pydiffusion is a free and open-source Python library designed to solve diffusion problems for both single-phase and multi-phase binary systems. The key features of pydiffusion include fast simulation of multi-phase diffusion and extraction of diffusion coefficients from experimental concentration profiles using forward simulation analysis. pydiffusion also provides various mathematical models for diffusion profile smoothing, diffusion coefficient evaluation, and data optimization. In pydiffusion, diffusion profiles and various phases are easy to define or read from the experimental datasets. Visualization tools based on Matplotlib are also provided to help users present or refine their simulations and analysis. Funding statement: The development of pydiffusion is supported by the US National Science Foundation (NSF) under Grant number CMMI-1333999, and it is part of an NSF Designing Materials to Revolutionize and Engineer our Future (DMREF) project.
{"title":"pydiffusion: A Python Library for Diffusion Simulation and Data Analysis","authors":"Zhangqi Chen, Qiaofu Zhang, Ji-Cheng Zhao","doi":"10.5334/jors.255","DOIUrl":"https://doi.org/10.5334/jors.255","url":null,"abstract":"pydiffusion is a free and open-source Python library designed to solve diffusion problems for both single-phase and multi-phase binary systems. The key features of pydiffusion include fast simulation of multi-phase diffusion and extraction of diffusion coefficients from experimental concentration profiles using forward simulation analysis. pydiffusion also provides various mathematical models for diffusion profile smoothing, diffusion coefficient evaluation, and data optimization. In pydiffusion, diffusion profiles and various phases are easy to define or read from the experimental datasets. Visualization tools based on Matplotlib are also provided to help users present or refine their simulations and analysis. Funding statement: The development of pydiffusion is supported by the US National Science Foundation (NSF) under Grant number CMMI-1333999, and it is part of an NSF Designing Materials to Revolutionize and Engineer our Future (DMREF) project.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47551786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vowel System Sandbox is a complex agent-based modelling tool which is intended for linguists and speech researchers to test hypotheses about how vowel sounds are transmitted and used through the generations in a language community, and thus how vowel systems may change over generational time. Written in Python 3, the code repository is on Github and can be run in Linux, Windows 7+ and MacOS. This is the first software that provides a computational model of sound change in language by implementing first principles of speech perception and production. Funding statement: This project was partially funded by Provost awards for Research, Scholarship and Creative Activity at Fresno State University.
{"title":"Vowel System Sandbox: Complex System Modelling of Language Change","authors":"S. Fulop, Hannah Scott","doi":"10.5334/JORS.198","DOIUrl":"https://doi.org/10.5334/JORS.198","url":null,"abstract":"Vowel System Sandbox is a complex agent-based modelling tool which is intended for linguists and speech researchers to test hypotheses about how vowel sounds are transmitted and used through the generations in a language community, and thus how vowel systems may change over generational time. Written in Python 3, the code repository is on Github and can be run in Linux, Windows 7+ and MacOS. This is the first software that provides a computational model of sound change in language by implementing first principles of speech perception and production. Funding statement: This project was partially funded by Provost awards for Research, Scholarship and Creative Activity at Fresno State University.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46229217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tensors.jl is a Julia package that provides efficient computations with symmetric and non-symmetric tensors. The focus is on the kind of tensors commonly used in e.g. continuum mechanics and fluid dynamics. Exploiting Julia’s ability to overload Unicode infix operators and using Unicode in identifiers, implemented tensor expressions commonly look very similar to their mathematical writing. This possibly reduces the number of bugs in implementations. Operations on tensors are often compiled into the minimum assembly instructions required, and, when beneficial, SIMD-instructions are used. Computations involving symmetric tensors take symmetry into account to reduce computational cost. Automatic differentiation is supported, which means that most functions written in pure Julia can be efficiently differentiated without having to implement the derivative by hand. The package is useful in applications where efficient tensor operations are required, e.g. in the Finite Element Method. Funding statement: Support for this research was provided by the Swedish Research Council (VR), grant no. 621-2013-3901 and grant no. 2015-05422.
{"title":"Tensors.jl — Tensor Computations in Julia","authors":"Kristoffer Carlsson, F. Ekre","doi":"10.5334/JORS.182","DOIUrl":"https://doi.org/10.5334/JORS.182","url":null,"abstract":"Tensors.jl is a Julia package that provides efficient computations with symmetric and non-symmetric tensors. The focus is on the kind of tensors commonly used in e.g. continuum mechanics and fluid dynamics. Exploiting Julia’s ability to overload Unicode infix operators and using Unicode in identifiers, implemented tensor expressions commonly look very similar to their mathematical writing. This possibly reduces the number of bugs in implementations. Operations on tensors are often compiled into the minimum assembly instructions required, and, when beneficial, SIMD-instructions are used. Computations involving symmetric tensors take symmetry into account to reduce computational cost. Automatic differentiation is supported, which means that most functions written in pure Julia can be efficiently differentiated without having to implement the derivative by hand. The package is useful in applications where efficient tensor operations are required, e.g. in the Finite Element Method. Funding statement: Support for this research was provided by the Swedish Research Council (VR), grant no. 621-2013-3901 and grant no. 2015-05422.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45125062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
transplant2mongo allows users to transform Standard Transplant Analysis and Research (STAR) ASCII data files from the Organ Procurement and Transplantation Network (OPTN) into a MongoDB database [1, 2]. The STAR data are a complex collection of tab-separated files with inter-related records that are not amenable to complex queries. A researcher planning to use OPTN STAR data can use transplant2mongo to convert the data into a MongoDB database and then use open-source tool software for analysis. The source code for transplant2mongo is available on GitHub at https://github.com/ceharvs/transplant2mongo and includes sample data files for initial testing and queries. Funding Statement: The software referenced herein is copyright of The MITRE Corporation and the result of MITRE’s Early Career Research program and work done in the Computational Science and Informatics program at George Mason University. Approved for Public Release; Distribution Unlimited. Case Number 18-0298. The author’s affiliation with The MITRE Corporation is provided for identification purposes only and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author.
{"title":"Transplant2Mongo: Python Scripts that Insert Organ Procurement and Transplantation Network (OPTN) Data in MongoDB","authors":"Christine Harvey, R. Weigel","doi":"10.5334/JORS.229","DOIUrl":"https://doi.org/10.5334/JORS.229","url":null,"abstract":"transplant2mongo allows users to transform Standard Transplant Analysis and Research (STAR) ASCII data files from the Organ Procurement and Transplantation Network (OPTN) into a MongoDB database [1, 2]. The STAR data are a complex collection of tab-separated files with inter-related records that are not amenable to complex queries. A researcher planning to use OPTN STAR data can use transplant2mongo to convert the data into a MongoDB database and then use open-source tool software for analysis. The source code for transplant2mongo is available on GitHub at https://github.com/ceharvs/transplant2mongo and includes sample data files for initial testing and queries. Funding Statement: The software referenced herein is copyright of The MITRE Corporation and the result of MITRE’s Early Career Research program and work done in the Computational Science and Informatics program at George Mason University. Approved for Public Release; Distribution Unlimited. Case Number 18-0298. The author’s affiliation with The MITRE Corporation is provided for identification purposes only and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44969515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Bond‐Lamberty, K. Dorheim, R. Cui, Russell Horowitz, Abigail C. Snyder, K. Calvin, Leyang Feng, R. Hoesly, Jill Horing, G. P. Kyle, R. Link, P. Patel, Christopher Roney, A. Staniszewski, S. Turner, Min Chen, F. Feijoo, C. Hartin, M. Hejazi, G. Iyer, Son H Kim, Yaling Liu, Cary Lynch, H. Mcjeon, Steven J. Smith, Stephanie T. Waldhoff, M. Wise, L. Clarke
The increasing data requirements of complex models demand robust, reproducible, and transparent systems to track and prepare models’ inputs. Here we describe version 1.0 of the gcamdata R package that processes raw inputs to produce the hundreds of XML files needed by the GCAM integrated human-earth systems model. It features extensive functional and unit testing, data tracing and visualization, and enforces metadata, documentation, and flexibility in its component data-processing subunits. Although this package is specific to GCAM, many of its structural pieces and approaches should be broadly applicable to, and reusable by, other complex model/data systems aiming to improve transparency, reproducibility, and flexibility. Funding statement: Primary support for this work was provided by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program. Additional support was provided by the U.S. Department of Energy Offices of Fossil Energy, Nuclear Energy, and Energy Efficiency and Renewable Energy and the U.S. Environmental Protection Agency.
{"title":"gcamdata: An R Package for Preparation, Synthesis, and Tracking of Input Data for the GCAM Integrated Human-Earth Systems Model","authors":"B. Bond‐Lamberty, K. Dorheim, R. Cui, Russell Horowitz, Abigail C. Snyder, K. Calvin, Leyang Feng, R. Hoesly, Jill Horing, G. P. Kyle, R. Link, P. Patel, Christopher Roney, A. Staniszewski, S. Turner, Min Chen, F. Feijoo, C. Hartin, M. Hejazi, G. Iyer, Son H Kim, Yaling Liu, Cary Lynch, H. Mcjeon, Steven J. Smith, Stephanie T. Waldhoff, M. Wise, L. Clarke","doi":"10.5334/JORS.232","DOIUrl":"https://doi.org/10.5334/JORS.232","url":null,"abstract":"The increasing data requirements of complex models demand robust, reproducible, and transparent systems to track and prepare models’ inputs. Here we describe version 1.0 of the gcamdata R package that processes raw inputs to produce the hundreds of XML files needed by the GCAM integrated human-earth systems model. It features extensive functional and unit testing, data tracing and visualization, and enforces metadata, documentation, and flexibility in its component data-processing subunits. Although this package is specific to GCAM, many of its structural pieces and approaches should be broadly applicable to, and reusable by, other complex model/data systems aiming to improve transparency, reproducibility, and flexibility. Funding statement: Primary support for this work was provided by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program. Additional support was provided by the U.S. Department of Energy Offices of Fossil Energy, Nuclear Energy, and Energy Efficiency and Renewable Energy and the U.S. Environmental Protection Agency.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44194055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Emsley, Torø Graven, N. Bird, Susan Griffiths, J. Suess, Lucy Shaw
Museums hold collections of objects. Interventions, such as audio descriptions, objects, and maps, make these accessible to the visitors with visual impairments but 2-dimensional objects, such as maps, photographs and paintings, can still present challenges. An inter-disciplinary project works to improve access to visual art works via audio and touch interfaces. The outputs include an improved understanding of the how to improve access to the art collections for the audience and a re-usable technology to deliver audio in a non-linear fashion to the audience within a gallery. We discuss the project’s development strand. The steps taken, such as participatory and experimental approaches, are considered with the issues that arose whilst working on the software, such as improving the communication how touch is used to perceive the world and the difficulties this posed. We pose ongoing research questions for non-visual interaction.
{"title":"Please Touch the Art: Experiences in Developing for the Visually Impaired","authors":"I. Emsley, Torø Graven, N. Bird, Susan Griffiths, J. Suess, Lucy Shaw","doi":"10.5334/JORS.231","DOIUrl":"https://doi.org/10.5334/JORS.231","url":null,"abstract":"Museums hold collections of objects. Interventions, such as audio descriptions, objects, and maps, make these accessible to the visitors with visual impairments but 2-dimensional objects, such as maps, photographs and paintings, can still present challenges. An inter-disciplinary project works to improve access to visual art works via audio and touch interfaces. The outputs include an improved understanding of the how to improve access to the art collections for the audience and a re-usable technology to deliver audio in a non-linear fashion to the audience within a gallery. We discuss the project’s development strand. The steps taken, such as participatory and experimental approaches, are considered with the issues that arose whilst working on the software, such as improving the communication how touch is used to perceive the world and the difficulties this posed. We pose ongoing research questions for non-visual interaction.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48599471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ARBTools is a Python library containing a Lekien-Marsden type tricubic spline method for interpolating three-dimensional scalar or vector fields presented as a set of discrete data points on a regular cuboid grid. ARBTools was developed for simulations of magnetic molecular traps, in which the magnitude, gradient and vector components of a magnetic field are required. Numerical integrators for solving particle trajectories are included, but the core interpolator can be used for any scalar or vector field. The only additional system requirements are NumPy.
{"title":"ARBTools: A Tricubic Spline Interpolator for Three-Dimensional Scalar or Vector Fields","authors":"P. A. Walker, U. Krohn, D. Carty","doi":"10.5334/JORS.258","DOIUrl":"https://doi.org/10.5334/JORS.258","url":null,"abstract":"ARBTools is a Python library containing a Lekien-Marsden type tricubic spline method for interpolating three-dimensional scalar or vector fields presented as a set of discrete data points on a regular cuboid grid. ARBTools was developed for simulations of magnetic molecular traps, in which the magnitude, gradient and vector components of a magnetic field are required. Numerical integrators for solving particle trajectories are included, but the core interpolator can be used for any scalar or vector field. The only additional system requirements are NumPy.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41543062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"BayesFit: A Tool for Modeling Psychophysical Data Using Bayesian Inference","authors":"Michael Slugocki, A. Sekuler, P. Bennett","doi":"10.5334/JORS.202","DOIUrl":"https://doi.org/10.5334/JORS.202","url":null,"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.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":"37 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41283596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Vernon, M. Hejazi, S. Turner, Yaling Liu, Caleb Braun, Xinya Li, R. Link
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.
{"title":"A Global Hydrologic Framework to Accelerate Scientific Discovery","authors":"C. Vernon, M. Hejazi, S. Turner, Yaling Liu, Caleb Braun, Xinya Li, R. Link","doi":"10.5334/JORS.245","DOIUrl":"https://doi.org/10.5334/JORS.245","url":null,"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.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47520747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Wheeler, Trevor Keller, Stephen J DeWitt, Andrea M Jokisaari, Daniel Schwen, Jonathan E Guyer, Larry K Aagesen, Olle G Heinonen, Michael R Tonks, Peter W Voorhees, James A Warren
Scientific communities struggle with the challenge of effectively and efficiently sharing content and data. An online portal provides a valuable space for scientific communities to discuss challenges and collate scientific results. Examples of such portals include the Micromagnetic Modeling Group (μMAG [1]), the Interatomic Potentials Repository (IPR [2, 3]) and on a larger scale the NIH Genetic Sequence Database (GenBank [4]). In this work, we present a description of a generic web portal that leverages existing online services to provide a framework that may be adopted by other small scientific communities. The first deployment of the PFHub framework supports phase-field practitioners and code developers participating in an effort to improve quality assurance for phase-field codes.
{"title":"PFHub: The Phase-Field Community Hub.","authors":"Daniel Wheeler, Trevor Keller, Stephen J DeWitt, Andrea M Jokisaari, Daniel Schwen, Jonathan E Guyer, Larry K Aagesen, Olle G Heinonen, Michael R Tonks, Peter W Voorhees, James A Warren","doi":"10.5334/jors.276","DOIUrl":"10.5334/jors.276","url":null,"abstract":"<p><p>Scientific communities struggle with the challenge of effectively and efficiently sharing content and data. An online portal provides a valuable space for scientific communities to discuss challenges and collate scientific results. Examples of such portals include the Micromagnetic Modeling Group (μMAG [1]), the Interatomic Potentials Repository (IPR [2, 3]) and on a larger scale the NIH Genetic Sequence Database (GenBank [4]). In this work, we present a description of a generic web portal that leverages existing online services to provide a framework that may be adopted by other small scientific communities. The first deployment of the PFHub framework supports phase-field practitioners and code developers participating in an effort to improve quality assurance for phase-field codes.</p>","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10938819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46485978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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