Tobin Magle presents on using the Open Science Framework (OSF) for collaborative data management. He discusses why data management is important throughout the research cycle. Key aspects of successful data management plans for collaborative projects include assigning roles, using a shared workspace like OSF, and implementing version control. OSF allows researchers to organize projects into components and files, link to add-ons, and control access for contributors.

1. Collaborative data
management using OSF
Tobin Magle
Data Management Specialist
Morgan Library
12-07-2016
http://www.slideshare.net/CTobinMagle/collaborative-data-management-using-osf

2. Outline
• Intro to data management services
• What is data management?
• Why should I care?
• Data Management Planning
• Collaboration tool: Open Science Framework

3. My Background: molecular microbiology
(1) CT Magle et al Infect Immun. 2014 Feb;82(2):618-25. doi: 10.1128/IAI.00444-13. Epub 2013 Nov 25.
(2) Sun W, Tanaka TQ, Magle CT, et al.. Sci Rep. 2014 Jan 17;4:3743. doi: 10.1038/srep03743.

4. Workshops

5. One on one meetings
• How do I write a DMP?
• How do I organize my data?
• How do I clean and format my data?
• How do I automate my analyses?
• How do I get my data ready to share?

6. Data archiving service
• CSU Digital Repository
• Over 100 Datasets
• Satisfy requirements for
manuscripts and grants
• At no cost <1 TB
• $150/TB for 5 years
• $300/TB for >5 years

7. Data Management Services
https://lib.colostate.edu/services/data-management

8. What is data
management?
The policies, practices and procedures needed to
manage the storage, access and preservation of data
produced from a research project

9. data management != data sharing
Collaboration

10. Why should I care?
• Good for research integrity
• Good for you
• Public good
• Collaboration is hard
Full lecture by Keith Baggerly, Bioinformatician
(University of Texas, MD Anderson Cancer Center)
https://www.youtube.com/watch?v=7gYIs7uYbMo
http://www.nytimes.com/2011/07/08/health/research/08genes.html

11. Where does data management
fit into research?
Throughout the whole research cycle

12. Hypothesis
The research cycle

13. Hypothesis
Experimental
design
The research cycle

14. Hypothesis Data
Experimental
design
The research cycle

15. Hypothesis Data
Experimental
design
Results
The research cycle

16. Hypothesis Data
Experimental
design
ResultsArticle
The research cycle

17. Hypothesis Data
Experimental
design
ResultsArticle
The research cycle

18. Hypothesis Data
Experimental
design
ResultsArticle
Data
Management
Plans
The research cycle

19. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Analysis
The research cycle

20. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
Closed
Data
Archiving
The research cycle

21. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
Code Reproducible
Research
Closed
Data
Archiving
The research cycle

22. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
Code Reproducible
Research
Reuse
Closed
Data
Archiving
The research cycle

23. Hypothesis
Raw
data
Experimental
design
Tidy
Data
ResultsArticle
Data
Management
Plans
Cleaning
Sharing
Analysis
Open Data
Code Reproducible
ResearchReuse
Closed
Data
Archiving
The research cycle
Version
Control
Metadata
Collaboration

24. What is a data management plan?
• A description of how you plan
to describe, preserve and
share your research data.
• Often required by funders
• Collaboration takes extra planning

25. Successful DMPs include
• A data inventory
• A strategy for describing the data
• A plan for preserving the data
• A method for access to the data
http://help.osf.io/m/60347/l/618674-creating-a-data-management-plan-dmp

26. Successful collaborative DMPs include
• A data inventory
• A strategy for describing the data
• A plan for preserving the data
• A method for access to the data
http://help.osf.io/m/60347/l/618674-creating-a-data-management-plan-dmp
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143734
• Assigned Roles
• Shared work space
• Context
• Version control

27. Shared workspace: OSF
• Components
• Add-ons
• Contributors
• Wiki
http://help.osf.io/m/collaborating/l/524109-using-the-wiki http://www.slideshare.net/DuraSpace/121014-
slides-roadmap-to-the-future-of-share

28. Organization rules
• Be consistent
• One directory per project
• Separate subdirectories for
• Raw data
• Processed data
• Code
• Output
• Make raw data read-only
• Make README files
http://help.osf.io/m/60347/l/611391-organizing-files

29. Components
• “Subprojects”
• Separate privacy settings,
contributors, wiki, add-ons, and
files.
• Examples:
• Different projects:
https://osf.io/82fba/
• Clinical: https://osf.io/gq4mz/
• Mix: https://osf.io/ezcuj/
• File types: https://osf.io/if7ug/
• Manuscript sections:
https://osf.io/zmja2/

30. Demo: add files and
components

31. Add ons
OSF

32. OpenSesame
Soon
OSF
29 grants to develop open tools and services: https://cos.io/pr/2015-09-24/

33. Demo: Link add ons

34. Context: Wiki
• Evolves during project
• Describe the project
• Goals
• Progress report
• Code book:
• ID systems (for records)
• Variable systems

35. Contributors
• Control who can see what
• Administrator
• Read/Write
• Read only
• Separate for each component

36. Demo: Add contributor

37. Version control
• Who did what when?
• Native in OSF
• Git Integration
• One file name,
many versions

38. Demo: version history

39. Need help?
• Email: tobin.magle@colostate.edu
• DMPTool: http://dmptool.org/
• OSF: https://osf.io/
• Data Management Services website:
http://lib.colostate.edu/services/data-management
• Slides: http://www.slideshare.net/CTobinMagle/collaborative-data-
management-using-osf