Aydinoglu, Arsev

English

University of Tennessee, Knoxville: Trace

University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange DataONE Sociocultural and Usability & Assessment Working Groups Communication and Information 2010 Emergence of a scientific collaboration: DataONE case study. Arsev Aydinoglu University of Tennessee - Knoxville, arsevu@gmail.com Follow this and additional works at: https://trace.tennessee.edu/utk_dataone  Part of the Library and Information Science Commons Recommended Citation Aydinoglu, A.U. (2010). Emergence of a scientific collaboration: DataONE case study. Presentation at the Society for the Social Studies of Science (4S) Annual Meeting, University of Tokyo, Tokyo, Japan. This Presentation is brought to you for free and open access by the Communication and Information at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in DataONE Sociocultural and Usability & Assessment Working Groups by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact trace@utk.edu. Contents Introduction BackgroundMethods DataONE (Preliminary) resultsIntroduction Tougher problems (global warming, energy demand, space research, financial crisis etc.) Big science  large-scale research projects that needs vast resources funded by national governments or groups of national governments. (Weingberg, 1961) Collaborative researchResearch question How do multidisciplinary, multinational, and multi-institutional scientific collaborations emerge?Scientific Collaborations Historical background Studies on scientific collaborations Gaps  The impact of new developments in ICT Complexity theory perspectiveComplexity Theory Non-linear systems Chaos theory Complexity theory Critically interacting components self-organize to form potentially evolving structures exhibiting a hierarchy of emergent system. (self-organizing systems discussion group, 2008). Multiple agents + interactions Applications in biology, computer science,  philosophy, organizational studies, communication, economics, international relations.Complex Adaptive SystemHull, 2008Complexity Theory Systems are potentially chaotic (complex). Systems move from one dynamic state to the other through a discrete bifurcation process. Forecasting is impossible, especially at a global scale and in the long term. When in a chaotic state: systems are "attracted" to an identifiable configuration; systems, generally, have a fractal form. Similar actions taken by systems in a complex state will never lead to the same result.Emergence Three waves of General System Theory (Sawyer, 2004) Parson’s Structural FunctionalismGeneral Systems Theory Complex Adaptive Systems Emergence Lower lever – higher levelEx: SMS Language or SlangEmergent behaviorhttp://www.softwarecreation.orgMethods Case study “research strategy which focuses on understanding the dynamics present within single settings” (Eisenhardt, 1989, 534) Deep understanding through rich data Multiple techniques and sources in data gathering Suitable for longitudinal studiesSample Information oriented sampling (Flyberg, 2006) Selecting a case that has the potential to provide the richest data Data Collection Semi-structured interviews w/ leadership team & cyberinfrastructure team (14) Natural observation (2 grand meetings + daily work)Online surveyHistorical background  Office of Cyberinfrastructure Supercomputer centers program (1980s) High performance computing and communications (1990s) Cyberinfrastructure (2000s) “the coordinated aggregation of software, hardware and other technologies as well as human expertise to support current and future discoveries in science and engineering and to integrate relevant and often disparate resources to provide a useful, usable and enabling computational and data framework characterized by broad access.” (NSF Cyberinfrastructure Workshop, 2006) Involved in tackling ‘grand challenges’.NSF’s DataNet Vision15 Provide the means to meet critical data needs  Create a structure that has a comprehensive range of expertise and ensures active participation. Achieve long term sustainability Provide for the full data management life cycle (creation, dissemination, & preservation), facilitate research, develop new tools and develop new capabilities for learning  Work with other DataNet PartnersThe DataNet program:  $100 Million for  5 DataNet Partners over 5 years ($20M each)The First Two Funded Projects: DataONE  and  the Data Conservancy(Observation Network for Earth)16Universal access about to life on earth and the environment that sustains it.Goals: To enable science Create sustainable & secure infrastructure Encourage broad and active community engagement Be useful to researchers, decision-makers, educators, studentsThe challenges of global changeSmith, Knapp, Collins. In press.Human impacts on land-based ecosystemsEcosystems and Human Well-BeingScientific challenges and data needs Global change is a complex scientific and societal challenge Community needs good data Good data… build good science make possible wise management  enable sound decisions Good data need… solid technical infrastructure sound organization community engagement – multiple disciplinesNature of the collaborationUnidisciplinary - researchers from a single discipline work together to address a common problem Multidisciplinary - researchers from different disciplines work independentlyor sequentially, each from his or her own disciplinary-specific perspective, to address a common problemInterdisciplinary - researchers from different disciplines work jointly to address a common problem and although some integration of their diverse perspectives occurs, participants remain anchored in their own fieldsTransdisciplinary - researchers from different disciplines work jointly to create a shared conceptual framework that integrates and moves beyond discipline-specific theories, concepts, and approaches, to address a common problem(Rosenfield, 1992)DataONERationale for selecting DataONE Timing Potential for emergent behavior Interaction among networks Systems functions cannot be localized Interaction w/ complex & sophisticated language ConveniencePreliminary Results 1/7 Non-linear relations (institutional & individual level)Preliminary Results 2/7 Counteracting forces (individual level) Different goals: “Fits to my research interest.” “For my tenure I need to be involved in such projects.” “I was called.” “It was my job to facilitate.”Preliminary Results 3/7 Strange attractors 1/2 NSF Solicitation = fundingNSFFunding!Funding! Funding!Scientific communityPreliminary Results 4/7 Strange Attractor 2/2 Severity of the problem‘Climate Change’Preliminary Results 5/7 No forecasting Developing the cyberinfrastructure Know what to develop but not how to, which steps to follow, it is evolving by itselfPreliminary Results 6/7 Interaction Frequent and continuous communication f2f Email Skype Collaboration websitePreliminary Results 7/7 Notes from fieldwork Constant recreation of working groups (higher level) Loose leadership style Emergence of an in-group language (e.g. acronym list)Waves (?)Future Research  Online survey (fractals)  DataONE & scientific collaborations Longitudinal study Community engagement  Interdisciplinarity & the role of Information Science Cyberinfrastructure Complexity theory

Emergence of a scientific collaboration: DataONE case study.

https://trace.tennessee.edu/cgi/viewcontent.cgi?article=1052&context=utk_dataone

Emergence of a scientific collaboration: DataONE case study.

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