UCLA: Data Management for Scientists
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The document provides guidance on improving data management practices for scientists. It recommends that scientists reduce their workload, reuse their ideas, and recycle their data by adopting better data management. This will help scientists be more efficient and productive in their work. The document also notes that many scientists currently do not receive training in data management, are unaware of best practices like using metadata, and do not share or archive their data. Adopting improved data management practices can help address these issues.
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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 Peter's lab Don't use - old data
Sample Type: Algal Washed Rocks
Date: Dec. 16
Tray ID and Sequence: Tray 004
13 15
Reference statistics: SD for delta C = 0.07 SD for delta 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
From
Stephanie
Hampton
(2010)
ESA
Workshop
on
Best
Practices](https://image.slidesharecdn.com/lascientists20110117-120117130512-phpapp01/85/UCLA-Data-Management-for-Scientists-10-320.jpg)
![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 Peter's lab Don't use - old data
Sample Type: Algal Washed Rocks
Date: Dec. 16
Tray ID and Sequence: Tray 004
13 15
Reference statistics: SD for delta C = 0.07 SD for delta 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
From
Stephanie
Hampton
(2010)
ESA
Workshop
on
Best
Practices](https://image.slidesharecdn.com/lascientists20110117-120117130512-phpapp01/85/UCLA-Data-Management-for-Scientists-11-320.jpg)
![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 Peter's lab Don't use - old data
Sample Type: Algal Washed Rocks
Date: Dec. 16
Tray ID and Sequence: Tray 004
13 15
Reference statistics: SD for delta C = 0.07 SD for delta 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 SampleID
-22.68 -22.22 ALG03
0.34 ALG05
4.31 3.66 ALG07
25376 ALG06 ALG04 ALG02 ALG01 ALG03 ALG07
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 Weight (mg)
-29.44 -28.98 2.91
1.74 0.62 2.91
-0.03 25382 3.04 2.95 Square 0.080178
R 3.01 3 2.99 2.92 2.9
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 Error
1.906378
B8 Lk Outlet Alg 3.04 31.43 -29.69 %C-29.23 6.85
1.07 0.95 35.560.30 25388 33.49 41.17
Observations43.74 11 4.51 1.59 4.37 33.58
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
delta 13C
-21.85
-21.11
0.45 4.72
-28.054.07 25392
-29.56 -27.32
ANOVA
-27.50 -22.68 -24.58 -21.06 -29.44
C1 ALG04 2.98 37.90 delta 13C_ca
-27.42 -26.96 -20.65
1.36 1.21 -27.590.56 25394 -29.10
c -26.86 -27.04
df SS -22.22
MS F -24.12
Significance F -20.60 -28.98
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 %N 0.48
1.17 2.30 1.68 1.97
Total 1.3610 35.55962 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
Coefficients
Standard Error t Stat P-value Lower 95%Upper 95%Lower 95.0%
Upper 95.0%
delta 15N_ca -1.62 -0.06 0.14 2.06
Intercept -4.297428 4.671099 3.66
0.34 -2.34 -2.17
-0.920003 0.381568 -14.8642 6.269341 -14.8642 6.269341 -0.03
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
0.00
-35.00 -30.00 -25.00 -20.00 -15.00 -10.00 -5.00 0.00
-1.00
-2.00
-3.00
12](https://image.slidesharecdn.com/lascientists20110117-120117130512-phpapp01/85/UCLA-Data-Management-for-Scientists-12-320.jpg)
![Random
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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 Peter's lab Don't use - old data
Sample Type: Algal Washed Rocks
Date: Dec. 16
Tray ID and Sequence: Tray 004
13 15
Reference statistics: SD for delta C = 0.07 SD for delta 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 Error
1.906378
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
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](https://image.slidesharecdn.com/lascientists20110117-120117130512-phpapp01/85/UCLA-Data-Management-for-Scientists-13-320.jpg)
UCLA: Data Management for Scientists
- 1. Data Management for Scientists Reduce your workload Reuse your ideas Recycle your data www.oddee.com Carly Strasser, PhD California Digital Library, UC Office of the President [email protected] www.carlystrasser.net
- 2. Roadmap 4. Toolbox 3. How to improve 2. Mistakes we make 1. Background
- 3. NSF funded DataNet Project Office of Cyberinfrastructure Community Cyberinfrastructure Engagement & Outreach From Flickr by wetwebwork Courtesy of DataONE
- 4. What role can libraries play in data education? Why don’t people What barriers to sharing share data? can we eliminate? Is data management Do attitudes about being taught? sharing differ among disciplines? How can we promote storing data in repositories?
- 5. Roadmap 4. Toolbox 3. How to improve 2. Mistakes we make 1. Background
- 6. From Flickr by DW0825 From Flickr by Flickmor From Flickr by deltaMike Digital data www.woodrow.org C. Strasser Courtesey of WHOI From Flickr by US Army Environmental Command
- 7. Digital data + Complex analyses
- 8. Data Models Maximum Likelihood estimation Matrix Models Images Tables Paper
- 9. UGLY TRUTH Many Earth | Environmental | Ecological scientists… 5shortessays.blogspot.com are not taught data management don’t know what metadata are can’t name data centers or repositories don’t share data publicly or store it in an archive aren’t convinced they should share data
- 10. 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 Peter's lab Don't use - old data Sample Type: Algal Washed Rocks Date: Dec. 16 Tray ID and Sequence: Tray 004 13 15 Reference statistics: SD for delta C = 0.07 SD for delta 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 From Stephanie Hampton (2010) ESA Workshop on Best Practices
- 11. 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 Peter's lab Don't use - old data Sample Type: Algal Washed Rocks Date: Dec. 16 Tray ID and Sequence: Tray 004 13 15 Reference statistics: SD for delta C = 0.07 SD for delta 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 From Stephanie Hampton (2010) ESA Workshop on Best Practices
- 12. 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 Peter's lab Don't use - old data Sample Type: Algal Washed Rocks Date: Dec. 16 Tray ID and Sequence: Tray 004 13 15 Reference statistics: SD for delta C = 0.07 SD for delta 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 SampleID -22.68 -22.22 ALG03 0.34 ALG05 4.31 3.66 ALG07 25376 ALG06 ALG04 ALG02 ALG01 ALG03 ALG07 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 Weight (mg) -29.44 -28.98 2.91 1.74 0.62 2.91 -0.03 25382 3.04 2.95 Square 0.080178 R 3.01 3 2.99 2.92 2.9 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 Error 1.906378 B8 Lk Outlet Alg 3.04 31.43 -29.69 %C-29.23 6.85 1.07 0.95 35.560.30 25388 33.49 41.17 Observations43.74 11 4.51 1.59 4.37 33.58 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 delta 13C -21.85 -21.11 0.45 4.72 -28.054.07 25392 -29.56 -27.32 ANOVA -27.50 -22.68 -24.58 -21.06 -29.44 C1 ALG04 2.98 37.90 delta 13C_ca -27.42 -26.96 -20.65 1.36 1.21 -27.590.56 25394 -29.10 c -26.86 -27.04 df SS -22.22 MS F -24.12 Significance F -20.60 -28.98 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 %N 0.48 1.17 2.30 1.68 1.97 Total 1.3610 35.55962 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 Coefficients Standard Error t Stat P-value Lower 95%Upper 95%Lower 95.0% Upper 95.0% delta 15N_ca -1.62 -0.06 0.14 2.06 Intercept -4.297428 4.671099 3.66 0.34 -2.34 -2.17 -0.920003 0.381568 -14.8642 6.269341 -14.8642 6.269341 -0.03 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 0.00 -35.00 -30.00 -25.00 -20.00 -15.00 -10.00 -5.00 0.00 -1.00 -2.00 -3.00 12
- 13. 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 Peter's lab Don't use - old data Sample Type: Algal Washed Rocks Date: Dec. 16 Tray ID and Sequence: Tray 004 13 15 Reference statistics: SD for delta C = 0.07 SD for delta 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 Error 1.906378 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 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
- 14. DATA HANGOVER What happened? From Flickr by SteveMcN
- 15. Where data end up From Flickr by diylibrarian www blog.order2disorder.com From Flickr by csessums Data Metadata From Flickr by csessums Recreated from Klump et al. 2006
- 16. Who cares? From Flickr by Redden-‐McAllister From Flickr by AJC1 www.rba.gov.au
- 17. Where data end up From Flickr by diylibrarian www Data www Metadata From Flickr by torkildr Recreated from Klump et al. 2006
- 18. Data Reuse Data Sharing Data Management
- 19. Trends in Data Archiving Journal publishers Joint Data Archiving Agreement
- 20. Trends in Data Archiving Journal publishers Joint Data Archiving Agreement Data Papers Ecological Archives, Beyond the PDF
- 21. Trends in Data Archiving Journal publishers Joint Data Archiving Agreement Data Papers etc. Ecological Archives, Beyond the PDF Funders Data management requirements
- 22. Roadmap 4. Toolbox 3. How to improve 2. Mistakes we make 1. Background
- 23. Best Practices for Data Management 1. Planning 2. Data collection & organization 3. Quality control & assurance 4. Metadata 5. Workflows 6. Data stewardship & reuse
- 24. 2. Data collection & organization Create unique identifiers • Decide on naming scheme early • Create a key • Different for each sample From Flickr by zebbie From Flickr by sjbresnahan
- 25. 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
- 26. 2. Data collection & organization Standardize • Reduce possibility of manual error by constraining entry choices Excel lists Data Google Docs Forms validataion Modified from K. Vanderbilt
- 27. 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
- 28. 2. Data collection & organization Use descriptive file names * • Unique • Reflect contents Bad: Mydata.xls Better: Eaffinis_nanaimo_2010_counts.xls 2001_data.csv best version.txt Study Year organism Site name What was measured *Not for everyone From R Cook, ESA Best Practices Workshop 2010
- 29. 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
- 30. 2. Data collection & organization Preserve information R script for processing & analysis • Keep raw data raw • Use scripts to process data & save them with data Raw data as .csv
- 31. 2. Data collection & oAll of the things that rganization make Excel great for data organization are bad for archiving! What to do? 1. Create archive-‐ready raw data 2. Put it somewhere special 3. Have your fun with fancy Excel techniques 4. Keep archiving in mind
- 32. 3. Quality control and quality assurance Define & enforce standards Double data entry Document changes Minimize manual data entry No missing, impossible, or anomalous values • Perform statistical summaries 60 • Use illegal data filter 50 • Look for outliers 40 30 20 10 0 0 5 10 15 20 25 30 35
- 33. 4. Metadata basics What is metadata?
- 34. 4. Metadata basics What is 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?
- 35. • Scientific context 4. Metadata basics • Scientific reason why the data were collected • What data were collected • Digital context • What instruments (including model & • Name of the data set serial number) were used • The name(s) of the data file(s) in the data • Environmental conditions during collection set • Where collected & spatial resolution When • Date the data set was last modified collected & temporal resolution • Example data file records for each data • Standards or calibrations used type file • Information about parameters • Pertinent companion files • How each was measured or produced • List of related or ancillary data sets • Units of measure • Software (including version number) • Format used in the data set used to prepare/read the data set • Precision & accuracy if known • Data processing that was performed • Information about data • Personnel & stakeholders • Definitions of codes used • Who collected • Quality assurance & control measures • Who to contact with questions • Known problems that limit data use (e.g. • Funders uncertainty, sampling problems) • How to cite the data set
- 36. 4. Metadata basics What is a metadata 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)
- 37. 4. Metadata basics What does a metadata record look like?
- 38. 5. Workflows Simplest workflows: commented scripts, flow charts Temperature data Data import into R Data in R Salinity format data Quality control & “Clean” T data cleaning & S data Analysis: mean, SD Summary statistics Graph production
- 39. 5. Workflows Fancy Schmancy: Kepler Resulting output https://kepler-‐project.org
- 40. 5. Workflows Workflows enable From Flickr by merlinprincesse 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
- 41. 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)
- 42. 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
- 43. 6. Data stewardship & reuse Where do I put it? Insitutional archive Discipline/specialty archive DataCite list of repostiories: www.datacite.org/repolist From Flickr by torkildr
- 44. 6. Data stewardship & reuse Data Citation: Why everyone should do it Allow readers to find data products Get credit for data and publications Promote 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 Learn more at www.datacite.org Modified from R. Cook
- 45. Best Practices for Data Management 1. Planning 2. Data collection & organization 3. Quality control & assurance 4. Metadata 5. Workflows 6. Data stewardship & reuse 7. Planning
- 46. 1. Planning What is a data management plan? A document that describes what you will do with your data during and after you complete your research DATA HANGOVER
- 47. 1. Planning Why should I prepare a DMP? Saves time Increases efficiency Easier to use data Others can understand & use data Credit for data products Funders require it
- 48. 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
- 49. 1. Types of data & other information • Types of data produced • Relationship to existing data • How/when/where will the data be captured or created? C. Strasser • How will the data be processed? • Quality assurance & quality control measures • Security: version control, backing up biology.kenyon.edu • Who will be responsible for data management during/after project? From Flickr by Lazurite
- 50. 2. Data & metadata standards • What metadata are needed to make the data meaningful? • How will you create or capture these metadata? Wired.com • Why have you chosen particular standards and approaches for metadata?
- 51. 3. Policies for access & sharing 4. Policies for re-‐use & re-‐distribution • Are you under any obligation to share data? • How, when, & where will you make the data available? • What is the process for gaining access to the data? • Who owns the copyright and/or intellectual property? • Will you retain rights before opening data to wider use? How long? • Are permission restrictions necessary? • Embargo periods for political/commercial/patent reasons? • Ethical and privacy issues? • Who are the foreseeable data users? • How should your data be cited?
- 52. 5. Plans for archiving & preservation • What data will be preserved for the long term? For how long? • Where will data be preserved? • What data transformations need to occur before preservation? • What metadata will be submitted alongside the datasets? • Who will be responsible for preparing data for preservation? Who will be the main contact person for the archived data? From Flickr by theManWhoSurfedTooMuch
- 53. Don’t forget: Budget • Costs of data preparation & documentation Hardware, software Personnel Archive fees • How costs will be paid Request funding! dorrvs.com
- 54. NSF’s Vision* DMPs and their evaluation will grow & change over time (similar to broader impacts) Peer review will determine next steps Community-‐driven guidelines – Different disciplines have different definitions of acceptable data sharing – Flexibility at the directorate and division levels – Tailor implementation of DMP requirement Evaluation will vary with directorate, division, & program officer *Unofficially Help from Jennifer Schopf, NSF
- 55. NSF’s Vision* DMPs are a good first step towards improving data stewardship – starting discussion – scientists learning about data management Additional expertise on panels to effectively evaluate DMPs (?) Working group will assess outcomes *Unofficially
- 56. Roadmap 4. Toolbox 3. How to improve 2. Mistakes we make 1. Background
- 57. DMPTool: dmp.cdlib.org Step-‐by-‐step wizard for generating DMP Create | edit | re-‐use | share | save | generate Open to community Links to institutional resources Directorate information & updates
- 58. E-‐notebooks • NoteBook • ORNL eNote • Evernote • Google Docs • Blogs • wikis • TheLabNotebook.com • iPad ELN • NoteBookMaker iPad ELN, the flexible electronic laboratory notebook TheLabNotebook.com"
- 59. CDL Services for UC Community Where should I put Data Repository my data? Deposit | Manage | Share | Preserve www.cdlib.org/services/uc3
- 60. CDL Services for UC Community Create & manage persistent identifiers • Precise identification of a dataset • Credit to data producers and data publishers • A link from the traditional literature to the data • Research metrics for datasets 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 www.cdlib.org/services/uc3
- 61. Why are you promoting Excel? • Open source add-‐in • Facilitate data management, sharing, archiving for scientists • Part of DataONE investigator toolkit • Collecting requirements for add-‐in from scientists, data centers, libraries dcxl.cdlib.org Funders: Gordon and Betty Moore Foundation, Microsoft Research
- 62. Why are you promoting Excel? • Everyone uses it • Features that make it good for data organization make it bad for archiving • Stopgap measure
- 63. B A C
- 64. www.dataone.org • Data Education Tutorials • Database of best practices & software tools • Links to DMPTool • Primer on data management From Flickr by Robert Hruzek
- 65. Data Management 101" dcxl.cdlib.org • Data Education Tutorials • Primer on data management • Other resources
- 66. Toolbox: DCXL blog: dcxl.cdlib.org
- 67. Lisa Federer dcxl.cdlib.org @dcxlCDL www.facebook.com/DCXLatCDL www.carlystrasser.net [email protected] @carlystrasser