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データライブラリアンとその育成:ラーニングコモンズとDigital Scholarship,オープンサイエンス

  • 1. f d
 f Digital Scholarshipb f ikeuchi.ui@gmail.com http://openscience.jp 2016 u ) 5th June
  • 2. Contents 1.  f 2.  f 3.  f f 4.  5. 
  • 3. 1
  • 5. d f .. 2015 6 23 . http://mainichi.jp/edu/news/20150623ddlk25100509000c.html
  • 7. https://www.coursera.org/university-programs/masters-in-computer-data-science University of Illinois at Urbana-Champaign •  2016 8 22 h •  $19,200i32 j •  f •  •  f •  f •  •  •  •  f
  • 11. G8 f f i2013j http://www.mofa.go.jp/mofaj/gaiko/page23_000044.html
  • 12. Global Open Data Index (2015) !  –  b b b b b b b b b i jb b b !  f f –  –  f –  “ f f b 31 . OKFJ. 2015 12 14 . http://okfn.jp/2015/12/14/opendataindex2015/
  • 15. f f !  f f !  f f !  f f “ – API –  f f .
  • 16. /大向一輝. オープンデータと図書館. カレントアウェアネス. 2014, (320), CA1825, p. 14-16. http://yokohamaopendata.jp/2014/03/04/yokohama-international-open-data-day-2014-「wikipedia-town」レポート/ f f "  " 
  • 21. f   !  f –  !  f –  f f f
  • 22. 4 1
  • 23. f f , . f . f . 2016 2 29 . https://jipsti.jst.go.jp/rda/
  • 24. European Open Science Cloud (EOSC) -‐‑‒ Governance Lifesciences Lead scientific users… …long tail of science Physics Earthsciences Economics Social sciences Scaleofscientificactivity(data-drivenscience) High-performance computing Data fusion across disciplines Big data analytics Data access and re-use Data manipulation and export Data discovery and catalogue Long-term funding Leverage of MS investment Legacy and sustainability IPR and privacy protection Federation Applied-engineering …… Humanities Citizen science Bottom-up governance European Data Infrastructure Big data storage Super-Computing High-speed connectivity Data and Services Trust d 0) d 0 ))) "  "  f f "  "  "  The European Open Science Cloud. EOSC Infoday, 2016-4-15. http://goo.gl/rEA7z7
  • 25. f . Ikeuchi, Ui. Research data sharing and open science. http://doi.org/10.6084/m9.figshare.1270371
  • 26. / i j :BEEi j f :PPSMSO Mb
  • 27. c 0 I NW ) 2 # a z! ) _ O ) - ,. , %,,& ) -3(-4) C VSMb5 CV O R MO O N_MSLSVS b) _ O) - ,/) H V) 0 0 _O 2/30) F O Mb
  • 28. 1 D A   D S U S   SI   U SS  A TRT NTS   7N NTS   SI I   8   SI I ,   6S N  R MTI   TI   S U S -‐‑‒   M  R N   S U S .   8 NLS   SI   S N   S U S /   A & LN NTS  TK   IN 0   A & LN NTS  TK   S N  U S 1   U N NTS i ) 0 j http://science.sciencemag.org/content/352/6290/1147.full.pdf+html
  • 29. f “ 2R _ 8 VV L S http://www.diasjp.net
  • 31. , f
  • 32. i2016 j ,DO O MR N 9O OV WO ;_ NS- ,1 V L V D 9 ;_ NS ; OM ) - ,1 ) /) R 5 aaa)S SaOL) S O NOP _V PSVO - ,1 V L VD#-19;_ NS ; OM -) NP -‐‑‒) ) ;_ NS
  • 33. ,, 1 2 2 Houghton, J; Gruen, N. Open Research Data: Report to the Australian National Data Service (ANDS). 2014, 45p. http://ands.org.au/resource/open-research-data-report.pdf %
  • 34. ,-‐‑‒ HS O F 6 O V) FRO SV LSVS b P O O MR N NOMVS O SNVb aS R SMVO O) 8_ O 7S V b) ,4 9OMOWLO - ,. R 5 N S) , ), ,1 T)M_L)- ,.),,) ,/S S 9 f (n=516)
  • 35. f c ,.C VSMSO N N O
  • 36. fi j ,/ Overview of funders' data policies. Digital Curation Centre. http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies,
  • 37. fi j ,0 Valen, Dan; Blanchat, Kelly. Overview of OSTP Responses. figshare. 2015. https://dx.doi.org/10.6084/m9.figshare.1367165.v7
  • 39. Altmetrics f f DOI DOI DOIデータ データ記述 論文 DB f UO_MRS GS) 9 _LVS RS N MR V Vb M WW_ SM S ) PS R O) - ,/) 9B 5 , )1 3/ W4)PS R O),-2 .2- *DOI(デジタルオブジェクト識別子)
  • 40.  t f f The Royal Society! Philosophical Transactions! http://rstl.royalsocietypublishing.org Nature Publishing Group ! Scientific Data! http://www.nature.com/sdata/ ))(
  • 41.  t f !  f ! Dryad -‐‑‒ R 5 aaa)N N b N) O P _ S R 5 aaa)P MO,,) _ T S (NOMV S (N (MS S ( S MS VO (PS V
  • 43. d f DOI !  fiJaLCj – 2014 10 h2015 10 i9 j – o f DOI p   ,   ,   . f DOI . . 2015, 58(10), p.763-770 http://doi.org/10.1241/johokanri.58.763
  • 44. f ! “Making Data Count” R 5 WNM)V )S ! Dataset Level Metrics R 5 M S) 9 O O OV O SM -‐‑‒-‐‑‒ f
  • 45. !  f –  i2013j –  c f i2015-j !  f –  m “ f n i2015j –  w i2016j -‐‑‒.B O EMSO MO
  • 49. f !  f –  f –  f !  f –  –  f -‐‑‒2B O EMSO MO DO O MR SL b
  • 50. 5 1 1
  • 51. .   “ f . f y z h “ h. 2015, p.20-21. http://www8.cao.go.jp/cstp/sonota/openscience/ c : aURAa “ f
  • 52. f Education and Training on handling of research data IG !  f 1.  research librarians; 2.  research administrators; 3.  research infrastructure managers / operators; 4.  researchers. .https://rd-alliance.org/node/971/all-wiki-index-by-group f
  • 53. ! Research Librarians –  Metadata (CE); Familiarity with various metadata standards; –  Familiar with different data documentation practices, not just metadata schemas (AN) –  Familiarity with various data standards (LW) –  Data management planning (CE) –  Data repositories (CE); knowledge of repositories and other digital initiatives (LW) Understanding the general requirments for preservable data or specific requirements for domain repositories (AN) –  Data preparation (CE) –  Marketing services (CE) –  Building collaborations (CE); interaction and cooperation with business and IT personnel (LW) –  Data backup and security (CE) –  Understand and create organisational culture change (LW) –  Experience of data and document management (LW) –  Training, advocacy and awareness-raising activities (LW) –  Communicating the importance of data stewardship, ability to make the case (AN) –  Managing and influencing business change activities (LW) –  Experience of working with an HE research establishment (LW) –  Understanding of RDM policy issues and best practice (LW) Funder requirements for data planning, handling, deposition (AN) –  Familiarity with RDM lifecycle (LW) .,https://rd-alliance.org/node/971/all-wiki-index-by-group f
  • 54. ! Research Librarians –  Ethical and legal requirements in handling data from human participants (VVdE) and other sensitive data types (AN) –  Review and appraisal of research data (VVdE) –  Data sharing/distribution/publication (AN) –  Intellectual property and Data licenses (AN) –  Defining the working space: What is research data? (AN) –  Migration between working file formats and preservation file formats (AN) –  File naming, organization, and versioning (AN) –  Familiarity with existence of available tools for data handling (AN) –  Identifying storage strategies for different data types, stages, quality. Selecting data for presearvation (AN) –  Understanding of research reproducibility (AN) –  From the Data Information Literacy (DIL) Program (AN) #  Discovery and Acquisition #  Ethics and Attribution #  Metadata and Data Description #  Cultures of Practice #  Data Management and Organization #  Data Curation and Reuse #  Data Quality and Documentation #  Data Processing and Analysis #  Data Visualization and Reuse #  Databases and Data Formats #  Data Conversion and Interoperability #  Data Preservation .-‐‑‒https://rd-alliance.org/node/971/all-wiki-index-by-group f
  • 55. f !  f –  f #  Data Management Plan –  f –  f –  b –  b f –  iDOIb j –  !  –  f –  f ..
  • 57. RDMRose 1.  bRDMbLIS 2.  RDM 3.  f 4.  RDM 5.  f e 6.  f 7.  f 8.  f d .0 RDMRose Learning Materials http://rdmrose.group.shef.ac.uk/?page_id=10#session-13-the-lis-role-in-rdm
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  • 72. 0 Research Data Infrastructures (MLIS 2014-2015) 1. Introductions, Syllabus Overview & Data Storage Part 1 2. Data Storage Part 2 3. Data Publication & Citation Part 1 4. Data Publication & Citation Part 2 5. Data Discovery 6. Immersive session with Researchers 7. Data Description & Data Standards 8. Data Repositories & Preservation Part 1 9. Data Repositories & Preservation Part 2 10. No class – Spring Recess 11. Data Sharing & Disciplinary Diversity 12. Citizen Science, Citizen Data 13. Data Science, Data Analytics & Data Visualization 14. Data, Society, Futures 15. Presentations & Summary Evaluation Lyon, Liz. Bridging the Data Talent Gap: Positioning the iSchool as an Agent for Change. 10th International Digital Curation Conference, Feb. 2015.
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