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Making	
  Research	
  Data	
  	
  
Discoverable	
  and	
  Usable	
  	
  
(It’s	
  the	
  metadata,	
  stupid!)	
  
Anita	
  de	
  Waard	
  
VP	
  Research	
  Data	
  Collabora7ons	
  
a.dewaard@elsevier.com	
  
	
  
	
  
	
  
h=p://researchdata.elsevier.com/	
  	
  	
  
Research	
  data	
  is	
  the	
  ‘new	
  hotness’…	
  	
  
§  Share	
  research	
  outputs	
  
§  Demonstrate	
  impact	
  to	
  public	
  
§  Data	
  availability	
  drives	
  growth	
  
§  Demonstrate	
  impact	
  	
  
§  Guarantee	
  permanence,	
  discoverability	
  	
  
§  Avoid	
  fraud	
  	
  
§  Generate,	
  track	
  outputs	
  
§  Comply	
  with	
  mandates	
  
§  Ensure	
  availability	
  
§  Archive,	
  track,	
  curate	
  
§  Support	
  researcher/ins7tu7on	
  
§  Archive	
  	
  
§  Add	
  cura7on	
  
§  Allow	
  reuse	
  	
  	
  
Todd	
  Vision,	
  DataDryad,	
  OAI8,	
  6/23/13:	
  	
  
“We	
  need	
  to	
  find	
  a	
  way	
  to	
  keep	
  Dryad	
  funded,	
  and	
  would	
  
love	
  to	
  hear	
  your	
  ideas	
  about	
  doing	
  that.”	
  
Phil	
  Bourne,	
  Associate	
  Vice	
  Chancellor,	
  UCSD,	
  4/13:	
  	
  
“We	
  are	
  thinking	
  about	
  the	
  university	
  as	
  a	
  digital	
  
enterprise.”	
  
Mike	
  Huerta,	
  Ass.	
  Director	
  NLM	
  O	
  of	
  Health	
  Info	
  at	
  NIH,	
  6/13:	
  	
  
“Today,	
  the	
  major	
  public	
  product	
  of	
  science	
  are	
  concepts,	
  wri=en	
  
down	
  in	
  papers.	
  But	
  tomorrow,	
  data	
  will	
  be	
  the	
  main	
  product	
  of	
  
science….	
  We	
  will	
  require	
  scien7sts	
  to	
  track	
  and	
  share	
  their	
  data	
  as	
  
least	
  as	
  well,	
  if	
  not	
  be=er,	
  than	
  they	
  are	
  sharing	
  their	
  ideas	
  today.”	
  	
  
Mara	
  Saule,	
  Dean	
  University	
  Libraries/CIO,	
  UVM,	
  5/13:	
  	
  
“We	
  need	
  to	
  do	
  something	
  about	
  data.”	
  
§  Derive	
  credit	
  
§  Comply	
  with	
  mandates	
  
§  Discover	
  and	
  use	
  	
  
§  Cite/acknowledge	
  
Gov	
  
Funding	
  
bodies	
  
University	
  
management	
  	
  
Researchers	
  
Librarians	
  
Data	
  	
  
Repositories	
  
Nathan	
  Urban,	
  PI	
  Urban	
  Lab,	
  CMU,	
  3/13:	
  	
  
“If	
  we	
  can	
  share	
  our	
  data,	
  we	
  can	
  write	
  a	
  paper	
  that	
  will	
  
knock	
  everybody’s	
  socks	
  off!”	
  
Roles	
  and	
  needs	
  wrt	
  Research	
  Data:	
  
Barbara	
  Ransom,	
  NSF	
  Program	
  Director	
  Earth	
  Sciences,	
  2/13:	
  	
  
“We’re	
  not	
  going	
  to	
  spend	
  any	
  more	
  money	
  for	
  you	
  to	
  go	
  out	
  
and	
  get	
  more	
  data!	
  We	
  want	
  you	
  first	
  to	
  show	
  us	
  how	
  you’re	
  
going	
  to	
  use	
  all	
  the	
  data	
  we	
  paid	
  y’all	
  to	
  collect	
  in	
  the	
  past!”	
  
Research	
  data	
  management	
  today:	
  
Using	
  an7bodies	
  
and	
  squishy	
  bits	
  	
  	
  
Grad	
  Students	
  experiment	
  
and	
  enter	
  details	
  into	
  their	
  
lab	
  notebook.	
  	
  
The	
  PI	
  then	
  tries	
  to	
  make	
  	
  
sense	
  of	
  their	
  slides,	
  
and	
  writes	
  a	
  paper.	
  	
  	
  
End	
  of	
  story.	
  	
  
Prepare	
  
Observe	
  
Analyze	
  
Ponder	
  
Communicate	
  
Prepare	
  
Observe	
  
Analyze	
  
Ponder	
  
Communicate	
  
Research	
  today	
  (in	
  biology)	
  is	
  o^en	
  
quite	
  insular:	
  	
  
But	
  life	
  is	
  VERY	
  complicated:	
  
h=p://en.wikipedia.org/wiki/File:Duck_of_Vaucanson.jpg	
  
•  Interspecies	
  variability:	
  A	
  specimen	
  is	
  not	
  a	
  species	
  
•  Gene	
  expression	
  variability:	
  Knowing	
  genes	
  is	
  not	
  	
  
knowing	
  how	
  they	
  are	
  expressed	
  
•  Microbiome:	
  An	
  animal	
  is	
  an	
  ecosystem	
  
•  Systems	
  biology:	
  A	
  whole	
  is	
  more	
  than	
  the	
  sum	
  of	
  its	
  
parts	
  	
  
	
  
Reduc7onist	
  science	
  	
  
does	
  not	
  work	
  
for	
  living	
  systems!	
  
What	
  if	
  the	
  data	
  were	
  connected?	
  
Prepare	
  
Analyze	
   Communicate	
  
Prepare	
  
Analyze	
   Communicate	
  
Observa7ons	
  
Observa7ons	
  
Observa7ons	
  
Across	
  labs,	
  experiments:	
  
track	
  reagents	
  and	
  how	
  
they	
  are	
  used	
  
Prepare	
  
Analyze	
   Communicate	
  
Prepare	
  
Analyze	
   Communicate	
  
Observa7ons	
  
Observa7ons	
  
Observa7ons	
  
Compare	
  outcome	
  of	
  
interac7ons	
  with	
  these	
  
en77es	
  
What	
  if	
  the	
  data	
  were	
  connected?	
  
Prepare	
  
Analyze	
   Communicate	
  
Prepare	
  
Analyze	
  Communicate	
  
Observa7ons	
  
Observa7ons	
  
Observa7ons	
  
Build	
  a	
  ‘virtual	
  reagent	
  
spectrogram’	
  by	
  comparing	
  	
  
how	
  different	
  en77es	
  	
  
interacted	
  in	
  different	
  
experiments	
   Think	
  
What	
  if	
  the	
  data	
  were	
  connected?	
  
Where	
  research	
  data	
  goes	
  now:	
  
>	
  50	
  My	
  Papers	
  
2	
  M	
  scien7sts	
  
2	
  My	
  papers/year	
  
Majority	
  of	
  data	
  
(90%?)	
  	
  is	
  stored	
  	
  
on	
  local	
  hard	
  drives	
  
Dryad:	
  
7,631	
  files	
  
	
  
Dataverse:	
  
0.6	
  My	
  
	
  
	
  
Ins7tu7onal	
  
Repositories	
  
	
  
Some	
  data	
  	
  
(8%?)	
  stored	
  in	
  large,	
  	
  
generic	
  data	
  	
  
repositories	
  
MiRB:	
  	
  	
  
25k	
  
PetDB:	
  	
  
1,5	
  k	
  
TAIR:	
  	
  	
  
72,1	
  k	
  
PDB:	
  	
  	
  
88,3	
  k	
  	
  
SedDB:	
  	
  
0.6	
  k	
  
A	
  small	
  por7on	
  of	
  data	
  	
  
(1-­‐2%?)	
  stored	
  in	
  small,	
  	
  
topic-­‐focused	
  
data	
  repositories	
  
1.	
  How	
  do	
  we	
  get	
  
researchers	
  to	
  curate,	
  store	
  
and	
  share	
  their	
  data?	
  	
  
2.	
  How	
  do	
  we	
  ensure	
  
long-­‐term	
  
sustainability	
  for	
  high-­‐
end	
  repositories?	
  
3.	
  What	
  role	
  do	
  
libraries/
ins7tu7ons	
  play?	
  	
  
de	
  Waard,	
  A.,	
  Burton,	
  S.	
  et	
  al.,	
  2013	
  
1.1.	
  An	
  a=empt	
  to	
  get	
  researchers	
  to	
  curate	
  
(but	
  only	
  parZally	
  share!)	
  their	
  data:	
  	
  
•  In	
  220	
  publica7ons	
  only	
  40%	
  of	
  an7bodies,	
  40%	
  of	
  cell	
  lines	
  and	
  25%	
  of	
  
constructs	
  can	
  be	
  manually	
  iden7fied	
  (Vasilevsky	
  et	
  al,	
  submi=ed)	
  
	
  
•  Proposal	
  (with	
  NIH/NIF	
  and	
  Force11	
  Group):	
  	
  
–  Adding	
  minimal	
  data	
  standards	
  
–  Tool	
  extracts	
  likely	
  reagents	
  /	
  resources	
  
–  User	
  interface	
  asks	
  author	
  to	
  confirm	
  or	
  select	
  
1.2.	
  What	
  to	
  do	
  in	
  the	
  mean7me?	
  	
  
49	
  publica7ons	
  193	
  publica7ons	
   76	
  publica7ons	
   214	
  publica7ons	
   210	
  publica7
Pilot	
  project	
  with	
  IEDA:	
  	
  
– Build	
  a	
  database	
  for	
  lunar	
  geochemistry	
  
– Write	
  joint	
  report	
  on	
  building	
  	
  
repository,	
  cura7on,	
  costs	
  and	
  	
  
challenges	
  
2.2	
  How	
  can	
  research	
  databases	
  
become	
  long-­‐term	
  sustainable?	
  	
  
With	
  WDS/RDA	
  WG:	
  	
  
•  Planning	
  survey	
  of	
  cost	
  recovery	
  models	
  for	
  research	
  
databases	
  
•  Input/inspira7on:	
  ICPSR	
  Sloane-­‐funded	
  project	
  
Sustaining	
  Domain	
  Repositories	
  for	
  Digital	
  Data’	
  
•  Developing	
  overarching	
  funding	
  model:	
  
2.2	
  Cost	
  recovery	
  ques7onnaire:	
  
Private
store	

Data producer	

or sponsor	

Access	

 Closed	

Flow of funds	

Data
publication	

Public	

Service	

Collaboration
Conclave	

	

Limited	

Subscription
content	

	

	

	

Commercial
overlay	

	

Limited	

 Academic Use/Limited	

Data user	

Flow of funds	

Examples	

 ICSPR,	

CERN-
LHC	

KEGG
GeoFacets	

Reaxys	

DRAFT - CC-BY-NC 2013, Todd Vision & Anita de Waard	

Many small
operations, e.g.
try-db.org,	

plhdb.org	

Dryad,	

arXiv,	

PDB	

Commercial
and
institutional
storage	


&	

or	

2.3.	
  A	
  first	
  stab	
  at	
  a	
  model:	
  
3.1.	
  Where	
  do	
  ins7tu7onal	
  repositories	
  fit	
  in?	
  	
  
Repository	
   Advantages	
  	
   Disadvantages	
  
Local	
  data	
  
repository	
  
Easy!	
  No	
  one	
  steals	
  
your	
  data.	
  	
  
No	
  one	
  sees	
  it.	
  	
  
Not	
  compliant	
  with	
  
requirements	
  
Generic	
  data	
  
repository	
  
Not	
  very	
  hard	
  to	
  do.	
  
Have	
  complied!	
  
Data	
  can’t	
  be	
  easily	
  
reused.	
  Credit?	
  
Ins7tu7onal	
  
Repository	
  
	
  
Can	
  use	
  exis7ng	
  IR?	
  
Tracking	
  and	
  
compliance	
  checks.	
  	
  	
  
Data	
  can’t	
  easily	
  be	
  
reused.	
  Credit?	
  
Domain-­‐specific	
  
data	
  repository	
  
Data	
  can	
  be	
  reused.	
  
Credit!	
  	
  
Lot	
  of	
  work	
  for	
  
curators.	
  Long-­‐term	
  
sustainable?	
  	
  
Effort,	
  Reuse,	
  Credit,	
  Compliance	
  
Habit,	
  Ease,	
  Privacy,	
  Control	
  	
  
	
  Higher	
  quality	
  metadata	
  
Funding	
  Agency:	
   University:	
  
Collaborators:	
  Domain	
  of	
  study:	
  Domain-­‐Specific	
  	
  
Data	
  Repository	
  
Local	
  	
  
Data	
  Repository	
  
Ins7tu7onal	
  	
  
Data	
  Repository	
  
Generic	
  
	
  Data	
  Repository	
  
AND	
  
THEY	
  ALL	
  
WANT	
  
DIFFERENT	
  
METADATA!!!!	
  
3.2.	
  The	
  poor	
  researcher:	
  	
  
Domain	
  repository	
  
3.3.	
  Possible	
  pilot	
  project:	
  
Domain	
  repository	
  
IR	
  Data	
  
Metadata:	
  
What	
  data	
  
was	
  stored/
viewed	
  
Meta
data	
  
Metadata:	
  
What	
  data	
  
was	
  stored/
viewed	
   •  Interview	
  ins7tu7ons	
  
•  Normalize	
  repor7ng	
  data	
  
•  Talking	
  to	
  	
  
•  IQSS,	
  Harvard	
  
•  ICPSR,	
  U	
  Mich	
  
•  DataDryad,	
  UNC	
  
•  Pangaea,	
  Germany	
  
3.4.	
  Ins7tu7onal	
  Pilot	
  study:	
  	
  
•  Planning	
  series	
  of	
  interviews	
  at	
  key	
  ins7tu7ons:	
  	
  
–  What	
  role	
  do	
  libraries/ins7tu7ons	
  play	
  wrt	
  research	
  data	
  
management?	
  	
  
–  What	
  tools/metadata	
  standards	
  are	
  used?	
  
–  What	
  aspects	
  of	
  data	
  deposi7on	
  is	
  the	
  Research	
  Office/
IR/Ins7tu7on	
  interested	
  in?	
  	
  
–  How	
  does	
  this	
  compare	
  with	
  what	
  scien7sts	
  want	
  and	
  do	
  
in	
  their	
  labs?	
  	
  	
  
•  Outcomes:	
  	
  
–  Share	
  knowledge	
  (within	
  ins7tu7on);	
  	
  
–  Write	
  joint	
  report	
  (anonymised)	
  	
  
–  Establish	
  joint	
  plan	
  of	
  ac7on	
  
Elsevier	
  Research	
  Data	
  Services:	
  	
  
•  2013/2013:	
  Series	
  of	
  pilots,	
  reviews,	
  and	
  reports:	
  
-  With	
  CMU:	
  Data/metadata	
  entry	
  and	
  sharing	
  
-  With	
  IEDA:	
  Repository	
  crea7on:	
  feasibility	
  study	
  &	
  report	
  
-  With	
  RDA:	
  Cost	
  of	
  Data	
  Repositories	
  ques7onnaire	
  
-  With	
  series	
  of	
  ins7tutes:	
  Interviews	
  re.	
  role	
  of	
  ins7tu7on	
  
•  Main	
  ques7ons:	
  	
  
-  What	
  are	
  key	
  needs?	
  	
  
-  Can	
  we	
  play	
  a	
  role:	
  skillsets,	
  partnerships?	
  	
  
-  Is	
  there	
  a	
  (transparent)	
  business	
  model	
  for	
  this?	
  
•  Principles:	
  	
  
–  Collabora7on	
  is	
  tailored	
  to	
  partner’s	
  needs,	
  using	
  local	
  resources;	
  	
  
–  Collabora7on	
  plan	
  is	
  MoU/Service-­‐Level	
  Agreement;	
  
–  At	
  all	
  7mes,	
  all	
  data,	
  reports	
  and	
  so^ware	
  are	
  open	
  and	
  shared.	
  	
  
In	
  summary:	
  	
  
1.  If	
  researchers	
  start	
  to	
  curate	
  and	
  share	
  their	
  
data…	
  
2.  And	
  research	
  databases	
  become	
  long-­‐term	
  
sustainable…	
  
3.  And	
  libraries,	
  data	
  repositories	
  and	
  grid	
  
infrastructures	
  start	
  to	
  work	
  together…	
  	
  
We	
  might	
  enable	
  a	
  knowledge	
  infrastructure	
  that	
  
allows	
  us	
  to	
  jointly	
  tackle	
  the	
  quesZons	
  of	
  life!	
  	
  
	
  
Many	
  ques7ons	
  remain:	
  
?  What	
  carrots	
  	
  and	
  s7cks	
  will	
  make	
  researchers	
  
share	
  their	
  data?	
  	
  
?  How	
  do	
  we	
  create	
  interoperable	
  metadata	
  
layers?	
  	
  
?  What	
  role	
  would	
  the	
  ins7tu7on/library	
  play?	
  	
  
?  What	
  are	
  sustainable	
  models,	
  moving	
  
forward?	
  	
  
?  Is	
  there	
  a	
  place	
  for	
  publishers,	
  in	
  all	
  this?	
  	
  
Thank	
  you!	
  
Collabora7ons	
  and	
  discussions	
  gratefully	
  acknowledged:	
  	
  
•  CMU:	
  Nathan	
  Urban,	
  Shreejoy	
  Tripathy,	
  Shawn	
  Burton,	
  Ed	
  Hovy	
  
•  UCSD:	
  Phil	
  Bourne,	
  Brian	
  Shoe=lander,	
  David	
  Minor,	
  Declan	
  Fleming,	
  
Ilya	
  Zaslavsky	
  
•  NIF:	
  Maryann	
  Martone,	
  Anita	
  Bandrowski	
  
•  MSU:	
  Brian	
  Bothner	
  
•  OHSU:	
  Melissa	
  Haendel,	
  Nicole	
  Vasilevsky	
  
•  California	
  Digital	
  Library:	
  Carly	
  Strasser,	
  John	
  Kunze,	
  Stephen	
  Abrams	
  
•  Columbia/IEDA:	
  Kers7n	
  Lehnert,	
  Leslie	
  Hsu	
  
•  ICPSR:	
  George	
  Altman,	
  Mary	
  Vardigan	
  
•  CNI:	
  Clifford	
  Lynch	
  
•  Harvard:	
  Michael	
  Kurtz,	
  Chris	
  Erdmann	
  
•  MIT:	
  Micah	
  Altman	
  
•  UVM:	
  Mara	
  Saurle	
  
•  RDA:	
  Simon	
  Hodson,	
  Michael	
  Diepenbroek	
  
Your	
  ques7ons?	
  	
  
Anita	
  de	
  Waard	
  
VP	
  Research	
  Data	
  Collabora7ons,	
  	
  
Elsevier	
  Research	
  Data	
  Services	
  (VT)	
  	
  
a.dewaard@elsevier.com	
  
h=p://researchdata.elsevier.com/	
  

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Talk at OHSU, September 25, 2013

  • 1. Making  Research  Data     Discoverable  and  Usable     (It’s  the  metadata,  stupid!)   Anita  de  Waard   VP  Research  Data  Collabora7ons   a.dewaard@elsevier.com         h=p://researchdata.elsevier.com/      
  • 2. Research  data  is  the  ‘new  hotness’…     §  Share  research  outputs   §  Demonstrate  impact  to  public   §  Data  availability  drives  growth   §  Demonstrate  impact     §  Guarantee  permanence,  discoverability     §  Avoid  fraud     §  Generate,  track  outputs   §  Comply  with  mandates   §  Ensure  availability   §  Archive,  track,  curate   §  Support  researcher/ins7tu7on   §  Archive     §  Add  cura7on   §  Allow  reuse       Todd  Vision,  DataDryad,  OAI8,  6/23/13:     “We  need  to  find  a  way  to  keep  Dryad  funded,  and  would   love  to  hear  your  ideas  about  doing  that.”   Phil  Bourne,  Associate  Vice  Chancellor,  UCSD,  4/13:     “We  are  thinking  about  the  university  as  a  digital   enterprise.”   Mike  Huerta,  Ass.  Director  NLM  O  of  Health  Info  at  NIH,  6/13:     “Today,  the  major  public  product  of  science  are  concepts,  wri=en   down  in  papers.  But  tomorrow,  data  will  be  the  main  product  of   science….  We  will  require  scien7sts  to  track  and  share  their  data  as   least  as  well,  if  not  be=er,  than  they  are  sharing  their  ideas  today.”     Mara  Saule,  Dean  University  Libraries/CIO,  UVM,  5/13:     “We  need  to  do  something  about  data.”   §  Derive  credit   §  Comply  with  mandates   §  Discover  and  use     §  Cite/acknowledge   Gov   Funding   bodies   University   management     Researchers   Librarians   Data     Repositories   Nathan  Urban,  PI  Urban  Lab,  CMU,  3/13:     “If  we  can  share  our  data,  we  can  write  a  paper  that  will   knock  everybody’s  socks  off!”   Roles  and  needs  wrt  Research  Data:   Barbara  Ransom,  NSF  Program  Director  Earth  Sciences,  2/13:     “We’re  not  going  to  spend  any  more  money  for  you  to  go  out   and  get  more  data!  We  want  you  first  to  show  us  how  you’re   going  to  use  all  the  data  we  paid  y’all  to  collect  in  the  past!”  
  • 3. Research  data  management  today:   Using  an7bodies   and  squishy  bits       Grad  Students  experiment   and  enter  details  into  their   lab  notebook.     The  PI  then  tries  to  make     sense  of  their  slides,   and  writes  a  paper.       End  of  story.    
  • 4. Prepare   Observe   Analyze   Ponder   Communicate   Prepare   Observe   Analyze   Ponder   Communicate   Research  today  (in  biology)  is  o^en   quite  insular:    
  • 5. But  life  is  VERY  complicated:   h=p://en.wikipedia.org/wiki/File:Duck_of_Vaucanson.jpg   •  Interspecies  variability:  A  specimen  is  not  a  species   •  Gene  expression  variability:  Knowing  genes  is  not     knowing  how  they  are  expressed   •  Microbiome:  An  animal  is  an  ecosystem   •  Systems  biology:  A  whole  is  more  than  the  sum  of  its   parts       Reduc7onist  science     does  not  work   for  living  systems!  
  • 6. What  if  the  data  were  connected?   Prepare   Analyze   Communicate   Prepare   Analyze   Communicate   Observa7ons   Observa7ons   Observa7ons   Across  labs,  experiments:   track  reagents  and  how   they  are  used  
  • 7. Prepare   Analyze   Communicate   Prepare   Analyze   Communicate   Observa7ons   Observa7ons   Observa7ons   Compare  outcome  of   interac7ons  with  these   en77es   What  if  the  data  were  connected?  
  • 8. Prepare   Analyze   Communicate   Prepare   Analyze  Communicate   Observa7ons   Observa7ons   Observa7ons   Build  a  ‘virtual  reagent   spectrogram’  by  comparing     how  different  en77es     interacted  in  different   experiments   Think   What  if  the  data  were  connected?  
  • 9. Where  research  data  goes  now:   >  50  My  Papers   2  M  scien7sts   2  My  papers/year   Majority  of  data   (90%?)    is  stored     on  local  hard  drives   Dryad:   7,631  files     Dataverse:   0.6  My       Ins7tu7onal   Repositories     Some  data     (8%?)  stored  in  large,     generic  data     repositories   MiRB:       25k   PetDB:     1,5  k   TAIR:       72,1  k   PDB:       88,3  k     SedDB:     0.6  k   A  small  por7on  of  data     (1-­‐2%?)  stored  in  small,     topic-­‐focused   data  repositories   1.  How  do  we  get   researchers  to  curate,  store   and  share  their  data?     2.  How  do  we  ensure   long-­‐term   sustainability  for  high-­‐ end  repositories?   3.  What  role  do   libraries/ ins7tu7ons  play?    
  • 10. de  Waard,  A.,  Burton,  S.  et  al.,  2013   1.1.  An  a=empt  to  get  researchers  to  curate   (but  only  parZally  share!)  their  data:    
  • 11. •  In  220  publica7ons  only  40%  of  an7bodies,  40%  of  cell  lines  and  25%  of   constructs  can  be  manually  iden7fied  (Vasilevsky  et  al,  submi=ed)     •  Proposal  (with  NIH/NIF  and  Force11  Group):     –  Adding  minimal  data  standards   –  Tool  extracts  likely  reagents  /  resources   –  User  interface  asks  author  to  confirm  or  select   1.2.  What  to  do  in  the  mean7me?     49  publica7ons  193  publica7ons   76  publica7ons   214  publica7ons   210  publica7
  • 12. Pilot  project  with  IEDA:     – Build  a  database  for  lunar  geochemistry   – Write  joint  report  on  building     repository,  cura7on,  costs  and     challenges   2.2  How  can  research  databases   become  long-­‐term  sustainable?    
  • 13. With  WDS/RDA  WG:     •  Planning  survey  of  cost  recovery  models  for  research   databases   •  Input/inspira7on:  ICPSR  Sloane-­‐funded  project   Sustaining  Domain  Repositories  for  Digital  Data’   •  Developing  overarching  funding  model:   2.2  Cost  recovery  ques7onnaire:  
  • 14. Private store Data producer or sponsor Access Closed Flow of funds Data publication Public Service Collaboration Conclave  Limited Subscription content    Commercial overlay  Limited Academic Use/Limited Data user Flow of funds Examples ICSPR, CERN- LHC KEGG GeoFacets Reaxys DRAFT - CC-BY-NC 2013, Todd Vision & Anita de Waard Many small operations, e.g. try-db.org, plhdb.org Dryad, arXiv, PDB Commercial and institutional storage  & or 2.3.  A  first  stab  at  a  model:  
  • 15. 3.1.  Where  do  ins7tu7onal  repositories  fit  in?     Repository   Advantages     Disadvantages   Local  data   repository   Easy!  No  one  steals   your  data.     No  one  sees  it.     Not  compliant  with   requirements   Generic  data   repository   Not  very  hard  to  do.   Have  complied!   Data  can’t  be  easily   reused.  Credit?   Ins7tu7onal   Repository     Can  use  exis7ng  IR?   Tracking  and   compliance  checks.       Data  can’t  easily  be   reused.  Credit?   Domain-­‐specific   data  repository   Data  can  be  reused.   Credit!     Lot  of  work  for   curators.  Long-­‐term   sustainable?     Effort,  Reuse,  Credit,  Compliance   Habit,  Ease,  Privacy,  Control      Higher  quality  metadata  
  • 16. Funding  Agency:   University:   Collaborators:  Domain  of  study:  Domain-­‐Specific     Data  Repository   Local     Data  Repository   Ins7tu7onal     Data  Repository   Generic    Data  Repository   AND   THEY  ALL   WANT   DIFFERENT   METADATA!!!!   3.2.  The  poor  researcher:    
  • 17. Domain  repository   3.3.  Possible  pilot  project:   Domain  repository   IR  Data   Metadata:   What  data   was  stored/ viewed   Meta data   Metadata:   What  data   was  stored/ viewed   •  Interview  ins7tu7ons   •  Normalize  repor7ng  data   •  Talking  to     •  IQSS,  Harvard   •  ICPSR,  U  Mich   •  DataDryad,  UNC   •  Pangaea,  Germany  
  • 18. 3.4.  Ins7tu7onal  Pilot  study:     •  Planning  series  of  interviews  at  key  ins7tu7ons:     –  What  role  do  libraries/ins7tu7ons  play  wrt  research  data   management?     –  What  tools/metadata  standards  are  used?   –  What  aspects  of  data  deposi7on  is  the  Research  Office/ IR/Ins7tu7on  interested  in?     –  How  does  this  compare  with  what  scien7sts  want  and  do   in  their  labs?       •  Outcomes:     –  Share  knowledge  (within  ins7tu7on);     –  Write  joint  report  (anonymised)     –  Establish  joint  plan  of  ac7on  
  • 19. Elsevier  Research  Data  Services:     •  2013/2013:  Series  of  pilots,  reviews,  and  reports:   -  With  CMU:  Data/metadata  entry  and  sharing   -  With  IEDA:  Repository  crea7on:  feasibility  study  &  report   -  With  RDA:  Cost  of  Data  Repositories  ques7onnaire   -  With  series  of  ins7tutes:  Interviews  re.  role  of  ins7tu7on   •  Main  ques7ons:     -  What  are  key  needs?     -  Can  we  play  a  role:  skillsets,  partnerships?     -  Is  there  a  (transparent)  business  model  for  this?   •  Principles:     –  Collabora7on  is  tailored  to  partner’s  needs,  using  local  resources;     –  Collabora7on  plan  is  MoU/Service-­‐Level  Agreement;   –  At  all  7mes,  all  data,  reports  and  so^ware  are  open  and  shared.    
  • 20. In  summary:     1.  If  researchers  start  to  curate  and  share  their   data…   2.  And  research  databases  become  long-­‐term   sustainable…   3.  And  libraries,  data  repositories  and  grid   infrastructures  start  to  work  together…     We  might  enable  a  knowledge  infrastructure  that   allows  us  to  jointly  tackle  the  quesZons  of  life!      
  • 21. Many  ques7ons  remain:   ?  What  carrots    and  s7cks  will  make  researchers   share  their  data?     ?  How  do  we  create  interoperable  metadata   layers?     ?  What  role  would  the  ins7tu7on/library  play?     ?  What  are  sustainable  models,  moving   forward?     ?  Is  there  a  place  for  publishers,  in  all  this?    
  • 22. Thank  you!   Collabora7ons  and  discussions  gratefully  acknowledged:     •  CMU:  Nathan  Urban,  Shreejoy  Tripathy,  Shawn  Burton,  Ed  Hovy   •  UCSD:  Phil  Bourne,  Brian  Shoe=lander,  David  Minor,  Declan  Fleming,   Ilya  Zaslavsky   •  NIF:  Maryann  Martone,  Anita  Bandrowski   •  MSU:  Brian  Bothner   •  OHSU:  Melissa  Haendel,  Nicole  Vasilevsky   •  California  Digital  Library:  Carly  Strasser,  John  Kunze,  Stephen  Abrams   •  Columbia/IEDA:  Kers7n  Lehnert,  Leslie  Hsu   •  ICPSR:  George  Altman,  Mary  Vardigan   •  CNI:  Clifford  Lynch   •  Harvard:  Michael  Kurtz,  Chris  Erdmann   •  MIT:  Micah  Altman   •  UVM:  Mara  Saurle   •  RDA:  Simon  Hodson,  Michael  Diepenbroek  
  • 23. Your  ques7ons?     Anita  de  Waard   VP  Research  Data  Collabora7ons,     Elsevier  Research  Data  Services  (VT)     a.dewaard@elsevier.com   h=p://researchdata.elsevier.com/