Bolibaugh, Cyclia

Jaquiery, Matt

Langford, Patrick

Owen, Nicholas

Rumsey, Sally

Towse, John

English

Lancaster E-Prints

Authors  (Z-A):  John  Towse ,  Sally  Rumsey ,  Nicholas  Owen ,  Patrick  Langford ,  Matt  Jaquiery  [Lead author],  Cylcia  Bolibaugh Data  sharing:  A  primer  from  UKRN What  is  data  sharing? Data  sharing  is  the  process  of  making research  data  available  for  wider  dissemination.  The FAIR principles  state  data  and  metadata  should  be: findable  (easy  to  find  for  both  humans and  computers); accessible  (accompanied  by  clear  instructions  for  access  and  authorisation); interoperable  (compatible  with  other  data  and/or  tools);  and reusable  (suitably  described  to allow  further  use).  Why  should  we  share  data? There  are  many  positive  reasons  to  share  data.  Sharing  data  opens  opportunities  for  scientific enquiry  through  the  promotion  of  innovation  via  new  data  uses  and  collaboration.  It  maximises transparency  and  helps  ensure  the  reliability  of  the  scientific  record.  😟 Might  people  find  mistakes  in  my  work? Sometimes  mistakes  are  found  in  shared  work,  but  finding  mistakes  is  good  -  it  means  we  no longer  believe  wrong  things.  Everyone  makes  mistakes,  and genuine  errors  are  unlikely  to  be damaging  to  your  work  or  career. 😟 Might  people  use  open  data  to  scoop  my  ongoing  research?  Scooping  almost  never  happens,  but  if  you’re  worried  about  it  you  can embargo  data  while sharing  so  that  you  have  exclusive  access  to  it  for  a  few  months  or  years. How  do  I  share  data? What  can  be  shared  with  whom?  Each  field  has  its  own  conventions,  challenges,  and  solutions.  The  tips  in  this  section  are general,  and  focus  on  human  data  (but  may  also  be  relevant  where  for  example  commercial, security  or  legal  implications  exist). It's  important  to  plan  for  sharing  of  research  data  from  the  start  of  your  project.  Most  human research  data  can  be  shared  ethically  and  legally  with  the  appropriate  use  of  informed consent , anonymisation , rights  management ,  and access  control .  Within  a  single  research project,  some  aspects  of  the  data  may  be  able  to  be  shared  completely  openly,  while  others may  need  to  be  protected  (e.g.  by  an  end-user  agreement).  Human  data Data  protection  laws  (e.g.  GDPR)  govern  the  processing  of personal  data ,  but  they  do  not apply  to  anonymised  data,  and  there  are  some exemptions  for  scientific  studies.  Be  aware  that some  seemingly anonymised  data  can  be re-identified .  Your  institution  or  funder  may  have additional  rules  regarding  data  management  (e.g.  requiring  a  Data  Management  Plan ).  Consent ✔ Inform  participants  about  what  data  will  be  stored,  how  it  will  be  preserved,  how  it  is  likely  to be  used  in  the  long-term,  and  how  confidentiality  will  be  protected.  The  UK  Data  Service  has  a bank  of  example  sharing-friendly  forms  for  different  types  of  data. ❌Ensure  consent  forms do  not promise  to  destroy  the  data  or  promise  that  the  data  will  only be  seen  or  accessed  by  the  research  team.      Anonymisation ✔ Consider  procedures  for anonymisation  at  the  beginning  of  your  project  in  conjunction  with the  planning  of  what  consent  you  will  seek  from  participants.  ✔ Work  with  anonymized  data  whenever  possible.  This  minimizes  the  risk  of  leaking personally  identifiable  information  and  ensures  that  anonymisation  does  not  distort  the  data. ✔ Remove  direct  and  indirect  identifiers  from  quantitative  and  qualitative  data.  ✔ Where  data  cannot  be  de-identified  sufficiently,  consider  imposing  access  controls  instead.  Access  Control Where  data  are  sensitive,  safeguarding  or  shielding  subsets  of  data  may  be  appropriate.  Most large  repositories  allow  managing  access  through  an End  User  Licence  which  stipulates conditions  for  access,  including  agreement  not  to  attempt  to  re-identify  individuals.  The  data  itself To  enable  shared  data  to  be  used  effectively,  they  should  be understandable  to  humans  and machines ,  which  means  conforming  to  standards. Meta-data  standards  facilitate  long-term  use and  integration;  some  also  apply  to specific  kinds  of  data .  The  DCC  keeps  a list  of  standards you  can  check. Roche  et  al  (2015)  provide  several  generally-applicable  recommendations  for making  data  complete  and  reusable,  including: ✔ Use clear  and  detailed  data  descriptions  and  metadata  to  increase  usability.  As  a  result, where  data  relate  to  an  accompanying  research  paper,  it  should  be  transparent  which  parts  of the  dataset  refer  to  which  claims  in  the  paper. ✔ Try  to  make  the  data  accessible  on  their  own  terms,  independent  of  another  paper, especially  where  additional  supplementary  data  can  be  provided.  ✔ Ensure  missing  data  or  data  exclusions  are  fully  annotated.  ✔ Provide  as  much  unprocessed  data  as  possible  so  users  can  “rebuild”  information. ✔ Include  analysis  code  and  processing  scripts  where  feasible. ❌Avoid  proprietary  formats  /  file  types  as  the only way  to  access  data.  Where  suitable, convert  the  files  to  open  or  standard  formats  before  storing  data  in  a  repository.  ✔ Consider  asking  friends  and  colleagues  to  review  the  data  submission  you  are  preparing  to ensure  quality  control,  especially  if  the  dataset  will  not  be  peer  reviewed.  Sharing  the  data 1. Find  a  repository  that  is  appropriate  for  your  dataset.  Make  sure  the  repository  has  the capabilities  you  need:  Is  a  general  or  specific  repository  more  suitable?  What  are  the file  size  limits?  How  long  will  the  data  be  available?  Do  you  need  to  manage  sharing permissions  or  set  up  an  embargo  period?  Will  you  get  a DOI or  similar persistent identifier ?  Check  the  terms  and  conditions  for  a  commitment  to  retain  your  data. You  can  search  a  list  of  repositories  by  discipline  at  https://www.re3data.org/  2. Provide  reuse  guidance: decide  on  an  appropriate  license  for  your  data;  be  clear  about how  you  would  like  the  dataset  cited .  Some  repositories  will  do  this  automatically, while  others  will  allow  you  to  customise  everything.  3. Share  the  persistent  URL!  Let  people  know  about  the  resource  you’ve  just  created  -  in  a paper,  at  a  conference,  on  social  media.  2    Data  should  be  as  open  as  possible and as  closed  as  necessary  Other  resources Check  out  institutional  help  (e.g.  the  Library)  for  advice,  guidance  and  experience. Digital  Curation  Centre :  Resources  and  information  http://www.dcc.ac.uk/resources  FAIRsharing :  Resource  on  data  and  metadata  standards  https://fairsharing.org/  MANTRA :  Free  online  course  on  digital  data  for  researchers  https://mantra.edina.ac.uk/  UK  Data  Archive :  Social,  economic  and  population  data  https://www.data-archive.ac.uk/  Caldicott  Principles :  For  sharing  medical  data  https://www.ukcgc.uk/manual/principles OpenAIRE :  Resource  on  ethical  and  legal  issues  https://www.openaire.eu/guides FOSTER  Open  Science :  Free  data  protection  and  ethics  course https://www.fosteropenscience.eu/learning/data-protection-and-ethics/#/ Cessda  Training:  Data  management  guide https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide   Reviewed  by  Chris  Gorgolewski  (0000-0003-3321-7583),  Olivier  Klein  (0000-0003-2737-8049),  Frederick Verbruggen  (0000-0002-7958-0719),  and  Tony  Weidbe rg  (0000-0002-5158-307X).  Edited  by  Emma  L  Henderson (0000-0002-5396-2321)  and  Jackie  Thompson  ( 0000-0003-2851-3636 ) Conflict  of  Interest  Statement:  Reviewer  Chris  Gorgolewski  works  for Google  Dataset  Search ( https://g.co/DatasetSearch ) This  work  is  licensed  under  CC  BY  3.0 3 

Data sharing : A primer from UKRN

https://eprints.lancs.ac.uk/id/eprint/139955/1/UKRN_Primer_Data_Sharing.pdf

Data sharing:A primer from UKRN

Data sharing:A primer from UKRN

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