Upper bounds for statistical uncertainties from normalization terms in neutron-scattering data-reduction

eROSITA (extended ROentgen Survey with an Imaging Telescope Array) is the primary instrument on-board the Russian/German Spectrum-Roentgen-Gamma (SRG) mission. It will perform the first all-sky imaging X-ray survey in the medium X-ray energy band. eROSITA is currently awaiting its launch from Baikonur in early 2019 into an L2 halo orbit. Preparations for eROSITA ground operations have been under way in parallel with the hardware development of the eROSITA telescope, comprising the areas mission planning, telescope operation and data reception, the operation of a data processing pipeline, and the creation of tools for data access and interactive data analysis. eROSITA mission: After a brief calibration and performance verification phase eROSITA will perform a four-year all-sky survey fully covering the sky eight times, which will be followed by several years of dedicated observations of interesting objects. Two ground antennas in Russia (near Moscow and in Siberia) will be available to provide several hours of daily ground contact for commanding and for data reception. eROSITA data rights will be equally divided between the Russian and German partners. Operation and data centers will exist at Max-Planck-Institut fur extraterrestrische Physics (MPE) in Garching, Germany and at the Russian Space Research Institute (IKI) in Moscow. Interfaces and procedures for mission planning, telescope operation and data exchange are closely coordinated between both sites. Mission planning and operation: Based on orbit simulations provided by the Russian side, a software environment for optimizing the desired sky coverage and observing efficiency while fulfilling visibility and solar constraints was set up. Agreed upon observing timelines will be converted to eROSITA and SRG command sequences fed to the Russian ground station for up-linking. MPE personnel will be on site either in Moscow or at MPE in Garching during each ground contact to conduct ondemand commanding and to assess instrument health and data quality. Data analysis pipeline and interactive data analysis: The eROSITA data processing pipeline consists of modules for data ingestion, event calibration, exposure calculation, source detection, and the creation of high-level source specific data products. It will be operated at MPE on a daily basis after each ground contact. A subset of the software tasks comprising the data analysis pipeline also functions as interactive data analysis tools. These can be grouped into tasks for X-ray event calibration, selection and binning, exposure, background and sensitivity map creation and for source detection. Data products are provided in a standards compliant FITS format for use with well-known high-level X-ray data analysis tools. A Web based graphical source catalog and data products viewer will allow easy data browsing. Since mid-2014 the interactive eROSITA data analysis package is available to the eROSITA user community, permitting the analysis of simulated eROSITA datasets, created by a sophisticated X-ray modeling and simulation tool.

The vision and mission of the Regional Head is one of the keys to the success of development in this city. One aspect that is of concern to the government is education as a form of human resource development. Zoning has an impact on the affordability of students to schools, especially public schools. This indirectly affects the capacity of the school. This study aims to determine the urgency of developing junior high school (called SMP) infrastructure with state status in the agenda setting stage. This research using mixed methods and interactive model data analysis. Interactive data analysis are popularized by Miles, Huberman and Saldana (2014), the result is that the infrastructure development of public junior high schools is needed, especially in areas affected by zone-political.

The pool of molecular interaction data is growing fast but nevertheless remains fragmented. Combining together data coming from heterogeneous sources is a crucial step towards a deeper understanding of the cell machinery. The Proteomics Standard Initiative offers mature standards (PSI-MI) to facilitate the exchange and analysis of molecular interaction data. After introducing the details of the latest version of the PSI-MI data model, we will present the implementation of PSI-MI in the IntAct project, which offers a platform for management and analysis of interaction data. Finally we will give some insight into realistically using molecular interaction data as a foundation for other research

By introducing data analysis techniques, analyzing current research in voting behavior, and using the computer for social science research, introductory American Government courses on the undergraduate level are undertaking ambitious goals. Using the SETUPS units for each presidential election since 1972, cadets at the Coast Guard Academy have engaged in a comprehensive approach to the study of American electoral behavior in an interactive data analysis mode. Although SETUPS modules are run at most colleges and universities in batch mode, there are imitations and drawbacks to batch mode. The Coast Guard Academy is able to utilize interactive data analysis as a result of its time-sharing arrangement with the Dartmouth College Time Sharing System (DCTS). In this article we will discuss the use of SETUPS in an interactive mode using the CIDAR interactive data analysis system.

This paper designs and implements a system which provides interactive data analysis services in cloud. These services allow users to analyze and explore large-scale in-cloud datasets quickly and interactively with a simple graphical user interface by leveraging the distributed computing ability of cloud data computing engine. The system decouples the analysis process and the computing engine. Users can construct the data flow graph in GUI, and review the results that are presented through the visualization tools. The back-end of the system is responsible for converting data flow graphs to the computation jobs that can be executed in cloud clusters. In addition, the system makes full use of the characteristics of overlapping results in the interactive analysis, and avoids repetitive computation through the caching mechanism. As a consequence, with this system, we make it easy and fast to perform interactive exploratory data analysis in cloud without losing flexibility.

In the age of big data, being able to make sense of data is an important key to success. Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data. The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods. It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes. The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data. A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways. Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis. The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis. The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology. Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains. Features: Dedicated to the synthesis of visual, interactive, and analysis methods Systematic top-down view on visualization, interaction, and automatic analysis Broad coverage of fundamental and advanced visualization techniques Comprehensive chapter on interacting with visual representations Extensive integration of automatic computational methods Accessible portrayal of cutting-edge visual analytics technology Foreword by Jack van Wijk For more information, you can also visit the author website, where the book's figures are made available under the CC BY Open Access license.

The interactive visual analysis of set-typed data, i.e., data with attributes that are of type set, is a rewarding area of research and applications. Valuable prior work has contributed solutions that enable the study of such data with individual set-typed dimensions. In this paper, we present CrossSet, a novel method for the joint study of two set-typed dimensions and their interplay. Based on a task analysis, we describe a new, multi-scale approach to the interactive visual exploration and analysis of such data. Two set-typed data dimensions are jointly visualized using a hierarchical matrix layout, enabling the analysis of the interactions between two set-typed attributes at several levels, in addition to the analysis of individual such dimensions. CrossSet is anchored at a compact, large-scale overview that is complemented by drill-down opportunities to study the relations between and within the set-typed dimensions, enabling an interactive visual multi-scale exploration and analysis of bivariate set-typed data. Such an interactive approach makes it possible to study single set-typed dimensions in detail, to gain an overview of the interaction and association between two such dimensions, to refine one of the dimensions to gain additional details at several levels, and to drill down to the specific interactions of individual set-elements from the set-typed dimensions. To demonstrate the effectiveness and efficiency of CrossSet, we have evaluated the new method in the context of several application scenarios.

SimVis is a novel technology for the interactive visual analysis of large and complex flow data which results from computational fluid dynamics (CFD) simulation. The new technology which has been researched and developed over the last years at the VRVis Research Center in Vienna, introduces a new approach for interactive graphical exploration and analysis of time-dependent data (computed on large three-dimensional grids, and resulting in a multitude of different scalar/vector values for each cell of these grids). In this paper the major new technological concepts of the SimVis approach are presented and real-world application examples are given.

The recent advances in natural language based interaction methodologies offer promising avenues to enhance the interactive processes within the human-machine dialogue of visual analytics. We envisage \textit{Multimodal Data Analytics} as a novel approach for conducting data analysis that builds on the strengths of visual analytics and natural language as an expressive interaction channel. We investigate the potential enhancements from such a multimodal approach and discusses the preliminary outline for a structured methodology to study the role of natural language in data analytics. Our approach builds on a simple model of human machine dialogue for interactive data analysis which we then propose to instantiate as visual analytics workflows -- representations to study and operationalise interactive data analysis routines empowered by natural language interaction.

Interactive spatial data analysis in medical geography

High-throughput quantitative genetic interaction (GI) measurements provide detailed information regarding the structure of the underlying biological pathways by reporting on functional dependencies between genes. However, the analytical tools for fully exploiting such information lag behind the ability to collect these data. We present a novel Bayesian learning method that uses quantitative phenotypes of double knockout organisms to automatically reconstruct detailed pathway structures. We applied our method to a recent data set that measures GIs for endoplasmic reticulum (ER) genes, using the unfolded protein response as a quantitative phenotype. The results provided reconstructions of known functional pathways including N-linked glycosylation and ER-associated protein degradation. It also contained novel relationships, such as the placement of SGT2 in the tail-anchored biogenesis pathway, a finding that we experimentally validated. Our approach should be readily applicable to the next generation of quantitative GI data sets, as assays become available for additional phenotypes and eventually higher-level organisms.

BackgroundBiological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets.DescriptionHere we describe a comprehensive database of Drosophila gene and protein interactions collected from a variety of sources, including low and high throughput screens, genetic interactions, and computational predictions. We also present a program for exploring multiple interaction data sets and for combining data from different sources. The program, referred to as the Interaction Map (IM) Browser, is a web-based application for searching and visualizing interaction data stored in a relational database system. Use of the application requires no downloads and minimal user configuration or training, thereby enabling rapid initial access to interaction data. IM Browser was designed to readily accommodate and integrate new types of interaction data as it becomes available. Moreover, all information associated with interaction measurements or predictions and the genes or proteins involved are accessible to the user. This allows combined searches and analyses based on either common or technique-specific attributes. The data can be visualized as an editable graph and all or part of the data can be downloaded for further analysis with other tools for specific applications. The database is available at ConclusionThe Drosophila Interactions Database described here places a variety of disparate data into one easily accessible location. The database has a simple structure that maintains all relevant information about how each interaction was determined. The IM Browser provides easy, complete access to this database and could readily be used to publish other sets of interaction data. By providing access to all of the available information from a variety of data types, the program will also facilitate advanced computational analyses.

Both interactive visualization and computational analysis methods are useful for data studies and an integration of both approaches is promising to successfully combine the benefits of both methodologies. In interactive data exploration and analysis workflows, we need successful means to quantitatively externalize results from data studies, amounting to a particular challenge for the usually qualitative visual data analysis. In this paper, we propose a hybrid approach in order to quantitatively externalize valuable findings from interactive visual data exploration and analysis, based on local linear regression models. The models are built on user-selected subsets of the data, and we provide a way of keeping track of these models and comparing them. As an additional benefit, we also provide the user with the numeric model coefficients. Once the models are available, they can be used in subsequent steps of the workflow. A model-based optimization can then be performed, for example, or more complex models can be reconstructed using an inversion of the local models. We study two datasets to exemplify the proposed approach, a meteorological data set for illustration purposes and a simulation ensemble from the automotive industry as an actual case study.

Rosetta is a science platform for resource-intensive, interactive data analysis which runs user tasks as software containers. It is built on top of a novel architecture based on framing user tasks as microservices – independent and self-contained units – which allows to fully support custom and user-defined software packages, libraries and environments. These include complete remote desktop and GUI applications, besides common analysis environments as the Jupyter Notebooks. Rosetta relies on Open Container Initiative containers, which allow for safe, effective and reproducible code execution; can use a number of container engines and runtimes; and seamlessly supports several workload management systems, thus enabling containerized workloads on a wide range of computing resources. Although developed in the astronomy and astrophysics space, Rosetta can virtually support any science and technology domain where resource-intensive, interactive data analysis is required.

In this introduction to the special issue of Computational Statistics on interactive graphical data analysis, we first briefly introduce the papers. They cover a lot of ground, from the updating of older statistical environments to the assessment of some of the newest, and there are several interesting examples of interactive graphics applied to data analysis problems. Afterwards, we describe a questionnaire we circulated in an effort to learn about the application of interactive graphical methods to data analysis in practice. We received too few responses to find the answers to our questions, but we did observe a few interesting things. In particular, we realized that conflicting uses of the term “interactive” are currently in use, muddying our discussions of software design and data analysis practice. We’ll discuss our observations in terms of the papers in this issue. We invite you to visit http://comst.wiwi.hu-berlin.de/issue0199.html for links to color versions of many of the graphics in the papers in this issue, as well as additional graphics provided by the authors.