The document describes stable isotope data from algal samples collected from Wash Cresc Lake. It includes a table with sample identifiers, weights, carbon and nitrogen composition percentages, delta C-13 and delta N-15 values, and calibration corrections for delta values. The table contains stable isotope measurements for 23 algal samples. Additional context is provided by notes that it is stable isotope data from algal samples collected on December 16th and references are given for the standard deviations of delta values.

1. From
Calisphere,
Couretsy
of
UC
Riverside,
California
Museum
of
Photography
From
Calisphere,
Courtesy
of
Thousand
Oaks
Library
Data
Management
for
Researchers
Tips,
Tools,
&
Why
You
Should
Care
Carly
Strasser,
PhD
California
Digital
Library
@carlystrasser
carly.strasser@ucop.edu
Cal
Poly
Oct
2013

2. Roadmap
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3. Best
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2. Why
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1. Background

3. What
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data
education?
NSF
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Why
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data
management
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taught?
How
can
we
promote
storing
data
in
repositories?

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

6. From
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via
Santa
Clara
University,
ark:/13030/kt696nc7j2
A
Brief
History
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Data
Collection
Or…
how
scientists
came
to
be
so
bad
at
data
management

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

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OT
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you
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18. 2
tables
Random
notes
C:Documents and SettingshamptonMy DocumentsNCEAS Distributed Graduate Seminars[Wash Cres Lake Dec 15 Dont_Use.xls]Sheet1
Stable Isotope Data Sheet
Sampling Site / Identifier: Wash Cresc Lake
Sample Type: Algal
Date: Dec. 16
Tray ID and Sequence: Tray 004
Reference statistics: SD for delta
Position
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
C1
C2
C3
SampleID
Weight (mg)
0.98
0.98
0.98
1.01
3.05
3.06
2.91
2.91
3.04
2.95
3.01
3
2.99
2.92
2.9
1.01
ref
0.99
ref
3.04
3.09
3.05
2.98
3.04
0.99
ref
ref
ref
ref
ref
ALG01
Lk Outlet Alg
ALG03
ALG05
ALG07
ALG06
ALG04
ALG02
ALG01
ALG03
ALG07
Lk Outlet Alg
ALG06
ALG02
ALG04
ALG05
13
From
Stephanie
Hampton
C = 0.07
%C
38.27
39.78
40.37
42.23
1.88
31.55
6.85
35.56
33.49
41.17
43.74
4.51
1.59
4.37
33.58
44.94
42.28
31.43
35.57
5.52
37.90
31.74
38.46
23.78
SD for delta
delta 13C
-25.05
-25.00
-24.99
-25.06
-24.34
-30.17
-21.11
-28.05
-29.56
-27.32
-27.50
-22.68
-24.58
-21.06
-29.44
-25.00
-24.87
-29.69
-27.26
-22.31
-27.42
-27.93
-25.09
delta 13C_ca
-24.59
-24.54
-24.53
-24.60
-23.88
-29.71
-20.65
-27.59
-29.10
-26.86
-27.04
-22.22
-24.12
-20.60
-28.98
-24.54
-24.41
-29.23
-26.80
-21.85
-26.96
-27.47
-24.63
%N
1.96
2.03
2.04
2.17
0.17
0.92
0.48
2.30
1.68
1.97
1.36
0.34
0.15
0.34
1.74
2.59
2.37
1.07
1.96
0.45
1.36
2.40
2.40
1.17
15
Peter's lab
Washed Rocks
Don't use - old data
Shore
-1.26
1.26
Avg Con
-27.22
0.32
N = 0.15
delta 15N
4.12
4.01
4.09
4.20
-1.65
0.87
-0.97
0.59
0.79
2.71
0.99
4.31
-1.69
-1.52
0.62
3.96
4.33
0.95
2.79
4.72
1.21
0.73
4.37
delta 15N_ca
3.47
3.36
3.44
3.55
-2.30
0.22
-1.62
-0.06
0.14
2.06
0.34
3.66
-2.34
-2.17
-0.03
3.31
3.68
0.30
2.14
4.07
0.56
0.08
3.72
Spec. No.
25354
25356
25358
25360
25362
25364
25366
25368
25370
25372
25374
25376
25378
25380
25382
25384
25386
25388
25390
25392
25394
25396
25398
c
c
c
c
c
c
From
Stephanie
Hampton
(2010)
ESA
Workshop
on
Best
Practices

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

20. Random
stats
output
C:Documents and SettingshamptonMy DocumentsNCEAS Distributed Graduate Seminars[Wash Cres Lake Dec 15 Dont_Use.xls]Sheet1
Stable Isotope Data Sheet
Sampling Site / Identifier: Wash Cresc Lake
Sample Type: Algal
Date: Dec. 16
Tray ID and Sequence: Tray 004
13
SampleID
Weight (mg)
0.98
0.98
0.98
1.01
3.05
3.06
2.91
2.91
3.04
2.95
3.01
3
2.99
2.92
2.9
1.01
ref
0.99
ref
3.04
3.09
3.05
2.98
3.04
0.99
ref
ref
ref
ref
ref
ALG01
Lk Outlet Alg
ALG03
ALG05
ALG07
ALG06
ALG04
ALG02
ALG01
ALG03
ALG07
Lk Outlet Alg
ALG06
ALG02
ALG04
ALG05
%C
38.27
39.78
40.37
42.23
1.88
31.55
6.85
35.56
33.49
41.17
43.74
4.51
1.59
4.37
33.58
44.94
42.28
31.43
35.57
5.52
37.90
31.74
38.46
23.78
Don't use - old data
Shore
-1.26
1.26
Avg Con
-27.22
0.32
15
Reference statistics: SD for delta C = 0.07
Position
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
C1
C2
C3
Peter's lab
Washed Rocks
SD for delta N = 0.15
delta 13C
-25.05
-25.00
-24.99
-25.06
-24.34
-30.17
-21.11
-28.05
-29.56
-27.32
-27.50
-22.68
-24.58
-21.06
-29.44
-25.00
-24.87
-29.69
-27.26
-22.31
-27.42
-27.93
-25.09
delta 13C_ca
-24.59
-24.54
-24.53
-24.60
-23.88
-29.71
-20.65
-27.59
-29.10
-26.86
-27.04
-22.22
-24.12
-20.60
-28.98
-24.54
-24.41
-29.23
-26.80
-21.85
-26.96
-27.47
-24.63
%N
1.96
2.03
2.04
2.17
0.17
0.92
0.48
2.30
1.68
1.97
1.36
0.34
0.15
0.34
1.74
2.59
2.37
1.07
1.96
0.45
1.36
2.40
2.40
1.17
delta 15N
4.12
4.01
4.09
4.20
-1.65
0.87
-0.97
0.59
0.79
2.71
0.99
4.31
-1.69
-1.52
0.62
3.96
4.33
0.95
2.79
4.72
1.21
0.73
4.37
delta 15N_ca Spec. No.
3.47
25354
3.36
25356
3.44
25358
3.55
25360
-2.30
25362
0.22
25364
-1.62
25366
-0.06
25368
0.14
25370
2.06
25372
0.34
25374
3.66
25376
-2.34
25378
-2.17
25380
-0.03
25382
3.31
25384
3.68
25386
0.30
25388
2.14
25390
4.07
25392
0.56
25394
0.08
25396
3.72
25398
c
c
c
SUMMARY OUTPUT
c
c
Regression Statistics
Multiple R 0.283158
R Square 0.080178
Adjusted R Square
-0.022024
Standard Error
1.906378
Observations
11
ANOVA
c
df
Regression
Residual
Total
SS
MS
F Significance F
1 2.851116 2.851116 0.784507 0.398813
9 32.7085 3.634278
10 35.55962
Coefficients
Standard 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
From
Stephanie
Hampton

21. C:Documents and SettingshamptonMy DocumentsNCEAS Distributed Graduate Seminars[Wash Cres Lake Dec 15 Dont_Use.xls]Sheet1
Stable Isotope Data Sheet
Sampling Site / Identifier: Wash Cresc Lake
Sample Type: Algal
Date: Dec. 16
Tray ID and Sequence: Tray 004
Reference statistics: SD for delta
Position
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
B1
B2
B3
B4
B5
B6
B7
B8
B9
B10
C1
C2
C3
SampleID
Weight (mg)
0.98
0.98
0.98
1.01
3.05
3.06
2.91
2.91
3.04
2.95
3.01
3
2.99
2.92
2.9
1.01
ref
0.99
ref
3.04
3.09
3.05
2.98
3.04
0.99
ref
ref
ref
ref
ref
ALG01
Lk Outlet Alg
ALG03
ALG05
ALG07
ALG06
ALG04
ALG02
ALG01
ALG03
ALG07
Lk Outlet Alg
ALG06
ALG02
ALG04
ALG05
13
C = 0.07
%C
38.27
39.78
40.37
42.23
1.88
31.55
6.85
35.56
33.49
41.17
43.74
4.51
1.59
4.37
33.58
44.94
42.28
31.43
35.57
5.52
37.90
31.74
38.46
23.78
SD for delta
delta 13C delta 13C_ca
-25.05
-24.59
-25.00
-24.54
-24.99
-24.53
-25.06
-24.60
-24.34
-23.88
-30.17
-29.71
-21.11
-20.65
-28.05
-27.59
-29.56
-29.10
-27.32
-26.86
-27.50
-27.04
SampleID
-22.68
-22.22
-24.58
-24.12
-21.06
-20.60
Weight (mg)
-29.44
-28.98
-25.00
-24.54
-24.87
-24.41
-29.69 %C-29.23
-27.26
-26.80
delta 13C
-22.31
-21.85
delta 13C_ca
-27.42
-26.96
-27.93
-27.47
-25.09
-24.63
%N
delta 15N
delta 15N_ca
15
Don't use - old data
Shore
-1.26
1.26
Avg Con
-27.22
0.32
N = 0.15
%N
1.96
2.03
2.04
2.17
0.17
0.92
0.48
2.30
1.68
1.97
1.36
ALG03
0.34
0.15
0.34
2.91
1.74
2.59
2.37
6.85
1.07
1.96
-21.11
0.45
-20.65
1.36
2.40
2.40
0.48
1.17
-0.97
-1.62
Peter's lab
Washed Rocks
delta 15N delta 15N_ca
4.12
3.47
4.01
3.36
4.09
3.44
4.20
3.55
-1.65
-2.30
0.87
0.22
-0.97
-1.62
0.59
-0.06
0.79
0.14
2.71
2.06
0.99
0.34
ALG05
4.31
3.66
-1.69
-2.34
-1.52
-2.17
2.91
0.62
-0.03
3.96
3.31
4.33
3.68
35.56
0.95
0.30
2.79
2.14
-28.05
4.72
4.07
-27.59
1.21
0.56
0.73
0.08
4.37
3.72
2.30
0.59
-0.06
Spec. No.
25354
25356
25358
25360
25362 c
25364
25366 c
25368
25370
25372
25374 c
ALG07
25376
25378 c
25380 c
25382 3.04
25384
25386
25388 33.49
25390
-29.56
25392
25394 -29.10
c
25396
25398
1.68
0.79
0.14
SUMMARY OUTPUT
ALG06
ALG04
Regression Statistics
Multiple R 0.283158
2.95 Square 0.080178
3.01
R
Adjusted R Square
-0.022024
Standard Error
1.906378
41.17
43.74
Observations
11
-27.32
-27.50
ANOVA
-26.86
-27.04
df
Regression
Residual
1.97
Total
2.71
2.06
Intercept
ALG02
ALG01
3
4.51
-22.68
-22.22
MS
2.99
1.59
-24.58
-24.12
Significance F
SS
F
1 2.851116 2.851116 0.784507 0.398813
9 32.7085 3.634278
1.3610 35.55962 0.34
0.15
0.99
4.31
-1.69
ALG03
ALG07
2.92
2.9
4.37
-21.06
-20.60
33.58
-29.44
-28.98
0.34
-1.52
1.74
0.62
-0.03
Coefficients
Standard Error t Stat
P-value Lower 95%Upper 95%
Lower 95.0%
Upper 95.0%
0.34
-2.34
-2.17
-4.297428 4.671099 3.66
-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
4.00
3.00
2.00
1.00
Series1
-35.00
-30.00
-25.00
-20.00
-15.00
-10.00
-5.00
0.00
0.00
-1.00
-2.00
-3.00
From
Stephanie
Hampton

22. What
should
you
be
doing?
From
Flickr
by
whatthefeed

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

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

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

26. 2.
Data
collection
&
organization
Create
unique
identifiers
From
Flickr
by
zebbie
• Decide
on
naming
scheme
early
• Create
a
key
• Different
for
each
sample
From
Flickr
by
sjbresnahan

27. 2.
Data
collection
&
organization
Standardize
• Consistent
within
columns
– only
numbers,
dates,
or
text
• Consistent
names,
codes,
formats
Modified
from
K.
Vanderbilt
From
Pink
Floyd,
The
Wall
themurkyfringe.com

28. 2.
Data
collection
&
organization
Standardize
• Reduce
possibility
of
manual
error
by
constraining
entry
choices
Excel
lists
Google
Docs
Data
Forms
validataion
Modified
from
K.
Vanderbilt

29. 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

30. 2.
Data
collection
&
organization
What
about
databases?
A
relational
database
is
A
set
of
tables
Relationships
among
the
tables
A
language
to
specify
&
query
the
tables
A
RDB
provides
Scalability:
millions+
records
Features
for
sub-­‐setting,
querying,
sorting
Reduced
redundancy
&
entry
errors
From
Mark
Schildhauer

31. 2.
Data
collection
&
organization
You
should
invest
time
in
learning
databases
if
your
data
sets
are
large
or
complex
Consider
investing
time
in
learning
databases
if
your
data
are
small
and
humble
you
ever
intend
to
share
your
data
you
are
<
30
years
old
From
Mark
Schildhauer

32. 2.
Data
collection
&
organization
Use
descriptive
file
names
*
• Unique
• Reflect
contents
Bad:
Mydata.xls
2001_data.csv
best
version.txt
Better:
Eaffinis_nanaimo_2010_counts.xls
Study
organism
Site
name
Year
What
was
measured
*Not
for
everyone
From
R
Cook,
ESA
Best
Practices
Workshop
2010

33. 2.
Data
collection
&
organization
Organize
files
logically
Biodiversity
Lake
Experiments
Biodiv_H20_heatExp_2005to2008.csv
Biodiv_H20_predatorExp_2001to2003.csv
…
Field
work
Biodiv_H20_PlanktonCount_2001toActive.csv
Biodiv_H20_ChlAprofiles_2003.csv
…
Grassland
From
S.
Hampton

34. 2.
Data
collection
&
organization
Preserve
information
• Keep
raw
data
raw
• Use
scripts
to
process
data
&
save
them
with
data
Raw
data
as
.csv
R
script
for
processing
&
analysis

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

36. 3.
Quality
control
and
quality
assurance
Before
data
collection
From
Flickr
by
StacieBee
• Define
&
enforce
standards
• Assign
responsibility
for
data
quality

37. 3.
Quality
control
and
quality
assurance
After
data
entry
• Check
for
missing,
impossible,
anomalous
values
• Perform
statistical
summaries
• Look
for
outliers
60
50
40
30
20
10
0
0
10
20
30
40

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

39. 4.
Metadata
basics
Why
are
you
What
is
promoting
metadata?
Excel?

40. 4.
Metadata
basics
From
Flickr
by
//ichael
Patric|{
Metadata
=
Data
reporting
WHO
created
the
data?
WHAT
is
the
content
of
the
data
set?
WHEN
was
it
created?
WHERE
was
it
collected?
HOW
was
it
developed?
WHY
was
it
developed?

41. 4.
Metadata
basics
•
Scientific
context
•
•
Environmental
conditions
during
collection
•
Where
collected
&
spatial
resolution
When
collected
&
temporal
resolution
•
The
name(s)
of
the
data
file(s)
in
the
data
set
What
instruments
(including
model
&
serial
number)
were
used
Standards
or
calibrations
used
Name
of
the
data
set
•
What
data
were
collected
•
Digital
context
Scientific
reason
why
the
data
were
collected
•
•
•
•
Date
the
data
set
was
last
modified
•
Example
data
file
records
for
each
data
type
file
•
Pertinent
companion
files
•
•
Information
about
parameters
List
of
related
or
ancillary
data
sets
How
each
was
measured
or
produced
•
Software
(including
version
number)
used
to
prepare/read
the
data
set
Units
of
measure
•
•
Format
used
in
the
data
set
•
•
•
Data
processing
that
was
performed
•
Precision
&
accuracy
if
known
Personnel
&
stakeholders
•
•
Quality
assurance
&
control
measures
•
Funders
Definitions
of
codes
used
•
Who
to
contact
with
questions
•
Information
about
data
•
Who
collected
•
•
Known
problems
that
limit
data
use
(e.g.
uncertainty,
sampling
problems)
How
to
cite
the
data
set

42. 4.
Metadata
basics
What
is
metadata?
Select
the
appropriate
standard
• Provides
structure
to
describe
data
Common
terms
|
definitions
|
language
|
structure
• Lots
of
different
standards
EML
,
FGDC,
ISO19115,
DarwinCore,…
• Tools
for
creating
metadata
files
Morpho
(EML),
Metavist
(FGDC),
NOAA
MERMaid
(CSGDM)

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

44. 5.
Workflows
Workflow:
how
you
get
from
the
raw
data
to
the
final
products
of
your
research
Simple
workflows:
flow
charts
Temperature
data
Salinity
data
“Clean”
T
&
S
data
Data
import
into
R
Data
in
R
format
Quality
control
&
data
cleaning
Analysis:
mean,
SD
Graph
production
Summary
statistics

45. 5.
Workflows
Workflow:
how
you
get
from
the
raw
data
to
the
final
products
of
your
research
Simple
workflows:
commented
scripts
• R,
SAS,
MATLAB
• Well-­‐documented
code
is…
Easier
to
review
Easier
to
share
Easier
to
repeat
analysis
%
#
$
&

46. 5.
Workflows
Fancy
Schmancy
workflows:
Kepler
Resulting
output
https://kepler-­‐project.org

47. 5.
Workflows
Workflows
enable
Reproducibility
Transparency
Executability
From
Flickr
by
merlinprincesse

48. 5.
Workflows
Minimally:
document
your
analysis
commented
code;
simple
flow-­‐chart
www.littlebytesoflife.com
Emerging
workflow
applications
will…
− Link
software
for
executable
end-­‐to-­‐end
analysis
− Provide
detailed
info
about
data
&
analysis
− Facilitate
re-­‐use
&
refinement
of
complex,
mn:
o ulti-­‐step
o
analyses
ng
S aring
omi
sh
C
− Enable
efficient
swapping
of
alternative
models
s!
kflow ent &
r
wo
algorithms
uirem
req
− Help
automate
tedious
tasks

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

50. 6.
Data
stewardship
&
reuse
From
Flickr
by
greensambaman
The
20-­‐Year
Rule
The
metadata
accompanying
a
data
set
should
be
written
for
a
user
20
years
into
the
future
RULE
(National
Research
Council
1991)

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

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

53. 6.
Data
stewardship
&
reuse
Practice
Data
Citation
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
Allows
readers
to
find
data
products
Get
credit
for
data
and
publications
Promotes
reproducibility
Better
measure
of
research
impact

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

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

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

57. NSF
DMP
Requirements
From
Grant
Proposal
Guidelines:
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

58. From
Flickr
by
OZinOH
The
Data
Management
Planning
Tool
DMPTool
Carly
Strasser
|
@carlystrasser
California
Digital
Library
5
August
2013
ESA
2013
SS
2

59. From
Flickr
by
dipster1
Toolbox

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

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

62. From Flickr by thewmatt
Get
help

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

64. Get
help
Toolbox:
DCXL
blog:
dcxl.cdlib.org

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

66. From
Flickr
by
Michael
Tinkler

67. From
Flickr
by
mikerosebery
There
is
a
social
contract
of
science:
we
have
an
obligation
to
ensure
dissemination,
validation,
&
advancement.
To
not
do
so
is
science
malpractice.
–
Brian
Hole,
Ubiquity
Press
at
UCL

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