Data Science Seminar: The data science revolution in Physics and Astronomy

Abstract Heading into the 2020s, Physics and Astronomy are undergoing experimental revolutions that will reshape our picture of the fabric of the Universe. The Large Hadron Collider (LHC), the largest particle physics project in the world, produces 30 petabytes of data annually that need to be sifted through, analysed, and modelled. In astrophysics, the Large Synoptic Survey Telescope (LSST) will be taking a high-resolution image of the full sky every 3 days, leading to data rates of 30 terabytes per night over ten years. These experiments endeavour to answer the question why 96% of the content of the universe currently elude our physical understanding. Both the LHC and LSST share the 5-dimensional nature of their data, with position, energy and time being the fundamental axes. This talk will present an overview of the experiments and data that is gathered, and outlines the challenges in extracting information. Common strategies employed are very similar to industrial data! Science problems (e.g., data filtering, machine learning, statistical interpretation) and provide a seed for exchange of knowledge between academia and industry. Speaker Biography Professor Mark Sullivan Mark Sullivan is a Professor of Astrophysics in the Department of Physics and Astronomy. Mark completed his PhD at Cambridge, and following postdoctoral study in Durham, Toronto and Oxford, now leads a research group at Southampton studying dark energy using exploding stars called "type Ia supernovae". Mark has many years' experience of research that involves repeatedly imaging the night sky to track the arrival of transient objects, involving significant challenges in data handling, processing, classification and analysis.

Scitable - Learn Science with Nature Education

Scitable is an open online teaching/learning portal combining high quality educational articles authored by editors at NPG with technology-based community features to fuel a global exchange of scientific insights, teaching practices, and study resources. Scitable currently contains articles in the field of genetics, and is intended for college undergraduate faculty and students. Future plans involve extension of Scitable to other fields within the life sciences, as well as to other audiences. Scitable brings together a library of scientific overviews with a worldwide community of scientists, researchers, teachers and students. Nature Education is a new division of Nature Publishing Group devoted to facilitating high quality, innovative, accessible science education in all countries of the world.

W3233 -- Networks and Complexity in Social Systems (Collective Dynamics Group ISERP -- Columbia University) Course Syllabus

The Networks and Complexity in Social Systems course commences with an overview of the nascent field of complex networks, dividing it into three related but distinct strands: Statistical description of large scale networks, viewed as static objects; the dynamic evolution of networks, where now the structure of the network is understood in terms of a growth process; and dynamical processes that take place on fixed networks; that is, "networked dynamical systems". (A fourth area of potential research ties all the previous three strands together under the rubric of co-evolution of networks and dynamics, but very little research has been done in this vein and so it is omitted.) The remainder of the course treats each of the three strands in greater detail, introducing technical knowledge as required, summarizing the research papers that have introduced the principal ideas, and pointing out directions for future development. With regard to networked dynamical systems, the course treats in detail the more specific topic of information propagation in networks, in part because this topic is of great relevance to social science, and in part because it has received the most attention in the literature to date.

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.