This document provides guidance on best practices for data stewardship for researchers. It discusses why data management is an important topic, including funder requirements for data sharing and increased emphasis on reproducibility. The document outlines best practices such as creating data management plans, storing data in repositories, and sharing data. Tips are provided on overcoming barriers to data sharing through education and promoting a culture shift toward recognition of data as a first-class research product.

1. Data
Stewardship
for
Researchers
Carly
Strasser,
PhD
California
Digital
Library
@carlystrasser
carly.strasser@ucop.edu
31
July
2013
CLIR
Symposium
From
Calisphere,
Couretsy
of
UC
Riverside,
California
Museum
of
Photography
Tips,
Tools,
&
Guidance
From
Calisphere,
Courtesy
of
Thousand
Oaks
Library

2. Roadmap
4. Toolbox
1. Background
2. Why
you
should
care
3. Best
practices

3. NSF
funded
DataNet
Project
Office
of
Cyberinfrastructure
Two
main
goals:
1. Build
a
network
for
data
repositories
2. Build
community
around
data
Focus
on
Earth
|
environmental
|
ecological
|
oceanographic
data

4. Why
don’t
people
share
data?
Is
data
management
being
taught?
Do
attitudes
about
sharing
differ
among
disciplines?
How
can
we
promote
storing
data
in
repositories?
What
barriers
to
sharing
can
we
eliminate?
What
role
can
libraries
play
in
data
education?

6. Why
is
data
management
a
hot
topic?
From
Flickr
by
Velo
Steve

7. Back in the day…
Da
Vinci
Curie
Newton
classicalschool.blogspot.com
Darwin

8. Digital
data
From
Flickr
by
Flickmor
From
Flickr
by
US
Army
Environmental
Command
From
Flickr
by
DW0825
C.
Strasser
Courtesey
of
WHOI
From
Flickr
by
deltaMike

9. Digital
data
+
Complex
workflows

10. From
Flickr
by
~Minnea~
Data
management
Documentation
Reproducibility

11. From
Flickr
by
iowa_spirit_walker
• Cost
• Confusion
about
standards
• Lack
of
training
• Fear
of
lost
rights
or
benefits
• No
incentives

12. THE
TRUTH
From
sandierpastures.com
Data
management
Metadata
Data
repositories
Data
sharing
RESEARCHERS
NEED TO KNOW
ABOUT

13. From
Flickr
by
johntrainor
Who
cares?

14. From
Flickr
by
hyperion327
From
Flickr
by
Redden-­‐McAllister

15. …
“Federal
agencies
investing
in
research
and
development
(more
than
$100
million
in
annual
expenditures)
must
have
clear
and
coordinated
policies
for
increasing
public
access
to
research
products.”
Back
in
February:

16. 1. Maximize
free
public
access
2. Ensure
researchers
create
data
management
plans
3. Allow
costs
for
data
preservation
and
access
in
proposal
budgets
4. Ensure
evaluation
of
data
management
plan
merits
5. Ensure
researchers
comply
with
their
data
management
plans
6. Promote
data
deposition
into
public
repositories
7. Develop
approaches
for
identification
and
attribution
of
datasets
8. Educate
folks
about
data
stewardship
From
Flickr
by
Joe
Crimmings
Photography

17. From
Flickr
by
twm1340
Culture
Shift
Ahead

18. science
source
notebook
content
access
data
government
knowledge
From
Flickr
by
cdsessums

19. flowingdata.com
Map
of
Scientific
Collaborations

20. From
Flickr
by
~shorts
and
longs
Publications
&
Their
Citation
&
data
availability

21. Data
are
being
recognized
as
first
class
products
of
research
From
Flickr
by
Richard
Moross

22. Data
management
plans
Data
sharing
mandates
Data
publications
Data
citation
From
Flickr
by
torkildr

23. Data
publications
Data
citation
Data
management
plans
Data
sharing
mandates

24. What
should
researchers
be
doing?
From
Flickr
by
whatthefeed
NOT
V

25. C:Documents and SettingshamptonMy DocumentsNCEAS Distributed Graduate Seminars[Wash Cres Lake Dec 15 Dont_Use.xls]Sheet1
Stable Isotope Data Sheet
Wash Cresc Lake Peter's lab Don't use - old data
Algal Washed Rocks
Dec. 16
Tray 004
SD for delta
13
C = 0.07 SD for delta
15
N = 0.15
Position SampleID Weight (mg) %C delta 13C delta 13C_ca %N delta 15N delta 15N_ca Spec. No.
A1 ref 0.98 38.27 -25.05 -24.59 1.96 4.12 3.47 25354
A2 ref 0.98 39.78 -25.00 -24.54 2.03 4.01 3.36 25356
A3 ref 0.98 40.37 -24.99 -24.53 2.04 4.09 3.44 25358
A4 ref 1.01 42.23 -25.06 -24.60 2.17 4.20 3.55 25360 Shore Avg Con
A5 ALG01 3.05 1.88 -24.34 -23.88 0.17 -1.65 -2.30 25362 c -1.26 -27.22
A6 Lk Outlet Alg 3.06 31.55 -30.17 -29.71 0.92 0.87 0.22 25364 1.26 0.32
A7 ALG03 2.91 6.85 -21.11 -20.65 0.48 -0.97 -1.62 25366 c
A8 ALG05 2.91 35.56 -28.05 -27.59 2.30 0.59 -0.06 25368
A9 ALG07 3.04 33.49 -29.56 -29.10 1.68 0.79 0.14 25370
A10 ALG06 2.95 41.17 -27.32 -26.86 1.97 2.71 2.06 25372
B1 ALG04 3.01 43.74 -27.50 -27.04 1.36 0.99 0.34 25374 c
B2 ALG02 3 4.51 -22.68 -22.22 0.34 4.31 3.66 25376
B3 ALG01 2.99 1.59 -24.58 -24.12 0.15 -1.69 -2.34 25378 c
B4 ALG03 2.92 4.37 -21.06 -20.60 0.34 -1.52 -2.17 25380 c
B5 ALG07 2.9 33.58 -29.44 -28.98 1.74 0.62 -0.03 25382
B6 ref 1.01 44.94 -25.00 -24.54 2.59 3.96 3.31 25384
B7 ref 0.99 42.28 -24.87 -24.41 2.37 4.33 3.68 25386
B8 Lk Outlet Alg 3.04 31.43 -29.69 -29.23 1.07 0.95 0.30 25388
B9 ALG06 3.09 35.57 -27.26 -26.80 1.96 2.79 2.14 25390
B10 ALG02 3.05 5.52 -22.31 -21.85 0.45 4.72 4.07 25392
C1 ALG04 2.98 37.90 -27.42 -26.96 1.36 1.21 0.56 25394 c
C2 ALG05 3.04 31.74 -27.93 -27.47 2.40 0.73 0.08 25396
C3 ref 0.99 38.46 -25.09 -24.63 2.40 4.37 3.72 25398
23.78 1.17
Reference statistics:
Sampling Site / Identifier:
Sample Type:
Date:
Tray ID and Sequence:
From
Stephanie
Hampton
(2010)
ESA
Workshop
on
Best
Practices
2
tables
Random
notes
From
Stephanie
Hampton

26. C:Documents and SettingshamptonMy DocumentsNCEAS Distributed Graduate Seminars[Wash Cres Lake Dec 15 Dont_Use.xls]Sheet1
Stable Isotope Data Sheet
Wash Cresc Lake Peter's lab Don't use - old data
Algal Washed Rocks
Dec. 16
Tray 004
SD for delta
13
C = 0.07 SD for delta
15
N = 0.15
Position SampleID Weight (mg) %C delta 13C delta 13C_ca %N delta 15N delta 15N_ca Spec. No.
A1 ref 0.98 38.27 -25.05 -24.59 1.96 4.12 3.47 25354
A2 ref 0.98 39.78 -25.00 -24.54 2.03 4.01 3.36 25356
A3 ref 0.98 40.37 -24.99 -24.53 2.04 4.09 3.44 25358
A4 ref 1.01 42.23 -25.06 -24.60 2.17 4.20 3.55 25360 Shore Avg Con
A5 ALG01 3.05 1.88 -24.34 -23.88 0.17 -1.65 -2.30 25362 c -1.26 -27.22
A6 Lk Outlet Alg 3.06 31.55 -30.17 -29.71 0.92 0.87 0.22 25364 1.26 0.32
A7 ALG03 2.91 6.85 -21.11 -20.65 0.48 -0.97 -1.62 25366 c
A8 ALG05 2.91 35.56 -28.05 -27.59 2.30 0.59 -0.06 25368
A9 ALG07 3.04 33.49 -29.56 -29.10 1.68 0.79 0.14 25370
A10 ALG06 2.95 41.17 -27.32 -26.86 1.97 2.71 2.06 25372
B1 ALG04 3.01 43.74 -27.50 -27.04 1.36 0.99 0.34 25374 c
B2 ALG02 3 4.51 -22.68 -22.22 0.34 4.31 3.66 25376
B3 ALG01 2.99 1.59 -24.58 -24.12 0.15 -1.69 -2.34 25378 c
B4 ALG03 2.92 4.37 -21.06 -20.60 0.34 -1.52 -2.17 25380 c
B5 ALG07 2.9 33.58 -29.44 -28.98 1.74 0.62 -0.03 25382
B6 ref 1.01 44.94 -25.00 -24.54 2.59 3.96 3.31 25384
B7 ref 0.99 42.28 -24.87 -24.41 2.37 4.33 3.68 25386
B8 Lk Outlet Alg 3.04 31.43 -29.69 -29.23 1.07 0.95 0.30 25388
B9 ALG06 3.09 35.57 -27.26 -26.80 1.96 2.79 2.14 25390
B10 ALG02 3.05 5.52 -22.31 -21.85 0.45 4.72 4.07 25392
C1 ALG04 2.98 37.90 -27.42 -26.96 1.36 1.21 0.56 25394 c
C2 ALG05 3.04 31.74 -27.93 -27.47 2.40 0.73 0.08 25396
C3 ref 0.99 38.46 -25.09 -24.63 2.40 4.37 3.72 25398
23.78 1.17
Reference statistics:
Sampling Site / Identifier:
Sample Type:
Date:
Tray ID and Sequence:
From
Stephanie
Hampton
(2010)
ESA
Workshop
on
Best
Practices
Wash
Cres
Lake
Dec
15
Dont_Use.xls
From
Stephanie
Hampton

27. C:Documents and SettingshamptonMy DocumentsNCEAS Distributed Graduate Seminars[Wash Cres Lake Dec 15 Dont_Use.xls]Sheet1
Stable Isotope Data Sheet
Wash Cresc Lake Peter's lab Don't use - old data
Algal Washed Rocks
Dec. 16
Tray 004
SD for delta
13
C = 0.07 SD for delta
15
N = 0.15
Position SampleID Weight (mg) %C delta 13C delta 13C_ca %N delta 15N delta 15N_ca Spec. No.
A1 ref 0.98 38.27 -25.05 -24.59 1.96 4.12 3.47 25354
A2 ref 0.98 39.78 -25.00 -24.54 2.03 4.01 3.36 25356
A3 ref 0.98 40.37 -24.99 -24.53 2.04 4.09 3.44 25358
A4 ref 1.01 42.23 -25.06 -24.60 2.17 4.20 3.55 25360 Shore Avg Con
A5 ALG01 3.05 1.88 -24.34 -23.88 0.17 -1.65 -2.30 25362 c -1.26 -27.22
A6 Lk Outlet Alg 3.06 31.55 -30.17 -29.71 0.92 0.87 0.22 25364 1.26 0.32
A7 ALG03 2.91 6.85 -21.11 -20.65 0.48 -0.97 -1.62 25366 c
A8 ALG05 2.91 35.56 -28.05 -27.59 2.30 0.59 -0.06 25368
A9 ALG07 3.04 33.49 -29.56 -29.10 1.68 0.79 0.14 25370
A10 ALG06 2.95 41.17 -27.32 -26.86 1.97 2.71 2.06 25372
B1 ALG04 3.01 43.74 -27.50 -27.04 1.36 0.99 0.34 25374 c SUMMARY OUTPUT
B2 ALG02 3 4.51 -22.68 -22.22 0.34 4.31 3.66 25376
B3 ALG01 2.99 1.59 -24.58 -24.12 0.15 -1.69 -2.34 25378 c Regression Statistics
B4 ALG03 2.92 4.37 -21.06 -20.60 0.34 -1.52 -2.17 25380 c Multiple R 0.283158
B5 ALG07 2.9 33.58 -29.44 -28.98 1.74 0.62 -0.03 25382 R Square 0.080178
B6 ref 1.01 44.94 -25.00 -24.54 2.59 3.96 3.31 25384 Adjusted R Square-0.022024
B7 ref 0.99 42.28 -24.87 -24.41 2.37 4.33 3.68 25386 Standard Error1.906378
B8 Lk Outlet Alg 3.04 31.43 -29.69 -29.23 1.07 0.95 0.30 25388 Observations 11
B9 ALG06 3.09 35.57 -27.26 -26.80 1.96 2.79 2.14 25390
B10 ALG02 3.05 5.52 -22.31 -21.85 0.45 4.72 4.07 25392 ANOVA
C1 ALG04 2.98 37.90 -27.42 -26.96 1.36 1.21 0.56 25394 c df SS MS F Significance F
C2 ALG05 3.04 31.74 -27.93 -27.47 2.40 0.73 0.08 25396 Regression 1 2.851116 2.851116 0.784507 0.398813
C3 ref 0.99 38.46 -25.09 -24.63 2.40 4.37 3.72 25398 Residual 9 32.7085 3.634278
23.78 1.17 Total 10 35.55962
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept -4.297428 4.671099 -0.920003 0.381568 -14.8642 6.269341 -14.8642 6.269341
X Variable 1-0.158022 0.17841 -0.885724 0.398813 -0.561612 0.245569 -0.561612 0.245569
Reference statistics:
Sampling Site / Identifier:
Sample Type:
Date:
Tray ID and Sequence:
Random
stats
output
From
Stephanie
Hampton

28. C:Documents and SettingshamptonMy DocumentsNCEAS Distributed Graduate Seminars[Wash Cres Lake Dec 15 Dont_Use.xls]Sheet1
Stable Isotope Data Sheet
Wash Cresc Lake Peter's lab Don't use - old data
Algal Washed Rocks
Dec. 16
Tray 004
SD for delta
13
C = 0.07 SD for delta
15
N = 0.15
Position SampleID Weight (mg) %C delta 13C delta 13C_ca %N delta 15N delta 15N_ca Spec. No.
A1 ref 0.98 38.27 -25.05 -24.59 1.96 4.12 3.47 25354
A2 ref 0.98 39.78 -25.00 -24.54 2.03 4.01 3.36 25356
A3 ref 0.98 40.37 -24.99 -24.53 2.04 4.09 3.44 25358
A4 ref 1.01 42.23 -25.06 -24.60 2.17 4.20 3.55 25360 Shore Avg Con
A5 ALG01 3.05 1.88 -24.34 -23.88 0.17 -1.65 -2.30 25362 c -1.26 -27.22
A6 Lk Outlet Alg 3.06 31.55 -30.17 -29.71 0.92 0.87 0.22 25364 1.26 0.32
A7 ALG03 2.91 6.85 -21.11 -20.65 0.48 -0.97 -1.62 25366 c
A8 ALG05 2.91 35.56 -28.05 -27.59 2.30 0.59 -0.06 25368
A9 ALG07 3.04 33.49 -29.56 -29.10 1.68 0.79 0.14 25370
A10 ALG06 2.95 41.17 -27.32 -26.86 1.97 2.71 2.06 25372
B1 ALG04 3.01 43.74 -27.50 -27.04 1.36 0.99 0.34 25374 c SUMMARY OUTPUT
B2 ALG02 3 4.51 -22.68 -22.22 0.34 4.31 3.66 25376
B3 ALG01 2.99 1.59 -24.58 -24.12 0.15 -1.69 -2.34 25378 c Regression Statistics
B4 ALG03 2.92 4.37 -21.06 -20.60 0.34 -1.52 -2.17 25380 c Multiple R 0.283158
B5 ALG07 2.9 33.58 -29.44 -28.98 1.74 0.62 -0.03 25382 R Square 0.080178
B6 ref 1.01 44.94 -25.00 -24.54 2.59 3.96 3.31 25384 Adjusted R Square-0.022024
B7 ref 0.99 42.28 -24.87 -24.41 2.37 4.33 3.68 25386 Standard Error1.906378
B8 Lk Outlet Alg 3.04 31.43 -29.69 -29.23 1.07 0.95 0.30 25388 Observations 11
B9 ALG06 3.09 35.57 -27.26 -26.80 1.96 2.79 2.14 25390
B10 ALG02 3.05 5.52 -22.31 -21.85 0.45 4.72 4.07 25392 ANOVA
C1 ALG04 2.98 37.90 -27.42 -26.96 1.36 1.21 0.56 25394 c df SS MS F Significance F
C2 ALG05 3.04 31.74 -27.93 -27.47 2.40 0.73 0.08 25396 Regression 1 2.851116 2.851116 0.784507 0.398813
C3 ref 0.99 38.46 -25.09 -24.63 2.40 4.37 3.72 25398 Residual 9 32.7085 3.634278
23.78 1.17 Total 10 35.55962
CoefficientsStandard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%Upper 95.0%
Intercept -4.297428 4.671099 -0.920003 0.381568 -14.8642 6.269341 -14.8642 6.269341
X Variable 1-0.158022 0.17841 -0.885724 0.398813 -0.561612 0.245569 -0.561612 0.245569
Reference statistics:
Sampling Site / Identifier:
Sample Type:
Date:
Tray ID and Sequence:
SampleID ALG03 ALG05 ALG07 ALG06 ALG04 ALG02 ALG01 ALG03 ALG07
Weight (mg) 2.91 2.91 3.04 2.95 3.01 3 2.99 2.92 2.9
%C 6.85 35.56 33.49 41.17 43.74 4.51 1.59 4.37 33.58
delta 13C -21.11 -28.05 -29.56 -27.32 -27.50 -22.68 -24.58 -21.06 -29.44
delta 13C_ca -20.65 -27.59 -29.10 -26.86 -27.04 -22.22 -24.12 -20.60 -28.98
%N 0.48 2.30 1.68 1.97 1.36 0.34 0.15 0.34 1.74
delta 15N -0.97 0.59 0.79 2.71 0.99 4.31 -1.69 -1.52 0.62
delta 15N_ca -1.62 -0.06 0.14 2.06 0.34 3.66 -2.34 -2.17 -0.03
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
-35.00 -30.00 -25.00 -20.00 -15.00 -10.00 -5.00 0.00
Series1
From
Stephanie
Hampton

29. From
Flickr
by
whatthefeed
What
should
researchers
be
doing?

30. data management
From
Flickr
by
Big
Swede
Guy
1. Planning
2. Data
collection
&
organization
3. Quality
control
&
assurance
4. Metadata
5. Workflows
6. Data
stewardship
&
reuse
Best
Practices

31. Create
unique
identifiers
• Decide
on
naming
scheme
early
• Create
a
key
• Different
for
each
sample
2.
Data
collection
&
organization
From
Flickr
by
sjbresnahan
From
Flickr
by
zebbie

32. Standardize
• Consistent
within
columns
– only
numbers,
dates,
or
text
• Consistent
names,
codes,
formats
Modified
from
K.
Vanderbilt
From
Pink
Floyd,
The
Wall
themurkyfringe.com
2.
Data
collection
&
organization

33. Google
Docs
Forms
Standardize
• Reduce
possibility
of
manual
error
by
constraining
entry
choices
Modified
from
K.
Vanderbilt
2.
Data
collection
&
organization
Excel
lists
Data
validataion

34. 2.
Data
collection
&
organization
Create
parameter
table
Create
a
site
table
From
doi:10.3334/ORNLDAAC/777
From
doi:10.3334/ORNLDAAC/777
From
R
Cook,
ESA
Best
Practices
Workshop
2010

35. Use
descriptive
file
names
• Unique
• Reflect
contents
From
R
Cook,
ESA
Best
Practices
Workshop
2010
Bad:
Mydata.xls
2001_data.csv
best
version.txt
Better:
Eaffinis_nanaimo_2010_counts.xls
Site
name
Year
What
was
measured
Study
organism
2.
Data
collection
&
organization
*Not
for
everyone
*

36. Organize
files
logically
Biodiversity
Lake
Experiments
Field
work
Grassland
Biodiv_H20_heatExp_2005to2008.csv
Biodiv_H20_predatorExp_2001to2003.csv
…
Biodiv_H20_PlanktonCount_2001toActive.csv
Biodiv_H20_ChlAprofiles_2003.csv
…
From
S.
Hampton
2.
Data
collection
&
organization

37. Preserve
information
• Keep
raw
data
raw
• Use
scripts
to
process
data
&
save
them
with
data
Raw
data
as
.csv
R
script
for
processing
&
analysis
2.
Data
collection
&
organization

38. data management
From
Flickr
by
Big
Swede
Guy
1. Planning
2. Data
collection
&
organization
3. Quality
control
&
assurance
4. Metadata
5. Workflows
6. Data
stewardship
&
reuse
Best
Practices

39. Before
data
collection
• Define
&
enforce
standards
• Assign
responsibility
for
data
quality
3.
Quality
control
and
quality
assurance
From
Flickr
by
StacieBee

40. After
data
entry
• Check
for
missing,
impossible,
anomalous
values
• Perform
statistical
summaries
• Look
for
outliers
3.
Quality
control
and
quality
assurance
0
10
20
30
40
50
60
0
10
20
30
40

41. data management
From
Flickr
by
Big
Swede
Guy
1. Planning
2. Data
collection
&
organization
3. Quality
control
&
assurance
4. Metadata
5. Workflows
6. Data
stewardship
&
reuse
Best
Practices

42. 4.
Metadata
basics
Why
are
you
promoting
Excel?
What
is
metadata?

43. • 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
• Where
collected
&
spatial
resolution
When
collected
&
temporal
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)
• How
to
cite
the
data
set
4.
Metadata
basics

44. • Provides
structure
to
describe
data
Common
terms
|
definitions
|
language
|
structure
4.
Metadata
basics
• Lots
of
different
standards
EML
,
FGDC,
ISO19115,
DarwinCore,…
• Tools
for
creating
metadata
files
Morpho
(EML),
Metavist
(FGDC),
NOAA
MERMaid
(CSGDM)
What
is
metadata?
Select
the
appropriate
standard

45. data management
From
Flickr
by
Big
Swede
Guy
1. Planning
2. Data
collection
&
organization
3. Quality
control
&
assurance
4. Metadata
5. Workflows
6. Data
stewardship
&
reuse
Best
Practices

46. Temperature
data
Salinity
data
Data
import
into
R
Analysis:
mean,
SD
Graph
production
Quality
control
&
data
cleaning
“Clean”
T
&
S
data
Summary
statistics
Data
in
R
format
5.
Workflows
Workflow:
how
you
get
from
the
raw
data
to
the
final
products
of
your
research
Simple
workflows:
flow
charts

47. • R,
SAS,
MATLAB
• Well-­‐documented
code
is…
Easier
to
review
Easier
to
share
Easier
to
repeat
analysis
5.
Workflows
Workflow:
how
you
get
from
the
raw
data
to
the
final
products
of
your
research
Simple
workflows:
commented
scripts
#
%
$
&

48. Fancy
Schmancy
workflows:
Kepler
Resulting
output
5.
Workflows
https://kepler-­‐project.org

49. Workflows
enable…
Reproducibility
can
someone
independently
validate
findings?
Transparency
others
can
understand
how
you
arrived
at
your
results
Executability
others
can
re-­‐run
or
re-­‐use
your
analysis
5.
Workflows
From
Flickr
by
merlinprincesse
Coming
Soon:
workflow
sharing
requirements!

50. data management
From
Flickr
by
Big
Swede
Guy
1. Planning
2. Data
collection
&
organization
3. Quality
control
&
assurance
4. Metadata
5. Workflows
6. Data
stewardship
&
reuse
Best
Practices

51. Use
stable
formats
csv,
txt,
tiff
Create
back-­‐up
copies
original,
near,
far
Periodically
test
ability
to
restore
information
6.
Data
stewardship
&
reuse
Modified from R. Cook

52. Store
your
data
in
a
repository
Institutional
archive
Discipline/specialty
archive
6.
Data
stewardship
&
reuse
From
Flickr
by
torkildr
Ask
a
librarian
Repos
of
repos:
databib.org
re3data.org

53. Allows
readers
to
find
data
products
Get
credit
for
data
and
publications
Promotes
reproducibility
Better
measure
of
research
impact
Example:
Sidlauskas,
B.
2007.
Data
from:
Testing
for
unequal
rates
of
morphological
diversification
in
the
absence
of
a
detailed
phylogeny:
a
case
study
from
characiform
fishes.
Dryad
Digital
Repository.
doi:10.5061/dryad.20
Persistent
Unique
Identifier
6.
Data
stewardship
&
reuse
Practice
Data
Citation

54. data management
From
Flickr
by
Big
Swede
Guy
1. Planning
2. Data
collection
&
organization
3. Quality
control
&
assurance
4. Metadata
5. Workflows
6. Data
stewardship
&
reuse
Best
Practices

55. A
document
that
describes
what
you
will
do
with
your
data
throughout
the
research
project
From Flickr by Barbies Land
What
is
a
data
management
plan?

56. DMP
for
funders:
A
short
plan
submitted
alongside
grant
applications
But they all have
different requirements
and express them in
different ways
From
Flickr
by
401(K)
2013
An
outline
of
– what
will
be
collected
– methods
– Standards
– Metadata
– sharing/access
– long-­‐term
storage
Includes
how
and
why

57. DMP
supplement
may
include:
1. the
types
of
data,
samples,
physical
collections,
software,
curriculum
materials,
and
other
materials
to
be
produced
in
the
course
of
the
project
2.
the
standards
to
be
used
for
data
and
metadata
format
and
content
(where
existing
standards
are
absent
or
deemed
inadequate,
this
should
be
documented
along
with
any
proposed
solutions
or
remedies)
3.
policies
for
access
and
sharing
including
provisions
for
appropriate
protection
of
privacy,
confidentiality,
security,
intellectual
property,
or
other
rights
or
requirements
4.
policies
and
provisions
for
re-­‐use,
re-­‐distribution,
and
the
production
of
derivatives
5.
plans
for
archiving
data,
samples,
and
other
research
products,
and
for
preservation
of
access
to
them
NSF
DMP
Requirements
From
Grant
Proposal
Guidelines:

58. • Types
of
data
• Existing
data
• How/when/where
created?
• How
processed?
• Quality
control
• Security
• Who
is
responsible
1. Types
of
data
&
other
information
biology.kenyon.edu
C.
Strasser
From
Flickr
by
Lazurite

59. Wired.com
• Metadata
needed
• How
captured
• Standards
2. Data
&
metadata
standards

60. • Obligation
to
share
• How/when/where
available
• Getting
access
• Copyright
/
IP
• Permission
restrictions
• Embargo
periods
• Ethics/privacy
• How
cited
3. Policies
for
access
&
sharing
4. Policies
for
re-­‐use
&
re-­‐distribution
From
Flickr
by
maryfrancesmain

61. • What
&
where
• Metadata
• Who’s
responsible
5. Plans
for
archiving
&
preservation
From
Flickr
by
theManWhoSurfedTooMuch

62. Don’t
forget
the
budget
dorrvs.com

63. NSF’s
Vision*
DMPs
and
their
evaluation
will
grow
&
change
over
time
Peer
review
will
determine
next
steps
Community-­‐driven
guidelines
Evaluation
will
vary
with
directorate,
division,
&
program
officer
*Unofficially

64. From
Flickr
by
celikins
Where
to
start?

65. From
Flickr
by
Andy
Graulund
Make
a
resolution
• Triage
on
current
projects
• Get
advisor,
lab
mates,
collaborators
on
board
• Do
better
next
time

66. Start
working
online
From
Flickr
by
karindalziel

67. From
Flickr
by
karindalziel
E-­‐notebooks
Online
science
http://datapub.cdlib.org/software-­‐for-­‐reproducibility-­‐part-­‐2-­‐the-­‐tools/
Reproducibility

69. From
Flickr
by
dipster1
Toolbox

70. Step-by-step wizard for generating DMP
create | edit | re-use | share
Free & open to community
dmptool.org
Write
a
DMP

71. databib.org
Where
should
I
put
my
data?
Find
a
repository

72. Get
help
FromFlickrbythewmatt

73. Get
help
from
your
library
From
Flickr
by
North
Carolina
Digital
Heritage
Center
From
Flickr
by
Madison
Guy

74. NSF
funded
DataNet
Project
Office
of
Cyberinfrastructure
www.dataone.org
Get
help

75. B
C
A

76. • Data
Education
Tutorials
• Database
of
best
practices
&
software
tools
• Primer
on
data
management
• Investigator
Toolkit
www.dataone.org

77. From
Flickr
by
Skakerman
A
word
about
Metrics…

78. Articles
are
the
butterfly
pinned
on
the
wall.
Pretty
but
not
very
useful.
They
are
only
the
advertisements
for
scholarship.
–
A.
Levi,
U.
Maryland
College
of
Information
Studies
From
Flickr
by
LisaW123

79. How to
incentivize
good data
stewardship?
Data
Citation
Altmetrics
(Alternative
Metrics)
From
Flickr
by
chriscook04

80. From
Flickr
by
dotpolka
Doing
science
is
a
privilege
–
not
a
right

81. There
is
a
social
contract
of
science:
we
have
an
obligation
to
ensure
dissemination,
validation,
&
advancement.
To
not
do
so
is
science
malpractice.
Who's
responsible?
Researchers,
publishers,
libraries,
repositories…
–
Brian
Hole,
Ubiquity
Press
at
UCL
From
Flickr
by
mikerosebery

82. From
Flickr
by
Michael
Tinkler

83. Data
Pub
Blog:
datapub.cdlib.org

84. My
website
Email
me
Tweet
me
My
slides
carlystrasser.net
carlystrasser@gmail.com
@carlystrasser
slideshare.net/carlystrasser