Title TBA: Revising the Abstract Submission Process.

Academic conferences are among the most prolific scientific activities, yet the current abstract submission and review process has serious limitations. We propose a revised process that would address these limitations, achieve some of the aims of Open Science, and stimulate discussion throughout the entire lifecycle of the scientific work

The LIGO Open Science Center

The LIGO Open Science Center (LOSC) fulfills LIGO's commitment to release, archive, and serve LIGO data in a broadly accessible way to the scientific community and to the public, and to provide the information and tools necessary to understand and use the data. In August 2014, the LOSC published the full dataset from Initial LIGO's "S5" run at design sensitivity, the first such large-scale release and a valuable testbed to explore the use of LIGO data by non-LIGO researchers and by the public, and to help teach gravitational-wave data analysis to students across the world. In addition to serving the S5 data, the LOSC web portal (losc.ligo.org) now offers documentation, data-location and data-quality queries, tutorials and example code, and more. We review the mission and plans of the LOSC, focusing on the S5 data release.Comment: 8 pages, 1 figure, proceedings of the 10th LISA Symposium, University of Florida, Gainesville, May 18-23, 2014; final published version; see losc.ligo.org for the S5 data release and more information about the LIGO Open Science Cente

BEAT: An Open-Source Web-Based Open-Science Platform

With the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques promising improved performance, generalization and robustness. Sadly, result reproducibility is often an overlooked feature accompanying original research publications, competitions and benchmark evaluations. The main reasons behind such a gap arise from natural complications in research and development in this area: the distribution of data may be a sensitive issue; software frameworks are difficult to install and maintain; Test protocols may involve a potentially large set of intricate steps which are difficult to handle. Given the raising complexity of research challenges and the constant increase in data volume, the conditions for achieving reproducible research in the domain are also increasingly difficult to meet. To bridge this gap, we built an open platform for research in computational sciences related to pattern recognition and machine learning, to help on the development, reproducibility and certification of results obtained in the field. By making use of such a system, academic, governmental or industrial organizations enable users to easily and socially develop processing toolchains, re-use data, algorithms, workflows and compare results from distinct algorithms and/or parameterizations with minimal effort. This article presents such a platform and discusses some of its key features, uses and limitations. We overview a currently operational prototype and provide design insights.Comment: References to papers published on the platform incorporate

Open Access: Science Publishing as Science Publishing Should Be

Full and unimpeded access (Open Access) to science literature is needed. It is not provided by the traditional subscription-based publishing model. Instead of criticizing Open Access and attacking its proponents, traditional publishers should make imaginative and innovative efforts to build their businesses around the needs of their customers rather than around their desire to continue a model that may be lucrative, but that is no longer satisfactory to science or society

Open up : the mission statement of the Control of Impulsive Action (Ctrl-ImpAct) lab on Open Science

The present paper is the mission statement of the Control of Impulsive Action (Ctrl-ImpAct) Lab regarding Open Science. As early-career researchers (ECRs) in the lab, we first state our personal motivation to conduct research based on the principles of Open Science. We then describe how we incorporate four specific Open Science practices (i.e., Open Methodology, Open Data, Open Source, and Open Access) into our scientific workflow. In more detail, we explain how Open Science practices are embedded into the so-called 'co-pilot' system in our lab. The 'co-pilot' researcher is involved in all tasks of the 'pilot' researcher, that is designing a study, double-checking experimental and data analysis scripts, as well as writing the manuscript. The lab has set up this co-pilot system to increase transparency, reduce potential errors that could occur during the entire workflow, and to intensify collaborations between lab members. Finally, we discuss potential solutions for general problems that could arise when practicing Open Science

Quantum Information Dynamics and Open World Science

One of the fundamental insights of quantum mechanics is that complete knowledge of the state of a quantum system is not possible. Such incomplete knowledge of a physical system is the norm rather than the exception. This is becoming increasingly apparent as we apply scientific methods to increasingly complex situations. Empirically intensive disciplines in the biological, human, and geosciences all operate in situations where valid conclusions must be drawn, but deductive completeness is impossible. This paper argues that such situations are emerging examples of {it Open World} Science. In this paradigm, scientific models are known to be acting with incomplete information. Open World models acknowledge their incompleteness, and respond positively when new information becomes available. Many methods for creating Open World models have been explored analytically in quantitative disciplines such as statistics, and the increasingly mature area of machine learning. This paper examines the role of quantum theory and quantum logic in the underpinnings of Open World models, examining the importance of structural features of such as non-commutativity, degrees of similarity, induction, and the impact of observation. Quantum mechanics is not a problem around the edges of classical theory, but is rather a secure bridgehead in the world of science to come

Discovering Job Preemptions in the Open Science Grid

The Open Science Grid(OSG) is a world-wide computing system which facilitates distributed computing for scientific research. It can distribute a computationally intensive job to geo-distributed clusters and process job's tasks in parallel. For compute clusters on the OSG, physical resources may be shared between OSG and cluster's local user-submitted jobs, with local jobs preempting OSG-based ones. As a result, job preemptions occur frequently in OSG, sometimes significantly delaying job completion time. We have collected job data from OSG over a period of more than 80 days. We present an analysis of the data, characterizing the preemption patterns and different types of jobs. Based on observations, we have grouped OSG jobs into 5 categories and analyze the runtime statistics for each category. we further choose different statistical distributions to estimate probability density function of job runtime for different classes.Comment: 8 page

New science on the Open Science Grid

The Open Science Grid (OSG) includes work to enable new science, new scientists, and new modalities in support of computationally based research. There are frequently significant sociological and organizational changes required in transformation from the existing to the new. OSG leverages its deliverables to the large-scale physics experiment member communities to benefit new communities at all scales through activities in education, engagement, and the distributed facility. This paper gives both a brief general description and specific examples of new science enabled on the OSG. More information is available at the OSG web site: www.opensciencegrid.org