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Preparing eScience Librarians for Managing Research Data RDAP 2012, New Orleans, LA Jian Qin School of InformaCon Studies Syracuse University
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Preparing eScience librarians -- RDAP 2012

May 25, 2015

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Jian Qin

Presentation at the RDAP2012 Education and Training panel, March 23, 2012, New Orleans, LA.
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Page 1: Preparing eScience librarians -- RDAP 2012

Preparing  eScience  Librarians  for  Managing  Research  Data  

RDAP  2012,  New  Orleans,  LA    

Jian  Qin    School  of  InformaCon  Studies  

Syracuse  University  

Page 2: Preparing eScience librarians -- RDAP 2012

NoCons  of  eScience  librarianship  

Part  of  team  transcending  disciplinary  boundaries    

AcCve  players  and  

contributors  of  data  curaCon  

ConsultaCve  services  for  data  use  and  management    

Leader  in  eScience  iniCaCves    

ProacCve  training  for  data  literacy    

RDAP  2012,  New  Orleans   2  

Page 3: Preparing eScience librarians -- RDAP 2012

•  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    

EducaCng  the  new  type  of  workforce  

RDAP  2012,  New  Orleans   3  

Page 4: Preparing eScience librarians -- RDAP 2012

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  

Page 5: Preparing eScience librarians -- RDAP 2012

Ability  to  arCculate  eScience  and  to  plan  and  develop  eScience  librarianship  projects  

•  ArCculate  eScience  process  and  data  lifecycle      

•  IdenCfy  user  needs  and  translate  the  needs  into  system  requirements    

•  Make  plans  for  eScience  librarianship  project  iniCaCon  and  implementaCon  

•  Conduct  research  on  data  related  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  

Page 6: Preparing eScience librarians -- RDAP 2012

Competency  in  scien-fic  data  management    

•  ArCculate  data  characterisCcs  

•  Analyze  domain  data  sets  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  

Page 7: Preparing eScience librarians -- RDAP 2012

Competency  in  cyberinfrastructure  technologies  

•  Maintain  informaCon  retrieval  interfaces  

•  Maintain  informaCon  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  

Page 8: Preparing eScience librarians -- RDAP 2012

Ability  to  collaborate,  communicate,  and  lead  in  eScience  librarianship  projects  

•  Develop  partnership  with  internal  and  external  organizaConal  units  and  collaborators    

•  Communicate  with  administrators  and  researchers    

•  Engage  researchers  in  data  management  processes    

•  IniCate  and  lead  in  eScience  librarianship  projects    

RDAP  2012,  New  Orleans   8  

Page 9: Preparing eScience librarians -- RDAP 2012

The  curriculum  

ScienCfic  Data  Management  (core)  

Cyberinfrastructure  and  ScienCfic  CollaboraCon  (core)  

Data  services  (capstone)  

Database  systems  (required  elecCve)  

Metadata  (required  elecCve)  

ScienCfic  Data  Management  (core)  

Cyberinfrastructure  and  ScienCfic  CollaboraCon  (core)  

Data  services  (capstone)  

Database  systems  (required  elecCve)  

Metadata  (required  elecCve)  

ScienCfic  Data  Management  (core)  

Cyberinfrastructure  and  ScienCfic  CollaboraCon  (core)  

Data  services  (capstone)  

Database  systems  (required  elecCve)  

Metadata  (required  elecCve)  

ScienCfic  Data  Management  (core)  

Cyberinfrastructure  (core)  

Data  services  (capstone)  

Database  systems  (required  elecCve)  

Metadata  (elecCve)  

Competency  in  scienCfic  data  management  

                                 Courses                                                        Primary  learning  outcomes  

Competency  in  cyberinfrastructure  technologies  

Ability  to  arCculate  eScience  and  to  plan  and  develop  eScience  librarianship  projects    

Ability  to  collaborate,  communicate,  and  lead  

in  eScience  librarianship  projects    

RDAP  2012,  New  Orleans   9  

Page 10: Preparing eScience librarians -- RDAP 2012

Theme  1:  building  fundamentals  

Overview  of  scienCfic  data  management  that  covers  

data  and  metadata  fundamentals  

1   Case  studies  that  use  pracCcal  examples  to  guide  students  step-­‐by-­‐step  in  

data  analysis  and  management  

2  

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

issues  

3  

RDAP  2012,  New  Orleans   10  

Page 11: Preparing eScience librarians -- RDAP 2012

Building  fundamentals:  data  formats  

RDAP  2012,  New  Orleans   11  

Overview  of  scienti.ic  data  management  that  

covers  data  and  metadata  fundamentals  

Data  level  

NASA’s    de-inition  of  data  processing  levels  

Level  0  

Reconstructed  unprocessed  instrument  data  at  full  resolutions.  

Level  1A  

Reconstructed,  unprocessed  instrument  data  at  full  resolution,  time  referenced,  and  annotated  with  ancillary  information,  but  not  applied  to  the  Level  0  data.  

Level  1B  

Level  1A  data  that  has  been  processed  to  sensor  units.  Not  all  instruments  will  have  a  Level  1B  equivalent.  

Processing  level  Level  4    Level  3    Level  2    Level  1B    Level  1A    Level  0  

Major  scienCfic  data  format  

Self-­‐descripCve  informaCon  existed  as  header  of  the  data  file  

Common  Data  Format  (CDF)  Flexible  Image  Transport  System  (FITS)  GRid  In  Binary  (GRIB)  Hierarchical  Data  Format  (HDF)  Network  Common  Data  Format  (netCDF)  

Page 12: Preparing eScience librarians -- RDAP 2012

Building  fundamentals:    Understanding  data  and  metadata  

RDAP  2012,  New  Orleans   12  

Processing  levels  

Data  formats  

Data  collecCons  

Lineage  vital  to  assessing  data  

quality  

Some  formats  contain  self-­‐descripCve  metadata  

Metadata  standards  need  to  be  adjusted  for  local  

descripCon  needs  

Page 13: Preparing eScience librarians -- RDAP 2012

Building  fundamentals:  data  literacy  

RDAP  2012,  New  Orleans   13  

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

Page 14: Preparing eScience librarians -- RDAP 2012

Theme  2:  Analysis  and  generalizaCon    

RDAP  2012,  New  Orleans   14  

Analysis  of  data  problems  is  an  analysis  of  domain  data,  requirements,  and  workflows  that  will  lead  to  the  development  of  soluCons.  

Page 15: Preparing eScience librarians -- RDAP 2012

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  

Page 16: Preparing eScience librarians -- RDAP 2012

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  

Page 17: Preparing eScience librarians -- RDAP 2012

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  

Page 18: Preparing eScience librarians -- RDAP 2012

Evolving  curriculum  

RDAP  2012,  New  Orleans   18  

Required  courses:  •  Database    •  Applied  Data  Science  

CAS  in  Data  Science  

Data  storage  and  

management    

Data  analyCcs  

Data  visualizaCon  

Systems  management  

Page 19: Preparing eScience librarians -- RDAP 2012

RDAP  2012,  New  Orleans   19  

eScience  Librarianship  Project  Website:  

hNp://eslib.ischool.syr.edu/