Re-thinking and re-engineering incentives for scholarly activities across the research enterprise inan open access environment

1. Launching collaborations with open science
Merrill Research Retreat
July 19, 2018
Adina Howe, PhD
Assistant Professor
Department of Agricultural and Biosystems Engineering
www.germslab.org
@teeniedeenie

2. Who are my collaborators?
• Locally
• Students
• Postdocs
• Faculty
• And beyond….
• Other institutions
• Our field
• Public stakeholders
2

3. STARTING WITH: COMMON
THEMES IN MY OPEN
COLLABORATIVE PROJECTS
3

4. Project (incentive): Build a swingset
4
http://www.instructables.com/id/Traditional-Garden-Tree-Swing/

5. A recurring project theme…
5https://www.tamingdata.com/wp-content/uploads/2010/07/tree-swing-project-management-large.png

6. Incentives: Why should ____ do open
science?
6
https://www.adamsmith.org/blog/economics/people-respond-to-incentives

7. Diversity
7

8. Standards: Necessary, painful, and rarely
well-implemented at scale
8
https://www.commercialintegrator.com/networks/information_technology/how-overcome-av-industry-reluctance-embrace-standards/

9. A recurring theme…
9
www.xkcd.com

10. The opportunities of open science?
10https://www.softwaretestinghelp.com/software-automation-testing-should-automate-project-testing/

11. Case study perspectives
• Open science in research (internal)
• Open science in research (external)
• Open science in teaching
11

12. Open science in research (internal)
All GERMS members can:
• Independently manage data + project
• Collaboratively code share
• Program in at least one language
• Version control and reproduce all analyses
• Reliably use computational resources provided
(Macbook + HPC access)
12

13. Case study #1:
• Jared F. (MS à PhD, joined in Fall 2015)
• Highly experiences in soil science
• No programming
• Interested in biological datasets to inform plant-
microbe interactions for soil health
• 3 year MS (+ 2 more years for PhD)
13

14. Case study #1: https://github.com/jflater/Incubation
• Ability to understand, communicate, and interpret
analyses?
• Data integrity?
• Reproducibility?
14

15. Case study #1: https://github.com/jflater/Incubation
• Ability to understand, communicate, and interpret
analyses?
• Data integrity?
• Reproducibility?
Analysis objectives for our students:
• Application of appropriate/creative techniques
• Ability to present results
• Recognize problems, anomalies, and expected
results
15

16. EXTERNAL COLLABORATIONS AND
OPEN SCIENCE
16

17. Case study #2:
• Standard operating procedures for collaborative
sample collection
https://github.com/germs-lab/SOPs
17

18. Case study #2:
• Standard operating procedures for collaborative
sample collection
https://github.com/germs-lab/SOPs
• Version controlled with known contributors
• Acknowledgements and citations are easier to
manage
18

19. Case study #3:
• Paul V. – Year 1 of PhD, B.S. in Math, light
programming history
• Collaboration with Stanford University
• Research collaborators are engaged but lightly
experienced in big data compute
• Research anecdote: Data integrity lessons
https://github.com/pommevilla/jrbp.community.analy
sis
19

20. TRAINING AND OPEN SCIENCE
20

21. Case study #5:
• Training programs – field specific (bench biologists
à computational)
21
1.92
2.45
2.27
1.59
3.24
3.57
3.38
3.21
Perception of computational ability
Computational understanding
Coding ability
Comfort with computational tasks
1.59 1.92 2.27 2.45 3.21 3.24 3.38 3.57
Likert scale value
pre post
EDAMAME Pre and Post AssessmentA

22. Workshops and bootcamps!
22
https://carpentries.org/
The Carpentries teach
foundational coding, and
data science skills to
researchers worldwide.
Software Carpentry and
Data Carpentry workshops
are based on our lessons.

23. Collaborative Outputs of Teaching:

24. “Re-thinking and re-engineering
incentives for scholarly activities across
the research enterprise in an open access
environment”
• What were my incentives?
• Personal values shaped from previous successes
• Commitment to training
• Start-up $s
• Integrative grants: training as a funded
component (NIH, USDA)
• Open-source training materials / workshops
24

25. Not a one size fits all solution….
25

26. Discussion
26
“Re-thinking and re-engineering incentives for
scholarly activities across the research enterprise in an
open access environment”
• Bottom up: Training the faculty and students
• Flexibility in success metrics
• Top Down: Pressure from administration/funding
agencies

27. THANK YOU FOR YOUR
ATTENTION!
27
www.germslab.org
adina@iastate.edu