Stochastic Spacetime and Brownian Motion of Test Particles

The operational meaning of spacetime fluctuations is discussed. Classical spacetime geometry can be viewed as encoding the relations between the motions of test particles in the geometry. By analogy, quantum fluctuations of spacetime geometry can be interpreted in terms of the fluctuations of these motions. Thus one can give meaning to spacetime fluctuations in terms of observables which describe the Brownian motion of test particles. We will first discuss some electromagnetic analogies, where quantum fluctuations of the electromagnetic field induce Brownian motion of test particles. We next discuss several explicit examples of Brownian motion caused by a fluctuating gravitational field. These examples include lightcone fluctuations, variations in the flight times of photons through the fluctuating geometry, and fluctuations in the expansion parameter given by a Langevin version of the Raychaudhuri equation. The fluctuations in this parameter lead to variations in the luminosity of sources. Other phenomena which can be linked to spacetime fluctuations are spectral line broadening and angular blurring of distant sources.Comment: 15 pages, 3 figures. Talk given at the 9th Peyresq workshop, June 200

Stress Tensor Correlators in the Schwinger-Keldysh Formalism

We express stress tensor correlators using the Schwinger-Keldysh formalism. The absence of off-diagonal counterterms in this formalism ensures that the +- and -+ correlators are free of primitive divergences. We use dimensional regularization in position space to explicitly check this at one loop order for a massless scalar on a flat space background. We use the same procedure to show that the ++ correlator contains the divergences first computed by `t Hooft and Veltman for the scalar contribution to the graviton self-energy.Comment: 14 pages, LaTeX 2epsilon, no figures, revised for publicatio

Moving Archival Practices Upstream: An Exploration of the Life Cycle of Ecological Sensing Data in Collaborative Field Research

The success of eScience research depends not only upon effective collaboration between scientists and technologists but also upon the active involvement of data archivists. Archivists rarely receive scientific data until findings are published, by which time important information about their origins, context, and provenance may be lost. Research reported here addresses the life cycle of data from collaborative ecological research with embedded networked sensing technologies. A better understanding of these processes will enable archivists to participate in earlier stages of the life cycle and to improve curation of these types of scientific data. Evidence from our interview study and field research yields a nine-stage life cycle. Among the findings are the cumulative effect of decisions made at each stage of the life cycle; the balance of decision-making between scientific and technology research partners; and the loss of certain types of data that may be essential to later interpretation

Data journeys: Capturing the socio-material constitution of data objects and flows

In this paper, we discuss the development and piloting of a new methodology for illuminating the socio-material con- stitution of data objects and flows as data move between different sites of practice. The data journeys approach contributes to the development of critical, qualitative methodologies that can address the geographic and temporal scale of emerging knowledge infrastructures, and capture the ‘life of data’ from their initial generation through to re-use in different contexts. We discuss the theoretical development of the data journeys methodology and the application of the approach on a project examining meteorological data on their journey from initial production through to being re- used in climate science and financial markets. We then discuss three key conceptual findings from this project about: (1) the socio-material constitution of digital data objects, (2) ‘friction’ in the movement of data through space and time and (3) the mutability of digital data as a material property that contributes to driving the movement of data between different sites of practice

What lies beneath?: Knowledge infrastructures in the subseafloor biosphere and beyond

We present preliminary findings from a three-year research project comprised of longitudinal qualitative case studies of data practices in four large, distributed, highly multidisciplinary scientific collaborations. This project follows a 2 ×× 2 research design: two of the collaborations are big science while two are little science, two have completed data collection activities while two are ramping up data collection. This paper is centered on one of these collaborations, a project bringing together scientists to study subseafloor microbial life. This collaboration is little science, characterized by small teams, using small amounts of data, to address specific questions. Our case study employs participant observation in a laboratory, interviews ( n=49n=49 to date) with scientists in the collaboration, and document analysis. We present a data workflow that is typical for many of the scientists working in the observed laboratory. In particular, we show that, although this workflow results in datasets apparently similar in form, nevertheless a large degree of heterogeneity exists across scientists in this laboratory in terms of the methods they employ to produce these datasets—even between scientists working on adjacent benches. To date, most studies of data in little science focus on heterogeneity in terms of the types of data produced: this paper adds another dimension of heterogeneity to existing knowledge about data in little science. This additional dimension makes more complex the task of management and curation of data for subsequent reuse. Furthermore, the nature of the factors that contribute to heterogeneity of methods suggest that this dimension of heterogeneity is a persistent and unavoidable feature of little science.Alfred P. Sloan Foundation (#20113194)Ope

WindS@UP: the e-science platform for windscanner.eu

The WindScanner e-Science platform architecture and the underlying premises are discussed. It is a collaborative platform that will provide a repository for experimental data and metadata. Additional data processing capabilities will be incorporated thus enabling in-situ data processing. Every resource in the platform is identified by a Uniform Resource Identifier (URI), enabling an unequivocally identification of the field(s) campaign(s) data sets and metadata associated with the data set or experience. This feature will allow the validation of field experiment results and conclusions as all managed resources will be linked. A centralised node (Hub) will aggregate the contributions of 6 to 8 local nodes from EC countries and will manage the access of 3 types of users: data-curator, data provider and researcher. This architecture was designed to ensure consistent and efficient research data access and preservation, and exploitation of new research opportunities provided by having this “Collaborative Data Infrastructure”. The prototype platform—WindS@UP—enables the usage of the platform by humans via a Web interface or by machines using an internal API (Application Programming Interface). Future work will improve the vocabulary (“application profile”) used to describe the resources managed by the platform.The WindScanner.eu|The European WindScanner Facility|is an ESFRI project (N: 312372) under the FP7-Infrastructures-2012-1. The authors are grateful to all colleagues in WP5 for the fruitful discussions, namely Dimitri Foussekis (CRES), Doron Callies (IWES Fraunhofer), Hans Verhoef (ECN), Harald Svendsen (Sintef), Jan Willem Wagenaar (ECN), Javier Sanz Rodrigo (CENER), Martin Bitter (Forwind), Mikael Sj oholm (DTU), Steen Arne S rensen (DTU) and Teresa Sim~oes (LNEG)

Understanding information need : an fMRI study

The raison d'etre of IR is to satisfy human information need. But, do we really understand information need? Despite advances in the past few decades in both the IR and relevant scientific communities, this question is largely unanswered. We do not really understand how an information need emerges and how it is physically manifested. Information need refers to a complex concept: at the very initial state of the phenomenon (i.e. at a visceral level), even the searcher may not be aware of its existence. This renders the measuring of this concept (using traditional behaviour studies) nearly impossible. In this paper, we investigate the connection between an information need and brain activity. Using functional Magnetic Resonance Imaging (fMRI), we measured the brain activity of twenty four participants while they performed a Question Answering (Q/A) Task, where the questions were carefully selected and developed from TREC-8 and TREC 2001 Q/A Track. The results of this experiment revealed a distributed network of brain regions commonly associated with activities related to information need and retrieval and differing brain activity in processing scenarios when participants knew the answer to a given question and when they did not and needed to search. We believe our study and conclusions constitute an important step in unravelling the nature of information need and therefore better satisfying it

The Emerging Scholarly Brain

It is now a commonplace observation that human society is becoming a coherent super-organism, and that the information infrastructure forms its emerging brain. Perhaps, as the underlying technologies are likely to become billions of times more powerful than those we have today, we could say that we are now building the lizard brain for the future organism.Comment: to appear in Future Professional Communication in Astronomy-II (FPCA-II) editors A. Heck and A. Accomazz

An Analysis of the Shapes of Interstellar Extinction Curves. V. The IR-Through-UV Curve Morphology

We study the IR-through-UV interstellar extinction curves towards 328 Galactic B and late-O stars. We use a new technique which employs stellar atmosphere models in lieu of unreddened "standard" stars. This technique is capable of virtually eliminating spectral mismatch errors in the curves. It also allows a quantitative assessment of the errors and enables a rigorous testing of the significance of relationships between various curve parameters, regardless of whether their uncertainties are correlated. Analysis of the curves gives the following results: (1) In accord with our previous findings, the central position of the 2175 A extinction bump is mildly variable, its width is highly variable, and the two variations are unrelated. (2) Strong correlations are found among some extinction properties within the UV region, and within the IR region. (3) With the exception of a few curves with extreme (i.e., large) values of R(V), the UV and IR portions of Galactic extinction curves are not correlated with each other. (4) The large sightline-to-sightline variation seen in our sample implies that any average Galactic extinction curve will always reflect the biases of its parent sample. (5) The use of an average curve to deredden a spectral energy distribution (SED) will result in significant errors, and a realistic error budget for the dereddened SED must include the observed variance of Galactic curves. While the observed large sightline-to-sightline variations, and the lack of correlation among the various features of the curves, make it difficult to meaningfully characterize average extinction properties, they demonstrate that extinction curves respond sensitively to local conditions. Thus, each curve contains potentially unique information about the grains along its sightline.Comment: To appear in the Astrophysical Journal, Part 1, July 1, 2007. Figures and Tables which will appear only in the electronic version of the Journal can be obtained via anonymous ftp from ftp://ftp.astronomy.villanova.edu . After logging in, change directories to "fitz/FMV_EXTINCTION". A README file describes the various files present in the director