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  • 1. 09.10.13 ALM Principles discussion document - Google Drive https://docs.google.com/a/martinfenner.org/document/d/1TWPzw0El3KGw-CJrA_VJaOmv9nTxfaggkhRRvUzbb_o/edit#heading=h.6q44zvf4m1bs 1/3 Usage and article measures: Community principles The  community  of  stakeholders  engaged  in  measuring  the  usage,  impact,  and  conversations around  scholarly  articles  adopts  these  community  principles. Preamble We  critique  and  measure  research  outputs  for  many  reasons,  to  understand  their  importance, their  relevance  to  a  particular  problem,  and  to  support  discovery  of  new  resources.  New  forms of  data  are  becoming  available  that  can  enrich  our  understanding  of  how  individual  research outputs  are  being  used:  who  is  using  them,  what  are  they  being  used  for,  and  when.  The responsible  application  of  these  new  data  to  the  challenges  of  research  assessment  places requirements  on  those  who  generate,  curate,  and  analyse  these  data. As  core  members  of  the  stakeholder  community  who  provide,  integrate,  analyse,  and  use  this data  we  affirm  our  commitment  to  providing  data  to  support  measuring  and  tracking  the  use  and interest  in  research  outputs  as  as  a  community  resource.  We  call  for  the  creation  of  a  shared community  infrastructure  for  aggregating  and  validating  article-­level  measures  and  for  the adoption  of  the  following  community  principles. Principles 1. Publishers  should  collect  data  on  the  usage  of  all  their  individual  articles. 2. Usage  data  should  be  comprehensive  and  should  include  usage  statistics;;  citations;;  and other  social  media  usage  and  references. 3. Data  should  be  freely  and  openly  available  with  reuse  permitted  to  the  fullest  extent possible.  Data  should  be  made  available  at  the  individual  article  level  and  in  bulk. 4. The  collection,  aggregation,  and  reporting  of  data  should  be  supported  by  best  practices agreed  by  the  community  with  development  towards  standards.  Data  providers  and  data aggregators  will  promote  good  practice  in  the  use  and  the  application  of  this  data. 5. Data  from  different  sources  and  service  providers  should  be  aggregated  via  an infrastructure  supported  by  the  community  to  facilitate  data  validation  and  comparisons  of data.  An  organization  taking  on  this  task  would  be  a  trusted  community  organization outside  the  control  of  any  one  major  stakeholder.
  • 2. 09.10.13 ALM Principles discussion document - Google Drive https://docs.google.com/a/martinfenner.org/document/d/1TWPzw0El3KGw-CJrA_VJaOmv9nTxfaggkhRRvUzbb_o/edit#heading=h.6q44zvf4m1bs 2/3 Issues Insufficiently  ambitious?  Greater  scope  for  data  sources Insufficiently  ambitious?  Greater  scope  for  object  types How  will  this  be  addressed  for  smaller  journals?  What  is  the  commitment  of  the community  to  help? What  is  the  value  of  a  centralised  “service”  or  clearing  house?  Would  a  standards body  be  better?  What  might  the  role  of  Crossref  be? Who  gets  to  decide?  Who  is  the  community?
  • 3. 09.10.13 ALM Principles discussion document - Google Drive https://docs.google.com/a/martinfenner.org/document/d/1TWPzw0El3KGw-CJrA_VJaOmv9nTxfaggkhRRvUzbb_o/edit#heading=h.6q44zvf4m1bs 3/3 I  create  derivative  data  or  value  added  services?  Do  you  mean  I  should  give  them away?  How  to  distinguish    the  “underlying”  data  and  what  is  my  derived  service offering? How  does  this  apply  to  social  media  data?  Should  we  collect  it  together?  What  are the  risks  in  using  one  collection  mechanism?  The  benefits? Who  should  be  driving  this,  and  what  is  the  intention? Is  now  the  right  time  for  this  -­‐  shouldn’t  we  be  focusing  on  pushing  and  delivering on  DORA  and  the  NISO  initiative  first