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Publisher: Ubiquity Press Limited   (Total: 47 journals)   [Sort by number of followers]

Showing 1 - 47 of 47 Journals sorted alphabetically
Ancient Asia     Open Access   (Followers: 10)
Archaeology Intl.     Open Access   (Followers: 20)
Architectural Histories     Open Access   (Followers: 12)
Belgian J. of Radiology     Open Access   (Followers: 1, SJR: 0.167, CiteScore: 0)
Bulletin of the History of Archaeology     Open Access   (Followers: 16)
Citizen Science : Theory and Practice     Open Access   (Followers: 1)
Comics Grid : J. of Comics Scholarship     Open Access   (Followers: 9)
Cultural Science J.     Open Access  
Data Science J.     Open Access   (Followers: 15, SJR: 0.23, CiteScore: 1)
European J. of Molecular and Clinical Medicine     Open Access  
Future Cities and Environment     Open Access   (Followers: 4)
Glocality     Open Access  
Glossa : A J. of General Linguistics     Open Access   (Followers: 4)
Health Psychology Bulletin     Open Access  
Insights : the UKSG journal     Open Access   (Followers: 109, SJR: 0.473, CiteScore: 0)
Intl. J. of Integrated Care     Open Access   (Followers: 10, SJR: 0.662, CiteScore: 2)
Intl. Review of Social Psychology / Revue Intl.e de Psychologie Sociale     Open Access   (Followers: 1, SJR: 0.421, CiteScore: 1)
J. of Circadian Rhythms     Open Access   (Followers: 2, SJR: 0.524, CiteScore: 1)
J. of Cognition     Open Access  
J. of Computer Applications in Archaeology     Open Access  
J. of Conservation and Museum Studies     Open Access   (Followers: 18)
J. of European Psychology Students     Open Access   (Followers: 1)
J. of Interactive Media in Education     Open Access   (Followers: 5)
J. of Molecular Signaling     Open Access   (SJR: 0.677, CiteScore: 2)
J. of Open Archaeology Data     Open Access   (Followers: 9)
J. of Open Hardware     Open Access  
J. of Open Humanities Data     Open Access   (Followers: 2)
J. of Open Psychology Data     Open Access   (Followers: 3)
J. of Open Research Software     Open Access   (Followers: 3)
J. of Portuguese Linguistics     Open Access  
KULA : knowldge creation, dissemination, and preservation studies     Open Access  
Laboratory Phonology : J. of the Association for Laboratory Phonology     Open Access   (Followers: 7)
Le foucaldien     Open Access  
MaHKUscript. J. of Fine Art Research     Open Access  
Metaphysics     Open Access  
Open Health Data     Open Access   (Followers: 4)
Open J. of Bioresources     Open Access   (Followers: 1)
Open Quaternary     Open Access   (Followers: 1)
Papers from the Institute of Archaeology     Open Access   (Followers: 15)
Physical Activity and Health     Open Access   (Followers: 1)
Present Pasts     Open Access   (Followers: 2)
Psychologica Belgica     Open Access   (Followers: 1, SJR: 0.426, CiteScore: 1)
Secularism and Nonreligion     Open Access  
Tilburg Law Review     Open Access   (Followers: 5, SJR: 0.289, CiteScore: 0)
Transactions of the Intl. Society for Music Information Retrieval     Open Access  
Utrecht J. of Intl. and European Law     Open Access   (Followers: 14)
Worldwide Waste : J. of Interdisciplinary Studies     Open Access  
Journal Cover
Journal of Open Research Software
Number of Followers: 3  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2049-9647
Published by Ubiquity Press Limited Homepage  [47 journals]
  • BayesFit: A tool for modeling psychophysical data using Bayesian inference

    • Abstract: BayesFit is a module for Python that allows users to fit models to psychophysical data using Bayesian inference. The module aims to make it easier to develop probabilistic models for psychophysical data in Python by providing users with a simple API that streamlines the process of defining psychophysical models, obtaining fits, extracting outputs, and visualizing fitted models. Our software implementation uses numerical integration as the primary tool to fit models, which avoids the complications that arise in using Markov Chain Monte Carlo (MCMC) methods [1]. The source code for BayesFit is available at https://github.com/slugocm/bayesfit and API documentation at http://www.slugocm.ca/bayesfit/. This module is extensible, and many of the functions primarily rely on Numpy [2] and therefore can be reused as newer versions of Python are developed to ensure researchers always have a tool available to ease the process of fitting models to psychophysical data. Published on 2019-01-17 10:28:53
       
  • A Global Hydrologic Framework to Accelerate Scientific Discovery

    • Abstract: With the ability to simulate historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degree, the Python package Xanthos version 1 provided a solid foundation for continuing advancements in global water dynamics science. The goal of Xanthos version 2 was to build upon previous investments by creating a Python framework where core components of the model (potential evapotranspiration (PET), runoff generation, and river routing) could be interchanged or extended without having to start from scratch. Xanthos 2 utilizes a component-style architecture which enables researchers to quickly incorporate and test cutting-edge research in a stable modeling environment prebuilt with diagnostics. Major advancements for Xanthos 2 were also achieved by the creation of a robust default configuration with a calibration module, hydropower modules, and new PET modules, which are now available to the scientific community.
      Funding statement: This research was supported by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. The views and opinions expressed in this paper are those of the authors alone. Published on 2019-01-07 00:00:00
       
  • QuestPlus: A MATLAB Implementation of the QUEST+ adaptive
           Psychometric Method

    • Abstract: QuestPlus is a MATLAB implementation of the QUEST+ adaptive psychometric method. It provides a rapid and flexible method of estimating the parameters of a psychophysical model, and is also capable of advising the user on the most appropriate stimuli to present, and on when to terminate testing. Of particular note is the algorithm’s ability to use prior information, its ability to determine the maximally informative stimulus on each trial, its ability to fit arbitrarily complex models, and its ability to vary multiple stimulus properties simultaneously. Funding statement: This work was supported by the NIHR Biomedical Research Centre located at (both) Moorfields Eye Hospital and the UCL Institute of Ophthalmology. Published on 2018-12-28 00:00:00
       
  • ugtm: A Python Package for Data Modeling and Visualization Using
           Generative Topographic Mapping

    • Abstract: ugtm is a Python package that implements generative topographic mapping (GTM), a dimensionality reduction algorithm by Bishop, Svensén and Williams. Because of its probabilistic framework, GTM can also be used to build classification and regression models, and is an attractive alternative to t-distributed neighbour embedding (t-SNE) or other non-linear dimensionality reduction methods. The package is compatible with scikit-learn, and includes a GTM transformer (eGTM), a GTM classifier (eGTC) and a GTM regressor (eGTR). The input and output of these functions are numpy arrays. The package implements supplementary functions for GTM visualization and kernel GTM (kGTM). The code is under MIT license and available on GitHub (https://github.com/hagax8/ugtm). For installation instructions and documentation, cf. https://ugtm.readthedocs.io. Funding statement: HG acknowledges funding from the US National Institute of Mental Health (PGC3: U01 MH109528). Published on 2018-12-19 00:00:00
       
  • Turtle Sport: An Open-Source Software for Communicating with GPS Sport
           Watches

    • Abstract: The aim of this article is to introduce an open-source software—Turtle Sport—that is capable of automatically importing the GPS traces of several types of GPS sport watches (Garmin, Polar, Suunto, Timex, TomTom, etc.) or of importing a number of GPS files. The GPS data are also uploaded locally to the researcher’s computer workstation, and not to Cloud, which may raise important ethical issues. Turtle Sport also allows users to: manage a number of users; visualize the traces and statistics for the races; and export the traces to external files (GPX, KML). Developed in Java, Turtle Sport is a stand-alone, multiplatform (Windows, Mac and Linux) and multi-language (11 languages supported) application. The software is available under GNU LGPL 2.1 Licence on SourceForge (https://sourceforge.net/projects/turtlesport/). Funding statement: The publication of the paper was supported by the Canada Research Chair in Environmental Equity (950-230813). Published on 2018-12-06 00:00:00
       
  • Building Mathematical Models of Biological Systems with modelbase

    • Abstract: The modelbase package is a free expandable Python package for building and analysing dynamic mathematical models of biological systems. Originally it was designed for the simulation of metabolic systems, but it can be used for virtually any deterministic chemical processes. modelbase provides easy construction methods to define reactions and their rates. Based on the rates and stoichiometries, the system of differential equations is assembled automatically. modelbase minimises the constraints imposed on the user, allowing for easy and dynamic access to all variables, including derived ones, in a convenient manner. A simple incorporation of algebraic equations is, for example, convenient to study systems with rapid equilibrium or quasi steady-state approximations. Moreover, modelbase provides construction methods that automatically build all isotope-specific versions of a particular reaction, making it a convenient tool to analyse non-steady state isotope-labelling experiments. Funding statement: This work was financially supported by the Deutsche Forschungsgemeinschaft “Cluster of Excellence on Plant Sciences” CEPLAS (EXC 1028). Published on 2018-11-16 17:53:24
       
  • Turbulucid: A Python Package for Post-Processing of Fluid Flow Simulations

    • Abstract: A Python package for post-processing of plane two-dimensional data from computational fluid dynamics simulations is presented. The package, called turbulucid, provides means for scripted, reproducible analysis of large simulation campaigns and includes routines for both data extraction and visualization. For the former, the Visualization Toolkit (VTK) is used, allowing for post-processing of simulations performed on unstructured meshes. For visualization, several matplotlib-based functions for creating highly customizable, publication-quality plots are provided. To demonstrate turbulucid's functionality it is here applied to post-processing a simulation of a flow over a backward-facing step. The implementation and architecture of the package are also discussed, as well as its reuse potential. Funding Statement: The work was supported by Grant No 621-2012-3721 from the Swedish Research Council. Published on 2018-11-02 08:43:39
       
  • Fidimag – A Finite Difference Atomistic and Micromagnetic Simulation
           Package

    • Abstract: Fidimag is an open-source scientific code for the study of magnetic materials at the nano- or micro-scale using either atomistic or finite difference micromagnetic simulations, which are based on solving the Landau-Lifshitz-Gilbert equation. In addition, it implements simple procedures for calculating energy barriers in the magnetisation through variants of the nudged elastic band method. This computer software has been developed with the aim of creating a simple code structure that can be readily installed, tested, and extended. An agile development approach was adopted, with a strong emphasis on automated builds and tests, and reproducibility of results. The main code and interface to specify simulations are written in Python, which allows simple and readable simulation and analysis configuration scripts. Computationally costly calculations are written in C and exposed to the Python interface as Cython extensions. Docker containers are shipped for a convenient setup experience. The code is freely available on GitHub and includes documentation and examples in the form of Jupyter notebooks. Funding Statement: We acknowledge financial support from EPSRC’s Centre for Doctoral Training in Next Generation Computational Modelling, (EP/L015382/1), EPSRC’s Doctoral Training Centre in Complex System Simulation (EP/G03690X/1), CONICYT Chilean scholarship programme Becas Chile (72140061), Horizon 2020 European Research Infrastructure project OpenDreamKit (676541), National Natural Science Foundation of China (11604169), and the Gordon and Betty Moore Foundation through Grant GBMF #4856, by the Alfred P. Sloan Foundation and by the Helmsley Trust. Published on 2018-09-06 13:16:49
       
  • vaCATE: A Platform for Automating Data Output from Compartmental Analysis
           by Tracer Efflux

    • Abstract: Compartmental analysis by tracer efflux (CATE) is fundamental to examinations of membrane transport, allowing study of solute movement among subcellular compartments with high temporal, spatial, and chemical resolution. CATE can provide a wealth of information about fluxes and pool sizes in complex systems, but is a mathematically intensive procedure, and there is a need for software designed to fully, easily, and dynamically analyse results from CATE experiments. Here we present vaCATE (Visualized Automation of Compartmental Analysis by Tracer Efflux), a software package that meets these criteria. A robust suite of test cases using CATE datasets from experiments with intact rice (Oryza sativa L.) root systems reveals the high fidelity of vaCATE and the ease with which parameters can be extracted, using a three-compartment model and a curve-stripping procedure to distinguish them on the basis of variable exchange rates. vaCATE was developed using Python 2.7 and can be used in most situations where compartmental analysis is required. Funding Statement: This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Ontario Graduate Scholarship Fund (OGS). Published on 2018-08-17 17:01:36
       
  • CERF – A Geospatial Model for Assessing Future Energy Production
           Technology Expansion Feasibility

    • Abstract: The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial model, written in Python and C++, that is designed to determine the on-the-ground feasibility of achieving a projected energy technology expansion plan. Integrated human-Earth systems models and grid expansion models typically do not have sufficient spatial, temporal, or process-level resolution to account for technology-specific siting considerations—for example, the value or costs of connecting a new power plant to the electric grid at a particular location or whether there is sufficient cooling water to support the installation of thermal power plants in a certain region. CERF was developed to specifically examine where power plant locations can be feasibly sited when considering high spatial resolution siting suitability data as well as the net locational costs (i.e., considering both net operating value and interconnection costs), at a spatial resolution of 1 km2. The outputs from CERF can provide insight into factors that influence energy system resilience under a variety of future scenarios can be used to refine model-based projections and be useful for capacity expansion planning exercises. CERF is open-source and publicly available via GitHub. Funding Statement: The original development of the CERF model was conducted under the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830. Further development and ongoing demonstration of CERF is supported by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program. Published on 2018-08-06 10:13:50
       
 
 
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