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Data Management for Scientists: Workshop at Ocean Sciences 2012

Here are some random notes on data from Peter's old lab: - Table with stable isotope data from algal samples collected at Wash Cresc Lake on Dec 16. Includes sample IDs, weights, %C, delta C-13 and N-15 values, and spectrometer numbers. - Reference standards analyzed to calculate sample delta values. SDs reported for delta C-13 and N-15 of reference standards. - Samples include algae from shore, lake outlet, and various collection points around lake labeled ALG01, 03, 05, 07. - Delta values range from -30.17 to -21.11 for C-13 and -1.65 to 0.87 for N-15

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Data  Management  for  Scientists     Reduce  your  workload   Reuse  your  ideas   Recycle  your  data     www.oddee.com   Carly  Strasser,  PhD   Ocean  Sciences  Meeting   California  Digital  Library,  UC  Office  of  the  President   February  2012   carly.strasser@ucop.edu   www.carlystrasser.net  
Roadmap   4.  Toolbox     3.  How  to  improve   2.  Data  management  landscape   1.  Background    
NSF  funded  DataNet  Project   Office  of  Cyberinfrastructure   Community   Cyberinfrastructure   Engagement  &   Outreach   From  Flickr  by  wetwebwork   Courtesy  of  DataONE  
What  role  can   libraries  play  in   data  education?   What  barriers  to  sharing   can  we  eliminate?   Why  don’t  people   share  data?   Is  data  management   Do  attitudes  about   being  taught?   sharing  differ   among  disciplines?   How  can  we  promote  storing   data  in  repositories?  
Roadmap   4.  Toolbox     3.  How  to  improve   2.  Data  management  landscape   1.  Background    
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  
Data   Models   Maximum   Likelihood   estimation   Matrix   Models   Images   Tables   Paper  
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    
Data  Hangover     What  happened?   From  Flickr  by  SteveMcN  
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   www Metadata   From  Flickr  by  torkildr   Recreated  from  Klump  et  al.  2006  
Data   Reuse   Data   Sharing   Data   Management  
Trends  in  Data  Archiving   Journal  publishers   Joint  Data  Archiving  Agreement     Data  Papers  etc.   Ecological  Archives,  Beyond  the  PDF     Funders   Data  management  requirements    
Roadmap   4.  Toolbox     3.  Best  practices   2.  Data  management  landscape   1.  Background    
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  
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  
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  
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
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 23  
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  
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  
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    
2.  Data  collection  &  organization   Identify  missing  data   •  Numeric  fields:  distinct  value  (e.g.  9999)   •  Text  fields:  NULL  or  NA     •  Use  data  flags  in  a  separate  column  to  qualify  empty  cells   M1  =  missing;  no   sample  collected   E1  =  estimated  from   grab  sample  
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   SPREADSHEETS: THE GOOD Quick  on  the  draw     Clickety-­‐click  and  you’re  ready  to  fire   Always  there  in  time      Everyone  has  Excel   Smarter  than  he  lets  on    Stats,  Pivot  tables,  VB  scripts   Cleans  up  real  pretty    Graphics,  fonts,  colors,  borders   From  Mark  Schildhauer  
2.  Data  collection  &  organization   SPREADSHEETS: THE BAD Shoot  first  ask  later   Click&fire  Click&fire  Click&fire   No  scruples    Delete  row,  click&fire,  ctrl-­‐x/ctrl-­‐c,  click&fire,  re-­‐sort,  save   Talks  a  good  story  but  not  much  education    Stats   From  Mark  Schildhauer  
2.  Data  collection  &  organization   SPREADSHEETS: THE UGLY Ill-­‐mannered   Takes  data  prisoner;  conflates  raw  and  summary  data   Gaudy   Use  of  visual  cues  as  metadata:  color,  font,  border   Shifty   Cross-­‐linking  worksheets  sets  up  “invisible”  dependencies   Shiftless   No  provenance   The  more  complicated  your  spreadsheet,  the  uglier  it  gets  for   use  with  other  software     From  Mark  Schildhauer  
2.  Data  collection  &  organization   All  of  the  things   that  make  Excel   great  for  data  are   bad  for  archiving!   1.  Create  archive-­‐ready  raw  data   2.  Put  it  somewhere  special   3.  Have  your  fun  with  fancy  Excel  techniques   4.  Keep  archiving  in  mind  
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   From  Mark  Schildhauer  
2.  Data  collection  &  organization   Sample  sites   samples   Samples   Species   *siteID   *sampleID   *sampleID   *speciesID   site_name   siteID   siteID   sample_date   species_name   latitude   sample_date   common_name   speciesID   longitude   speciesID   height   family   description   height   flowering   order   flowering   flag   comments   flag   comments   *  Denotes  the  primary  key   From  Mark  Schildhauer  
2.  Data  collection  &  organization   Databases  often  enforce  good  practice     Must  define     A   B   C   D   E    Tables   1   2   3   10   11    Attributes   4   5   6   12   13   14   15    Relationships  (constraints)   7   8   9   16   17     Databases  provide:    Scalability:  millions+  records    Features  for  sub-­‐setting,  querying,  sorting    Scripted  language:  SQL      Reduced  redundancy  &  potential  data  entry  errors   From  Mark  Schildhauer  
2.  Data  collection  &  organization   Spreadsheets   Databases   •  Good  for  simple,  self-­‐contained   •  Works  well  with  lots  of  data   charts,  graphs,  calculations   •  Easy  to  query  and  subset  data   •  Handy  for  collecting  raw  data   •  Data  fields  are  constrainted   •  Flexible  cell  content  type   •  Columns  cannot  be  sorted   But…   independently  of  each  other   •  Hard  to  subset  or  sort   •  Normalization  reduces  data  entry   •  Lack  “record”  integrity:  can  sort  a   and  potential  for  error   column  independently  of  all  others   But…   •  Harder  to  maintain  as  complexity   •  More  to  learn     and  size  of  data  grows   •  Harder  to  use   From  Mark  Schildhauer  
2.  Data  collection  &  organization   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   www.top20training.com   From  Mark  Schildhauer  
2.  Data  collection  &  organization   Use  descriptive  file  names   PhDcomics.com  
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  
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  
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  
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  
3.  Quality  control  and  quality  assurance   Before  data  collection   •  Define  &  enforce  standards   •  Assign  responsibility  for  data  quality   From  Flickr  by  StacieBee  
3.  Quality  control  and  quality  assurance   During  data  collection/entry   •  Minimize  manual  entry   •  Use  double  entry   •  Use  text-­‐to-­‐speech  program   to  read  data  back   •  Use  a  database   •  Document  changes   From  Flickr  by  schock  
3.  Quality  control  and  quality  assurance   After  data  entry   •  Check  for  missing,  impossible,   anomalous  values   •  Perform  statistical  summaries     •  Look  for  outliers   •  Normal  probability  plots   •  Regression   •  Scatter  plots   60   50   40   •  Maps   30   20   10   0   0   10   20   30   40    
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  
4.  Metadata  basics   Why  are  you   What  is   promoting   metadata?   Excel?  
4.  Metadata  basics      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?  
•  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  
4.  Metadata  basics   What  is   metadata?   Select  the  appropriate   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)        
4.  Metadata  basics   What  ds  a   What  ioes   metadata   standard?   look  like?  
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  
5.  Workflows   Workflow:  how  you  get  from  the  raw  data  to  the  final   products  of  your  research     Simple  workflows:  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  
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   &  
5.  Workflows   Fancy  Schmancy  workflows:  Kepler   Resulting  output   https://kepler-­‐project.org  
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    
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,  multi-­‐step   analyses   −  Enable  efficient  swapping  of  alternative  models  &   algorithms   −  Help  automate  tedious  tasks  
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  
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)  
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   Store  your  data  in  a  repository   Institutional  archive   Discipline/specialty  archive   DataCite  list  of  repostiories:    www.datacite.org/repolist         From  Flickr  by  torkildr  
6.  Data  stewardship  &  reuse   Data  Citation   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     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.  Workflows   6.  Data  stewardship  &  reuse   7.  Planning   &  data  management  plans  in   particular  
1.  Planning   What  is  a  data  management  plan?   A  document  that  describes  what  you  will  do  with  your  data   during  your  research  and  after  you  complete  your  research   Data   Hangover    
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    
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  
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  
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?  
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?  
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  
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  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  
Roadmap   4.  Toolbox     3.  Best  practices   2.  Data  management  landscape   1.  Background    
E-­‐notebooks  &  online  science       •  NoteBook   •  ORNL  eNote     •  Evernote   •  Google  Docs   •  Blogs   •  wikis   •  TheLabNotebook.com   •  NoteBookMaker   TheLabNotebook.com!
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  
CDL  Services:  www.cdlib.org/services/uc3     Data  Repository   Deposit    |    Manage    |    Share    |    Preserve   •  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    
Why  are  you   promoting   Excel?   •  Open  source  add-­‐in   •  Facilitate  data  management,  sharing,  archiving  for  scientists   •  Focus  on  atmospheric,  ecological,  hydrological,  and   oceanographic  data   •  Collecting  requirements  for  add-­‐in  from  scientists,  data   centers,  libraries   Funders:  Gordon  and  Betty  Moore  Foundation,  Microsoft  Research  
Why  are  you   promoting   Excel?   Everyone  uses  it   Stopgap  measure       Funders:  Gordon  and  Betty  Moore  Foundation,  Microsoft  Research  
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  
www.carlystrasser.net   Resources" Slideshare link: this presentation"
Handy  References   Best  Practices  for  Preparing  Environmental  Data  Sets   to  Share  and  Archive.  September  2010.  Hook,   Santhana  Vannan,  Beaty,  Cook,  &  Wilson   http://daac.ornl.gov/PI/BestPractices-­‐2010.pdf   Some  Simple  Guidelines  for  Effective  Data   Management.  Borer,  Seabloom,  Jones,  &  Schildhauer.     Bull  Ecol  Soc  Amer,  April  2009:  205-­‐214.    
Feedback!   http://tinyurl.com/OS2012WS Ocean  Sciences   dcxl.cdlib.org   2012  WorkShop   @dcxlCDL   www.facebook.com/DCXLatCDL   www.carlystrasser.net   carlystrasser@gmail.com   @carlystrasser  

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Data Management for Scientists: Workshop at Ocean Sciences 2012

  • 1.
    Data  Management  for  Scientists     Reduce  your  workload   Reuse  your  ideas   Recycle  your  data     www.oddee.com   Carly  Strasser,  PhD   Ocean  Sciences  Meeting   California  Digital  Library,  UC  Office  of  the  President   February  2012   carly.strasser@ucop.edu   www.carlystrasser.net  
  • 2.
    Roadmap   4.  Toolbox     3.  How  to  improve   2.  Data  management  landscape   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?   What  barriers  to  sharing   can  we  eliminate?   Why  don’t  people   share  data?   Is  data  management   Do  attitudes  about   being  taught?   sharing  differ   among  disciplines?   How  can  we  promote  storing   data  in  repositories?  
  • 6.
    Roadmap   4.  Toolbox     3.  How  to  improve   2.  Data  management  landscape   1.  Background    
  • 7.
    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  
  • 8.
    Digital  data   +     Complex  analyses  
  • 9.
    Data   Models   Maximum   Likelihood   estimation   Matrix   Models   Images   Tables   Paper  
  • 10.
    Data   Models   Maximum   Likelihood   estimation   Matrix   Models   Images   Tables   Paper  
  • 11.
    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    
  • 12.
    Data  Hangover     What  happened?   From  Flickr  by  SteveMcN  
  • 13.
    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  
  • 14.
    Who  cares?     From  Flickr  by  Redden-­‐McAllister   From  Flickr  by  AJC1   www.rba.gov.au  
  • 15.
    Where  data  end  up   From  Flickr  by  diylibrarian   www Data   www Metadata   From  Flickr  by  torkildr   Recreated  from  Klump  et  al.  2006  
  • 16.
    Data   Reuse   Data   Sharing   Data   Management  
  • 17.
    Trends  in  Data  Archiving   Journal  publishers   Joint  Data  Archiving  Agreement     Data  Papers  etc.   Ecological  Archives,  Beyond  the  PDF     Funders   Data  management  requirements    
  • 18.
    Roadmap   4.  Toolbox     3.  Best  practices   2.  Data  management  landscape   1.  Background    
  • 19.
    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  
  • 20.
    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  
  • 21.
    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  
  • 22.
    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
  • 23.
    C:Documents and SettingshamptonMyDocumentsNCEAS 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 23  
  • 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   Identify  missing  data   •  Numeric  fields:  distinct  value  (e.g.  9999)   •  Text  fields:  NULL  or  NA     •  Use  data  flags  in  a  separate  column  to  qualify  empty  cells   M1  =  missing;  no   sample  collected   E1  =  estimated  from   grab  sample  
  • 28.
    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  
  • 29.
    2.  Data  collection  &  organization   SPREADSHEETS: THE GOOD Quick  on  the  draw     Clickety-­‐click  and  you’re  ready  to  fire   Always  there  in  time      Everyone  has  Excel   Smarter  than  he  lets  on    Stats,  Pivot  tables,  VB  scripts   Cleans  up  real  pretty    Graphics,  fonts,  colors,  borders   From  Mark  Schildhauer  
  • 30.
    2.  Data  collection  &  organization   SPREADSHEETS: THE BAD Shoot  first  ask  later   Click&fire  Click&fire  Click&fire   No  scruples    Delete  row,  click&fire,  ctrl-­‐x/ctrl-­‐c,  click&fire,  re-­‐sort,  save   Talks  a  good  story  but  not  much  education    Stats   From  Mark  Schildhauer  
  • 31.
    2.  Data  collection  &  organization   SPREADSHEETS: THE UGLY Ill-­‐mannered   Takes  data  prisoner;  conflates  raw  and  summary  data   Gaudy   Use  of  visual  cues  as  metadata:  color,  font,  border   Shifty   Cross-­‐linking  worksheets  sets  up  “invisible”  dependencies   Shiftless   No  provenance   The  more  complicated  your  spreadsheet,  the  uglier  it  gets  for   use  with  other  software     From  Mark  Schildhauer  
  • 32.
    2.  Data  collection  &  organization   All  of  the  things   that  make  Excel   great  for  data  are   bad  for  archiving!   1.  Create  archive-­‐ready  raw  data   2.  Put  it  somewhere  special   3.  Have  your  fun  with  fancy  Excel  techniques   4.  Keep  archiving  in  mind  
  • 33.
    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   From  Mark  Schildhauer  
  • 34.
    2.  Data  collection  &  organization   Sample  sites   samples   Samples   Species   *siteID   *sampleID   *sampleID   *speciesID   site_name   siteID   siteID   sample_date   species_name   latitude   sample_date   common_name   speciesID   longitude   speciesID   height   family   description   height   flowering   order   flowering   flag   comments   flag   comments   *  Denotes  the  primary  key   From  Mark  Schildhauer  
  • 35.
    2.  Data  collection  &  organization   Databases  often  enforce  good  practice     Must  define     A   B   C   D   E    Tables   1   2   3   10   11    Attributes   4   5   6   12   13   14   15    Relationships  (constraints)   7   8   9   16   17     Databases  provide:    Scalability:  millions+  records    Features  for  sub-­‐setting,  querying,  sorting    Scripted  language:  SQL      Reduced  redundancy  &  potential  data  entry  errors   From  Mark  Schildhauer  
  • 36.
    2.  Data  collection  &  organization   Spreadsheets   Databases   •  Good  for  simple,  self-­‐contained   •  Works  well  with  lots  of  data   charts,  graphs,  calculations   •  Easy  to  query  and  subset  data   •  Handy  for  collecting  raw  data   •  Data  fields  are  constrainted   •  Flexible  cell  content  type   •  Columns  cannot  be  sorted   But…   independently  of  each  other   •  Hard  to  subset  or  sort   •  Normalization  reduces  data  entry   •  Lack  “record”  integrity:  can  sort  a   and  potential  for  error   column  independently  of  all  others   But…   •  Harder  to  maintain  as  complexity   •  More  to  learn     and  size  of  data  grows   •  Harder  to  use   From  Mark  Schildhauer  
  • 37.
    2.  Data  collection  &  organization   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   www.top20training.com   From  Mark  Schildhauer  
  • 38.
    2.  Data  collection  &  organization   Use  descriptive  file  names   PhDcomics.com  
  • 39.
    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  
  • 40.
    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  
  • 41.
    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  
  • 42.
    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  
  • 43.
    3.  Quality  control  and  quality  assurance   Before  data  collection   •  Define  &  enforce  standards   •  Assign  responsibility  for  data  quality   From  Flickr  by  StacieBee  
  • 44.
    3.  Quality  control  and  quality  assurance   During  data  collection/entry   •  Minimize  manual  entry   •  Use  double  entry   •  Use  text-­‐to-­‐speech  program   to  read  data  back   •  Use  a  database   •  Document  changes   From  Flickr  by  schock  
  • 45.
    3.  Quality  control  and  quality  assurance   After  data  entry   •  Check  for  missing,  impossible,   anomalous  values   •  Perform  statistical  summaries     •  Look  for  outliers   •  Normal  probability  plots   •  Regression   •  Scatter  plots   60   50   40   •  Maps   30   20   10   0   0   10   20   30   40    
  • 46.
    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  
  • 47.
    4.  Metadata  basics   Why  are  you   What  is   promoting   metadata?   Excel?  
  • 48.
    4.  Metadata  basics      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?  
  • 49.
    •  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  
  • 50.
    4.  Metadata  basics   What  is   metadata?   Select  the  appropriate   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)        
  • 51.
    4.  Metadata  basics   What  ds  a   What  ioes   metadata   standard?   look  like?  
  • 52.
    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  
  • 53.
    5.  Workflows   Workflow:  how  you  get  from  the  raw  data  to  the  final   products  of  your  research     Simple  workflows:  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  
  • 54.
    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   &  
  • 55.
    5.  Workflows   Fancy  Schmancy  workflows:  Kepler   Resulting  output   https://kepler-­‐project.org  
  • 56.
    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    
  • 57.
    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,  multi-­‐step   analyses   −  Enable  efficient  swapping  of  alternative  models  &   algorithms   −  Help  automate  tedious  tasks  
  • 58.
    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  
  • 59.
    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)  
  • 60.
    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  
  • 61.
    6.  Data  stewardship  &  reuse   Store  your  data  in  a  repository   Institutional  archive   Discipline/specialty  archive   DataCite  list  of  repostiories:    www.datacite.org/repolist         From  Flickr  by  torkildr  
  • 62.
    6.  Data  stewardship  &  reuse   Data  Citation   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     Learn  more  at  www.datacite.org   Modified from R. Cook  
  • 63.
    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   &  data  management  plans  in   particular  
  • 64.
    1.  Planning   What  is  a  data  management  plan?   A  document  that  describes  what  you  will  do  with  your  data   during  your  research  and  after  you  complete  your  research   Data   Hangover    
  • 65.
    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    
  • 66.
    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  
  • 67.
    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  
  • 68.
    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?  
  • 69.
    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?  
  • 70.
    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  
  • 71.
    Don’t  forget:  Budget   •  Costs  of  data  preparation  &  documentation   Hardware,  software   Personnel   Archive  fees   •  How  costs  will  be  paid     Request  funding!   dorrvs.com  
  • 72.
    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  
  • 73.
    Roadmap   4.  Toolbox     3.  Best  practices   2.  Data  management  landscape   1.  Background    
  • 74.
    E-­‐notebooks  &  online  science       •  NoteBook   •  ORNL  eNote     •  Evernote   •  Google  Docs   •  Blogs   •  wikis   •  TheLabNotebook.com   •  NoteBookMaker   TheLabNotebook.com!
  • 75.
    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  
  • 76.
    CDL  Services:  www.cdlib.org/services/uc3     Data  Repository   Deposit    |    Manage    |    Share    |    Preserve   •  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    
  • 77.
    Why  are  you   promoting   Excel?   •  Open  source  add-­‐in   •  Facilitate  data  management,  sharing,  archiving  for  scientists   •  Focus  on  atmospheric,  ecological,  hydrological,  and   oceanographic  data   •  Collecting  requirements  for  add-­‐in  from  scientists,  data   centers,  libraries   Funders:  Gordon  and  Betty  Moore  Foundation,  Microsoft  Research  
  • 78.
    Why  are  you   promoting   Excel?   Everyone  uses  it   Stopgap  measure       Funders:  Gordon  and  Betty  Moore  Foundation,  Microsoft  Research  
  • 79.
    www.dataone.org   •  Data  Education  Tutorials   •  Database  of  best  practices    &  software  tools   •  Links  to  DMPTool   •  Primer  on  data  management   From  Flickr  by  Robert   Hruzek  
  • 80.
  • 81.
    www.carlystrasser.net   Resources" Slideshare link: this presentation"
  • 82.
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