The document discusses framing e-Research as a computerization movement based on Kling and Iacono's framework. It analyzes the origins and visions of e-Research, which sought to revolutionize research through grid infrastructure. However, challenges emerged in implementing this vision, as grid technologies were not always usable by domain researchers and did not facilitate the dynamic sharing envisioned. The document examines how the e-Research vision could be recalibrated to provide more open and flexible infrastructure.
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These slides were presented in a session that we organized at the American Association for Advancement of Science (AAAS) meeting in Chicago, February 2009.
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Canada is a data and technological society. There is no sector that is uninformed by data or unmediated by code, algorithms, software and infrastructure. Consider the Internet of Things (IoT), smart cities, and precision agriculture; or smart fisheries, forestry, and energy and of course governing. In a data based and technological society, leadership is the responsibility of all citizens, a parent, teacher, scholar, administrator, public servant, nurse and doctor, mayor and councillor, fisher, builder, business person, industrialist, MP, MLA, PM, and so on. In other words leadership is distributed and requires people power. This form of citizenship, according to Andrew Feenberg, Canada Research Chair in Philosophy of Technology, requires agency, knowledge and the capacity to act or power. In this GovMaker Keynote I will introduce the concept of technological citizenship, I will discuss what principled public interest governing might look like, and how we might go about critically applying philosophy in our daily practice. In terms of practice I will discuss innovative policy and regulation such as the right to repair movement, EU legislation such as the right to explanation, data subjects and the right to access and also data sovereignty from a globalization and an indigenous perspective.
Social network analysis is a method of big data analysis which reveals the nature
of connections between objects, including implicit connections. This is a tool of interest
since it can be applied to large data sets, manual processing of which is very laborintensive,
while automated processing through self-learning linguistic engines requires
a lot of resources. In this regard a study was carried out: it was aimed at development
and testing of social network analysis tools and creating a research algorithm which is
applicable to solve a wide range of analytical and search tasks. The current image of
Russia and its activities in the Arctic was chosen as a case.
The research algorithm helps to discover implicit patterns and trends, relate
information flows and events with relevant newsworthy events and news stories to form
a “clear” view of the study object and key actors which this object is associated with.
The work contributes to filling the gap in scientific literature, caused by insufficient
development of applied issues of using social network analysis to solve managerial
tasks, while theoretical papers, which describe the theory and methodology of such an
analysis, are abundant.
Looking at the past of infrastructure development for research data in the context of infrastructure development patterns and experiences from the evolution of the IEDA data facility to inform future pathways and developments. A major focus of the lecture is on the FAIR principles and the issues surrounding reusability of data.
These slides were presented in a session that we organized at the American Association for Advancement of Science (AAAS) meeting in Chicago, February 2009.
Abstract: New laboratory devices, sensor networks, high-throughput instruments, and numerical simulation systems are producing data at rates that are both without precedent and rapidly growing. The resulting increases in the size, number, and variety of data are revolutionizing scientific practice. These changes demand new computing infrastructures and tools. Until recently, most laboratories and collaborations managed their own data, operated their own computers, and used remote high-performance computers only when required. We are moving to a paradigm in which data will primarily be located and managed on remote clusters, grids, and data centers. In this symposium, we will examine the computing infrastructure designed to serve this emerging era of data-intensive computing from three perspectives: (1) that of grid computing, which enables the creation of virtual organizations that can share remote and distributed resources over the Internet; (2) that of data centers, which are transitioning to providers of integrated storage, data, compute, and collaboration services (the offering of one or more of these integrated services over the Internet is beginning to be called cloud computing); and (3) that of e-science, in which grids, Web 2.0 technologies, and new collaboration and analysis services are merging and changing the way science is conducted. Each speaker will focus on one perspective but also compare and contrast with the others.
Canada is a data and technological society. There is no sector that is uninformed by data or unmediated by code, algorithms, software and infrastructure. Consider the Internet of Things (IoT), smart cities, and precision agriculture; or smart fisheries, forestry, and energy and of course governing. In a data based and technological society, leadership is the responsibility of all citizens, a parent, teacher, scholar, administrator, public servant, nurse and doctor, mayor and councillor, fisher, builder, business person, industrialist, MP, MLA, PM, and so on. In other words leadership is distributed and requires people power. This form of citizenship, according to Andrew Feenberg, Canada Research Chair in Philosophy of Technology, requires agency, knowledge and the capacity to act or power. In this GovMaker Keynote I will introduce the concept of technological citizenship, I will discuss what principled public interest governing might look like, and how we might go about critically applying philosophy in our daily practice. In terms of practice I will discuss innovative policy and regulation such as the right to repair movement, EU legislation such as the right to explanation, data subjects and the right to access and also data sovereignty from a globalization and an indigenous perspective.
Social network analysis is a method of big data analysis which reveals the nature
of connections between objects, including implicit connections. This is a tool of interest
since it can be applied to large data sets, manual processing of which is very laborintensive,
while automated processing through self-learning linguistic engines requires
a lot of resources. In this regard a study was carried out: it was aimed at development
and testing of social network analysis tools and creating a research algorithm which is
applicable to solve a wide range of analytical and search tasks. The current image of
Russia and its activities in the Arctic was chosen as a case.
The research algorithm helps to discover implicit patterns and trends, relate
information flows and events with relevant newsworthy events and news stories to form
a “clear” view of the study object and key actors which this object is associated with.
The work contributes to filling the gap in scientific literature, caused by insufficient
development of applied issues of using social network analysis to solve managerial
tasks, while theoretical papers, which describe the theory and methodology of such an
analysis, are abundant.
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Geographic Information Management TransformationPat Kenny
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Why documenting research data? Is it worth the extra effort? learnings from t...ILRI
A presentation by Traore et al. at the Workshop on Dealing with Drivers of Rapid Change in Africa: Integration of Lessons from Long-term Research on INRM, ILRI, Nairobi, June 12-13, 2008.
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Some notes on how to train data science talent and exploit the fact that the membrane between academia and industry has become more permeable.
From Data Platforms to Dataspaces: Enabling Data Ecosystems for Intelligent S...Edward Curry
Digital transformation is driving a new wave of large-scale datafication in every aspect of our world. Today our society creates data ecosystems where data moves among actors within complex information supply chains that can form around an organization, community, sector, or smart environment. These ecosystems of data can be exploited to transform our world and present new challenges and opportunities in the design of intelligent systems. This talk presents my recent work on using the dataspace paradigm as a best-effort approach to data management within data ecosystems. The talk explores the theoretical foundations and principles of dataspaces and details a set of specialized best-effort techniques and models to enable loose administrative proximity and semantic integration of heterogeneous data sources. Finally, I share my perspectives on future dataspace research challenges, including multimedia data, data governance and the role of dataspaces to enable large-scale data sharing within Europe to power data-driven AI.
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ADAPT is revolutionising the way people can seamlessly interact with digital content, systems and each other and enabling users to achieve unprecedented levels of access and efficiency. - Prof. Declan O'Sullivan, Trinity College Dublin. Address given at Ordnance Survey Ireland GI R&D Initiatives, Tuesday, 22 March 2016, 13:00 to 20:30 (GMT), Maynooth University.
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Slides from my talk at the Melbourne Software Freedom Day, 21st September 2013, on the topic of Free and Open Source Software (FOSS) in public decision-making, particularly in the policy areas of climate change and transportation.
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Talking 'bout a revolution: Framing e-Research as a computerization movement
1. Talking 'bout a revolution: Framing e-Research as a computerization movement e-Research ‘08 Conference September 2008 Grace de la Flor Eric T. Meyer [email_address] Oxford University Computing Laboratory Wolfson Building Oxford OX1 3QD
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4. Framing e-Research as a computerization movement - "A kind of movement whose advocates focus on computer-based systems as instruments to bring about a new social order" (Kling & Iacono, 1988: 228) - communicate key ideological beliefs about "what computing is good for" and how project participants "should manage and organize access to computing" (Ibid: 227) - "computerization movements communicate key ideological beliefs about the links between computerization and a preferred social order which help legitimize computerization for many potential adopters" (Ibid)
5. Technological action frames in e-Research? "simplify and condense elements of complex technologies and their potential use...[enabling]...groups of people to interact about what they might mean" (Iacono & Kling 2001) 1) The positioning of Grid technology as a means to "provide an effective and efficient platform for the empowerment of specific communities of researchers to innovate and eventually revolutionize what they do, how they do it, and who participates" (Atkins et al, 2003) 2) The proposition of an "imminent data deluge" (Hey & Trefethen, 2003b) which will require "data access, integration and federation capabilities" using Grid technologies 3) Promoting "digital scholarship" through the embedding of Grid technology in the humanities and social sciences will led to "new intellectual products" making it necessary to "give high priority to building tools and collections" (ACLS report, 2006: 7)
6. Tracing the origins of e-Research - A 'technology-led initiative' - indicating an imbalance at the early phases of large-scale projects where "technologies have been developed without taking social aspects into account" (Schroeder & Fry, 2007: 567) - Technological visions developed far in advance of implementation & give meaning and direction by providing pre-defined solutions of how technology should operate, which may be used later to justify key decisions about the direction of software development (de la Flor et al, 2007) - Three factors that may have contributed to the development of the e-Research vision - Big science - Scientific computing research - Metacomputing - The interactions between both big science and computer science have been complementary; with big science providing requirements and research directions to scientific computing research and supercomputing providing technological advances that could be utilised in big science projects.
7. Supercomputing and big science - Big science "For many the age of Big Science was ushered in by the Manhattan Project during World War II, when the building of the atomic bomb involved the mobilization of much of the U.S. community of physical scientists in an engineering project of unprecedented magnitude. By the early 1960s, with the advent of NASA and the national space program, the term 'Big Science' was firmly affixed as a label for projects that required large-scale organization, massive commitments of funds, and complex technological systems" (Capshew & Rader, 1992: 3-4). - in silico experiments - Complex numerical models to test predictions or to analyse very large amounts of data - Results are investigated through the use of simulations and visualisation tools - Supercomputing - Provide high performance computing and mass data storage capacities - Supports big science research - NSF five national supercomputing centers (Princeton, San Diego, Illinois, Cornell, Pittsburgh) "past efforts in supercomputing and high-performance networking are being subsumed into a broader, integrated vision of a more capable, ubiquitous, and accessible cyberinfrastructure" (Freeman et al, 2005: 682)
8. Metacomputing and the Grid - A new set of protocols for network architecture - Transform networked access to information and tools; analogy of change ( De Roure et al (2003: 90) - 'publishing paradigm' - data can be shared in a distributed manner as images, documents and software tools - 'interactional paradigm' - Instruments and facilities at remote locations could be accessed directly - Grid protocols make possible the virtual consolidation of resources through a network "ultimately, one would hope that the Grid will be the operating system of the Internet" (Berman et al, 2003: 40)
9. A new science & engineering research agenda - Vision documents - US : Revolutionizing Science and Engineering Through Cyberinfrastructure (Atkins et al, 2003) - UK : The UK e-Science core programme and the Grid (Hey & Trefethen, 2002) & e-Science and its Implications (Hey & Trefethen, 2003a) - Establishment of a research programme "Scientific and engineering research has been crucial in both the creation and the advanced application of the amazing products of the digital revolution begun some sixty years ago – a revolution that increasingly undergirds our modern world" (Atkins et al, 2003) "indeed a further revolution – in how we create, disseminate, and preserve scientific and engineering knowledge"(Atkins et al, 2003) "The Core Programme can assist the e-science projects in building their discipline-centric grids and learn how to interconnect and federate multiple grids in a controlled way. If we can build such grids to production quality, operating 24 hours a day, seven days a week, 52 weeks a year, and that do not require computing experts to use them, then e-science really does have the potential to change the way we do scientific research in all our universities and research institutes" (Hey & Trefethen, 2003a: 1813).
10. e-Science: managing the data deluge - e-Research: support research goals. Grid technologies are presented as a means for fostering distributed research collaboration, sharing of data and remote instruments, enabling access to heterogeneous datasets and providing high-performance computing power and mass storage capacity "data generated from sensors, satellites, high-performance computer simulations, high-throughput devices, scientific images and so on will soon dwarf all of the scientific data collected in the whole history of scientific exploration" (Hey & Trefethen, 2003b: 812) "It has been argued that alongside the two traditional methodologies of science - theory and experiment - computational science has now emerged as a third methodology. With the advent of scientific data warehouses such as virtual observatories we may be seeing the emergence of a fourth methodology, that of collection-based science" (Hey & Trefethen, 2003a: 1823)
11. e-Social science: enabling digital scholarship "the development of more realistic models of complex social phenomena, the production and analysis of larger datasets (such as surveys, censuses, textual corpora, videotapes, cognitive neuroimaging records, and administrative data) that more completely record human behavior, the integration and coordination of disparate datasets to enable deeper investigation, and the collection of better data through experiments and simulations on the Internet" (Berman & Brady, 2005: 5) - The argument from e-Research proponents is that the social sciences and humanities can benefit from the use of Grid technologies. However, some researchers have discussed challenges in mapping the computational requirements for the physical sciences directly on to the social sciences and humanities (Carusi & Jirotka, 2007; Meyer et al, 2008)
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13. Reconfiguring practices in domain research & access to infrastructure - "An infrastructure occurs when the tensions between local and global is resolved. That is, an infrastructure occurs when local practices are afforded by a larger-scale technology, which can be used in a natural, ready-to-hand fashion" (Star & Ruhleder, 1996: 114) - Can Grid infrastructure be used in a natural & ready-to-hand fashion ? - Challenges of use: “ I've got a Grid API against which I had to program in order to get images over and so on. Was that usable for me as a user of Grid technologies? Probably not.” (ER02a-06) . - Avoidance of use: "One of the definitions of what we're doing is setting up a Grid, 'cause we're connecting experimental facilities together over a network. You could call it a Grid...[but]...anything that we do we program ourselves" (ER03a-08) .
14. So what exactly is the anatomy of the Grid? - Highly controlled "The sharing that we are concerned with is not primarily file exchange but rather direct access to computers, software, data, and other resources, as is required by a range of collaborative problem-solving and resource-brokering strategies emerging in industry, science, and engineering. This sharing is, necessarily, highly controlled, with resource providers and consumers defining clearly and carefully just what is shared, who is allowed to share and the conditions under which sharing occurs. A set of individuals and/or institutions defined by such sharing rules form what we call a virtual organization" (Foster et al, 2001: 172). - Highly dynamic "Because of their focus on dynamic, cross-organizational sharing, Grid technologies complement rather than compete with existing distributed computing technologies...Grid technologies can be used to establish dynamic markets for computing and storage resources, hence overcoming the limitations of current static configurations" (p. 202). "Just as the Web revolutionized information sharing by providing a universal protocol and syntax (HTTP and HTML) for information exchange, so we require standard protocols and syntaxes for general resource sharing" (p. 205). - It is important to note here that the Web revolutionized information sharing because its protocols are easy to use & does not "require computing experts to use" (Hey & Trefethen, 2003a: 1813)
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16. Thanks & questions Talking 'bout a revolution: Framing e-Research as a computerization movement e-Research ‘08 Conference September 2008 Grace de la Flor Eric T. Meyer [email_address] Oxford University Computing Laboratory Wolfson Building Oxford OX1 3QD
Editor's Notes
My name is Grace de la Flor and I’m a D.Phil student at the Computing laboratory at the University of Oxford. My research focus is on developing novel methods for requirements capture that will address the specific concerns of e-Science & e-Social Science projects - in particular; approaches to requirements for large-scale, distributed projects that address both 1) understanding researcher work practices and 2) requirements for collaboration My collaborators on this paper are MJ & SL who are based at the Computing Lab in Oxford and AW who was at Oxford but is now with Microsoft In this talk I will present an overview of our initial case study research findings set out to understand the challenges of 1) designing e-Science applications and 2) the management of e-Science projects