This presentation was provided by Carly Strasser of the Chan Zuckerberg Initiative during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.

1. Effective data management
and its role in open research
Carly Strasser, PhD | @carlystrasser
Chan Zuckerberg Initiative
NISO Effective Data Management
September 2021
Images from unsplash.com unless otherwise attributed

2. Why open research?
What is open research?
Data management basics
Incentives for openness

3. Open Science Advocacy & Education
Research

4. Why open research?
What is open research?
Data management basics
Incentives for openness

5. Research has
changed:
New opportunities

6. Public
Domain,
commons.wikimedia.org

7. Public
Domain,
commons.wikimedia.org

8. Olivier H. Beauchesne
https://olihb.com/2014/08/11/map-of-scientific-collaboration-redux/

9. Research has
changed
New
challenges

12. From Flickr by Alex Roberts
We need to update the
culture of research to
be more open

13. Why open research?
What is open research?
Data management basics
Incentives for openness

14. Open research is the practice of
sharing research processes and outputs
publicly in a timely and effective manner,
so that other researchers can learn and build on them.
proposals
preprints
datasets
images
software/code
workflows
reviews
publications
What should be
shared?

15. reproducibility
credibility
reuse
participation
collaboration
Open research enables

16. Open research is the practice of
sharing research processes and outputs
publicly in a timely and effective manner,
so that other researchers can learn and build on them.

17. Data
Paper just got published!
Guess I’ll share my data now

18. Modified from https://pacharanero.github.io/2020-howto-opensource-rewired-dev-conf/
D
a
t
a
Public domain

19. Open data is only useful if it’s FAIR
Data

20. Getting to FAIR data requires good data management

21. Why open research?
What is open research?
Data management basics
Incentives for openness

22. Plan
Basics

23. When do you plan?
Yes.
manage

24. Standard Data Management Plans
1. Types of data
2. Data & metadata standards
3. Policies
4. Plans for preservation
5. Budget
More from Maria later!

25. Plan
Collect & organize
Basics

26. Collect & organize data so it’s
FAIR from the start
(for both the researcher themselves and others)
● Use good naming schema for files
● Organize files logically
● Use spreadsheets thoughtfully
● Use an appropriate metadata schema throughout
the project

27. Digital context
• Name of the data set
• The name(s) of the data file(s) in the
data set
• Date the data set was last modified
• Example data file records for each data
type file
• Pertinent companion files
• List of related or ancillary data sets
• Software (including version number)
used to prepare/read the data set
• Data processing that was performed
Personnel & stakeholders
• Who collected
• Who to contact with questions
• Funders
Scientific context
• Scientific reason why the data were collected
• What data were collected
• What instruments (including model & serial
number) were used
• Environmental conditions during collection
• Temporal & spatial resolution
• Standards or calibrations used
Information about parameters
• How each was measured or produced
• Units of measure
• Format used in the data set
• Precision & accuracy if known
Information about data
• Definitions of codes used
• Quality assurance & control measures
• Known problems that limit data use (e.g.
uncertainty, sampling problems)

28. Plan
Collect & organize
Store & back up
Basics

29. What software?
What hardware?
What personnel?
How often?
Test system
Set reminders

30. Plan
Collect & organize
Store & back up
Document processes
Basics

31. The R in FAIR is the hardest

35. The R in FAIR is the hardest
…and it’s much easier with code

37. Plan
Collect & organize
Store & back up
Document processes
Preserve for the long term
Basics

38. Choose a good data repository

39. is best
custom data use
agreements
are bad
is okay
Use standard, machine-readable
licenses and waivers

40. Why open research?
What is open research?
Data management basics
Incentives for openness

41. What’s the
holdup?
● Cost
● Lack of training
● Fear of lost rights or benefits
● No incentives

42. ● Cost
● Lack of training
● Fear of lost rights or benefits
● No incentives
What’s the
holdup?

43. Reproducibility for
Everyone (R4E)
www.repro4everyone.org
carpentries.org

44. ● Cost
● Lack of training
● Fear of lost rights or benefits
● No incentives
What’s the
holdup?

45. Open research is the practice of
sharing research processes and outputs
publicly in a timely and effective manner,
so that other researchers can learn and build on them.
proposals
preprints
datasets
images
software/code
workflows
reviews
publications

46. From
Flickr
by
Selena
N.
B.
H.
Journal articles are the LAST output
...and the only thing that “counts”

47. Open research is the practice of
sharing research processes and outputs
publicly in a timely and effective manner,
so that other researchers can learn and build on them.
proposals
preprints
datasets
images
software/code
workflows
reviews
publications
How can we get
everything to
count?

48. Tenure & promotion
Collaboration
Requirements
Mandates
Better tools
& metrics

49. Identifiers for
everything
People
Organizations
Publications
Datasets
Protocols
Software/code
...

50. Identifiers enable citation
data
software
genetic code
reagents
protocols
>

51. Identifiers enable citation
and credit

52. Paper
Notebook
Software
Preprint Data

53. Open science is the future

54. website
email
twitter
strasser.github.io
carlystrasser@gmail.com
@carlystrasser
Thank you!