UC Riverside: Data Management for Scientists

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  • 1. Data  Management  for  Scientists     Reduce  your  workload   Reuse  your  ideas   Recycle  your  data     www.oddee.com  Carly  Strasser,  PhD   UC  Riverside  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.  Mistakes  we  make  1.  Background    
  • 3. 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?  
  • 4. Who  we  are  
  • 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 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  
  • 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 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  
  • 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 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 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 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
  • 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   wwwMetadata   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    Data  Papers  etc.  Ecological  Archives,  Beyond  the  PDF  
  • 20. Trends  in  Data  Archiving  Journal  publishers  Joint  Data  Archiving  Agreement    Data  Papers  etc.  Ecological  Archives,  Beyond  the  PDF    Funders  Data  management  requirements    
  • 21. Roadmap   4.  Toolbox     3.  How  to  improve   2.  Mistakes  we  make  1.  Background    
  • 22. Best  Practices  for  Data  Management   1.  Planning   2.  Data  collection  &  organization   3.  Quality  control  &  assurance   4.  Metadata   5.  Workflows   6.  Data  stewardship  &  reuse  
  • 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  
  • 24. 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  
  • 25. 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    
  • 26. 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  
  • 27. 2.  Data  collection  &  organization  Use  descriptive  file  names   PhDcomics.com  
  • 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     60   50   40   30   20   10   0   0   5   10   15   20   25   30   35  
  • 33. 4.  Metadata  basics   Why  are  you   What  is   promoting   metadata?   Excel?  
  • 34. 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?  
  • 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   What  is   metadata   metadata?   standard?  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)    
  • 37. 4.  Metadata  basics  
  • 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  my  data?   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  your  research  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. Roadmap   4.  Toolbox     3.  How  to  improve   2.  Mistakes  we  make  1.  Background    
  • 56. 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  
  • 57. E-­‐notebooks  •  NoteBook  •  ORNL  eNote    •  Evernote  •  Google  Docs  •  Blogs  •  wikis  •  TheLabNotebook.com  •  iPad  ELN  •  NoteBookMaker   iPad ELN, the flexible electronic laboratory notebook TheLabNotebook.com"
  • 58. CDL  Services  for  UC  Community   Where  should  I  put   Data  Repository   my  data?   Deposit    |    Manage    |    Share    |    Preserve   www.cdlib.org/services/uc3  
  • 59. 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  
  • 60. 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  
  • 61. Why  are  you   promoting   Excel?  Everyone  uses  it  Stopgap  measure      
  • 62. B  A   C  
  • 63. www.dataone.org  •  Data  Education  Tutorials  •  Database  of  best  practices     &  software  tools  •  Links  to  DMPTool  •  Primer  on  data  management   From  Flickr  by  Robert  Hruzek  
  • 64. Data Management 101"dcxl.cdlib.org  •  Data  Education  Tutorials  •  Other  resources  
  • 65. From  tripwow.tripadvisor.com,  Travelpod  member  Sutiramisu  
  • 66. Process  1.  Assess  needs  2.  Gather  requirements  3.  Build  requirements   document  4.  Build  community  
  • 67. Requirements  1.  Must  work  for  Excel  users  without  the  add-­‐in  2.  No  additional  software  (other  than  add-­‐in  and  Excel)  necessary  3.  Can  be  used  offline  4.  Perform  CSV  compatibility  checks,  reporting,  and  automated  fixes  5.  Add  Metadata  to  data  file   a.  Can  use  existing  metadata  as  a  template   b.  Add-­‐in  can  automatically  generate  some  of  the  metadata   where  the  info  is  available  from  the  file  6.  Generate  a  citation  for  the  data  file  7.  Deposit  data  and  metadata  in  a  repository  
  • 68. The  Great  Debate  Add-­‐in    •  Little  pieces  of  software    •  Download  to  extend  the  capabilities  of  Excel  •  Appear  as  “ribbon”  Web-­‐based  application    •  Require  the  web:  www  +  wba  •  Do  not  require  that  you  download  a  program  •  Websites  that  do  something  with  info/files  provided  by  user  •  Examples:  Facebook,  YouTube  
  • 69. Add-­‐in   New  &   Download   improved   add-­‐in   DCXL   spreadsheet   add-­‐in   Check   Create   Connect   Compatibility   Metadata   to   repository   1.  Parse  for  compatibility   1.  Make  template   1.  Version  control   2.  Report  potential  errors   2.  Auto-­‐fill   2.  Backing  up   3.  Allow  user-­‐directed   3.  Parameter  list  selection   3.  Retrieve  info:   error  correction   4.  Citation  generation   Authentication   5.  DOI  connection   Keyword  list   Metadata  standard   Citation  format   Acceptable  file  formats    
  • 70. Summary:  Add-­‐in  The Good The Bad•  Integrated  in  workflow   •  Windows  only  •  Familiar  UI,  functionality   •  Install  &  updates  required  •  Smaller  shift   •  Not  as  generalizable/•  Available  offline   extensible   •  Not  as  easy  for  community   to  get  involved  
  • 71. Web  application   New  &   Upload   Web-­‐based   improved   spreadsheet   application   spreadsheet   Check   Create   Connect   Compatibility   Metadata   to   repository   1.  Parse  for  compatibility   1.  Make  template   1.  Version  control   2.  Report  potential  errors   2.  Auto-­‐fill   2.  Backing  up   3.  Allow  user-­‐directed   3.  Parameter  list  selection   3.  Retrieve  info:   error  correction   4.  Citation  generation   Authentication   5.  DOI  connection   Keyword  list   Metadata  standard   Citation  format   Acceptable  file  formats    
  • 72. Summary:  Web  based  The Good The Bad•  Easier  to  maintain,  update   •  Not  familiar  •  Can  use  with  Mac   •  Requires  new  UI  •  Generalizable/extensible   •  Not  integrated  in  Excel  •  Community  involvement   •  Offline  use  not  guaranteed   possible  
  • 73. Moving  forward…  •  Simple,  clean  user  interface  •  Connect  to  web  application  from  within  Excel  •  Offline  use  of  web  application,  especially  ability  to   create  metadata  offline  
  • 74. Send  me  feedback!   From  Flickr  by  hashmil  Comment  on  the  blog   dcxl.cdlib.org   Email  me   carlystrasser@gmail.com   Tweet  me   @carlystrasser   FB  message  me   DCXLatCDL  
  • 75. Diane  Bisom   Ann  Frenkel   Dr.  Ruth  Jackson  dcxl.cdlib.org  @dcxlCDL  www.facebook.com/DCXLatCDL   www.carlystrasser.net   carlystrasser@gmail.com   @carlystrasser