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Preparing	
  eScience	
  Librarians	
  for	
  
Managing	
  Research	
  Data	
  




        RDAP	
  2012,	
  New	
  Orleans,	
  LA	
  
                        	
  
                    Jian	
  Qin	
  	
  
      School	
  of	
  InformaCon	
  Studies	
  
          Syracuse	
  University	
  
NoCons	
  of	
  eScience	
  librarianship	
  


                                                                                ProacCve	
  
                                                                               training	
  for	
  
                                                                               data	
  literacy	
  	
  
        ConsultaCve	
  
                                                           Leader	
  in	
  
        services	
  for	
  
                                                            eScience	
  
       data	
  use	
  and	
  
                                                           iniCaCves	
  	
  
       management	
  	
   AcCve	
  players	
  
                                  and	
  
                              contributors	
  
                                 of	
  data	
  
       Part	
  of	
  team	
     curaCon	
  
       transcending	
  
        disciplinary	
  
        boundaries	
  	
  


                                   RDAP	
  2012,	
  New	
  Orleans	
                                      2	
  
EducaCng	
  the	
  new	
  type	
  of	
  
workforce	
  
                •  ScienCfic	
  data	
  literacy	
  	
  (SDL)	
  
                   project	
  (hNp://sdl.syr.edu),	
  2007-­‐2009	
  

                •  E-­‐Science	
  Librarianship	
  Curriculum	
  
                   project	
  	
  (eSLib	
  hNp://eslib.ischool.syr.edu),	
  
                   2009-­‐2012,	
  in	
  partnership	
  with	
  
                   Cornell	
  University	
  Library	
  	
  


                            RDAP	
  2012,	
  New	
  Orleans	
              3	
  
A	
  curriculum	
  for	
  eScience	
  
librarianship	
  
•  Overall	
  learning	
  objecCves:	
  
   –  Ability	
  to	
  arCculate	
  eScience	
  and	
  to	
  plan	
  and	
  
      develop	
  eScience	
  librarianship	
  projects	
  
   –  Competency	
  in	
  scienCfic	
  data	
  management	
  
   –  Competency	
  in	
  cyberinfrastructure	
  technologies	
  
   –  Ability	
  to	
  collaborate,	
  communicate,	
  and	
  lead	
  in	
  
      eScience	
  librarianship	
  projects	
  



                                 RDAP	
  2012,	
  New	
  Orleans	
             4	
  
Ability	
  to	
              •  ArCculate	
  eScience	
  process	
  and	
  
                                data	
  lifecycle	
  	
  	
  
arCculate	
                  •  IdenCfy	
  user	
  needs	
  and	
  translate	
  
eScience	
  and	
  to	
         the	
  needs	
  into	
  system	
  
                                requirements	
  	
  
plan	
  and	
  develop	
  
                             •  Make	
  plans	
  for	
  eScience	
  
eScience	
                      librarianship	
  project	
  iniCaCon	
  and	
  
librarianship	
                 implementaCon	
  
                             •  Conduct	
  research	
  on	
  data	
  related	
  
projects	
                      issues	
  such	
  as	
  insCtuConal	
  data	
  
                                policy,	
  support	
  services,	
  and	
  
                                technology	
  adopCon	
  	
  
                             •  Write	
  grant	
  proposals	
  for	
  
                                obtaining	
  funding	
  to	
  support	
  
                                eScience	
  librarianship	
  projects	
  	
  


                              RDAP	
  2012,	
  New	
  Orleans	
              5	
  
•  ArCculate	
  data	
  
Competency	
  in	
   characterisCcs	
  
scien-fic	
  data	
   •  Analyze	
  domain	
  data	
  sets	
  
management	
            and	
  develop	
  data	
  models	
  
	
                   •  Define	
  metadata	
  element	
  
                        sets	
  	
  
                     •  Develop	
  specialized	
  
                        metadata	
  for	
  data	
  curaCon,	
  
                        preservaCon,	
  and	
  access	
  
                     •  Create	
  metadata	
  records	
  for	
  
                        scienCfic	
  data	
  sets	
  
                         RDAP	
  2012,	
  New	
  Orleans	
     6	
  
•  Maintain	
  informaCon	
  
Competency	
  in	
           retrieval	
  interfaces	
  
cyberinfrastruct          •  Maintain	
  informaCon	
  
ure	
  technologies	
        exchange	
  networks	
  
                          •  Program,	
  write	
  code,	
  and	
  
                             manipulate	
  scripts	
  
                          •  Use	
  content	
  management	
  
                             systems	
  
                          •  IdenCfy	
  and	
  model	
  data/
                             work	
  flows	
  
                          •  Assess	
  research	
  needs	
  for	
  
                             and	
  performance	
  of	
  CI	
  tools	
  

                           RDAP	
  2012,	
  New	
  Orleans	
               7	
  
Ability	
  to	
            •  Develop	
  partnership	
  with	
  
collaborate,	
                internal	
  and	
  external	
  
communicate,	
  and	
         organizaConal	
  units	
  and	
  
lead	
  in	
  eScience	
      collaborators	
  	
  
librarianship	
            •  Communicate	
  with	
  
projects	
                    administrators	
  and	
  
                            researchers	
  	
  
                         •  Engage	
  researchers	
  in	
  data	
  
                            management	
  processes	
  	
  
                         •  IniCate	
  and	
  lead	
  in	
  
                            eScience	
  librarianship	
  
                            projects	
  	
  
                          RDAP	
  2012,	
  New	
  Orleans	
        8	
  
The	
  curriculum	
  
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  Courses	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  Primary	
  learning	
  outcomes	
  




                                                                                                                                                                                                                                 in	
  eScience	
  librarianship	
  projects	
  	
  
                                                                                                                                                                                                                                 Ability	
  to	
  collaborate,	
  communicate,	
  and	
  lead	
  
                         ScienCfic	
  Data	
                                                                            Competency	
  in	
  scienCfic	
  data	
  
                         Management	
  (core)	
                                                                        management	
  
                                                                                                                       Competency	
  in	
  
                         Cyberinfrastructure	
  
                         (core)	
                                                                                      cyberinfrastructure	
  technologies	
  

                                                                                                                       Ability	
  to	
  arCculate	
  eScience	
  and	
  to	
  
                         Data	
  services	
  (capstone)	
                                                              plan	
  and	
  develop	
  eScience	
  
                                                                                                                       librarianship	
  projects	
  	
  
                         Database	
  systems	
  
                         (required	
  elecCve)	
  

                         Metadata	
  (elecCve)	
  

                                                                                                   RDAP	
  2012,	
  New	
  Orleans	
                                                                                                                              9	
  
Theme	
  1:	
  building	
  fundamentals	
  
        1	
                                                                 2	
              Case	
  studies	
  that	
  use	
  
                Overview	
  of	
  scienCfic	
  data	
                                      pracCcal	
  examples	
  to	
  guide	
  
                management	
  that	
  covers	
                                             students	
  step-­‐by-­‐step	
  in	
  
                   data	
  and	
  metadata	
                                                   data	
  analysis	
  and	
  
                      fundamentals	
                                                             management	
  



3	
       Using	
  scienCfic	
  data,	
  which	
  involves	
  
           discussions	
  of	
  data	
  quality,	
  data	
  
           repositories	
  and	
  discovery,	
  data	
  
            analysis	
  and	
  presentaCon,	
  and	
  
            ethics	
  and	
  intellectual	
  property	
  
                               issues	
  

                                                    RDAP	
  2012,	
  New	
  Orleans	
                                               10	
  
Building	
  fundamentals:	
  data	
  
formats	
  
 Overview	
  of	
  scienti.ic	
  
data	
  management	
  that	
  
   covers	
  data	
  and	
  
metadata	
  fundamentals	
  

 Data	
           NASA’s	
  	
  de-inition	
  of	
  data	
                                 Processing	
  level	
  
 level	
  
                     processing	
  levels	
                                                     Level	
  4	
                      Self-­‐descripCve	
  
                                                                                                	
                                informaCon	
  existed	
  as	
  
 Level	
     Reconstructed	
  unprocessed	
  instrument	
  
 0	
         data	
  at	
  full	
  resolutions.	
  
                                                                                                Level	
  3	
                      header	
  of	
  the	
  data	
  file	
  
                                                                                                	
  
 Level	
     Reconstructed,	
  unprocessed	
  instrument	
                                      Level	
  2	
  
 1A	
        data	
  at	
  full	
  resolution,	
  time	
  referenced,	
                         	
  
             and	
  annotated	
  with	
  ancillary	
  information,	
                                                          Common	
  Data	
  Format	
  (CDF)	
  
                                                                                                Level	
  1B	
                 Flexible	
  Image	
  Transport	
  System	
  (FITS)	
  
             but	
  not	
  applied	
  to	
  the	
  Level	
  0	
  data.	
  
                                                                                                	
                            GRid	
  In	
  Binary	
  (GRIB)	
  
 Level	
     Level	
  1A	
  data	
  that	
  has	
  been	
  processed	
  to	
                    Level	
  1A	
                 Hierarchical	
  Data	
  Format	
  (HDF)	
  
 1B	
        sensor	
  units.	
  Not	
  all	
                                                   	
                            Network	
  Common	
  Data	
  Format	
  (netCDF)	
  
             instruments	
  will	
  have	
  a	
  Level	
  1B	
                                  Level	
  0	
  
             equivalent.	
  
                                                                                                                       Major	
  scienCfic	
  data	
  format	
  

                                                                                 RDAP	
  2012,	
  New	
  Orleans	
                                                                 11	
  
Building	
  fundamentals:	
  	
  
Understanding	
  data	
  and	
  metadata	
  

                                               Data	
  
                                             formats	
  
        Processing	
  
          levels	
                                                                     Data	
  
                                                                                    collecCons	
  
                              Some	
  formats	
  contain	
  self-­‐
 Lineage	
  vital	
  to	
       descripCve	
  metadata	
  
 assessing	
  data	
                                                        Metadata	
  standards	
  need	
  
     quality	
                                                               to	
  be	
  adjusted	
  for	
  local	
  
                                                                                   descripCon	
  needs	
  




                                      RDAP	
  2012,	
  New	
  Orleans	
                                       12	
  
Building	
  fundamentals:	
  data	
  
literacy	
  




     IL:	
  ACRL.	
  (2010).	
  	
  
     DL:	
  Finn,	
  Charles,	
  W.P.	
  (Tech	
  &	
  Learning,	
  2004)	
  
     SDL:	
  Qin,	
  J.	
  &	
  J.	
  D’Ignazio,	
  (Journal	
  of	
  Library	
  Metadata,	
  2010)	
  
     	
  	
  


                                             RDAP	
  2012,	
  New	
  Orleans	
                            13	
  
Theme	
  2:	
  Analysis	
  and	
  
   generalizaCon	
  	
  
Analysis	
  of	
  data	
  problems	
  is	
  an	
  
analysis	
  of	
  domain	
  data,	
  
requirements,	
  and	
  workflows	
  
that	
  will	
  lead	
  to	
  the	
  
development	
  of	
  soluCons.	
  




                                             RDAP	
  2012,	
  New	
  Orleans	
     14	
  
Analysis	
  and	
  generalizaCon:	
  engaging	
  
in	
  real	
  research	
  projects	
  	
  
•  Engage	
  students	
  in	
  research	
  and	
  service	
  
   projects	
  
   –  Data	
  policy	
  analysis	
  
   –  Data	
  management	
  consultaCon	
  
   –  Interviews	
  and	
  survey	
  design	
  
•  Course	
  projects	
  
   –  Real-­‐world	
  data	
  management	
  problems	
  



                            RDAP	
  2012,	
  New	
  Orleans	
     15	
  
Theme	
  3:	
  collaboraCon	
  and	
  communicaCon	
  

 •  Community	
  of	
  pracCce	
  
 •  InsCtuConalizaCon	
  of	
  data	
  services	
  
    –  Data	
  policies	
  
    –  Compliance	
  to	
  funding	
  agency	
  policies	
  and	
  
       mandates	
  
    –  Infrastructural	
  data	
  services	
  at	
  insCtuConal,	
  
       community,	
  and	
  naConal	
  levels	
  
 •  Awareness,	
  incenCves,	
  and	
  training	
  

                              RDAP	
  2012,	
  New	
  Orleans	
        16	
  
CollaboraCon	
  and	
  communicaCon	
  

•  Mentoring	
  by	
  Cornell	
  librarians,	
  led	
  by	
  Gail	
  
   Steinhart	
  
•  Internships	
  in	
  academic	
  libraries	
  and/or	
  
   research	
  centers	
  
•  Guest	
  speakers	
  to	
  classes	
  
•  Engaging	
  students	
  in	
  research	
  and	
  service	
  
   projects	
  


                              RDAP	
  2012,	
  New	
  Orleans	
         17	
  
Evolving	
  curriculum	
  
                      CAS	
  in	
  Data	
  Science	
  
             Required	
  courses:	
  
             •  Database	
  	
  
             •  Applied	
  Data	
  Science	
  



Data	
  storage	
  
                           Data	
                          Data	
           Systems	
  
    and	
  
                         analyCcs	
                    visualizaCon	
     management	
  
management	
  	
  




                                 RDAP	
  2012,	
  New	
  Orleans	
                    18	
  
eScience	
  Librarianship	
  Project	
  
             Website:	
  
 hNp://eslib.ischool.syr.edu/	
  	
  


             RDAP	
  2012,	
  New	
  Orleans	
     19	
  

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Preparing eScience librarians -- RDAP 2012

  • 1. Preparing  eScience  Librarians  for   Managing  Research  Data   RDAP  2012,  New  Orleans,  LA     Jian  Qin     School  of  InformaCon  Studies   Syracuse  University  
  • 2. NoCons  of  eScience  librarianship   ProacCve   training  for   data  literacy     ConsultaCve   Leader  in   services  for   eScience   data  use  and   iniCaCves     management     AcCve  players   and   contributors   of  data   Part  of  team   curaCon   transcending   disciplinary   boundaries     RDAP  2012,  New  Orleans   2  
  • 3. EducaCng  the  new  type  of   workforce   •  ScienCfic  data  literacy    (SDL)   project  (hNp://sdl.syr.edu),  2007-­‐2009   •  E-­‐Science  Librarianship  Curriculum   project    (eSLib  hNp://eslib.ischool.syr.edu),   2009-­‐2012,  in  partnership  with   Cornell  University  Library     RDAP  2012,  New  Orleans   3  
  • 4. A  curriculum  for  eScience   librarianship   •  Overall  learning  objecCves:   –  Ability  to  arCculate  eScience  and  to  plan  and   develop  eScience  librarianship  projects   –  Competency  in  scienCfic  data  management   –  Competency  in  cyberinfrastructure  technologies   –  Ability  to  collaborate,  communicate,  and  lead  in   eScience  librarianship  projects   RDAP  2012,  New  Orleans   4  
  • 5. Ability  to   •  ArCculate  eScience  process  and   data  lifecycle       arCculate   •  IdenCfy  user  needs  and  translate   eScience  and  to   the  needs  into  system   requirements     plan  and  develop   •  Make  plans  for  eScience   eScience   librarianship  project  iniCaCon  and   librarianship   implementaCon   •  Conduct  research  on  data  related   projects   issues  such  as  insCtuConal  data   policy,  support  services,  and   technology  adopCon     •  Write  grant  proposals  for   obtaining  funding  to  support   eScience  librarianship  projects     RDAP  2012,  New  Orleans   5  
  • 6. •  ArCculate  data   Competency  in   characterisCcs   scien-fic  data   •  Analyze  domain  data  sets   management   and  develop  data  models     •  Define  metadata  element   sets     •  Develop  specialized   metadata  for  data  curaCon,   preservaCon,  and  access   •  Create  metadata  records  for   scienCfic  data  sets   RDAP  2012,  New  Orleans   6  
  • 7. •  Maintain  informaCon   Competency  in   retrieval  interfaces   cyberinfrastruct •  Maintain  informaCon   ure  technologies   exchange  networks   •  Program,  write  code,  and   manipulate  scripts   •  Use  content  management   systems   •  IdenCfy  and  model  data/ work  flows   •  Assess  research  needs  for   and  performance  of  CI  tools   RDAP  2012,  New  Orleans   7  
  • 8. Ability  to   •  Develop  partnership  with   collaborate,   internal  and  external   communicate,  and   organizaConal  units  and   lead  in  eScience   collaborators     librarianship   •  Communicate  with   projects   administrators  and   researchers     •  Engage  researchers  in  data   management  processes     •  IniCate  and  lead  in   eScience  librarianship   projects     RDAP  2012,  New  Orleans   8  
  • 9. The  curriculum                                    Courses                                                        Primary  learning  outcomes   in  eScience  librarianship  projects     Ability  to  collaborate,  communicate,  and  lead   ScienCfic  Data   Competency  in  scienCfic  data   Management  (core)   management   Competency  in   Cyberinfrastructure   (core)   cyberinfrastructure  technologies   Ability  to  arCculate  eScience  and  to   Data  services  (capstone)   plan  and  develop  eScience   librarianship  projects     Database  systems   (required  elecCve)   Metadata  (elecCve)   RDAP  2012,  New  Orleans   9  
  • 10. Theme  1:  building  fundamentals   1   2   Case  studies  that  use   Overview  of  scienCfic  data   pracCcal  examples  to  guide   management  that  covers   students  step-­‐by-­‐step  in   data  and  metadata   data  analysis  and   fundamentals   management   3   Using  scienCfic  data,  which  involves   discussions  of  data  quality,  data   repositories  and  discovery,  data   analysis  and  presentaCon,  and   ethics  and  intellectual  property   issues   RDAP  2012,  New  Orleans   10  
  • 11. Building  fundamentals:  data   formats   Overview  of  scienti.ic   data  management  that   covers  data  and   metadata  fundamentals   Data   NASA’s    de-inition  of  data   Processing  level   level   processing  levels   Level  4   Self-­‐descripCve     informaCon  existed  as   Level   Reconstructed  unprocessed  instrument   0   data  at  full  resolutions.   Level  3   header  of  the  data  file     Level   Reconstructed,  unprocessed  instrument   Level  2   1A   data  at  full  resolution,  time  referenced,     and  annotated  with  ancillary  information,   Common  Data  Format  (CDF)   Level  1B   Flexible  Image  Transport  System  (FITS)   but  not  applied  to  the  Level  0  data.     GRid  In  Binary  (GRIB)   Level   Level  1A  data  that  has  been  processed  to   Level  1A   Hierarchical  Data  Format  (HDF)   1B   sensor  units.  Not  all     Network  Common  Data  Format  (netCDF)   instruments  will  have  a  Level  1B   Level  0   equivalent.   Major  scienCfic  data  format   RDAP  2012,  New  Orleans   11  
  • 12. Building  fundamentals:     Understanding  data  and  metadata   Data   formats   Processing   levels   Data   collecCons   Some  formats  contain  self-­‐ Lineage  vital  to   descripCve  metadata   assessing  data   Metadata  standards  need   quality   to  be  adjusted  for  local   descripCon  needs   RDAP  2012,  New  Orleans   12  
  • 13. Building  fundamentals:  data   literacy   IL:  ACRL.  (2010).     DL:  Finn,  Charles,  W.P.  (Tech  &  Learning,  2004)   SDL:  Qin,  J.  &  J.  D’Ignazio,  (Journal  of  Library  Metadata,  2010)       RDAP  2012,  New  Orleans   13  
  • 14. Theme  2:  Analysis  and   generalizaCon     Analysis  of  data  problems  is  an   analysis  of  domain  data,   requirements,  and  workflows   that  will  lead  to  the   development  of  soluCons.   RDAP  2012,  New  Orleans   14  
  • 15. Analysis  and  generalizaCon:  engaging   in  real  research  projects     •  Engage  students  in  research  and  service   projects   –  Data  policy  analysis   –  Data  management  consultaCon   –  Interviews  and  survey  design   •  Course  projects   –  Real-­‐world  data  management  problems   RDAP  2012,  New  Orleans   15  
  • 16. Theme  3:  collaboraCon  and  communicaCon   •  Community  of  pracCce   •  InsCtuConalizaCon  of  data  services   –  Data  policies   –  Compliance  to  funding  agency  policies  and   mandates   –  Infrastructural  data  services  at  insCtuConal,   community,  and  naConal  levels   •  Awareness,  incenCves,  and  training   RDAP  2012,  New  Orleans   16  
  • 17. CollaboraCon  and  communicaCon   •  Mentoring  by  Cornell  librarians,  led  by  Gail   Steinhart   •  Internships  in  academic  libraries  and/or   research  centers   •  Guest  speakers  to  classes   •  Engaging  students  in  research  and  service   projects   RDAP  2012,  New  Orleans   17  
  • 18. Evolving  curriculum   CAS  in  Data  Science   Required  courses:   •  Database     •  Applied  Data  Science   Data  storage   Data   Data   Systems   and   analyCcs   visualizaCon   management   management     RDAP  2012,  New  Orleans   18  
  • 19. eScience  Librarianship  Project   Website:   hNp://eslib.ischool.syr.edu/     RDAP  2012,  New  Orleans   19