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Comparing	
  	
  
digital	
  apples	
  to	
  digital	
  apples:	
  
An	
  update	
  on	
  NISO’s	
  Alterna8ve	
  
Assessment	
  Ini8a8ve
Todd Carpenter	
  
Executive Director, NISO
ALA Annual
June 28, 2014June	
  28,	
  2014	
   1	
  
!  Non-­‐profit	
  industry	
  trade	
  associa9on	
  	
  
accredited	
  by	
  ANSI	
  
!  Mission	
  of	
  developing	
  and	
  maintaining	
  technical	
  
standards	
  related	
  to	
  informa9on,	
  documenta9on,	
  
discovery	
  and	
  distribu9on	
  of	
  published	
  materials	
  
and	
  media	
  
!  Volunteer	
  driven	
  organiza9on:	
  400+	
  contributors	
  
spread	
  out	
  across	
  the	
  world	
  
!  Responsible	
  (directly	
  and	
  indirectly)	
  for	
  standards	
  
like	
  ISSN,	
  DOI,	
  Dublin	
  Core	
  metadata,	
  DAISY	
  digital	
  
talking	
  books,	
  OpenURL,	
  MARC	
  records,	
  and	
  ISBN	
  
About	
  	
  
June	
  28,	
  2014	
   2	
  
3	
  June	
  28,	
  2014	
  
What are the
infrastructure elements
of alternative
assessments?	
  
June	
  28,	
  2014	
   4	
  
Basic Definitions

(So we are all talking
about the same thing)	
  
June	
  28,	
  2014	
   5	
  
June	
  28,	
  2014	
   6	
  
Element Identification	
  
June	
  28,	
  2014	
   7	
  
At what granularity?	
  
June	
  28,	
  2014	
   8	
  
How long do we measure?	
  
June	
  28,	
  2014	
   9	
  
June	
  28,	
  2014	
   10	
  
Consistency across providers	
  
Source:	
  Scott	
  Chamberlain,	
  Consuming	
  Article-­‐Level	
  Metrics:
Observations	
  And	
  Lessons	
  From	
  Comparing	
  Aggregator	
  Provider	
  Data,	
  
Information	
  Standards	
  Quarterly,	
  Summer	
  2013,	
  Vol	
  25,	
  Issue	
  2.	
  June	
  28,	
  2014	
   11	
  
TRUST	
  
=	
  
June	
  28,	
  2014	
   12	
  
What is NISO working toward?	
  June	
  28,	
  2014	
   13	
  
June	
  28,	
  2014	
   14	
  
June	
  28,	
  2014	
   15	
  
Steering	
  CommiZee	
  	
  
•  Euan	
  Adie,	
  Altmetric	
  
•  Amy	
  Brand,	
  Harvard	
  University	
  
•  Mike	
  Buschman,	
  Plum	
  Analy9cs	
  
•  Todd	
  Carpenter,	
  NISO	
  
•  Mar9n	
  Fenner,	
  Public	
  Library	
  of	
  Science	
  (PLoS)	
  (Chair)	
  
•  Michael	
  Habib,	
  Reed	
  Elsevier	
  
•  Gregg	
  Gordon,	
  Social	
  Science	
  Research	
  Network	
  (SSRN)	
  
•  William	
  Gunn,	
  Mendeley	
  
•  Nebe	
  Lagace,	
  NISO	
  
•  Jamie	
  Liu,	
  American	
  Chemical	
  Society	
  (ACS)	
  
•  Heather	
  Piwowar,	
  ImpactStory	
  
•  John	
  Sack,	
  HighWire	
  Press	
  
•  Peter	
  Shepherd,	
  Project	
  Counter	
  
•  Chris9ne	
  Stohn,	
  Ex	
  Libris	
  
•  Greg	
  Tananbaum,	
  SPARC	
  (Scholarly	
  Publishing	
  	
  Academic	
  Resources	
  Coali9on)	
  
June	
  28,	
  2014	
   16	
  
Alterna8ve	
  Assessment	
  
Ini8a8ve	
  	
  
	
  
Phase	
  1	
  Mee8ngs	
  
October	
  9,	
  2013	
  	
  -­‐	
  San	
  Francisco,	
  CA	
  
December	
  11,	
  2013	
  -­‐	
  Washington,	
  DC	
  
January	
  23-­‐24	
  -­‐	
  Philadelphia,	
  PA	
  
Round	
  of	
  1-­‐on-­‐1	
  interviews	
  –	
  March/Apr	
  
	
  
Phase	
  1	
  report	
  Published	
  June	
  9,	
  2014
June	
  28,	
  2014	
   17	
  
Mee9ngs’	
  General	
  Format	
  
•  Collocated	
  with	
  other	
  industry	
  mee9ng	
  
•  Morning:	
  lightning	
  talks,	
  post-­‐it	
  brainstorming	
  
•  Aiernoon:	
  discussion	
  groups	
  	
  
– X	
  
– Y	
  
– Z	
  	
  
– Report	
  back/react	
  
•  Live	
  streamed	
  (video	
  recordings	
  are	
  available)	
  
June	
  28,	
  2014	
   18	
  
Mee9ng	
  Lightning	
  Talks	
  
•  Expecta9ons	
  of	
  researchers	
  
•  Exploring	
  disciplinary	
  differences	
  in	
  the	
  use	
  of	
  social	
  media	
  in	
  
scholarly	
  communica9on	
  
•  Altmetrics	
  as	
  part	
  of	
  the	
  services	
  of	
  a	
  large	
  university	
  library	
  
system	
  
•  Deriving	
  altmetrics	
  from	
  annota9on	
  ac9vity	
  
•  Altmetrics	
  for	
  Ins9tu9onal	
  Repositories:	
  Are	
  the	
  metadata	
  
ready?	
  
•  Snowball	
  Metrics:	
  Global	
  Standards	
  for	
  Ins9tu9onal	
  
Benchmarking	
  
•  Interna9onal	
  Standard	
  Name	
  Iden9fier	
  
•  Altmetric.com,	
  Plum	
  Analy9cs,	
  Mendeley	
  reader	
  survey	
  
•  TwiZer	
  Inconsistency	
  
June	
  28,	
  2014	
   19	
  
“Lightning	
  by	
  snowpeak	
  is	
  licensed	
  under	
  CC	
  BY	
  2.0	
  
June	
  28,	
  2014	
   20	
  
SF	
  Mee9ng	
  Discussions	
  
•  Business	
  	
  Use	
  cases	
  
–  Publishers	
  want	
  to	
  serve	
  authors,	
  make	
  money	
  
–  People	
  don’t	
  value	
  a	
  standard,	
  they	
  value	
  something	
  that	
  helps	
  them	
  
–  …	
  Couldn’t	
  iden9fy	
  a	
  logical	
  standard	
  need	
  that	
  actors	
  in	
  the	
  space	
  would	
  value,	
  
and	
  best	
  prac9ces	
  are	
  of	
  interest	
  
•  Quality	
  	
  Data	
  science	
  
–  Themes:	
  context,	
  valida9on,	
  provenance,	
  quality,	
  descrip9on	
  	
  metadata	
  
–  We'll	
  never	
  get	
  to	
  the	
  point	
  where	
  assessment	
  can	
  be	
  done	
  without	
  a	
  human	
  
in	
  the	
  loop,	
  but	
  discovery	
  and	
  recommenda9on	
  can	
  
•  Defini9ons	
  
–  Define	
  “ALM”	
  and	
  “Altmetrics”	
  
–  Map	
  the	
  landscape	
  
–  We'll	
  never	
  get	
  to	
  the	
  point	
  where	
  assessment	
  can	
  be	
  done	
  without	
  a	
  human	
  in	
  
the	
  loop,	
  but	
  discovery	
  and	
  recommenda9on	
  can	
  
June	
  28,	
  2014	
   21	
  
DC	
  Mee9ng	
  Discussions	
  
•  Business	
  and	
  Use	
  Cases	
  
•  Discovery	
  
–  metrics	
  only	
  get	
  generated	
  if	
  material	
  is	
  discovered	
  
•  Qualita9ve	
  vs.	
  Quan9ta9ve	
  
•  Iden9fying	
  Stakeholders	
  and	
  their	
  Values	
  
–  stakeholders	
  in	
  outcomes	
  /	
  stakeholders	
  in	
  process	
  of	
  crea9ng	
  metrics	
  
–  shared	
  values	
  but	
  tensions	
  
–  branding	
  
•  Defini9ons/Defining	
  Impact	
  
–  metrics	
  and	
  analyses	
  
–  what	
  led	
  to	
  success	
  of	
  cita9on?	
  
–  how	
  to	
  be	
  certain	
  we	
  are	
  measuring	
  the	
  right	
  things	
  
•  Future	
  Proofing	
  
–  what	
  won't	
  change	
  
–  impact	
  -­‐	
  hard	
  to	
  establish	
  across	
  disciplines	
  
June	
  28,	
  2014	
   22	
  
Philly	
  Mee9ng	
  Discussions	
  
•  Defini9ons	
  
–  Define	
  life	
  cycle	
  of	
  scholarly	
  output	
  and	
  associated	
  metrics	
  
–  Qualita9ve	
  versus	
  Quan9ta9ve	
  aspects	
  -­‐	
  what	
  is	
  possible	
  to	
  define	
  here	
  
–  Consider	
  other	
  aspects	
  of	
  these	
  data	
  collec9ons	
  
•  Standards	
  
–  Develop	
  defini9ons	
  (what	
  is	
  a	
  download?	
  what	
  is	
  a	
  view?)	
  
–  Differen9ate	
  between	
  scholarly	
  impact	
  versus	
  popular/social	
  use	
  
–  Define	
  sources/characteris9cs	
  for	
  metrics	
  (social,	
  commercial,	
  scholarly)	
  
•  Data	
  Integrity	
  
–  Counter	
  biases/gaming	
  
–  Associa9on	
  with	
  credible	
  en99es	
  -­‐	
  e.g.	
  ORCID	
  ID	
  v.	
  gmail	
  account	
  
–  Reproduceability	
  is	
  key	
  
–  Everyone	
  needs	
  to	
  be	
  at	
  the	
  table	
  to	
  establish	
  overall	
  credibility	
  
•  Use	
  cases	
  (3X)	
  
June	
  28,	
  2014	
   23	
  
30	
  One-­‐on-­‐One	
  Interviews	
  
June	
  28,	
  2014	
   24	
  
Poten8al	
  work	
  themes	
  
Defini9ons	
  
Applica9on	
  to	
  types	
  of	
  research	
  outputs	
  
Discovery	
  implica9ons	
  
Research	
  evalua9on	
  
Data	
  quality	
  and	
  gaming	
  
Grouping,	
  aggrega9ng,	
  and	
  granularity	
  
Context	
  	
  
Adop9on	
  
June	
  28,	
  2014	
   25	
  
Poten9al	
  work	
  themes	
  
Defini8ons	
  
Applica9on	
  to	
  types	
  of	
  research	
  outputs	
  
Discovery	
  implica9ons	
  
Research	
  evalua9on	
  
Data	
  quality	
  and	
  gaming	
  
Grouping,	
  aggrega9ng,	
  and	
  granularity	
  
Context	
  	
  
Adop9on	
  
June	
  28,	
  2014	
   26	
  
Poten9al	
  work	
  themes	
  
Defini9ons	
  
Applica8on	
  to	
  types	
  of	
  research	
  outputs	
  
Discovery	
  implica9ons	
  
Research	
  evalua9on	
  
Data	
  quality	
  and	
  gaming	
  
Grouping,	
  aggrega9ng,	
  and	
  granularity	
  
Context	
  	
  
Adop9on	
  
June	
  28,	
  2014	
   27	
  
Poten9al	
  work	
  themes	
  
Defini9ons	
  
Applica9on	
  to	
  types	
  of	
  research	
  outputs	
  
Discovery	
  implica8ons	
  
Research	
  evalua9on	
  
Data	
  quality	
  and	
  gaming	
  
Grouping,	
  aggrega9ng,	
  and	
  granularity	
  
Context	
  	
  
Adop9on	
  
June	
  28,	
  2014	
   28	
  
Poten9al	
  work	
  themes	
  
Defini9ons	
  
Applica9on	
  to	
  types	
  of	
  research	
  outputs	
  
Discovery	
  implica9ons	
  
Research	
  evalua8on	
  
Data	
  quality	
  and	
  gaming	
  
Grouping,	
  aggrega9ng,	
  and	
  granularity	
  
Context	
  	
  
Adop9on	
  
June	
  28,	
  2014	
   29	
  
Poten9al	
  work	
  themes	
  
Defini9ons	
  
Applica9on	
  to	
  types	
  of	
  research	
  outputs	
  
Discovery	
  implica9ons	
  
Research	
  evalua9on	
  
Data	
  quality	
  and	
  gaming	
  
Grouping,	
  aggrega9ng,	
  and	
  granularity	
  
Context	
  	
  
Adop9on	
  
June	
  28,	
  2014	
   30	
  
Poten9al	
  work	
  themes	
  
Defini9ons	
  
Applica9on	
  to	
  types	
  of	
  research	
  outputs	
  
Discovery	
  implica9ons	
  
Research	
  evalua9on	
  
Data	
  quality	
  and	
  gaming	
  
Grouping,	
  aggrega8ng,	
  and	
  granularity	
  
Context	
  	
  
Adop9on	
  
June	
  28,	
  2014	
   31	
  
Poten9al	
  work	
  themes	
  
Defini9ons	
  
Applica9on	
  to	
  types	
  of	
  research	
  outputs	
  
Discovery	
  implica9ons	
  
Research	
  evalua9on	
  
Data	
  quality	
  and	
  gaming	
  
Grouping,	
  aggrega9ng,	
  and	
  granularity	
  
Context	
  	
  
Adop9on	
  
June	
  28,	
  2014	
   32	
  
Poten9al	
  work	
  themes	
  
Defini9ons	
  
Applica9on	
  to	
  types	
  of	
  research	
  outputs	
  
Discovery	
  implica9ons	
  
Research	
  evalua9on	
  
Data	
  quality	
  and	
  gaming	
  
Grouping,	
  aggrega9ng,	
  and	
  granularity	
  
Context	
  	
  
Adop8on	
  	
  Promo8on	
  
June	
  28,	
  2014	
   33	
  
Alterna8ve	
  Assessment	
  
Ini8a8ve	
  	
  
	
  
Phase	
  2	
  
Presenta8ons	
  of	
  Phase	
  1	
  report	
  (June	
  2014)	
  
Priori8za8on	
  Effort	
  (June	
  -­‐	
  Aug,	
  2014)	
  
Project	
  approval	
  (Sept	
  2014)	
  
Working	
  group	
  forma8on	
  (Oct	
  2014)	
  
Consensus	
  Development	
  (Nov	
  2014	
  -­‐	
  Dec	
  2015)	
  
Trial	
  Use	
  Period	
  (Dec	
  15	
  -­‐	
  Mar	
  16)	
  
Publica8on	
  of	
  final	
  recommenda8ons	
  (Jun	
  16) 	
  June	
  28,	
  2014	
   34	
  
We	
  all	
  want	
  our	
  own	
  
June	
  28,	
  2014	
   35	
  
For more information:
www.niso.org/topics/tl/altmetrics_initiative/
June	
  28,	
  2014	
   36	
  
Questions?
Todd Carpenter
Executive Director
tcarpenter@niso.org
National Information Standards Organization (NISO)
3600 Clipper Mill Road, Suite 302
Baltimore, MD 21211 USA
+1 (301) 654-2512
www.niso.org
June	
  28,	
  2014	
   37	
  

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Carpenter Talk at Elsevier Booth During ALA Annual about NISO alternative assessment project

  • 1. Comparing     digital  apples  to  digital  apples:   An  update  on  NISO’s  Alterna8ve   Assessment  Ini8a8ve Todd Carpenter   Executive Director, NISO ALA Annual June 28, 2014June  28,  2014   1  
  • 2. !  Non-­‐profit  industry  trade  associa9on     accredited  by  ANSI   !  Mission  of  developing  and  maintaining  technical   standards  related  to  informa9on,  documenta9on,   discovery  and  distribu9on  of  published  materials   and  media   !  Volunteer  driven  organiza9on:  400+  contributors   spread  out  across  the  world   !  Responsible  (directly  and  indirectly)  for  standards   like  ISSN,  DOI,  Dublin  Core  metadata,  DAISY  digital   talking  books,  OpenURL,  MARC  records,  and  ISBN   About     June  28,  2014   2  
  • 3. 3  June  28,  2014  
  • 4. What are the infrastructure elements of alternative assessments?   June  28,  2014   4  
  • 5. Basic Definitions (So we are all talking about the same thing)   June  28,  2014   5  
  • 7. Element Identification   June  28,  2014   7  
  • 8. At what granularity?   June  28,  2014   8  
  • 9. How long do we measure?   June  28,  2014   9  
  • 10. June  28,  2014   10  
  • 11. Consistency across providers   Source:  Scott  Chamberlain,  Consuming  Article-­‐Level  Metrics: Observations  And  Lessons  From  Comparing  Aggregator  Provider  Data,   Information  Standards  Quarterly,  Summer  2013,  Vol  25,  Issue  2.  June  28,  2014   11  
  • 12. TRUST   =   June  28,  2014   12  
  • 13. What is NISO working toward?  June  28,  2014   13  
  • 14. June  28,  2014   14  
  • 15. June  28,  2014   15  
  • 16. Steering  CommiZee     •  Euan  Adie,  Altmetric   •  Amy  Brand,  Harvard  University   •  Mike  Buschman,  Plum  Analy9cs   •  Todd  Carpenter,  NISO   •  Mar9n  Fenner,  Public  Library  of  Science  (PLoS)  (Chair)   •  Michael  Habib,  Reed  Elsevier   •  Gregg  Gordon,  Social  Science  Research  Network  (SSRN)   •  William  Gunn,  Mendeley   •  Nebe  Lagace,  NISO   •  Jamie  Liu,  American  Chemical  Society  (ACS)   •  Heather  Piwowar,  ImpactStory   •  John  Sack,  HighWire  Press   •  Peter  Shepherd,  Project  Counter   •  Chris9ne  Stohn,  Ex  Libris   •  Greg  Tananbaum,  SPARC  (Scholarly  Publishing    Academic  Resources  Coali9on)   June  28,  2014   16  
  • 17. Alterna8ve  Assessment   Ini8a8ve       Phase  1  Mee8ngs   October  9,  2013    -­‐  San  Francisco,  CA   December  11,  2013  -­‐  Washington,  DC   January  23-­‐24  -­‐  Philadelphia,  PA   Round  of  1-­‐on-­‐1  interviews  –  March/Apr     Phase  1  report  Published  June  9,  2014 June  28,  2014   17  
  • 18. Mee9ngs’  General  Format   •  Collocated  with  other  industry  mee9ng   •  Morning:  lightning  talks,  post-­‐it  brainstorming   •  Aiernoon:  discussion  groups     – X   – Y   – Z     – Report  back/react   •  Live  streamed  (video  recordings  are  available)   June  28,  2014   18  
  • 19. Mee9ng  Lightning  Talks   •  Expecta9ons  of  researchers   •  Exploring  disciplinary  differences  in  the  use  of  social  media  in   scholarly  communica9on   •  Altmetrics  as  part  of  the  services  of  a  large  university  library   system   •  Deriving  altmetrics  from  annota9on  ac9vity   •  Altmetrics  for  Ins9tu9onal  Repositories:  Are  the  metadata   ready?   •  Snowball  Metrics:  Global  Standards  for  Ins9tu9onal   Benchmarking   •  Interna9onal  Standard  Name  Iden9fier   •  Altmetric.com,  Plum  Analy9cs,  Mendeley  reader  survey   •  TwiZer  Inconsistency   June  28,  2014   19   “Lightning  by  snowpeak  is  licensed  under  CC  BY  2.0  
  • 20. June  28,  2014   20  
  • 21. SF  Mee9ng  Discussions   •  Business    Use  cases   –  Publishers  want  to  serve  authors,  make  money   –  People  don’t  value  a  standard,  they  value  something  that  helps  them   –  …  Couldn’t  iden9fy  a  logical  standard  need  that  actors  in  the  space  would  value,   and  best  prac9ces  are  of  interest   •  Quality    Data  science   –  Themes:  context,  valida9on,  provenance,  quality,  descrip9on    metadata   –  We'll  never  get  to  the  point  where  assessment  can  be  done  without  a  human   in  the  loop,  but  discovery  and  recommenda9on  can   •  Defini9ons   –  Define  “ALM”  and  “Altmetrics”   –  Map  the  landscape   –  We'll  never  get  to  the  point  where  assessment  can  be  done  without  a  human  in   the  loop,  but  discovery  and  recommenda9on  can   June  28,  2014   21  
  • 22. DC  Mee9ng  Discussions   •  Business  and  Use  Cases   •  Discovery   –  metrics  only  get  generated  if  material  is  discovered   •  Qualita9ve  vs.  Quan9ta9ve   •  Iden9fying  Stakeholders  and  their  Values   –  stakeholders  in  outcomes  /  stakeholders  in  process  of  crea9ng  metrics   –  shared  values  but  tensions   –  branding   •  Defini9ons/Defining  Impact   –  metrics  and  analyses   –  what  led  to  success  of  cita9on?   –  how  to  be  certain  we  are  measuring  the  right  things   •  Future  Proofing   –  what  won't  change   –  impact  -­‐  hard  to  establish  across  disciplines   June  28,  2014   22  
  • 23. Philly  Mee9ng  Discussions   •  Defini9ons   –  Define  life  cycle  of  scholarly  output  and  associated  metrics   –  Qualita9ve  versus  Quan9ta9ve  aspects  -­‐  what  is  possible  to  define  here   –  Consider  other  aspects  of  these  data  collec9ons   •  Standards   –  Develop  defini9ons  (what  is  a  download?  what  is  a  view?)   –  Differen9ate  between  scholarly  impact  versus  popular/social  use   –  Define  sources/characteris9cs  for  metrics  (social,  commercial,  scholarly)   •  Data  Integrity   –  Counter  biases/gaming   –  Associa9on  with  credible  en99es  -­‐  e.g.  ORCID  ID  v.  gmail  account   –  Reproduceability  is  key   –  Everyone  needs  to  be  at  the  table  to  establish  overall  credibility   •  Use  cases  (3X)   June  28,  2014   23  
  • 24. 30  One-­‐on-­‐One  Interviews   June  28,  2014   24  
  • 25. Poten8al  work  themes   Defini9ons   Applica9on  to  types  of  research  outputs   Discovery  implica9ons   Research  evalua9on   Data  quality  and  gaming   Grouping,  aggrega9ng,  and  granularity   Context     Adop9on   June  28,  2014   25  
  • 26. Poten9al  work  themes   Defini8ons   Applica9on  to  types  of  research  outputs   Discovery  implica9ons   Research  evalua9on   Data  quality  and  gaming   Grouping,  aggrega9ng,  and  granularity   Context     Adop9on   June  28,  2014   26  
  • 27. Poten9al  work  themes   Defini9ons   Applica8on  to  types  of  research  outputs   Discovery  implica9ons   Research  evalua9on   Data  quality  and  gaming   Grouping,  aggrega9ng,  and  granularity   Context     Adop9on   June  28,  2014   27  
  • 28. Poten9al  work  themes   Defini9ons   Applica9on  to  types  of  research  outputs   Discovery  implica8ons   Research  evalua9on   Data  quality  and  gaming   Grouping,  aggrega9ng,  and  granularity   Context     Adop9on   June  28,  2014   28  
  • 29. Poten9al  work  themes   Defini9ons   Applica9on  to  types  of  research  outputs   Discovery  implica9ons   Research  evalua8on   Data  quality  and  gaming   Grouping,  aggrega9ng,  and  granularity   Context     Adop9on   June  28,  2014   29  
  • 30. Poten9al  work  themes   Defini9ons   Applica9on  to  types  of  research  outputs   Discovery  implica9ons   Research  evalua9on   Data  quality  and  gaming   Grouping,  aggrega9ng,  and  granularity   Context     Adop9on   June  28,  2014   30  
  • 31. Poten9al  work  themes   Defini9ons   Applica9on  to  types  of  research  outputs   Discovery  implica9ons   Research  evalua9on   Data  quality  and  gaming   Grouping,  aggrega8ng,  and  granularity   Context     Adop9on   June  28,  2014   31  
  • 32. Poten9al  work  themes   Defini9ons   Applica9on  to  types  of  research  outputs   Discovery  implica9ons   Research  evalua9on   Data  quality  and  gaming   Grouping,  aggrega9ng,  and  granularity   Context     Adop9on   June  28,  2014   32  
  • 33. Poten9al  work  themes   Defini9ons   Applica9on  to  types  of  research  outputs   Discovery  implica9ons   Research  evalua9on   Data  quality  and  gaming   Grouping,  aggrega9ng,  and  granularity   Context     Adop8on    Promo8on   June  28,  2014   33  
  • 34. Alterna8ve  Assessment   Ini8a8ve       Phase  2   Presenta8ons  of  Phase  1  report  (June  2014)   Priori8za8on  Effort  (June  -­‐  Aug,  2014)   Project  approval  (Sept  2014)   Working  group  forma8on  (Oct  2014)   Consensus  Development  (Nov  2014  -­‐  Dec  2015)   Trial  Use  Period  (Dec  15  -­‐  Mar  16)   Publica8on  of  final  recommenda8ons  (Jun  16)  June  28,  2014   34  
  • 35. We  all  want  our  own   June  28,  2014   35  
  • 37. Questions? Todd Carpenter Executive Director tcarpenter@niso.org National Information Standards Organization (NISO) 3600 Clipper Mill Road, Suite 302 Baltimore, MD 21211 USA +1 (301) 654-2512 www.niso.org June  28,  2014   37