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The End(s) of e-Research



Ralph Schroeder, Professor, MSc Programme Director
Eric T. Meyer, Research Fellow, DPhil Programme Director




October 25, 2012

                                                           @etmeyer
   research using
   digital tools and data
   for the distributed and collaborative
   production of knowledge
   e-Research is not a separate entity; it
    consists merely of computational
    support for other disciplines, and these
    are where the real research is taking
    place.
Source: Meyer, E.T., Schroeder, R. (2009). Untangling the Web of e-Research: Towards a Sociology of Online Knowledge. Journal of Informetrics
3(3):246-260
 We are all becoming e-Researchers;
  successful e-Research will become so
  mundane and expected that it will
  disappear from daily notice, like other
 infrastructures.
   Grid computing (the original incarnation
    of e-Science) was displaced by web
    services, then by the cloud; the cloud is
    now giving way to ‘big data’, which will
    no doubt be replaced by something else.
Research computing
Supercomputing


         The Grid


                    Web 2.0


                              Clouds


                                       Big Data
Research computing


Supercomputing

            The Grid

                       Web 2.0

                                 Clouds

                                          Big Data
Number of academic articles including mentions of computational approaches to research in their title,
abstract, or keywords. Source: Scopus queries by the authors. * 2012 only includes data through September.
Cloud computing: 3k-4k per month




Number of news articles including mentions of big data. Source: Lexis/Nexis queries by the authors.
   Hacking: styles of science (after Crombie)
     1. taxonomic
     2. statistical
     3. modelling
     4. observation and measurement
     5. historico-genetic development
     6. mathematical postulation
     +7. laboratory
     (+8. algorithmic?)
Styles of science, but also mathematization and other forms of
symbolic manipulation via cataloguing, image analysis, etc.
   Sciences: algorithms across the styles (modelling,
    statistics,…), data deluge,...
   Social Sciences: statistics, image analysis, mapping,…
   Humanities: patterns in words, numbers, images,
    sounds,… (ie. Google Books)
   Arts: audience engagement, new forms of performance,
    …
Particle Physics and EGEE: The world’s largest e-Science collaboration




Source: CERN, CERN-EX-0712023, http://cdsweb.cern.ch/record/1203203
The End(s) of e-Research
Social Sciences: Growing influence of new tools and
approaches
              VOSON (NodeXL version)




Ackland, R. (2010), "WWW Hyperlink Networks," Chapter 12 in D. Hansen, B. Shneiderman and M. Smith (eds),
Analyzing Social Media Networks with NodeXL: Insights from a connected world. Morgan-Kaufmann.
Social Sciences: Search engine behaviour

     Waller’s analysis of Australian Google Users

     Key findings:
            - Mainly leisure
            - < 2% contemporary issues
            - No perceptible ‘class’ differences

     Novel advance:
            - Unprecedented insight into what people search for

     Challenge:
            - Replicability
            - Securing access to commercial data

V. Waller, “Not Just Information: Who Searches for What on the Search Engine Google?”,
Journal of the American Society for Information Science and Technology, 62(4): 761-75, 2011.
Humanities: Large-scale text analysis

 Michel et al. ‘culturomic’ analysis of 5 Million Digitized Google
 Books and Perc analysis of the same data

 Key findings:
        - Patterns of key terms
        - Industrialization tied to shift from abstract to concrete
          words

 Novel advance:
        - Replicability, extension to other areas, systematic
          analysis of cultural materials

 Challenge:
        - Data quality
Fig. 1 Culturomic analyses study millions of books at once.




           J. Michel al. Quantitative Analysis of Culture Using Millions of Digitized
           Books. Science: Vol. 331 no. 6014 pp. 176-182. 2010.

Published by AAAS
Evolution of popularity of the top 100 n-grams over the past five centuries.




                             Perc M. (2012) Journal of the Royal Society Interface doi:10.1098/rsif.2012.0491

                             See: http://goo.gl/2URVT

©2012 by The Royal Society   Slide from John Lavagnino, King’s College London
Digital transformations of research

    Computational
    Manipulability +
 Research Technologies
  (Mathematization)

                          Transformations of
                            Research Front
                         (For different fields)

   Socio-Technical
    Organization
  (Computerization
    movements)
See http://www.oii.ox.ac.uk/research/projects/?id=98
Oxford Internet Institute



          Ralph Schroeder                             Eric T. Meyer
     ralph.schroeder@oii.ox.ac.uk                eric.meyer@oii.ox.ac.uk
http://www.oii.ox.ac.uk/people/?id=26    http://www.oii.ox.ac.uk/people/?id=120
                                                        @etmeyer


   With support from:

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The End(s) of e-Research

  • 1. The End(s) of e-Research Ralph Schroeder, Professor, MSc Programme Director Eric T. Meyer, Research Fellow, DPhil Programme Director October 25, 2012 @etmeyer
  • 2. research using  digital tools and data  for the distributed and collaborative  production of knowledge
  • 3. e-Research is not a separate entity; it consists merely of computational support for other disciplines, and these are where the real research is taking place.
  • 4. Source: Meyer, E.T., Schroeder, R. (2009). Untangling the Web of e-Research: Towards a Sociology of Online Knowledge. Journal of Informetrics 3(3):246-260
  • 5.  We are all becoming e-Researchers; successful e-Research will become so mundane and expected that it will disappear from daily notice, like other  infrastructures.
  • 6. Grid computing (the original incarnation of e-Science) was displaced by web services, then by the cloud; the cloud is now giving way to ‘big data’, which will no doubt be replaced by something else.
  • 7. Research computing Supercomputing The Grid Web 2.0 Clouds Big Data
  • 8. Research computing Supercomputing The Grid Web 2.0 Clouds Big Data
  • 9. Number of academic articles including mentions of computational approaches to research in their title, abstract, or keywords. Source: Scopus queries by the authors. * 2012 only includes data through September.
  • 10. Cloud computing: 3k-4k per month Number of news articles including mentions of big data. Source: Lexis/Nexis queries by the authors.
  • 11. Hacking: styles of science (after Crombie) 1. taxonomic 2. statistical 3. modelling 4. observation and measurement 5. historico-genetic development 6. mathematical postulation +7. laboratory (+8. algorithmic?) Styles of science, but also mathematization and other forms of symbolic manipulation via cataloguing, image analysis, etc.
  • 12. Sciences: algorithms across the styles (modelling, statistics,…), data deluge,...  Social Sciences: statistics, image analysis, mapping,…  Humanities: patterns in words, numbers, images, sounds,… (ie. Google Books)  Arts: audience engagement, new forms of performance, …
  • 13. Particle Physics and EGEE: The world’s largest e-Science collaboration Source: CERN, CERN-EX-0712023, http://cdsweb.cern.ch/record/1203203
  • 15. Social Sciences: Growing influence of new tools and approaches VOSON (NodeXL version) Ackland, R. (2010), "WWW Hyperlink Networks," Chapter 12 in D. Hansen, B. Shneiderman and M. Smith (eds), Analyzing Social Media Networks with NodeXL: Insights from a connected world. Morgan-Kaufmann.
  • 16. Social Sciences: Search engine behaviour Waller’s analysis of Australian Google Users Key findings: - Mainly leisure - < 2% contemporary issues - No perceptible ‘class’ differences Novel advance: - Unprecedented insight into what people search for Challenge: - Replicability - Securing access to commercial data V. Waller, “Not Just Information: Who Searches for What on the Search Engine Google?”, Journal of the American Society for Information Science and Technology, 62(4): 761-75, 2011.
  • 17. Humanities: Large-scale text analysis Michel et al. ‘culturomic’ analysis of 5 Million Digitized Google Books and Perc analysis of the same data Key findings: - Patterns of key terms - Industrialization tied to shift from abstract to concrete words Novel advance: - Replicability, extension to other areas, systematic analysis of cultural materials Challenge: - Data quality
  • 18. Fig. 1 Culturomic analyses study millions of books at once. J. Michel al. Quantitative Analysis of Culture Using Millions of Digitized Books. Science: Vol. 331 no. 6014 pp. 176-182. 2010. Published by AAAS
  • 19. Evolution of popularity of the top 100 n-grams over the past five centuries. Perc M. (2012) Journal of the Royal Society Interface doi:10.1098/rsif.2012.0491 See: http://goo.gl/2URVT ©2012 by The Royal Society Slide from John Lavagnino, King’s College London
  • 20. Digital transformations of research Computational Manipulability + Research Technologies (Mathematization) Transformations of Research Front (For different fields) Socio-Technical Organization (Computerization movements)
  • 22. Oxford Internet Institute Ralph Schroeder Eric T. Meyer ralph.schroeder@oii.ox.ac.uk eric.meyer@oii.ox.ac.uk http://www.oii.ox.ac.uk/people/?id=26 http://www.oii.ox.ac.uk/people/?id=120 @etmeyer With support from: