Infrastructure, Standards, and Policies for Research Data Management
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Infrastructure, Standards, and Policies for Research Data Management

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This presentation discusses the needs and importance of research data management and introduces the concept of research data management as an infrastructure service. Although many resources have been ...

This presentation discusses the needs and importance of research data management and introduces the concept of research data management as an infrastructure service. Although many resources have been made available for research data management, most of them are developed as “islands” and lack linking mechanisms. The lack of integrated and interconnected resources has contributed to high cost and duplicated efforts in data management operations. The vision of research data management as an infrastructure service is not only to improve the efficiency of research data management but also the productivity of the research enterprise. Each of the three dimensions—infrastructure, standards, and policies—addresses a critical aspect of research data management to make the data infrastructure services work.

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Infrastructure, Standards, and Policies for Research Data Management Infrastructure, Standards, and Policies for Research Data Management Presentation Transcript

  • Infrastructure,  Standards,   and  Policies  for  Research   Data  Management     Jian  Qin   School  of  Informa0on  Studies,  Syracuse  U,  USA     COINFO  2013,  Wuhan,  China,  2013-­‐10-­‐26  
  • About  this  presenta0on   1.  Concepts  about   data  infrastructure   services   2.  Problems  &  gaps   in  data   management   services   3.  Problems  and   gaps   4.  Data   management   infrastructure   service  dimensions   10/26/2013   COINFO2013,  Wuhan,  China   2  
  • Some   background   about  the  topic   Infrastructure,   standards,  and  policy   10/26/2013   COINFO2013,  Wuhan,  China   3  
  • Infrastructure   hVp://www.merriam-­‐webster.com/dic0onary/infrastructure     The  underlying  founda0on  or  basic   framework  (as  of  a  system  or  organiza0on).     The  system  of  public  works  of  a  country,   state,  or  region.       The  resources  (as  personnel,  buildings,  or   equipment)  required  for  an  ac0vity.       10/26/2013   COINFO2013,  Wuhan,  China   4  
  • Data  infrastructure   “a  sustainable  data  infrastructure  that  will  be   discoverable,  searchable,  accessible,  and  usable  to  the   en0re  research  and  educa0on  community.”     “usable  by  mul0ple  scien0fic  disciplines…”     “…that  can  support  and  provide  data  solu0ons  to  a   broader  range  of  scien0fic  disciplines  while  reducing   duplica0ve  efforts.”     hVp://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504776         10/26/2013   COINFO2013,  Wuhan,  China   5  
  • Standards   Scien=fic  data  formats   Metadata  standards  for  scien=fic   data   10/26/2013   COINFO2013,  Wuhan,  China   6  
  • Data  policies   § Access  and  use   § Management   § Storage  and  backup   § Metadata     § Sharing     § Preserva0on   § Intellectual  property  rights   § Security     10/26/2013   COINFO2013,  Wuhan,  China   7  
  • Examples  of  data  infrastructure  services   §  The  Ins0tute  for  Quan0ta0ve  Social  Science  repository:   hVp://www.iq.harvard.edu/   §  Inter-­‐University  Consor0um  for  Poli0cal  and  Social  Research   (ICPSR):  hVp://www.icpsr.umich.edu/icpsrweb/landing.jsp     §  The  Dryad  Digital  Repository:    hVp://datadryad.org/     §  Data  Observa0on  Network  for  Earth:  hVp://www.dataone.org/     §  Datalib:  hVp://databib.org/  (a  registry/directory/catalog  of   research  data  repositories)   §  Registry  of  Research  Data  Repositories:   hVp://www.re3data.org/     10/26/2013   COINFO2013,  Wuhan,  China   8  
  • Major  problems   § “Challenges  and  opportuni0es,”  Introduc0on  to  special   sec0on  Dealing  with  Data.  Science,  11  February  2011:  Vol.  331,  pp.   692-­‐693.     § 20%  of  respondents  regularly  use  or  analyze  data   sets  exceeding  100  GB   § 7%  use  data  sets  exceeding  1  TB   § About  50%  store  their  data  only  in  their  laboratories   § Lack  of  common  metadata  and  archives  for  using   and  storing  data   § No  funding  to  support  archiving   10/26/2013   COINFO2013,  Wuhan,  China   9  
  • Gaps  in  data  management  services     Data  lifecycle   Community  data   repositories   Gaps:  lack  of  =me,  lack  of  staff  support,  and  lack   of  tools  for  crea=ng  meaningful  metadata     (data  products  development  services)   Ins0tu0onal     data  repositories   Verified,  Derived,   calculated,  …  data     Gaps:  lack  of  standards  and  tools  to   support  managing  ac=ve  data     (data  staging  services)   Laptops,   personal  hard   drives,  etc.   10/26/2013   Verified,   archived  data   COINFO2013,  Wuhan,  China   Raw  data   Ac0ve  data   10  
  • Why  the  gaps?   Raw  data,   ac0ve  data   Technical   factors   Calculated,   derived  …  data   Verified,   archived  data   Lack  of  tools  to  help  DM  at  different  stages  of  a  research  lifecycle   Data  repositories  do  not  always  provide  tools  for  pre-­‐submission  staging   Organiza0onal   Lack  of  repeatable,  reliable  prac0ces  to  ensure  effec0ve  DM   Lack  of  ins0tu0onal  policies  to  support  and  assess  DM  prac0ces   factors   Lack  of  DM  training  programs     Behavioral   factors   10/26/2013   Researchers  have  no  0me  for  performing  DM  tasks       No  mo0va0on  to  invest  0me  in  DM   Concerns  for  losing  compe00ve  advantages   COINFO2013,  Wuhan,  China   11  
  • 10/26/2013   COINFO2013,  Wuhan,  China   12  
  • 10/26/2013   COINFO2013,  Wuhan,  China   13  
  • Research  data  management   A  series  of  services  that  an  organiza0on  develops  and   implements  through  ins0tu0onalized  data  policies,   technological  infrastructures,  and  informa0on  standards.       Image  credit:  DataONE  best  prac0ces  hVp://www.dataone.org/best-­‐prac0ces       10/26/2013   COINFO2013,  Wuhan,  China   14  
  • Principle  of     Infrastructure  as  a  Service  (IaaS)     “a  standardized,  highly  automated   offering,  where  compute  resources,   complemented  by  storage  and   networking  capabili0es  are  owned  and   hosted  by  a  service  provider  and  offered   to  customers  on-­‐demand.”     Gartner,  “IT  glossary”,  hVp://www.gartner.com/it-­‐glossary/infrastructure-­‐as-­‐a-­‐service-­‐iaas/.   10/26/2013   COINFO2013,  Wuhan,  China   15  
  • Nature  of  an  infrastructure   § Embeddedness.  Infrastructure  is  sunk  into,  inside  of,  other  structures,   social  arrangements,  and  technologies.   § Transparency.  Infrastructure  does  not  have  to  be  reinvented  each  0me   of  assembled  for  each  task,  but  invisibly  supports  those  tasks.   § Reach  or  scope  beyond  a  single  event  or  a  local  prac=ce.   § Learned  as  part  of  membership.     § Links  with  conven=ons  of  prac=ce.     § Embodiment  of  standards.     § Built  on  an  installed  base.   Star,  S.L.  &  Ruhleder,  K.  (1996).   Steps  toward  an  ecology  of   infrastructure:  Design  and  access  for   large  informa0on  space.  Informa0on   Systems  Research,  7(1):  111-­‐134.     § Becomes  visible  upon  breakdown.   § Is  fixed  in  modular  increments,  not  all  at  once  or  globally.     10/26/2013   COINFO2013,  Wuhan,  China   16  
  • Three  dimensions  of  data  infrastructure   services   Infrastructure   10/26/2013   Networks,  systems,   databases,  sooware   tools,  data  services   COINFO2013,  Wuhan,  China   17  
  • What  is  ins0tu0onaliza0on?   Why  do  you  need  ins0tu0onalize  research  data  management?   How  can  you  ins0tu0onalize  RDM?   Infrastructure   Networks,  systems,   databases,  sooware   tools,  data  services   10/26/2013   COINFO2013,  Wuhan,  China   18  
  • How  much  do  you  know  about  data  and  metadata?   How  does  the  nature  of  data  affect  metadata?   How  does  metadata  affect  data  access,  sharing,   reuse,  and  long-­‐term  preserva0on?   Infrastructure   Networks,  systems,   databases,  sooware   tools,  data  services   10/26/2013   COINFO2013,  Wuhan,  China   19  
  • Infrastructure   Networks,  systems,   databases,  sooware   tools,  data  services   What  is  data  infrastructure  and  Data  infrastructure  services?   Why  do  you  need  to  build  a  data  infrastructure?   What  is  the  key  in  building  a  data  infrastructure?   10/26/2013   COINFO2013,  Wuhan,  China   20  
  • Data  infrastructure  services  and   research  libraries   Research     librarianship   Data   librarianship   Data   science   10/26/2013   Need   more  R&D     Library   IT   Data   infrastructure   services   Data   infrastructure   IT     management   COINFO2013,  Wuhan,  China   21  
  • Building  data  infrastructure  services   •  To  change  in  composi0on  or  structure  (what   we  are/what  we  do)   •  To  change  the  outward  form  or  appearance   (how  we  are  viewed/understood)     •  To  change  in  character  or  condi0on  (how  we   do  it)   hVp://www.arl.org/storage/documents/publica0ons/2012-­‐hrsym-­‐pres-­‐neal-­‐j.pdf     10/26/2013   COINFO2013,  Wuhan,  China   22   22  
  • In  summary…   The  keyword  for  data  infrastructure  services  is:   That  includes:     •  Ins0tu0onalizing  DM   •  Developing  and  implemen0ng  standards  for  DM   •  Developing  and  implemen0ng  data  infrastructure   10/26/2013   COINFO2013,  Wuhan,  China   23