Exchange Of Data Sets Research Articles

Design of System‐of‐System Acquisition Analysis Using Machine Learning

A system of system’s ability to function is derived from the integration of systems from different sources. An SOS’s systems serve two purposes: first, to accomplish their own specific aims, and second, to provide resources to the SOS as a whole. In the last few decades, machine learning and data analytics have been widely used in system design and acquisitions. Every organisation that acquires a sophisticated system employs some type of data analytics to evaluate the system’s independent objectives, which is universally accepted. Data analytics and decision‐making regarding the independent system is rarely shared across SOS stakeholders, even though the systems contribute to and benefit from the larger SOS. The goal of this research is to determine how the exchange of data sets and the corresponding analytics by SOS stakeholders can improve SOS capacity. Predicting SOS capabilities by exchanging relevant data sets and prescribing information connections between systems, we propose to use machine learning techniques. This article serves as an intermediate analysis of the above research work and aims to estimate the benefit of information sharing among the SOS stakeholders. In this research, we have applied different machine learning models to the IBM HR analytics data set to determine the corresponding analytics by SOS stakeholders that can improve SOS capacity. We propose using machine learning techniques to forecast SOS capabilities through the sharing of relevant data sets, and we prescribe the information linkages across systems to make this possible. This paper provides an update on the progress being made toward the aforementioned research project, and its primary focus is on developing a method to put a dollar amount on the benefits of information sharing among the many parties involved in the SOS.

ION GNSS software‐defined radio metadata standard

The past several years have seen a proliferation of software-defined radio (SDR) data collection systems and processing platforms designed for or applicable to satellite navigation (satnav) applications. These systems necessarily produce datasets in a wide range of different formats. To correctly interpret this SDR data, essential information such as the packed sample format and sampling rate is needed. Communicating this metadata between creators and users has historically been an ad-hoc, cumbersome, and error-prone process. To address this issue, the satnav SDR community developed a metadata standard and normative software library to automate this process, thus simplifying the exchange of datasets and promoting the interoperability of satnav SDR systems. The standard was ratified and formally accepted as an Institute of Navigation Standard in January 2020. This article describes the ION GNSS SDR metadata standard and associated open-source software project. All content associated with the standard is available on sdr.ion.org.

The Biological Object Notation (BON): a structured file format for biological data

The large size and high complexity of biological data can represent a major methodological challenge for the analysis and exchange of data sets between computers and applications. There has also been a substantial increase in the amount of metadata associated with biological data sets, which is being increasingly incorporated into existing data formats. Despite the existence of structured formats based on XML, biological data sets are mainly formatted using unstructured file formats, and the incorporation of metadata results in increasingly complex parsing routines such that they become more error prone. To overcome these problems, we present the “biological object notation” (BON) format, a new way to exchange and parse nearly all biological data sets more efficiently and with less error than other currently available formats. Based on JavaScript Object Notation (JSON), BON simplifies parsing by clearly separating the biological data from its metadata and reduces complexity compared to XML based formats. The ability to selectively compress data up to 87% compared to other file formats and the reduced complexity results in improved transfer times and less error prone applications.

Community and Copyright: The Building of an OpenGIS Data Repository in Buffalo, NY

GIScience 2016 Short Paper Proceedings Community and Copyright: The Building of an OpenGIS Data Repository in Buffalo, NY Monica Stephens 1 Department of Geography, University at Buffalo (SUNY), 105 Wilkeson Quadrangle, Buffalo, NY 14261, United States Email: mgstephe@buffalo.edu Abstract This project examines the challenges and opportunities that arose in the first phase of building an open Geospatial data portal for Buffalo, NY. Community groups, academics, and non-profits articulated a desire to have an exchange of datasets and information that was accessible and stored in one place. However, the City of Buffalo, NY was unable and unwilling to share local data or provide a top-down initiative for collecting, storing, and disseminating geospatial information. Other locales like Detroit and Pittsburgh have relied on non-governmental actors to take the lead establishing data repositories with open data policies that were later adopted by the public sector. Through a Civic Engagement and Public Partnership grant with the United Way of Buffalo and Erie County, we built a prototype of an open GIS-data portal. 1. Introduction Open data are often thought of as a foundation on which to develop new goods, services, and applications that could significantly improve quality of life and community development. Open data are freely available to everyone to use without the barriers conventionally imposed by copyrights, patents, or other forms of information control. Open data provides an opportunity for communities to improve transparency and democratic control of government, augment the efficiency and effectiveness of public services, and generate new knowledge and insight into processes of social, political, economic and environmental change. Although open data systems have been strongly championed by city governments in places like New York City, Chicago, and San Francisco, other locales like Detroit (http://portal.datadrivendetroit.org) and Pittsburgh (http://www.wprdc.org/background) have relied on non-governmental actors to take the lead in establishing data repositories with open data policies subsequently following from the public sector. In Buffalo, New York, where local government has not adopted public policy related to open data, we partnered with community organizations to build a repository to collect and disseminate geospatial information. 1.1 Open Data in Buffalo In Buffalo, NY local government has resisted sharing or disseminating datasets collected by the city or county. Community groups and non-profits provided motivation to share data by re-creating updated versions of community data. For example, the ‘ReTree the District’ project used smartphones and volunteers to crowdsource a house-by-house tree census of one neighbourhood in the city (Krolikowski 2015). The dataset they created was of higher quality and integrity than the data held by the city forester and was later adopted by the city. Another community group, frustrated that the city budget was released only as single- page, non-machine readable PDFs (City of Buffalo 2015), scraped the budget website and posted on GitHub a version accessible for analysis, the city later submitted the budget to OpenBooks. The United Way of Buffalo and Erie County (UWBEC) recognized the need for data and technical applications available to the non-profit community, so in 2014 they

Stimulating information sharing, collaboration and learning in operations research with libOR

The exchange of data sets within the operations research community suffers from three main issues: (1) data sets are exchanged as plain text files; (2) data sets are offered on centrally managed websites; and (3) the results of applying algorithms to these data sets are unavailable. These issues result in an inefficient sharing of research artifacts. In this paper, we present libOR, a Web-based library of data sets for the operations research community. The organization of libOR is based on the open source and open content approach. The project has three main objectives: (1) stimulate information sharing of research data; (2) increase collaboration to increase scientific advancement; and (3) stimulate learning from approaches undertaken by other researchers in the domain. Early feedback from operations researchers seems to indicate that the advantages offered by libOR are greatly welcomed.

Lineage retrieval for scientific data processing: a survey

Scientific research relies as much on the dissemination and exchange of data sets as on the publication of conclusions. Accurately tracking the lineage (origin and subsequent processing history) of scientific data sets is thus imperative for the complete documentation of scientific work. Researchers are effectively prevented from determining, preserving, or providing the lineage of the computational data products they use and create, however, because of the lack of a definitive model for lineage retrieval and a poor fit between current data management tools and scientific software. Based on a comprehensive survey of lineage research and previous prototypes, we present a metamodel to help identify and assess the basic components of systems that provide lineage retrieval for scientific data products.

On-line three-parameter data uptake, analysis, and display device for flow cytometry and other applications.

Multiparameter flow cytometric measurements are of growing interest in the study of complex features of biological cells. With state of the art instrumentation, three-parameter (3-P) data handling is relatively complicated and time consuming and the display methods are not satisfactory. As an alternative, an interactive 3-P analyzing module, Cytomic 123 is described, which displays 3-P fields during and immediately after data uptake in the form of a cubic array of 32,768 channels. The fields can be randomly rotated by hardware and software. The event frequencies in the field are primarily visualized by brightness modulation of the display dots. Additionally, the display of the field may be confined to user selected ranges of event frequencies, which may also be superposed to mixed frequency displays. A set of preprogrammed functions is available for the following tasks: (a) uptake of 3-P histograms combined with on-line control of the transducer pulses, (b) automatic uptake of a series of 2-P time correlated histograms in the cube, (c) generation and numerical evaluation of sections and projections of cube histograms, (d) interactive generation and evaluation of spatial subfields for integration, or as sorting matrix by successive erosion of section planes, or reprojection of projection windows, and (e) isometric display of sections and projections and exchange of data sets with other Cytomic modules or other data systems, especially the Cytomic 12 module, whose 2-P capabilities can be used. The module is built with low cost Z80 microprocessor eurocards. A standard oscilloscope serves as a display unit.