BRAEMBL community survey report

Active researchers and employees are learning these skills "on the job"

Researchers need training that is immediate, accessible, appropriate for their level and relevant to their domain.

This training needs to include not only technical skills, but ways of thinking about data to provide learners with the knowledge of what is possible along with the confidence to continue self-guided learning.

Additionally, we need to give researchers the opportunity to engage in deliberate practice as they learn these skills, starting with strong foundational skills and receiving feedback as they learn

• Learning objectives: Best practices in software development and data analysis & management
• Delivery approach: intensive, hands-on teaching strategies in 2-day workshops, informed by educational pedagogy
• Delivery mechanism: collaboratively developed lessons available on-line for free use, instructors trained to teach
• Assessment: assessment surveys for both assessing learning outcomes and improving content

Advantages:

• Don't need to write something new
• Lessons taught frequently, so constantly improved
• Lessons can be agile, and adpated for new releases or tools
• Openly licensed means that they can be used in our workshops, or in other contexts
• Modular format means things can be mixed and matched

Challenges: - Discovery of high-quality or appropriate materials - Lessons might not be maintained

Data Carpentry workshops are for any researcher who has data they want to analyze, and no prior computational experience is required. This hands-on workshop teaches basic concepts, skills and tools for working more effectively with data.

• Focused on data - teaches how to manage and analyze data in an effective and reproducible way.
• Initial focus is on workshops for novices - there are no prerequisites, and no prior knowledge computational experience is assumed.
• Domain specific by design – currently have lessons in ecology, in genomics developed with iPlant, in geospatial data developed with NEON, and on APIs with rOpenSci

Biology

• Data organization with spreadsheets
• OpenRefine for data cleaning
• R or Python for data analysis and visualization
• SQL for data management

Genomics

• data organization and metadata
• cloud computing
• command line
• automating bioinformatics workflows
• R for data analysis

Geospatial data

• working with spatial and vector data in R

• Focused on better software development practices, for writing software or analysis scripts
• Domain agnostic

• Introduction to programming with Python or R
• github for version control
• command line for automation

http://software-carpentry.org/lessons/

• Learner outcomes
• Instructor outcomes
• Community!

• People learn new techincal skills
• They have a community - no more 'lone bioinformatician'
• People become better communicators
• Gain value from giving back and empowering people with the skills they have learned are valuable

An active and engaged community of instructors and learners, both using and advocating for best practices in effective and reproducible research

How best to support, sustain and grow this community?

• Active researchers and employees are very motivated, but need 'on the job' training
• Training for active researchers needs to teach in the gaps
• Different challenges & opportunities in the training process that potentially require different strategies for support and different groups to develop and deliver

• Support training within your organization
• Support training efforts for people before they get to your organization
• Good documentation of software
• Enabling 'power tools' for all

• Over 900 volunteers worldwide that teach and develop lessons
• Greg Wilson, who founded Software Carpentry
• The Steering Committees of Software and Data Carpentry
• Software and Data Carpentry staff: Jonah Duckles, Greg Wilson, Erin Becker, Maneesha Sane and Kari Jordan

• Alfred P. Sloan Foundation
• Gordon and Betty Moore Foundation
• National Science Foundation