OctoBot: An Open-Source Orchestration System for a Wide Range Network Activity Generation

2021 ◽  
Author(s):  
Aris Cahyadi Risdianto ◽  
Ee-Chien Chang
Keyword(s):  
Open Source ◽  
Network Activity ◽  
Wide Range ◽  
Activity Generation

An open-source R-package and web application for high-quality probabilistic predictions in hydrology

2021 ◽  
Author(s):  
Jason Hunter ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
David McInerney
Keyword(s):  
Open Source ◽  
Web Application ◽  
R Package ◽  
Error Model ◽  
Objective Functions ◽  
High Quality ◽  
Wide Range ◽  
Probabilistic Error

<p>Probabilistic predictions provide crucial information regarding the uncertainty of hydrological predictions, which are a key input for risk-based decision-making. However, they are often excluded from hydrological modelling applications because suitable probabilistic error models can be both challenging to construct and interpret, and the quality of results are often reliant on the objective function used to calibrate the hydrological model.</p><p>We present an open-source R-package and an online web application that achieves the following two aims. Firstly, these resources are easy-to-use and accessible, so that users need not have specialised knowledge in probabilistic modelling to apply them. Secondly, the probabilistic error model that we describe provides high-quality probabilistic predictions for a wide range of commonly-used hydrological objective functions, which it is only able to do by including a new innovation that resolves a long-standing issue relating to model assumptions that previously prevented this broad application.  </p><p>We demonstrate our methods by comparing our new probabilistic error model with an existing reference error model in an empirical case study that uses 54 perennial Australian catchments, the hydrological model GR4J, 8 common objective functions and 4 performance metrics (reliability, precision, volumetric bias and errors in the flow duration curve). The existing reference error model introduces additional flow dependencies into the residual error structure when it is used with most of the study objective functions, which in turn leads to poor-quality probabilistic predictions. In contrast, the new probabilistic error model achieves high-quality probabilistic predictions for all objective functions used in this case study.</p><p>The new probabilistic error model and the open-source software and web application aims to facilitate the adoption of probabilistic predictions in the hydrological modelling community, and to improve the quality of predictions and decisions that are made using those predictions. In particular, our methods can be used to achieve high-quality probabilistic predictions from hydrological models that are calibrated with a wide range of common objective functions.</p>

scholarly journals The Open Global Glacier Model (OGGM) v1.0

2018 ◽  
Author(s):  
Fabien Maussion ◽  
Anton Butenko ◽  
Julia Eis ◽  
Kévin Fourteau ◽  
Alexander H. Jarosch ◽  
...  
Keyword(s):  
Mass Balance ◽  
Open Source ◽  
Sea Level ◽  
Computational Cost ◽  
Balance Model ◽  
Climate Data ◽  
Estimation Model ◽  
Model Framework ◽  
Validation Data ◽  
Wide Range

Abstract. Despite of their importance for sea-level rise, seasonal water availability, and as source of geohazards, mountain glaciers are one of the few remaining sub-systems of the global climate system for which no globally applicable, open source, community-driven model exists. Here we present the Open Global Glacier Model (OGGM, http://www.oggm.org), developed to provide a modular and open source numerical model framework for simulating past and future change of any glacier in the world. The modelling chain comprises data downloading tools (glacier outlines, topography, climate, validation data), a preprocessing module, a mass-balance model, a distributed ice thickness estimation model, and an ice flow model. The monthly mass-balance is obtained from gridded climate data and a temperature index melt model. To our knowledge, OGGM is the first global model explicitly simulating glacier dynamics: the model relies on the shallow ice approximation to compute the depth-integrated flux of ice along multiple connected flowlines. In this paper, we describe and illustrate each processing step by applying the model to a selection of glaciers before running global simulations under idealized climate forcings. Even without an in-depth calibration, the model shows a very realistic behaviour. We are able to reproduce earlier estimates of global glacier volume by varying the ice dynamical parameters within a range of plausible values. At the same time, the increased complexity of OGGM compared to other prevalent global glacier models comes at a reasonable computational cost: several dozens of glaciers can be simulated on a personal computer, while global simulations realized in a supercomputing environment take up to a few hours per century. Thanks to the modular framework, modules of various complexity can be added to the codebase, allowing to run new kinds of model intercomparisons in a controlled environment. Future developments will add new physical processes to the model as well as tools to calibrate the model in a more comprehensive way. OGGM spans a wide range of applications, from ice-climate interaction studies at millenial time scales to estimates of the contribution of glaciers to past and future sea-level change. It has the potential to become a self-sustained, community driven model for global and regional glacier evolution.

scholarly journals Cortical Engagement Metrics During Reactive Balance Are Associated With Distinct Aspects of Balance Behavior in Older Adults

2021 ◽  
Vol 13 ◽  
Author(s):  
Jacqueline A. Palmer ◽  
Aiden M. Payne ◽  
Lena H. Ting ◽  
Michael R. Borich
Keyword(s):  
Older Adults ◽  
Balance Control ◽  
Network Activity ◽  
Beta Activity ◽  
Balance Function ◽  
Potential Biomarker ◽  
Beta Power ◽  
Wide Range ◽  
Motor Cortical ◽  
Reactive Balance

Heightened reliance on the cerebral cortex for postural stability with aging is well-known, yet the cortical mechanisms for balance control, particularly in relation to balance function, remain unclear. Here we aimed to investigate motor cortical activity in relation to the level of balance challenge presented during reactive balance recovery and identify circuit-specific interactions between motor cortex and prefrontal or somatosensory regions in relation to metrics of balance function that predict fall risk. Using electroencephalography, we assessed motor cortical beta power, and beta coherence during balance reactions to perturbations in older adults. We found that individuals with greater motor cortical beta power evoked following standing balance perturbations demonstrated lower general clinical balance function. Individual older adults demonstrated a wide range of cortical responses during balance reactions at the same perturbation magnitude, showing no group-level change in prefrontal- or somatosensory-motor coherence in response to perturbations. However, older adults with the highest prefrontal-motor coherence during the post-perturbation, but not pre-perturbation, period showed greater cognitive dual-task interference (DTI) and elicited stepping reactions at lower perturbation magnitudes. Our results support motor cortical beta activity as a potential biomarker for individual level of balance challenge and implicate prefrontal-motor cortical networks in distinct aspects of balance control involving response inhibition of reactive stepping in older adults. Cortical network activity during balance may provide a neural target for precision-medicine efforts aimed at fall prevention with aging.

scholarly journals micrIO: An Open-Source Autosampler and Fraction Collector for Automated Microfluidic Input-Output

2019 ◽  
Author(s):  
Scott A. Longwell ◽  
Polly M. Fordyce
Keyword(s):  
Open Source ◽  
Positive Pressure ◽  
Cross Contamination ◽  
Wide Spread ◽  
Fraction Collector ◽  
Input And Output ◽  
Wide Range ◽  
Molecular Separations ◽  
Multiwell Plates

Microfluidic devices are an empowering technology for many labs, enabling a wide range of applications spanning high-throughput encapsulation, molecular separations, and long-term cell culture. In many cases, however, their utility is limited by a ‘world-to-chip’ barrier that makes it difficult to serially interface samples with these devices. As a result, many researchers are forced to rely on low-throughput, manual approaches for managing device input and output (IO) of samples, reagents, and effluent. Here, we present a hardware-software platform for automated microfluidic IO (micrIO). The platform, which is uniquely compatible with positive-pressure microfluidics, comprises an ‘AutoSipper’ for input and a Fraction Collector for output. To facilitate wide-spread adoption, both are open-source builds constructed from components that are readily purchased online or fabricated from included design files. The software control library, written in Python, allows the platform to be integrated with existing experimental setups and to coordinate IO with other functions such as valve actuation and assay imaging. We demonstrate these capabilities by coupling both the AutoSipper and Fraction Collector to a microfluidic device that produces beads with distinct spectral codes, and an analysis of the collected bead fractions establishes the ability of the platform to draw from and output to specific wells of multiwell plates with no detectable cross-contamination between samples.

scholarly journals Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex

2021 ◽  
pp. 47-59
Author(s):  
Sacha J. van Albada ◽  
Jari Pronold ◽  
Alexander van Meegen ◽  
Markus Diesmann
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Network Models ◽  
Macaque Monkey ◽  
Source Model ◽  
Model Specification ◽  
Data Sets ◽  
Neural Network Models ◽  
Wide Range ◽  
Ict Infrastructure

AbstractWe are entering an age of ‘big’ computational neuroscience, in which neural network models are increasing in size and in numbers of underlying data sets. Consolidating the zoo of models into large-scale models simultaneously consistent with a wide range of data is only possible through the effort of large teams, which can be spread across multiple research institutions. To ensure that computational neuroscientists can build on each other’s work, it is important to make models publicly available as well-documented code. This chapter describes such an open-source model, which relates the connectivity structure of all vision-related cortical areas of the macaque monkey with their resting-state dynamics. We give a brief overview of how to use the executable model specification, which employs NEST as simulation engine, and show its runtime scaling. The solutions found serve as an example for organizing the workflow of future models from the raw experimental data to the visualization of the results, expose the challenges, and give guidance for the construction of an ICT infrastructure for neuroscience.

scholarly journals JTrack: A Digital Biomarker Platform for Remote Monitoring of Daily-Life Behaviour in Health and Disease

2021 ◽  
Vol 9 ◽  
Author(s):  
Mehran Sahandi Far ◽  
Michael Stolz ◽  
Jona M. Fischer ◽  
Simon B. Eickhoff ◽  
Juergen Dukart
Keyword(s):  
Data Management ◽  
Open Source ◽  
Remote Monitoring ◽  
Daily Life ◽  
Smartphone Application ◽  
Valuable Insight ◽  
Motion Sensors ◽  
General Data Protection Regulation ◽  
Wide Range ◽  
Health And Disease

Health-related data being collected by smartphones offer a promising complementary approach to in-clinic assessments. Despite recent contributions, the trade-off between privacy, optimization, stability and research-grade data quality is not well met by existing platforms. Here we introduce the JTrack platform as a secure, reliable and extendable open-source solution for remote monitoring in daily-life and digital-phenotyping. JTrack is an open-source (released under open-source Apache 2.0 licenses) platform for remote assessment of digital biomarkers (DB) in neurological, psychiatric and other indications. JTrack is developed and maintained to comply with security, privacy and the General Data Protection Regulation (GDPR) requirements. A wide range of anonymized measurements from motion-sensors, social and physical activities and geolocation information can be collected in either active or passive modes by using JTrack Android-based smartphone application. JTrack also provides an online study management dashboard to monitor data collection across studies. To facilitate scaling, reproducibility, data management and sharing we integrated DataLad as a data management infrastructure. Smartphone-based Digital Biomarker data may provide valuable insight into daily-life behaviour in health and disease. As illustrated using sample data, JTrack provides as an easy and reliable open-source solution for collection of such information.

SHEMAT-Suite: a parallel open source simulator for flow, heat and mass transport in porous media

2021 ◽  
Author(s):  
Johannes Keller ◽  
Johanna Fink ◽  
Norbert Klitzsch
Keyword(s):  
Porous Media ◽  
Mass Transport ◽  
Open Source ◽  
Source Code ◽  
Modular Structure ◽  
Numerical Code ◽  
Heat And Mass Transport ◽  
Transport In Porous Media ◽  
Wide Range ◽  
Flow Heat

<p>We present SHEMAT-Suite, a numerical code for simulating flow, heat, and mass transport in porous media that has been published as an open source code recently. The functionality of SHEMAT-Suite comprises pure forward computation, deterministic Bayesian inversion, and stochastic Monte Carlo<br>simulation and data assimilation. Additionally, SHEMAT-Suite features a multi-level OpenMP parallelization. Along with the source code of the software, extensive documentation and a suite of test models is provided.</p><p>SHEMAT-Suite has a modular structure that makes it easy for users to adapt the code to their needs. Most importantly, there is an interface for defining the functional relationship between dynamic variables and subsurface parameters. Additionally, user-defined input and output can be implemented without interfering with the core of the code. Finally, at a deeper level, linear solvers and preconditioners can be added to the code.</p><p>We present studies that have made use of the code's HPC capabilities. SHEMAT-Suite has been applied to large-scale groundwater models for a wide range of purposes, including studying the formation of convection cells, assessing geothermal potential below an office building, or modeling submarine groundwater discharge since the last ice age. The modular structure of SHEMAT-Suite has also led to diverse applications, such as glacier modeling, simulation of borehole heat exchangers, or Optimal Experimental Design applied to the placing of geothermal boreholes.</p><p>Further, we present ongoing developments for improving the performance of SHEMAT-Suite, both by refactoring the source code and by interfacing SHEMAT-Suite with up-to-date HPC software. Examples of this include interfacing SHEMAT-Suite with the Portable Data Interface (PDI) for improved data management, interfacing SHEMAT-Suite with PetSC for MPI-parallel solvers, and interfacing SHEMAT-Suite with PDAF for parallel EnKF algorithms.</p><p>The goal for the open source SHEMAT-Suite is to provide a rigorously tested core code for flow, heat and transport simulation, Bayesian and stochastic inversion, while at the same time enabling a wide range of scientific research through straightforward user interaction.</p>

JuTrack: A Digital Biomarker Platform for Remote Monitoring in Neuropsychiatric and Psychiatric Diseases (Preprint)

2021 ◽  
Author(s):  
Mehran Sahandi Far ◽  
Michael Stolz ◽  
Jona Marcus Fischer ◽  
Simon B Eickhoff ◽  
Juergen Dukart
Keyword(s):  
Data Management ◽  
Open Source ◽  
Remote Monitoring ◽  
Daily Life ◽  
Smartphone Application ◽  
Valuable Insight ◽  
Motion Sensors ◽  
Management Tools ◽  
General Data Protection Regulation ◽  
Wide Range

BACKGROUND Health-related data being collected by smartphones offer a promising complementary approach to in-clinic assessments. OBJECTIVE Here we introduce the JuTrack platform as a secure, reliable and extendable open-source solution for remote monitoring in daily-life and digital phenotyping. METHODS JuTrack consists of an Android-based smartphone application and a web-based project management dashboard. A wide range of anonymized measurements from motion-sensors, social and physical activities and geolocation information can be collected in either active or passive modes. The dashboard also provides management tools to monitor and manage data collection across studies. To facilitate scaling, reproducibility, data management and sharing we integrated DataLad as a data management infrastructure. JuTrack was developed to comply with security, privacy and the General Data Protection Regulation (GDPR) requirements. RESULTS JuTrack is an open-source (released under open-source Apache 2.0 licenses) platform for remote assessment of digital biomarkers (DB) in neurological, psychiatric and other indications. The main components of the JuTrack platform and examples of data being collected using JuTrack are presented here. CONCLUSIONS Smartphone-based Digital Biomarker data may provide valuable insight into daily life behaviour in health and disease. JuTrack provides an easy and reliable open-source solution for collection of such data.

Bridging the Academia-Industry Gap in Software Engineering

2014 ◽  
pp. 373-396 ◽  
Author(s):  
Bonnie K. MacKellar ◽  
Mihaela Sabin ◽  
Allen B. Tucker
Keyword(s):  
Software Engineering ◽  
Computer Science ◽  
Open Source ◽  
Real World ◽  
Software Application ◽  
Development Framework ◽  
Wide Range ◽  
Approach To Teaching ◽  
Development Practice ◽  
Open Source Development

Too often, computer science programs offer a software engineering course that emphasizes concepts, principles, and practical techniques, but fails to engage students in real-world software experiences. The authors have developed an approach to teaching undergraduate software engineering courses that integrates client-oriented project development and open source development practice. They call this approach the Client-Oriented Open Source Software (CO-FOSS) model. The advantages of this approach are that students are involved directly with a client, nonprofits gain a useful software application, and the project is available as open source for other students or organizations to extend and adapt. This chapter describes the motivation, elaborates the approach, and presents the results in substantial detail. The process is agile and the development framework is transferrable to other one-semester software engineering courses in a wide range of institutions.

scholarly journals The Open Global Glacier Model (OGGM) v1.1

2019 ◽  
Vol 12 (3) ◽  
pp. 909-931 ◽  
Author(s):  
Fabien Maussion ◽  
Anton Butenko ◽  
Nicolas Champollion ◽  
Matthias Dusch ◽  
Julia Eis ◽  
...  
Keyword(s):  
Mass Balance ◽  
Open Source ◽  
Sea Level ◽  
Computational Cost ◽  
Balance Model ◽  
Climate Data ◽  
Estimation Model ◽  
Model Framework ◽  
Validation Data ◽  
Wide Range

Abstract. Despite their importance for sea-level rise, seasonal water availability, and as a source of geohazards, mountain glaciers are one of the few remaining subsystems of the global climate system for which no globally applicable, open source, community-driven model exists. Here we present the Open Global Glacier Model (OGGM), developed to provide a modular and open-source numerical model framework for simulating past and future change of any glacier in the world. The modeling chain comprises data downloading tools (glacier outlines, topography, climate, validation data), a preprocessing module, a mass-balance model, a distributed ice thickness estimation model, and an ice-flow model. The monthly mass balance is obtained from gridded climate data and a temperature index melt model. To our knowledge, OGGM is the first global model to explicitly simulate glacier dynamics: the model relies on the shallow-ice approximation to compute the depth-integrated flux of ice along multiple connected flow lines. In this paper, we describe and illustrate each processing step by applying the model to a selection of glaciers before running global simulations under idealized climate forcings. Even without an in-depth calibration, the model shows very realistic behavior. We are able to reproduce earlier estimates of global glacier volume by varying the ice dynamical parameters within a range of plausible values. At the same time, the increased complexity of OGGM compared to other prevalent global glacier models comes at a reasonable computational cost: several dozen glaciers can be simulated on a personal computer, whereas global simulations realized in a supercomputing environment take up to a few hours per century. Thanks to the modular framework, modules of various complexity can be added to the code base, which allows for new kinds of model intercomparison studies in a controlled environment. Future developments will add new physical processes to the model as well as automated calibration tools. Extensions or alternative parameterizations can be easily added by the community thanks to comprehensive documentation. OGGM spans a wide range of applications, from ice–climate interaction studies at millennial timescales to estimates of the contribution of glaciers to past and future sea-level change. It has the potential to become a self-sustained community-driven model for global and regional glacier evolution.

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