Developing Data Services to Support eScience/eResearch

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Developing Data Services to Support eScience/eResearch

  1. 1. School  of  Information                  Studies   Syracuse  University  Developing  Data  Services  to  Support  eScience/eResearch   2012  Priscilla  M.  Mayden  Lecture   eScience  and  the  Evolution  of  Library  Services     Jian  Qin   School  of  Information  Studies   Syracuse  University   http://eslib.ischool.syr.edu/     February  22,  2012  
  2. 2. School  of  Information  Studies                                   Syracuse  University   The  morning  ahead  An  environmental  scan  •  E-­‐Science,  cyberinfrastructure,  and  data  •  What  do  all  these    have  to  do  with  me?   Case  study:  The  gravitational  wave   research  data  management     Group  work:  Role  play  in  developing   data  management  initiatives     Priscilla M. Mayden Lecture 2012, Utah 2
  3. 3. School  of  Information                  Studies   Syracuse  University   An  environmental  scan   •  E-­‐Science,  cyberinfrastructure,  and  data   •  What  do  all  these    have  to  do  with  me?  Overview  of  E-­‐Science  and  Data   Characteristics  of  e-­‐science   Data  sets,  data  collections,  and  data   repositories   Why  does  it  matter  to  libraries?  
  4. 4. School  of  Information  Studies                                   Syracuse  University   E-­‐Science          “In  the  future,  e-­‐Science  will  refer  to  the  large  scale  science  that  will  increasingly  be  carried  out  through  distributed  global  collaborations  enabled  by  the  Internet.  ”     National e-Science Center. (2008). Defining e-Science. http://www.nesc.ac.uk/nesc/define.html Priscilla M. Mayden Lecture 2012, Utah 4
  5. 5. School  of  Information  Studies                                   Syracuse  University   Characteris>cs  of  e-­‐science  •  Digital  data  driven  •  Distributed  •  Collaborative  •  Trans-­‐disciplinary  •  Fuses  pillars  of  science   –  Experiment   –  Theory   Greer,  Chris.  (2008).  E-­‐Science:  Trends,   Transformations  &  Responses.  In:   –  Model/simulation   Reinventing  Science  Librarianship:   Models  for  the  Future,  October  2008.   –  Observation/correlation   http://www.arl.org/bm~doc/ ff08greer.pps     Priscilla M. Mayden Lecture 2012, Utah 5
  6. 6. School  of  Information  Studies                                   Syracuse  University    Shi?  in  Science  Paradigms   Thousand   A  few  hundred   A  few  decades   Today   years  ago   years  ago   ago   Data exploration (eScience) unify theory, experiment, and simulation A computational -- Data captured by approach instruments or generated by simulating simulator Theoretical complex -- Processed by software branch phenomena -- Information/Knowledge using models, stored in computer generalizations -- Scientist analyzes Science was database/files using data empirical management and statisticsdescribing natural Gray,  J.  &  Szalay,  A.  (2007).  eScience  –  A  transformed  scienti_ic  method.   phenomena http://research.microsoft.com/en-­‐us/um/people/gray/talks/NRC-­‐CSTB_eScience.ppt     Priscilla M. Mayden Lecture 2012, Utah 6
  7. 7. School  of  Information  Studies                                   Syracuse  University   Gray,  J.  &  Szalay,  A.  (2007).  eScience  –  A  transformed   scienti_ic  method.  http://research.microsoft.com/en-­‐us/ X-­‐Informa>cs   um/people/gray/talks/NRC-­‐CSTB_eScience.ppt  •  The  evolution  of  X-­‐Informatics  and  Computational-­‐X                                                     for  each  discipline  X  •  How  to  codify  and  represent  our  knowledge       Experiments & Instruments Other Archives facts questions Literature facts ? answers Simulations The Generic Problems •  Data  ingest       •  Query  and  Visualization  tools     •  Managing  a  petabyte   •  Building  and  executing  models   •  Common  schema   •  Integrating  data  and  Literature       •  How  to  organize  it     •  Documenting  experiments   •  How  to  reorganize  it   •  Curation  and  long-­‐term  preservation   •  How  to  share  with  others   Priscilla M. Mayden Lecture 2012, Utah 7
  8. 8. School  of  Information  Studies                                   Syracuse  University   Useful  resources   Part 2: Health and Wellbeing •  The healthcare singularity and the age of semantic medicine •  Healthcare delivery in developing countries: challenges and potential solutions •  Discovering the wiring diagram of the brain •  Toward a computational microscope for neurobiology •  A unified modeling approach to data-intensivehttp://research.microsoft.com/en- healthcareus/collaboration/fourthparadigm/ •  Visualization in process algebra models of biological systems Priscilla M. Mayden Lecture 2012, Utah 8
  9. 9. School  of  Information  Studies                                   Syracuse  University  What  are  data?    What  are  some  of  the  major  data  formats?  Why  data  formats?  FUNDAMENTALS  OF  DATA   Priscilla M. Mayden Lecture 2012, Utah 9
  10. 10. School  of  Information  Studies                                   Syracuse  University   What  are  data?  (1)   An  artist’s  conception  (above)  depicts  fundamental  NEON  observatory  instrumentation  and  systems  as  well  as  potential  spatial  organization  of   the  environmental  measurements  made  by  these  instruments  and   systems.  http://www.nsf.gov/pubs/2007/nsf0728/nsf0728_4.pdf     Priscilla M. Mayden Lecture 2012, Utah 10
  11. 11. School  of  Information  Studies                                   Syracuse  University  What  are  data?  (2)   Priscilla M. Mayden Lecture 2012, Utah 11
  12. 12. School  of  Information  Studies                                   Syracuse  University   Medical  and  health  data  StandardizationComplianceSecurityhttp://www.weforum.org/issues/charter-health-data Priscilla M. Mayden Lecture 2012, Utah 12
  13. 13. School  of  Information  Studies                                   Syracuse  University  The  mul>-­‐dimensions  of  data   Research orientation Data types Data formats Levels of processing Priscilla M. Mayden Lecture 2012, Utah 13
  14. 14. School  of  Information  Studies                                   Syracuse  University  Scien>fic  data  formats   Common  data  format   Image  formats   Matrix  formats   Microarray  _ile  formats   Communication  protocols   Priscilla M. Mayden Lecture 2012, Utah 14
  15. 15. School  of  Information  Studies                                   Syracuse  University   Scien>fic  &  medical  data  formats    •  Medical  and  Physiological  Data   •  Chemical  Formats   Formats   –  XYZ  —  XYZ  molecule  geometry  _ile   –  BDF  —  BioSemi  data  format   (.xyz)   (.bdf)   –  MOL  —  MDL  MOL  format  (.mol)   –  EDF  —  European  data   –  MOL2  —  Tripos  MOL2  format  (.mol2)   format  (.edf)   –  SDF  —  MDL  SDF  format  (.sdf)  •  Molecular  Biology  data  Formats   –  SMILES  —  SMILES  chemical  format   –  PDB  —  Protein  Data  Bank   (.smi)   format  (.pdb)   •  Bioinformatics  Formats   –  MMCIF  —  MMCIF  3D   molecular  model  format  (.cif)   –  GenBank  —  NCBI  GenBank  sequence   format  (.gb,  .gbk)  •  Medical  Imaging   –  FASTA  —  bioinformatics  sequence   –  DICOM  —  DICOM  annotated   format  (.fasta,  .fa,  .fsa,  .mpfa)   medical  images  (.dcm,  .dic)   –  NEXUS  —  NEXUS  phylogenetic  data   format  (.nex,  .ndk)   Priscilla M. Mayden Lecture 2012, Utah 15
  16. 16. School  of  Information  Studies                                   Syracuse  University   Why  data  formats?  •  Archiving   •  Transmission   – Preservation  for   – delivery  across     posterity   • hardware    •  Storage   • software     – Availability  for   • administrative     “arbitrary”  access   – system  boundaries     •  Analysis   – availability  for   processing     Priscilla M. Mayden Lecture 2012, Utah 16
  17. 17. School  of  Information  Studies                                   Syracuse  University   Summary    •  Scienti_ic  data  formats  are  closely  tied  to  scienti_ic   computing   –  Data  structure,  model,  and  attributes   –  Self-­‐descriptive  with  header/metadata   –  API  for  manipulating  the  data   –  Interoperability:  conversion  between  different  formats  •  No  one-­‐format-­‐_its-­‐all  standard  •  Each  standard  has  one  or  more  tools  for  creating,   editing,  and  annotating  dataset       Priscilla M. Mayden Lecture 2012, Utah 17
  18. 18. School  of  Information  Studies                                   Syracuse  University  What  is  a  dataset?  What  are  some  of  the  metadata  standards  for  describing  datasets?  What  is  data  management?  DATASETS,  METADATA,  AND  DATA  MANAGEMENT   Priscilla M. Mayden Lecture 2012, Utah 18
  19. 19. School  of  Information  Studies                                   Syracuse  University   Dataset  classifica>on   Volume  Large-­‐volume  Small-­‐volume   Priscilla M. Mayden Lecture 2012, Utah 19
  20. 20. School  of  Information  Studies                                   Syracuse  University  Ecological data example: Instantaneous streamflow by watershedhttp://www.hubbardbrook.org/data/dataset.php?id=1 Priscilla M. Mayden Lecture 2012, Utah 20
  21. 21. School  of  Information  Studies                                   Syracuse  University   Diabetes data and trends— Country level estimates: http://apps.nccd.cdc.gov/ DDT_STRS2/ NationalDiabetesPrevale nceEstimates.aspx? mode=PHY ; Diabetes Data & Trends home page: http://apps.nccd.cdc.gov/ ddtstrs/default.aspxPriscilla M. Mayden Lecture 2012, Utah 21
  22. 22. Clinical trials data management: School  of  Information  Studies                                   Syracuse  University  http://www.clinicaltrials.gov/ct2/show/NCT00006286?term=TADS+NIMH&rank=1 Priscilla M. Mayden Lecture 2012, Utah 22
  23. 23. School  of  Information  Studies                                   Syracuse  University   Common  in  the  examples  •  Attributes  of  a  dataset  tell  users/managers:   –  What  the  dataset  is  about   –  How  data  was  collected   –  To  which  project  the  data  is  related   –  Who  were  responsible  for  data  collection   –  Who  you  may  contact  to  obtain  the  data   –  What  publications  the  data  have  generated   –  ??   Priscilla M. Mayden Lecture 2012, Utah 23
  24. 24. School  of  Information  Studies                                   Syracuse  University  Metadata  standards  in  medical  &  health  sciences   Structure   Semantics   Medical     Bioinfomatics   NCBI TaxonomyHealthcare     images   NCBO Bioportal UMLS MeSH (Medical Subject GenBank   Headings) GenBank   HL7   DICOM   GenBank   SNOMED CT (Systematized Nomenclature of Medicine-- Clinical Terms) Priscilla M. Mayden Lecture 2012, Utah 24
  25. 25. School  of  Information  Studies                                   Syracuse  University  Priscilla M. Mayden Lecture 2012, Utah 25
  26. 26. School  of  Information  Studies                                   Syracuse  University  Research  data  collec>ons   Size Metadata Management Standards Larger,   Multiple, Organized discipline-­‐ comprehensive Institutionalized, based   Heroic individual Smaller,   None or inside the team-­‐based   random team Priscilla M. Mayden Lecture 2012, Utah 26
  27. 27. School  of  Information  Studies                                   Syracuse  University   Research  collec>ons  •  Limited  processing  or  long-­‐term   management•  Not  conformed  to  any  data   standards•  Varying  sizes  and  formats  of  data   _iles  •  Low  level  of  processing,  lack  of   plan  for  data  products  •  Low  awareness  of  metadata   standards  and  data  management   issues   Priscilla M. Mayden Lecture 2012, Utah 27
  28. 28. School  of  Information  Studies                                   Syracuse  University  Resource  collec>ons   •  Authored  by  a   community  of   investigators,  within   a  domain  or  science   or  engineering   •  Developed  with   community  level   standards   •  Life  time  is  between   mid-­‐  and  long-­‐term   Priscilla M. Mayden Lecture 2012, Utah 28
  29. 29. School  of  Information  Studies                                   Syracuse  University   Reference  collec>on  •  Example:  Global  Biodiversity  Information  Facility   –  Created  by  large  segments  of  science  community     –  Conform  to  robust,  well-­‐established  and  comprehensive   standards,  e.g.   •  ABCD  (Access  to  Biological  Collection  Data)     •  Darwin  Core     •  DiGIR  (Distributed  Generic  Information  Retrieval)     •  Dublin  Core  Metadata  standard     •  GGF    (Global  Grid  Forum)     •  Invasive  Alien  Species  Pro_ile     •  LSID  (Life  Sciences  Identi_ier)     •  OGC  (Open  Geospatial  Consortium)   Priscilla M. Mayden Lecture 2012, Utah 29
  30. 30. School  of  Information  Studies                                   Syracuse  University   Datasets,  data  collec>ons,  and  data   repositories     System for storing, managing, preserving, and•  Data  collections  are  built  for   providing access to larger  segments  of  science   datasets and  engineering   Data  •  Datasets   repository   –  typically  centered  around  an   A repository may event  or  a  study   contain one or more –  contain  a  single  _ile  or  multiple   data collections _iles  in  various  formats   A data collection may –  coupled  with  documentation   contain one or more about  the  background  of  data   datasets collection  and  processing   A dataset may contain one or more Priscilla M. Mayden Lecture 2012, Utah data files 30
  31. 31. School  of  Information  Studies                                   Syracuse  University  Data  management  for  science  research  •  De_inition  from  Wikipedia:   http://en.wikipedia.org/wiki/Data_management    •  Key  concepts  in  data  management:   –  Data  ownership   –  Data  collection   –  Data  storage   How do they relate to –  Data  protection   responsible conduct of –  Data  retention   research? –  Data  analysis   http://ori.hhs.gov/images/ –  Data  sharing   ddblock/data.pdf –  Data  reporting   Priscilla M. Mayden Lecture 2012, Utah 31
  32. 32. School  of  Information  Studies                                   Syracuse  University   An  aPempt  to  define  DM  •  In  the  context  of  libraries:   –  Data  management  is  a  process  in  which  librarians  plan,   design,  and  implement  data  services  to  support  eScience/ eResearch.     –  Data  services  that  libraries  may  provide:   •  Institutional  or  community  data  repositories   •  Data  management  plan  for  pre-­‐  and  post-­‐award  of  grants   •  Metadata  creation,  linking,  and  discovery   •  Data  archiving,  preservation,  and  curation   •  Consultation  for  research  group’s  data  management  projects     •  Data  management  and  data  literacy  training  for  graduate  students   and  faculty   Priscilla M. Mayden Lecture 2012, Utah 32
  33. 33. School  of  Information  Studies                                   Syracuse  University   Ini>a>ves  in  research  libraries   Data support and Libraries involved in services in supporting eScience: institutions: 73% 45% •  Pressure  points:   –  Lack  of  resources   –  Dif_iculty  acquiring  the  appropriate  staff  and   expertise  to  provide  eScience  and  data   management  or  curation  services   –  Lack  of  a  unifying  direction  on  campus  Source: Soehner, C., Steeves, C. & Ward, J. (2010). E-Science and data support services: Astudy of ARL member institution. http://www.arl.org/bm~doc/escience_report2010.pdf Priscilla M. Mayden Lecture 2012, Utah 33
  34. 34. School  of  Information  Studies                                   Syracuse  University   Data  preserva>on  challenges  •  Data  formats   –  Vary  in  data  types,  e.g.  vector  and  raster  data  types     –  Format  conversions,  e.g.  from  an  old  version  to  a  newer   one  •  Data  relations     –  e.g.  there  are  data  models,  annotations,  classi_ication   schemes,  and  symbolization  _iles  for  a  digital  map  •  Semantic  issues   –  Naming  datasets  and  attributes   Priscilla M. Mayden Lecture 2012, Utah 34
  35. 35. School  of  Information  Studies                                   Syracuse  University   Data  access  challenges  •  Reliability    •  Authenticity  •  Leverage  technology  to  make  data  access   easier  and  more  effective   –  Cross-­‐database  search   –  Integration  applications   –  “Science-­‐ready”  datasets   Priscilla M. Mayden Lecture 2012, Utah 35
  36. 36. School  of  Information  Studies                                   Syracuse  University   Suppor>ng  digital  research  data  •  Lifecycle  of  research  data   –  Create:  data  creation/capture/gathering  from   laboratory  experiments,  _ield  work,  surveys,  devices,   media,  simulation  output…   –  Edit:  organize,  annotate,  clean,  _ilter…   –  Use/reuse:  analyze,  mine,  model,  derive  additional   data,  visualize,  input  to  instruments  /computers   –  Publish:  disseminate,  create,  portals  /data.   Databases,  associate  with  literature   –  Preserve/destroy:  store  /  preserve,  store  /replicate  / preserve,  store  /  ignore,  destroy…   Priscilla M. Mayden Lecture 2012, Utah 36
  37. 37. School  of  Information  Studies                                   Syracuse  University   Suppor>ng  data  management  The data deluge Researchers need:Numerical, image, video Specialized search engines to discoverModels, simulations, bit the data they needstreams Powerful data miningXML, CVS, DB, HTML tools to use and analyze the data Priscilla M. Mayden Lecture 2012, Utah 37
  38. 38. School  of  Information  Studies                                   Syracuse  University   Research  data  management   Community Institution eScience   librarian  Financial andpolicy support Science Data content User domain idiosyncrasies requirementsEvolving and interconnecting –Institutional   Community   National   International   repository   repository   repository   repository   Priscilla M. Mayden Lecture 2012, Utah 38
  39. 39. School  of  Information  Studies                                   Syracuse  University   Implica>ons  to  scholarly  communica>on   process   Publishing     Curation   Archiving   Data  publishing;   Maintaining,  preserving   The  long-­‐term  New  scholarly  publishing   and  adding  value  to   storage,  retrieval,  and   models—open  access,   digital  research  data   use  of  scienti_ic  data   institutional  and   throughout  its  lifecycle.   and  methods.  community    repositories,   self-­‐publishing,  library   publishing,  ....     Priscilla M. Mayden Lecture 2012, Utah 39
  40. 40. School  of  Information  Studies                                   Syracuse  University   Summary    •  E-­‐Science  development  has  raised   expectations  to  research  libraries   –  Working  knowledge  and  skills  in  e-­‐Science   –  Focus  on  process  (data  and  team  science)   rather  than  product  (reference  services)   –  Proactive,  collaborative,  integrative,  and   interdisciplinary   Priscilla M. Mayden Lecture 2012, Utah 40
  41. 41. School  of  Information                  Studies   Syracuse  University   Case  Study:    Learning  Data  Management   Needs  from  Scien>sts  
  42. 42. School  of  Information  Studies                                   Syracuse  University  Gravita>onal  Wave  (GW)  Research   Priscilla M. Mayden Lecture 2012, Utah 42
  43. 43. School  of  Information  Studies                                   Syracuse  University   What  is  the  problem?  •  Tracking  data  output  and  work_lows  is   dif_icult  due  to  lack  of  provenance  data  •  Search  of  datasets  is  limited  due  to  lack  of   speci_ic  options    •  Within  the  LIGO  community,  data  sharing  and   reuse  is  dif_icult  without  provenance  metadata   Data provenance case study 43
  44. 44. School  of  Information  Studies                                   Syracuse  University   Understand  the  research  workflow  •  Interview  the  scientist   –  Listening  (good  listening  skills)   –  Asking  questions  (don’t  be  afraid  of  asking   questions)   –  Use  your  librarian  brain  to  ingest  the   conversation:   •  How  does  the  research  _low  from  one  point  to  next?   •  What  consists  of  the  research  input  and  output  at  each   stage  of  research  in  terms  of  data?     Priscilla M. Mayden Lecture 2012, Utah 44
  45. 45. Mapping  out  the  knowledge  v0.1     School  of  Information  Studies                                   Syracuse  University   Priscilla M. Mayden Lecture 2012, Utah 45
  46. 46. Mapping  out  the  knowledge  v0.2     School  of  Information  Studies                                   Syracuse  University   Priscilla M. Mayden Lecture 2012, Utah 46
  47. 47. Mapping  out  the  knowledge  v1.0     School  of  Information  Studies                                   Syracuse  University   Priscilla M. Mayden Lecture 2012, Utah 47
  48. 48. School  of  Information  Studies                                   Syracuse  University   Lessons  learned  •  Science  is  learnable  even  if  you  don’t  have  a  subject   background   –  Learn  enough  to  understand  the  research  process  and  work_low  •  Scientists  are  eager  to  get  help  •  Librarians  need  to  be  technical-­‐minded   –  Data,  metadata,  database   –  Structures,  models,  work_lows  •  Librarians  need  to  be  good  listeners  while  staying  good   conversation  leaders   –  Know  when  and  how  to  lead  the  conversation  to  get  what  you   need  for  data  management  planning  and  implementation   –  Do  your  homework  on  the  subject  so  that  you  can  be  an   intelligent  listener   Priscilla M. Mayden Lecture 2012, Utah 48
  49. 49. School  of  Information                  Studies   Syracuse  University  Case  Discussions  
  50. 50. School  of  Information  Studies                                   Syracuse  University   Case  Study  #1:  To  build  or  not  to  build  a  data   repository?     by the researchers in this institution.A university library has developed an institutional repository for preserving andproviding access to the scholarly outputNow the new challenge arises from e-science research demanding datamanagement plan by the funding agency and the linking between publicationsand data by the authors and users. You already know that some faculty usetheir disciplinary data repository for submitting their datasets (e.g., GenBank formicrobiology research data). The problem you face now is whether aninstitutional data repository should be built for those who do “small science” anddon’t have funding nor expertise to manage their data.Questions to be addressed:•  What are the strategies you will use to approach the problem?•  What are the possible solutions for the problem?•  What are some of the tradeoffs for the solutions you will adopt? Priscilla M. Mayden Lecture 2012, Utah 50
  51. 51. School  of  Information  Studies                                   Syracuse  University   Case  study  #2:  Developing  a  data   taxonomy    The concept of research data management is a stranger to many faculty aswell as your library staff. What is data? What is a data set? These seeminglysimple terms can be very confusing and have different interpretations indifferent context and disciplines. As part of the data management strategies,you decide to develop an authoritative data taxonomy for the campus researchcommunity. This data taxonomy will benefit the creation and use of institutionaldata policies, data repository or repositories, and data management plansrequired of funding agencies.Questions to be addressed:•  What should the data taxonomy include?•  What form should it take, a database-driven website or a static HTML page?•  Who should be the constituencies in this process?•  Who will be the maintainer once the taxonomy is released? Priscilla M. Mayden Lecture 2012, Utah 51
  52. 52. School  of  Information  Studies                                   Syracuse  University  Case  study  #3:  Developing  a  data  policy    Data policies play an important role in governing how the data will be managed,shared, and accessed. It is also an instrument that will fend off potential legalproblems. Data policies have several types: data access and use, datapublishing, and data management. Your university’s Office of SponsoredResearch has some existing policy on data, but it is neither systematic norcomplete. Many of the terms were defined years ago and did not cover the newareas such as the embargo period of data. As the university has decided tobuild a data repository for managing and preserving datasets, a data policy hasbecome one of the top priorities for both the institution and the data repository.Questions to be addressed:•  What should the data policy include?•  Who should be the constituencies in this process?•  Who will be the interpretation authority for the data policy? Priscilla M. Mayden Lecture 2012, Utah 52
  53. 53. School  of  Information  Studies                                   Syracuse  University   Case  study  #4:  Cataloging  datasets  Describing datasets is the process of creating metadata for datasets. Inscientific disciplines, several metadata standards have been developed, e.g.,the Content Standard for Digital Geospatial Metadata (CSDGM), Darwin Core,and Ecological Metadata Language (EML). Each of these metadata standardscontains hundreds of elements and requires both metadata and subjectknowledge training in order to use them. Besides, creating one record usingany of these standards will require a tremendous time investment. But youlibrary does not have such specialized personnel nor have the fund to hire newpersons for the job. The existing staff has some general metadata skills such asDublin Core. In deciding the metadata schema for your data repository, youneed to address these questions:•  Should I adopt a scientific metadata standard or develop one tailored to ourneed?•  How can I learn what metadata elements are critical to dataset submitters andsearchers?•  What are some of the benefits and disadvantages for adopting a standard ordeveloping a local schema? Priscilla M. Mayden Lecture 2012, Utah 53
  54. 54. School  of  Information  Studies                                   Syracuse  University  Case  study  #5:  Evalua>ng  data  repository  tools   Research data as a driving force for e-science is inherently a tool-intensive field. Tools related to data management can be divided into two broad categories: those for creating metadata records and those for data repository management. An academic institution decided to build their own data repository as part of the supporting service for researchers to meet the data management plan requirement of funding agencies. This data repository development task was handed down to the library. You the library director have to decide whether to develop an in-house system or use an off-the-shelf software system. As usual, you put together a taskforce to find a solution to this challenge. The questions to be addressed by the taskforce include: •  What are the options available to us? •  What evaluation criteria are the most important to our goal? •  What are the limitations for us to adopt one option or the other? •  How will this option be interoperate with existing institutional repository system? Or, can the existing repository system used for data repository purposes? Priscilla M. Mayden Lecture 2012, Utah 54

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