Accelerating Science: A Computing Research Agenda

The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine natural phenomena through the computational lens, i.e., using algorithmic or information processing abstractions of the underlying processes; and our ability to acquire, share, integrate and analyze disparate types of data. However, there is a huge gap between our ability to acquire, store, and process data and our ability to make effective use of the data to advance discovery. Despite successful automation of routine aspects of data management and analytics, most elements of the scientific process currently require considerable human expertise and effort. Accelerating science to keep pace with the rate of data acquisition and data processing calls for the development of algorithmic or information processing abstractions, coupled with formal methods and tools for modeling and simulation of natural processes as well as major innovations in cognitive tools for scientists, i.e., computational tools that leverage and extend the reach of human intellect, and partner with humans on a broad range of tasks in scientific discovery (e.g., identifying, prioritizing formulating questions, designing, prioritizing and executing experiments designed to answer a chosen question, drawing inferences and evaluating the results, and formulating new questions, in a closed-loop fashion). This calls for concerted research agenda aimed at: Development, analysis, integration, sharing, and simulation of algorithmic or information processing abstractions of natural processes, coupled with formal methods and tools for their analyses and simulation; Innovations in cognitive tools that augment and extend human intellect and partner with humans in all aspects of science.Comment: Computing Community Consortium (CCC) white paper, 17 page

The LSST Data Mining Research Agenda

We describe features of the LSST science database that are amenable to scientific data mining, object classification, outlier identification, anomaly detection, image quality assurance, and survey science validation. The data mining research agenda includes: scalability (at petabytes scales) of existing machine learning and data mining algorithms; development of grid-enabled parallel data mining algorithms; designing a robust system for brokering classifications from the LSST event pipeline (which may produce 10,000 or more event alerts per night); multi-resolution methods for exploration of petascale databases; indexing of multi-attribute multi-dimensional astronomical databases (beyond spatial indexing) for rapid querying of petabyte databases; and more.Comment: 5 pages, Presented at the "Classification and Discovery in Large Astronomical Surveys" meeting, Ringberg Castle, 14-17 October, 200

Physicists, stamp collectors, human mobility forecasters

One of the two reviewers studied in high school to be a physicist. In the end, he became something else, but he never lost his awe of physics. The other reviewer never intended to become a physicist, but he sometimes asks himself why he didn’t become one. Today, they are both sociologists who practice their science on an action theory basis and believe that regularities exist in the world of social actions which can be perceived, understood, explained – and even used for making predictions

Identification and Comparison of Gray Literature in Two Polar Libraries: Australian Antarctic Division and Scott Polar Research Institute

Gray literature collections were investigated and compared at the libraries of the Australian Antarctic Division (AAD) and the Scott Polar Research Institute (SPRI) in order to improve accessibility. These collections are important to Arctic and Antarctic researchers, but are problematic because they are not well documented, often have limited access, and are arranged by subject using a classification system specific to polar libraries. Tangible results of the project include estimates of the number of gray literature items in the polar subject categories for the two libraries, along with a template of a user’s finding aid to these collections. In addition, 172 sources from four Antarctic expeditions in the early part of the 20th century were selected as a representative sample; 64 from AAD and 108 from SPRI. While small, the sample was a focused topic with enough variety of materials to provide good examples for accessibility issues. Inquiries are continually received at AAD and SPRI for information related to these four expeditions, so improved access will be beneficial for both researchers and the two institutions. Making the material more available is also very timely, anticipating renewed interest from the public with the approaching centennial celebrations of two of the expeditions coming up in 2010 and 2011. Despite the similar subject nature of the collections, only ten items were duplicated in the two libraries. Solutions for improving access, such as linking the gray literature collections to broader initiatives are addressed in more detail in the final report. Providing the references in a metadata format to include in an online catalog or linked to a website will increase visibility and use of the materials. Suggestions for improving the arrangement of the materials and reducing duplication within the collections are also discussed in the final report available on my blog. http://www.consortiumlibrary.org/blogs/dcarle/sabbatical/Summary / Project Background / Project Methodology Part I / Results Part I / Project Methodology and Results Part II / Discussion and Recommendations / Additional Activities / Additional Accomplishments / Additional Professional Activities / Project Goals Not Completed/ Benefitsof Sabbatical / Conclusion and Acknowledgement

The Importance of Social and Government Learning in Ex Ante Policy Evaluation

We provide two methodological insights on \emph{ex ante} policy evaluation for macro models of economic development. First, we show that the problems of parameter instability and lack of behavioral constancy can be overcome by considering learning dynamics. Hence, instead of defining social constructs as fixed exogenous parameters, we represent them through stable functional relationships such as social norms. Second, we demonstrate how agent computing can be used for this purpose. By deploying a model of policy prioritization with endogenous government behavior, we estimate the performance of different policy regimes. We find that, while strictly adhering to policy recommendations increases efficiency, the nature of such recipes has a bigger effect. In other words, while it is true that lack of discipline is detrimental to prescription outcomes (a common defense of failed recommendations), it is more important that such prescriptions consider the systemic and adaptive nature of the policymaking process (something neglected by traditional technocratic advice)

Macro-iterativity : a qualitative multi-arc design for studying complex issues and big questions

The impact and relevance of our discipline's research is determined by its ability to engage the big questions of the grand challenges we face today. Our central argument is that we need innovative methods that engage large-scope phenomena, not least because these phenomena benefit from going beyond individual study design. We introduce the concept of macro-iterativity which involves multiple iterations that move between, and link across, a set of research cycles. We offer a multi-arc research design that comprises the discovery arc and extension arc and three extension logics through which scholars can combine these arcs of inquiry in a coherent way. Based on this research design, we develop a roadmap that guides scholars through the four steps of how to engage in multi-arc research along with the main techniques and outputs. We argue that a multi-arc design supports the move toward more generative theorizing that is required for researching problems dealing with the complex issues and big questions of our time.PostprintPeer reviewe

Reflections On Science And Technoscience

Technoscientific research, a kind of scientific research conducted within the decontextualized approach (DA), uses advanced technology to produce instruments, experimental objects, and new objects and structures, that enable us to gain knowledge of states of affairs of novel domains, especially knowledge about new possibilities of what we can do and make, with the horizons of practical, industrial, medical or military innovation, and economic growth and competition, never far removed from view. The legitimacy of technoscientific innovations can be appraised only in the course of considering fully what sorts of objects technoscientific objects are: objects that embody scientific knowledge confirmed within DA; physical/chemical/biological objects, realizations of possibilities discovered in research conducted within DA, brought to realization by means of technical/experimental/instrumental interventions; and components of social/ecological systems, objects that embody the values of technological progress and (most of them) values of capital and the market. What technoscientific objects are - their powers, tendencies, sources of their being, effects on human beings and social/economic systems, how they differ from non technoscientific objects - cannot be grasped from technoscientific inquiry alone; scientific inquiry that is not reducible to that conducted within DA is also needed. The knowledge that underlies and explains the efficacy of technoscientific objects is never sufficient to grasp what sorts of object they are and could become. Science cannot be reduced to technoscience

DIGITAL: multidisciplinary and multidimensional in the classrooms

In this paper our aim is to analyse and present some pedagogical paths that prefigure and guide the teaching-learning devices developed "around" the digital tools. In this context issues related to the implementation with teaching methodologies and teaching techniques acquire a new dimension due to the need of transpose them into online learning environments (technologies to teach to technologies to learn). This starting point is a deep understanding from the analysis of actors in the online learning process: student, teacher, platform and e- contents. Thus, it is our goal in this chapter to promote digital education, think of teaching methods, tools and learning processes, to adapted to eLearninginfo:eu-repo/semantics/publishedVersio