libraries and research data management – what works? - sheila corrall - immersive informatics...

Immersive  Informa-cs  Educa-on  for  Research  Data  Specialists  

Sheila  Corrall  Professor  and  Chair  

Library  &  Informa-on  Science  

www.ischool.piA.edu/lis  

Digital  Cura-on  Educa-on  in  the  USA  •  Massive  investment  in  RDM  educa-on  and  capacity  building  by  Ins-tute  of  Museum  &  Library  Studies  –  $9M  on  curriculum  development,  scholarships,  etc.  

•  Around  33%  of  US  schools  offer  one  or  more  courses  –  10  special  tracks,  7  Master’s  programs,  4  cer-ficates  –  target  audiences  include  LIS  Master’s  and  PhD  students,  

exis-ng  prac--oners  and  graduates  in  other  disciplines  –  experien-al  learning  via  internships  and  field  experiences  

•  Other  ini-a-ves  include  annual  training  ins-tutes  and  library-­‐led  data  literacy  for  Sci  &  Eng  students  

(Corrall,  2012;  Corrall,  Kennan  &  Afzal,  2013)  

Family  of  Data  Scien-st  Roles  •  Data  engineer  –  focus  on  so_ware  development,  coding,  

programming,  tools  

•  Data  analyst  –  focus  on  business/scien-fic  analy-cs  and  sta-s-cs,  e.g.,  R,  SAS,  Excel,  to  support  researchers  and  modelers,  business  

•  Data  librarian  –  focus  on  advocacy,  research  data  management/informa-cs  in  a  university/ins-tute  

•  Data  steward  –  focus  on  long  term  digital  preserva-on,  repositories,  archives,  data  centers  

•  Data  journalist  –  focus  on  telling  stories  and  news  (Lyon,  2012)  

Re-­‐engineering  Libraries  Re-­‐skilling  Librarians  •  New  prac-ce  modes  

–  from  liaison  to  immersive  

immersiveInforma-cs  –  co-­‐developed  by  UKOLN,  University  of  Bath  and  University  of  Melbourne  

•  Immersive  sessions  in  lab  (Lyon,  2012)  

RDM  Seminar  @  PiA  iSchool  Course  Developer  and  Instructor:  Dr.  Liz  Lyon  1.  Introduc-ons  and  

Overview  2.  Data  Landscape  3.  Ins-tu-ons  and  Data  4.  Data  Requirements  and  

Capability  5.  Roadmaps,  Strategy  and  

Planning  6.  Data  Management  Plans  7.  Disciplinary  Data  1  

8.  Immersive  Analysis  with  Faculty  Researchers  

9.  Legal  and  Ethical  Issues  10.  Disciplinary  Data  2  11.  Data  Centers  12.  Data  Advocacy,  Skills,  

Training  13.  Data  Sustainability  and  

Costs  14.  Roadmap  Presenta-ons  

Spring  2014,  Fall  2014  

Pt.  2  Research  Data  Infrastructures  Course  Developer  and  Instructor:  Dr.  Liz  Lyon  1.  Introduc-ons  and  

Overview  2.  Data  Standards  3.  Data  Cita-on  and  Metrics  4.  Data  Publica-on  5.  Data  Discovery  6.  Immersive  Session  with  

Researchers  7.  Disciplinary  Data  3  

8.  Data  Storage  9.  Data  Repositories  10. Data  Preserva-on  

(Long-­‐term)  11. Ci-zen  Science,  

Ci-zen  Data  12. Data  Science  13. Data,  Society,  

Futures  14. Presenta-ons  and  

Summary  Evalua-on  

Fall  2014,  Spring  2015  

1.  Briefing  paper  on  developing  a  university  data  policy  

2.  Cri-cal  appraisal  of  selected  data  tools  for  data  audits  and  data  management  plans  

3.  Disciplinary  posi-on  paper  

4.  Develop  an  RDM  Roadmap  or  Business  Case  Presenta-on  (for  a  professional  audience)  with  suppor-ng  evidence  and  documenta-on  

? RDM  Assignments  

Student  Feedback  “It  was  great  to  see  a  real-­‐life  example  of  how  a  lab  generates  and  uses  data.”  

“We  learned  not  only  about  the  specifics  of  their  research  but  about  the  lifecycle  of  data.”  

“This  was  a  valuable  experience.    It  was  very  prac-cal  and  illuminated  some  of  the  struggles  that  one  may  encounter  in  discussing  data  as  its  own  area  of  research.”  

Course  Features  •  Case  studies  and  global  exemplars  

•  Methodologies  and  tools  

•  Invited  speakers  •  Prepare  for  prac-ce  •  Disciplinary  data  focus  •  Innova-ve  immersive  session  in  the  lab  

Sample  MLIS  RDM  Track  Course  Planner  Core/Founda+onal  Courses    (required)  •  Understanding  

Informa-on  •  Library  and  

Archival  Compu-ng  

•  Organizing  and  Retrieving  Informa-on  

•  Managing  and  Leading  LIS  

Specializa+on  Courses  (recommended)  •  Research  Data  

Management  •  Research  Data  

Infrastructures  •  Informa-on  

Architecture  •  Metadata  •  Data  and  

Informa-on  Visualiza-on  

Suggested  Elec+ves    (choose  3  or  more)  •  Academic  Libraries  •  Research  Methods  •  Scholarly  

Communica-on  •  Geographic  

Informa-on  Systems  

•  Preserving  Digital  Culture  

•  Field  Experience  •  Individual  Project  

Other  Specialist  Data  Courses    at  the  PiAsburgh  iSchool  

MS  in  Informa-on  Science  Big  Data  Analy-cs  Specializa-on  

www.ischool.piA.edu/ist/degrees/specializa-ons/big-­‐data.php  

MSIS  Big  Data  Analy-cs  Specializa-on  Pre-­‐requisite  courses  •  Programming  •  Sta-s-cs  •  Mathema-cs  •  Data  Structures  •  JAVA  Required  courses  •  Decision  Analysis  &  Decision  

Support  Systems  •  Algorithm  Design  •  Database  Management  •  Advanced  Topics  in  Database  

Management  •  Data  Analy-cs  +  Two  elec+ves  

One  course  from  •  Intro  to  Info  Storage  &  Retrieval      •  Data  Mining  One  course  from  •  Introduc-on  to  Neural  Networks  •  Social  Compu-ng  Two  courses  from  •  Geospa-al  Informa-on  Systems  •  Mobile  GIS  &  Loca-on-­‐Based  Services  •  Network  Science  •  Informa-on  Visualiza-on  •  Security  &  Privacy  •  Advanced  Topics  in  GIS  •  Network  Performance  •  Computer  Networking  

Ques-ons  and  Comments  

Sheila  Corrall  scorrall@piA.edu  

Liz  Lyon  elyon@piA.edu