scholarly journals Dynamics in and dynamics of networks using DyNSimF

2021 ◽  
Author(s):  
Maarten van den Ende ◽  
Mathijs Mayer ◽  
Sacha Epskamp ◽  
Michael Lees ◽  
Han van der Maas
Keyword(s):  
Network Analysis ◽  
Data Collection ◽  
Open Source ◽  
Network Science ◽  
Complexity Science ◽  
Formal Theories

Advancements of formal theories, network science, and data collection technologies make network analysis and simulation an increasingly crucial tool in complexity science. We present DyNSimF; the first open-source package that allows for the modeling of com- plex interacting dynamics on a network a well as dynamics of (the structure of) a net- work. The package can deal with weighted as well as directional connections, is scalable and efficient, and includes a utility-based edge-altering framework. DyNSimF includes visualization methods and tools to help analyze models. It is designed to be easily ex- tendable and makes use of NetworkX graphs. It aims to be easy to learn and to work with, enabling non-experts to focus on the development of models, while at the same time being highly customizable and extensible to allow for complex custom models.

Geographies of transport II

2016 ◽  
Vol 41 (3) ◽  
pp. 355-364 ◽  
Author(s):  
Tim Schwanen
Keyword(s):  
Big Data ◽  
Network Analysis ◽  
Complex Networks ◽  
Network Science ◽  
Complexity Science ◽  
Transport Systems ◽  
Transport Geography ◽  
Space And Time ◽  
Critical Reflexivity ◽  
Mixed Blessing

Geographical scholarship on transport has been boosted by the emergence of big data and advances in the analysis of complex networks in other disciplines, but these developments are a mixed blessing. They allow transport as object of analysis to exist in new ways and raise the profile of geography in interdisciplinary spaces dominated by physics and complexity science. Yet, they have also brought back concerns over the privileging of generality over particularity. This is because they have once more made acceptable and even normalized a focus on supposedly universal laws that explain the functioning of mobility systems and on space and time independent explanations of hierarchies, inequalities and vulnerabilities in transport systems and patterns. Geographical scholarship on transport should remain open to developments in big data and network science but would benefit from more critical reflexivity on the limitations and the historical and geographical situatedness of big data and on the conceptual shortcomings of network science. Big data and network analysis need to be critiqued and re-appropriated, and examples of how this can be done are starting to emerge. Openness, critique and re-appropriation are especially important in a context where transport geography decentralizes away from its Euro-American core, and the development pathways of transport and mobility in localities beyond that core deserve their own, unique explanations.

Developing a sustainable Digital Health Innovation Ecosystem in a rural and resource-poor region: A monitored networking approach (Preprint)

2020 ◽  
Author(s):  
Arunangsu Chatterjee ◽  
Sebastian Stevens ◽  
Sheena Asthana ◽  
Ray B Jones
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Data Collection ◽  
Digital Health ◽  
Resource Poor Setting ◽  
Quadruple Helix ◽  
Innovation Ecosystem ◽  
Resource Poor ◽  
Quadruple Helix Model ◽  
Over Time

BACKGROUND Digital health (DH) innovation ecosystems (IE) are key to the development of new e-health products and services. Within an IE, third parties can help promote innovation by acting as knowledge brokers and the conduits for developing inter-organisational and interpersonal relations, particularly for smaller organisations. Kolehmainen’s quadruple helix model suggests who the critical IE actors are, and their roles. Within an affluent and largely urban setting, such ecosystems evolve and thrive organically with minimal intervention due to favourable economic and geographical conditions. Facilitating and sustaining a thriving DH IE within a resource-poor setting can be far more challenging even though far more important for such peripheral economics and the health and well-being of those communities. OBJECTIVE Taking a rural and remote region in the UK, as an instance of an IE in a peripheral economy, we adapt the quadruple helix model of innovation, apply a monitored social networking approach using McKinsey’s Three Horizons of growth to explore: • What patterns of connectivity between stakeholders develop within an emerging digital health IE? • How do networks develop over time in the DH IE? • In what ways could such networks be nurtured in order to build the capacity, capability and sustainability of the DH IE? METHODS Using an exploratory single case study design for a developing digital health IE, this study adopts a longitudinal social network analysis approach, enabling the authors to observe the development of the innovation ecosystem over time and evaluate the impact of targeted networking interventions on connectivity between stakeholders. Data collection was by an online survey and by a novel method, connection cards. RESULTS Self-reported connections between IE organisations increased between the two waves of data collection, with Small and Medium-sized Enterprises (SMEs) and academic institutions the most connected stakeholder groups. Patients involvement improved over time but still remains rather peripheral to the DH IE network. Connection cards as a monitoring tool worked really well during large events but required significant administrative overheads. Monitored networking information categorised using McKinsey’s Three Horizons proved to be an effective way to organise networking interventions ensuring sustained engagement. CONCLUSIONS The study reinforces the difficulty of developing and sustaining a DH IE in a resource-poor setting. It demonstrates the effective monitored networking approach supported by Social Network Analysis allows to map the networks and provide valuable information to plan future networking interventions (e.g. involving patients or service users). McKinsey’s Three Horizons of growth-based categorisation of the networking assets help ensure continued engagement in the DH IE contributing towards its long-term sustainability. Collecting ongoing data using survey or connection card method will become more labour intensive and ubiquitous ethically driven data collection methods can be used in future to make the process more agile and responsive.

scholarly journals Managing a Community Data Collection with Open Source Software

2020 ◽  
Author(s):  
Roland Schweitzer ◽  
Ethan Davis ◽  
Sean Arms ◽  
Robert Simons ◽  
Kevin O'Brien ◽  
...  
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Open Source Software ◽  
Community Data

Industry 4.0 Digital Technologies for data collection and control

2021 ◽  
pp. 43-58
Author(s):  
S. S. Yudachev ◽  
P. A. Monakhov ◽  
N. A. Gordienko
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Software Defined Radio ◽  
Block Diagram ◽  
Data Access ◽  
Gnu Radio ◽  
Time Data ◽  
Object A ◽  
Data Propagation ◽  
And Control

This article describes an attempt to create open source LabVIEW software, equivalent to data collection and control software. The proposed solution uses GNU Radio, OpenCV, Scilab, Xcos, and Comedi in Linux. GNU Radio provides a user-friendly graphical interface. Also, GNU Radio is a software-defined radio that conducts experiments in practice using software rather than the usual hardware implementation. Blocks for data propagation, code deletion with and without code tracking are created using the zero correlation zone code (ZCZ, a combination of ternary codes equal to 1, 0, and –1, which is specified in the program). Unlike MATLAB Simulink, GNU Radio is open source, i. e. free, and the concepts can be easily accessed by ordinary people without much programming experience using pre-written blocks. Calculations can be performed using OpenCV or Scilab and Xcos. Xcos is an application that is part of the Scilab mathematical modeling system, and it provides developers with the ability to design systems in the field of mechanics, hydraulics and electronics, as well as queuing systems. Xcos is a graphical interactive environment based on block modeling. The application is designed to solve problems of dynamic and situational modeling of systems, processes, devices, as well as testing and analyzing these systems. In this case, the modeled object (a system, device or process) is represented graphically by its functional parametric block diagram, which includes blocks of system elements and connections between them. The device drivers listed in Comedi are used for real-time data access. We also present an improved PyGTK-based graphical user interface for GNU Radio. English version of the article is available at URL: https://panor.ru/articles/industry-40-digital-technology-for-data-collection-and-management/65216.html

scholarly journals ImageJ and 3D Slicer: open source 2/3D morphometric software

2019 ◽  
Author(s):  
Fiona Pye ◽  
Nussaȉbah B Raja ◽  
Bryan Shirley ◽  
Ádám T Kocsis ◽  
Niklas Hohmann ◽  
...  
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Programming Languages ◽  
Quality Data ◽  
Software Projects ◽  
High Quality Data ◽  
3D Slicer ◽  
Data Collection And Analysis ◽  
Digital Methods ◽  
Imagej Plugin

In a world where an increasing number of resources are hidden behind paywalls and monthly subscriptions, it is becoming crucial for the scientific community to invest energy into freely available, community-maintained systems. Open-source software projects offer a solution, with freely available code which users can utilise and modify, under an open source licence. In addition to software accessibility and methodological repeatability, this also enables and encourages the development of new tools. As palaeontology moves towards data driven methodologies, it is becoming more important to acquire and provide high quality data through reproducible systematic procedures. Within the field of morphometrics, it is vital to adopt digital methods that help mitigate human bias from data collection. In addition,m mathematically founded approaches can reduce subjective decisions which plague classical data. This can be further developed through automation, which increases the efficiency of data collection and analysis. With these concepts in mind, we introduce two open-source shape analysis software, that arose from projects within the medical imaging field. These are ImageJ, an image processing program with batch processing features, and 3DSlicer which focuses on 3D informatics and visualisation. They are easily extensible using common programming languages, with 3DSlicer containing an internal python interactor, and ImageJ allowing the incorporation of several programming languages within its interface alongside its own simplified macro language. Additional features created by other users are readily available, on GitHub or through the software itself. In the examples presented, an ImageJ plugin “FossilJ” has been developed which provides semi-automated morphometric bivalve data collection. 3DSlicer is used with the extension SPHARM-PDM, applied to synchrotron scans of coniform conodonts for comparative morphometrics, for which small assistant tools have been created.

scholarly journals NetFACS: Using network science to understand facial communication systems

2021 ◽  
Author(s):  
Alexander Mielke ◽  
Bridget M. Waller ◽  
Claire Pérez ◽  
Alan V. Rincon ◽  
Julie Duboscq ◽  
...  
Keyword(s):  
Network Analysis ◽  
Communication Systems ◽  
Network Science ◽  
Coding System ◽  
Communication Tool ◽  
Resampling Methods ◽  
Facial Movements ◽  
The Face ◽  
Analyse Data ◽  
And Function

AbstractUnderstanding facial signals in humans and other species is crucial for understanding the evolution, complexity, and function of the face as a communication tool. The Facial Action Coding System (FACS) enables researchers to measure facial movements accurately, but we currently lack tools to reliably analyse data and efficiently communicate results. Network analysis can provide a way to use the information encoded in FACS datasets: by treating individual AUs (the smallest units of facial movements) as nodes in a network and their co-occurrence as connections, we can analyse and visualise differences in the use of combinations of AUs in different conditions. Here, we present ‘NetFACS’, a statistical package that uses occurrence probabilities and resampling methods to answer questions about the use of AUs, AU combinations, and the facial communication system as a whole in humans and non-human animals. Using highly stereotyped facial signals as an example, we illustrate some of the current functionalities of NetFACS. We show that very few AUs are specific to certain stereotypical contexts; that AUs are not used independently from each other; that graph-level properties of stereotypical signals differ; and that clusters of AUs allow us to reconstruct facial signals, even when blind to the underlying conditions. The flexibility and widespread use of network analysis allows us to move away from studying facial signals as stereotyped expressions, and towards a dynamic and differentiated approach to facial communication.

e-mission: An Open-Source, Smartphone Platform for Collecting Human Travel Data

2018 ◽  
Vol 2672 (42) ◽  
pp. 1-12 ◽  
Author(s):  
K. Shankari ◽  
Mohamed Amine Bouzaghrane ◽  
Samuel M. Maurer ◽  
Paul Waddell ◽  
David E. Culler ◽  
...  
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Travel Behavior ◽  
Full Time ◽  
Transportation Engineering ◽  
Complex Projects ◽  
Data User ◽  
Time Work ◽  
Set Up ◽  
Extensible Software

GPS-equipped smartphones provide new methods to collect data about travel behavior, including travel survey apps that incorporate automated location sensing. Previous approaches to this have involved proprietary or one-off tools that are inconsistent and difficult to evaluate. In contrast, e-mission is an open-source, extensible software platform that consists of ( a) an app for survey participants to install on their Android or iOS smartphones and ( b) cloud-hosted software for managing the collected data. e-mission collects continuous location data, user-initiated annotations, and responses to contextual, platform initiated survey questions. New studies can be set up using the existing University of California, Berkeley, infrastructure with no additional coding, or the platform can be extended for more complex projects. This paper reviews the requirements for smartphone travel data collection, describes the architecture and capabilities of the e-mission platform, and evaluates its performance in a pilot deployment. The results show that the platform is usable, with over 150 installations in a month; stable, with over 85% of users retaining it for more than 3 days; and extensible, with interface and survey customizations accomplished in a little over a week of full-time work by a transportation engineering researcher. We hope that e-mission will be a useful tool for app-based data collection and will serve as a catalyst for related research.

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