UCLAtalk_for_librarians_20120118

Data Management and the Digital Curation for Excel (DCXL) Project Carly Strasser University of California Curation Center at CDL

NSF funded DataNet Project Of@ice of Cyberinfrastructure Enabling universal access to data about life on earth and the environment that sustains it

NSF funded DataNet Project Of@ice of Cyberinfrastructure Community Cyberinfrastructure Engagement & Outreach From Flickr by wetwebwork Courtesy of DataONE

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

Roadmap 5.  Tools 4.  DCXL 3.  Best practices for scientists 2.  Barriers to best practices 1.  Mistakes scientists make

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

Digital data + Complex analyses

Data Models Maximum Likelihood estimation Matrix Models Images Tables Paper

2 tables 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 Peters lab Dont 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

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 Peters lab Dont 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

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 Peters lab Dont 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

Collaboration and Data Sharing 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 Peters lab Dont 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 13

Random stats 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 Peters lab Dont 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

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

Who cares? From Flickr by Redden-‐McAllister From Flickr by AJC1 www.rba.gov.au

Where data end up From Flickr by diylibrarian www Data wwwMetadata Recreated from Klump et al. 2006

Data Reuse Data Sharing Data Management

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

Barriers Cost Time cultblender.wordpress.com Software, Personnel hardware

Barriers Cost: time, personnel, software, hardware Culture of Science •  Not the norm •  Lack of training •  Disparate data

Barriers Cost: time, personnel, software, hardware Culture of Science Loss of rights or bene:its Misuse of data Missed opportunities Con@lict

Barriers Cost: time, personnel, software, hardware Culture of Science Loss of rights or bene:its Lack of incentives Time consuming & expensive Reward structure Few requirements

Are Undergrads Learning About Data Management? Importance Versus Assessment •  Metadata generation 40 •  Software choice 35 •  File naming 30 •  QAQC Important 25 •  Backing up 20 •  Work@lows 15 •  Data sharing 10 •  Data re-‐use •  Meta-‐analysis 5 •  Reproducibility 0 If it’s important, why 0 •  Notebook protocols 10 20 30 40 Assessed isn’t it taught? •  Databases

Barriers to Teaching Data Management Too Not a Not advanced priority appropriate level Students Time don’t know No software Lab No training Covered Too in Lab big

Best Practices for Data Management 1.  Planning 2.  Data collection & organization 3.  Quality control & assurance 4.  Metadata 5.  Work@lows 6.  Data stewardship & reuse

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

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

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

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

2. Data collection & organization Use descriptive Tile names * •  Unique •  Re@lect contents Bad: Mydata.xls Better: Eaf@inis_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

2. Data collection & organization Organize Tiles 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

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

2. Data collection & organization All of the things that 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

3. Quality control and quality assurance De@ine & enforce standards Double data entry Document changes Minimize manual data entry No missing, impossible, or anomalous values •  Perform statistical summaries •  Use illegal data @ilter •  Look for outliers 60 50 40 30 20 10 0 0 5 10 15 20 25 30 35

•  ScientiTic context 4. Metadata basics •  Scienti@ic 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 @ile(s) in the •  Environmental conditions during data set collection •  Date the data set was last modi@ied •  Where collected & spatial resolution •  Example data @ile records for each data When collected & temporal resolution type @ile •  Standards or calibrations used •  Pertinent companion @iles •  Information about parameters •  List of related or ancillary data sets •  How each was measured or produced •  Software (including version number) •  Units of measure used to prepare/read the data set •  Format used in the data set •  Data processing that was performed •  Precision & accuracy if known •  Personnel & stakeholders •  Information about data •  Who collected •  De@initions of codes used •  Who to contact with questions •  Quality assurance & control measures •  Funders •  Known problems that limit data use (e.g. uncertainty, sampling problems) •  How to cite the data set

4. Metadata basics •  Provides structure to describe data Common terms | deVinitions | language | structure •  Lots of different standards EML , FGDC, ISO19115, DarwinCore,… •  Tools for creating metadata @iles Morpho (EML), Metavist (FGDC), NOAA MERMaid (CSGDM)

5. WorkTlows Simplest workTlows: commented scripts, Vlow 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

5. WorkTlows Fancy Schmancy: Kepler Resulting output https://kepler-‐project.org

5. WorkTlows WorkTlows enable From Flickr by merlinprincesse Reproducibility can someone independently validate Vindings? Transparency others can understand how you arrived at your results Executability others can re-‐run or re-‐use your analysis

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

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

6. Data stewardship & reuse Data Citation: Why everyone should do it Allow readers to @ind 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 diversi@ication in the absence of a detailed phylogeny: a case study from characiform @ishes. Dryad Digital Repository. doi:10.5061/dryad.20 Learn more at www.datacite.org Modified from R. Cook

Best Practices for Data Management 1.  Planning 2.  Data collection & organization 3.  Quality control & assurance 4.  Metadata 5.  Work@lows 6.  Data stewardship & reuse 7.  Planning

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

1. Planning Why should scientists prepare a DMP? Saves time Increases ef@iciency Easier to use data Others can understand & use data Credit for data products Funders require it

A few words about NSF Data Management Plans

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, con@identiality, 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

Don’t forget: Budget •  Costs of data preparation & documentation Hardware, software Personnel Archive fees •  How costs will be paid Request funding! dorrvs.com

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 de@initions of acceptable data sharing –  Flexibility at the directorate and division levels –  Tailor implementation of DMP requirement Evaluation will vary with directorate, division, & program of@icer *UnofVicially Help from Jennifer Schopf, NSF

NSF’s Vision* DMPs are a good Tirst 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 *UnofVicially

“A transformation in the conduct of a segment of scientiVic research by enabling and promoting publishing, sharing, and archiving of tabular data” Increase interoperability = Sharing publishability = Publishing archivability = Archiving Focus on atmospheric, ecological, hydrological, and oceanographic data

Open Source & Free Excel Add-‐in Software program that extends the capabilities of larger programs Complements basic Excel functionality From www.webopedia.com www.ablebits.com

DCXL Project Deliverables •  Excel add-‐in •  Publicly available source code •  Technical documentation •  End user documentation •  Publicly available requirements

Process Assess needs •  Quantitative –  Surveys

Process Assess needs •  Quantitative ? –  Surveys –  Quick poll •  Qualitative –  Interviews

Process Assess needs Gather requirements Locations Conferences UC campus visits Remote/web-‐based

Process Assess needs Gather requirements Stakeholders & contributors Libraries Scientists Repositories Experts: MSR, GBMF Personnel on related projects

Process Assess needs Gather requirements !Build requirements document ! "#$%&!"()*+!#,-*)./!0.-!1234+!5-.643)! ! !"#$%&#()$#*#+,"% %%%%%!+)-#$"),.%/0%123)0/$+)2%1($2,)/+%1#+,#$4%123)0/$+)2%5)6),23%7)8$2$.% %%

Requirements 1 Ensure compatibility for Excel users without the add-‐in 2 Check the data Tile for CSV compatibility 2.1 Excel performs a CSV compatibility check on the data Vile 2.2 Excel generates a Compatibility Report 3 Generate metadata that is linked to the data Tile 3.1 The user opens an existing metadata document as a template 3.2 The user initiates a new metadata document 3.3 Excel populates Level 1 metadata Vields 3.4 The user populates Level 2 metadata Vields 3.5 The user generates labels for parameter metadata 3.6 The user requests standards for keywords

Requirements 4 Generate a citation for the data Tile 5 Deposit into a repository 5.1 The user authenticates via an existing relationship with the designated repository 5.2 The user is directed to establish a relationship with a repository 5.3 The user links an identiVier to the data Vile via the designated repository 5.4 Excel performs Pre-‐Archiving Tasks 5.5 The user submits the Excel Vile for deposition 6 Appendix A: Metadata Types 7 Appendix B: Citation Format 8 Appendix C: Dictionary of Terms

Process Assess needs Gather requirements Build requirements document Build community Libraries Scientists Repositories Programmers/ Developers

Why are you promoting Excel? •  Everyone uses it •  Features that make it good for data organization make it bad for archiving •  Stopgap measure

Get Involved dcxl.cdlib.org @dcxlCDL www.facebook.com/ DCXLatCDL

UC3 Services Where should I put Data Repository my data? Deposit | Manage | Share | Preserve www.cdlib.org/services/uc3

UC3 Services How do I get a unique identiVier? Create & manage persistent identi@iers •  Precise identi@ication of a dataset •  Credit to data producers and data publishers •  A link from the traditional literature to the data •  Research metrics for datasets www.cdlib.org/services/uc3

DataONE www.dataone.org •  Data Education Tutorials •  Database of best practices & software tools •  Links to DMPTool •  Primer on data management From Flickr by Robert Hruzek

Data Management 101"dcxl.cdlib.org •  Data Education Tutorials •  Primer on data management •  Other resources

Toolbox: DCXL blog: dcxl.cdlib.org

Lisa Federer dcxl.cdlib.org @dcxlCDL www.facebook.com/DCXLatCDL www.carlystrasser.net carlystrasser@gmail.com @carlystrasser

UCLAtalk_for_librarians_20120118

by Carly Strasser on Jan 19, 2012

Accessibility

Categories

Upload Details

Usage Rights

Statistics

UCLAtalk_for_librarians_20120118 — Presentation Transcript