some good methods you’ve used or seen used?
- What are the merits and drawbacks of institutional support for data archival and sharing?

Grab some ☕️ and 🍩
Research and Computational Data Management with Dr. Katherine Ireland and Dr. Camila Lívio

Please fill out the post workshop survey!