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Bottom-up, Top-Down, and other social and technological dynamics of e-Research

Bottom-up, Top-Down, and other social and technological dynamics of e-Research. Kathryn Eccles, Eric Meyer, Ralph Schroeder Oxford e-Social Science Project ( OeSS ) Oxford Internet Institute University of Oxford. Presented at the UK All Hands Conference, Oxford, December 2009.

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Bottom-up, Top-Down, and other social and technological dynamics of e-Research

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  1. Bottom-up, Top-Down, and other social and technological dynamics of e-Research Kathryn Eccles, Eric Meyer, Ralph Schroeder Oxford e-Social Science Project (OeSS) Oxford Internet Institute University of Oxford Presented at the UK All Hands Conference, Oxford, December 2009

  2. Top-Down and Bottom-Up • Two predominant ways of thinking about e-Research • Both are misleading… • Top-down because • It suggests supporting a ‘society’ of researchers (in fact, niches) • It suggests supporting structure (in fact, provisional efforts only) • Bottom-up because • It includes individual ‘garage’ efforts that are not sustained and/or cumulative • It includes individual efforts that are not shared - technically, socially, or in terms of goals • A definition of e-Research: distributed, shared digital tools or data for knowledge production

  3. Beyond Top-Down and Bottom-Up • Social movements around technological systems or artifactual cores • The conditions for stabilizing these new technologies…(not necessarily technological superiority) • Coalitions • Compatibility • Task uncertainty • Specific to e-Research is that these are distributed and shared, so commitment is an added condition • Diffusion, ‘community’, sustainability are also necessary

  4. Top-down and bottom-up again… • The two may not be mutually exclusive, but one or other may depend on the outcome of certain conditions (community creation, resources, etc.) • Resources, data, and tools have different conditions (as ideal types, even if they overlap)… • Resources (not part of knowledge production) depend mainly on maintenance and user support • Data depends on user contributions, standards, and competition with other data sets • Tools depend on user-friendliness or robustness, usefulness and critical mass

  5. Some examples • Swiss BioGrid: a set of tools and data without an ‘infrastructural’ home • Swedish National Data Service: an infrastructure with a community of users in the making • Genetic Association Identification Network: a shared data infrastructure with still-emerging standards • Pynchon Wiki: a rapidly forming community with indefinite task finalization • EGEE: a polymorphous large organization in organizational transition to another(s?) (EGI) • VOSON: a popular social science tool in search of resources for robustness

  6. Implications • Adoption cannot be conceived of in individual terms for e-Research technologies • Conditions for resources are different (also in underpinning natural science, social science and humanities) from tools and data • A fourth category is the ‘accidental e-Researcher’ – example: Web 2.0 tools (though the same conditions as for tools, data and resources apply) • The stabilization of socio-technical systems at different scales, functionality levels, and stages…

  7. Oxford Internet InstituteUniversity of OxfordRalph SchroederSenior Research Fellowralph.schroeder@oii.ox.ac.uk http://people.oii.ox.ac.uk/schroeder Kathryn EcclesResearch Fellowkathryn.eccles@oii.ox.ac.ukhttp://www.oii.ox.ac.uk/people/faculty.cfm?id=138 Eric T. MeyerResearch Felloweric.meyer@oii.ox.ac.ukhttp://people.oii.ox.ac.uk/meyer Oxford e-Social Science Project

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