{"title":"prospect: an R package to link leaf optical properties\u0000with their chemical and structural properties with the leaf model\u0000PROSPECT","authors":"J. Féret, F. de Boissieu","doi":"10.21105/joss.06027","DOIUrl":"https://doi.org/10.21105/joss.06027","url":null,"abstract":"","PeriodicalId":503081,"journal":{"name":"Journal of Open Source Software","volume":"23 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864400","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}
Peerasait Prachaseree, S. Mohammadzadeh, B. Dortdivanlioglu, E. Lejeune
{"title":"FEniCS-arclength: A numerical continuation package in\u0000FEniCS for nonlinear problems in solid mechanics","authors":"Peerasait Prachaseree, S. Mohammadzadeh, B. Dortdivanlioglu, E. Lejeune","doi":"10.21105/joss.05727","DOIUrl":"https://doi.org/10.21105/joss.05727","url":null,"abstract":"","PeriodicalId":503081,"journal":{"name":"Journal of Open Source Software","volume":"47 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868770","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}
Peerasait Prachaseree, S. Mohammadzadeh, B. Dortdivanlioglu, E. Lejeune
{"title":"FEniCS-arclength: A numerical continuation package in\u0000FEniCS for nonlinear problems in solid mechanics","authors":"Peerasait Prachaseree, S. Mohammadzadeh, B. Dortdivanlioglu, E. Lejeune","doi":"10.21105/joss.05727","DOIUrl":"https://doi.org/10.21105/joss.05727","url":null,"abstract":"","PeriodicalId":503081,"journal":{"name":"Journal of Open Source Software","volume":"21 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808785","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}
Baptiste Delaporte, Thomas Arsouze, G. Keppel, Swen Jullien, Christophe Menkes, Thomas Ibanez
StormR is an R package allowing the easy extraction of storm track data from a provided database and the generation of surface wind fields (speed and direction) as reconstructed from storm track data and a parametric cyclone model. Then StormR allows us to compute three summary statistics (the maximum sustained wind speed, the power dissipation index, and the duration of exposure to winds reaching a given wing speed along the cyclone life span) and to plot the results. We suggest to use the IBTrACS (International Best Track Archive for Climate Stewardship) database as input (Knapp et al., 2010, 2018). This database provides a fairly comprehensive record of tropical storms and cyclones with a 3-hours temporal resolution since 1841. However any storm track data can be used as long as the mandatory fields are provided. Storm track data can be extracted using a specified point location, a
{"title":"StormR: An R package to quantify and map the tropical\u0000storms and cyclones’ winds characteristics","authors":"Baptiste Delaporte, Thomas Arsouze, G. Keppel, Swen Jullien, Christophe Menkes, Thomas Ibanez","doi":"10.21105/joss.05766","DOIUrl":"https://doi.org/10.21105/joss.05766","url":null,"abstract":"StormR is an R package allowing the easy extraction of storm track data from a provided database and the generation of surface wind fields (speed and direction) as reconstructed from storm track data and a parametric cyclone model. Then StormR allows us to compute three summary statistics (the maximum sustained wind speed, the power dissipation index, and the duration of exposure to winds reaching a given wing speed along the cyclone life span) and to plot the results. We suggest to use the IBTrACS (International Best Track Archive for Climate Stewardship) database as input (Knapp et al., 2010, 2018). This database provides a fairly comprehensive record of tropical storms and cyclones with a 3-hours temporal resolution since 1841. However any storm track data can be used as long as the mandatory fields are provided. Storm track data can be extracted using a specified point location, a","PeriodicalId":503081,"journal":{"name":"Journal of Open Source Software","volume":"2 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139438125","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}
Ziheng ‘Leo’ Li, Haowen ‘John’ Wei, Ziwen Xie, Yunxiang Peng, June Pyo Suh, Steven Feiner, Paul Sajda
{"title":"PhysioLabXR: A Python Platform for Real-Time,\u0000Multi-modal, Brain–Computer Interfaces and Extended Reality\u0000Experiments","authors":"Ziheng ‘Leo’ Li, Haowen ‘John’ Wei, Ziwen Xie, Yunxiang Peng, June Pyo Suh, Steven Feiner, Paul Sajda","doi":"10.21105/joss.05854","DOIUrl":"https://doi.org/10.21105/joss.05854","url":null,"abstract":"","PeriodicalId":503081,"journal":{"name":"Journal of Open Source Software","volume":"10 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139438758","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}
Assessing and comparing player skill in online multiplayer gaming environments is essential for fair matchmaking and player engagement. Traditional ranking models like Elo and Glicko-2, designed for two-player games, are insufficient for the complexity of multi-player, asymmetric team-based matches. To address this gap, the OpenSkill library offers a suite of sophisticated, fast, and adaptable models tailored for such dynamics. Drawing from Bayesian inference methods, OpenSkill provides a more accurate representation of individual player contributions and speeds up the computation of ranks. This paper introduces the OpenSkill library, featuring a Python implementation of the Plackett-Luce model among others, highlighting its performance advantages and predictive accuracy against proprietary systems like TrueSkill. OpenSkill is a valuable tool for game developers and researchers, ensuring a responsive and fair gaming experience by efficiently adjusting player rankings based on game outcomes. The library's support for time decay and diligent documentation further aid in its practical application, making it a robust solution for the nuanced world of multiplayer ranking systems. This paper also acknowledges areas for future enhancement, such as partial play and contribution weighting, emphasizing the library's ongoing development to meet the evolving needs of online gaming communities.
{"title":"OpenSkill: A faster asymmetric multi-team, multiplayer\u0000rating system","authors":"Vivek Joshy","doi":"10.21105/joss.05901","DOIUrl":"https://doi.org/10.21105/joss.05901","url":null,"abstract":"Assessing and comparing player skill in online multiplayer gaming environments is essential for fair matchmaking and player engagement. Traditional ranking models like Elo and Glicko-2, designed for two-player games, are insufficient for the complexity of multi-player, asymmetric team-based matches. To address this gap, the OpenSkill library offers a suite of sophisticated, fast, and adaptable models tailored for such dynamics. Drawing from Bayesian inference methods, OpenSkill provides a more accurate representation of individual player contributions and speeds up the computation of ranks. This paper introduces the OpenSkill library, featuring a Python implementation of the Plackett-Luce model among others, highlighting its performance advantages and predictive accuracy against proprietary systems like TrueSkill. OpenSkill is a valuable tool for game developers and researchers, ensuring a responsive and fair gaming experience by efficiently adjusting player rankings based on game outcomes. The library's support for time decay and diligent documentation further aid in its practical application, making it a robust solution for the nuanced world of multiplayer ranking systems. This paper also acknowledges areas for future enhancement, such as partial play and contribution weighting, emphasizing the library's ongoing development to meet the evolving needs of online gaming communities.","PeriodicalId":503081,"journal":{"name":"Journal of Open Source Software","volume":"45 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442335","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}
{"title":"CLPFUDatabase: A suite of R packages for energy\u0000conversion chain analysis","authors":"M. Heun, Zeke Marshall, E. Aramendia","doi":"10.21105/joss.06057","DOIUrl":"https://doi.org/10.21105/joss.06057","url":null,"abstract":"","PeriodicalId":503081,"journal":{"name":"Journal of Open Source Software","volume":"34 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139443031","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}
Jessica M. Otis, James Safley, Megan Brett, Lincoln Mullen
{"title":"DataScribe: An Omeka S module for structured data\u0000transcription","authors":"Jessica M. Otis, James Safley, Megan Brett, Lincoln Mullen","doi":"10.21105/joss.05661","DOIUrl":"https://doi.org/10.21105/joss.05661","url":null,"abstract":"","PeriodicalId":503081,"journal":{"name":"Journal of Open Source Software","volume":"35 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448111","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}
Pablo Timoner, F. Hierink, Loïc Baecher, Caroline Fuhrer, Nicolas Ray
{"title":"inAccessMod: An R package to automate data downloading\u0000and processing for AccessMod","authors":"Pablo Timoner, F. Hierink, Loïc Baecher, Caroline Fuhrer, Nicolas Ray","doi":"10.21105/joss.05879","DOIUrl":"https://doi.org/10.21105/joss.05879","url":null,"abstract":"","PeriodicalId":503081,"journal":{"name":"Journal of Open Source Software","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448806","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}
Andrew S. Rosen, Max Gallant, Janine George, Janosh Riebesell, Hrushikesh Sahasrabuddhe, Jimmy-Xuan Shen, Mingjian Wen, Matthew L. Evans, G. Petretto, D. Waroquiers, G. Rignanese, Kristin A. Persson, Anubhav Jain, A. Ganose
{"title":"Jobflow: Computational Workflows Made Simple","authors":"Andrew S. Rosen, Max Gallant, Janine George, Janosh Riebesell, Hrushikesh Sahasrabuddhe, Jimmy-Xuan Shen, Mingjian Wen, Matthew L. Evans, G. Petretto, D. Waroquiers, G. Rignanese, Kristin A. Persson, Anubhav Jain, A. Ganose","doi":"10.21105/joss.05995","DOIUrl":"https://doi.org/10.21105/joss.05995","url":null,"abstract":"","PeriodicalId":503081,"journal":{"name":"Journal of Open Source Software","volume":"35 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448100","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}
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