Luke Johnston, Helene Juel, Bettina Lengger, Daniel R Witte, H. Chatwin, Malene R Christiansen, A. Isaksen
The amount of biological data created increases every year, driven largely by technologies such as high-throughput -omics, real-time monitoring, or high resolution imaging in addition to greater access to routine administrative data and larger study populations. This not only presents operational challenges, but also highlights considerable needs for the skills and knowledge to manage, process, and analyze this data (Brownson et al., 2015). Along with the open science movement on the rise, methods and analytic processes are also increasingly expected to be open and transparent and for scientific studies to be reproducible (Watson, 2015).
{"title":"r-cubed: Guiding the overwhelmed scientist from random wrangling to Reproducible Research in R","authors":"Luke Johnston, Helene Juel, Bettina Lengger, Daniel R Witte, H. Chatwin, Malene R Christiansen, A. Isaksen","doi":"10.21105/jose.00122","DOIUrl":"https://doi.org/10.21105/jose.00122","url":null,"abstract":"The amount of biological data created increases every year, driven largely by technologies such as high-throughput -omics, real-time monitoring, or high resolution imaging in addition to greater access to routine administrative data and larger study populations. This not only presents operational challenges, but also highlights considerable needs for the skills and knowledge to manage, process, and analyze this data (Brownson et al., 2015). Along with the open science movement on the rise, methods and analytic processes are also increasingly expected to be open and transparent and for scientific studies to be reproducible (Watson, 2015).","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67736305","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}
Pub Date : 2021-01-01Epub Date: 2021-12-17DOI: 10.21105/jose.00138
Marlena Duda, Kelly L Sovacool, Negar Farzaneh, Vy Kim Nguyen, Sarah E Haynes, Hayley Falk, Katherine L Furman, Logan A Walker, Rucheng Diao, Morgan Oneka, Audrey C Drotos, Alana Woloshin, Gabrielle A Dotson, April Kriebel, Lucy Meng, Stephanie N Thiede, Zena Lapp, Brooke N Wolford
We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our Open Educational Resources on GitHub.
{"title":"Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum.","authors":"Marlena Duda, Kelly L Sovacool, Negar Farzaneh, Vy Kim Nguyen, Sarah E Haynes, Hayley Falk, Katherine L Furman, Logan A Walker, Rucheng Diao, Morgan Oneka, Audrey C Drotos, Alana Woloshin, Gabrielle A Dotson, April Kriebel, Lucy Meng, Stephanie N Thiede, Zena Lapp, Brooke N Wolford","doi":"10.21105/jose.00138","DOIUrl":"10.21105/jose.00138","url":null,"abstract":"<p><p>We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our Open Educational Resources on GitHub.</p>","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8851894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39939809","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}
In general relativity, Schwarzschild coordinates for a black hole have desirable properties such as asymptotic matching with flat-space spherical coordinates; but other coordinate systems can be used which have other advantages such as removing the non-physical coordinate singularity at the event horizon. Following Schwarzschild’s original publication in 1916 of his spherically symmetrical solution to the vacuum Einstein field equations, a variety of coordinate transformations have been described that highlight different features of the Schwarzschild metric. These include: Kruskal-Szekeres (Kruskal, 1960; Szekeres, 1960), Eddington-Finkelstein (Eddington, 1924; Finkelstein, 1958), Gullstrand-Painleve (Gullstrand, 1922; Painlevé, 1921), Lemaitre (Lemaître, 1933), and various Penrose transforms with or without a black hole (Hawking & Ellis, 1973). These are described in many undergraduate GR textbooks such as Schutz (2009) and Carroll (2019).
{"title":"General relativity in R: visual representation of Schwarzschild space using different coordinate systems","authors":"R. Hankin","doi":"10.21105/JOSE.00091","DOIUrl":"https://doi.org/10.21105/JOSE.00091","url":null,"abstract":"In general relativity, Schwarzschild coordinates for a black hole have desirable properties such as asymptotic matching with flat-space spherical coordinates; but other coordinate systems can be used which have other advantages such as removing the non-physical coordinate singularity at the event horizon. Following Schwarzschild’s original publication in 1916 of his spherically symmetrical solution to the vacuum Einstein field equations, a variety of coordinate transformations have been described that highlight different features of the Schwarzschild metric. These include: Kruskal-Szekeres (Kruskal, 1960; Szekeres, 1960), Eddington-Finkelstein (Eddington, 1924; Finkelstein, 1958), Gullstrand-Painleve (Gullstrand, 1922; Painlevé, 1921), Lemaitre (Lemaître, 1933), and various Penrose transforms with or without a black hole (Hawking & Ellis, 1973). These are described in many undergraduate GR textbooks such as Schutz (2009) and Carroll (2019).","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47590651","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}
The concept of genetic drift in population genetics is a notoriously difficult concept to teach and to learn. DraggleSimulator is an open-source simulation web application written using Google’s Angular framework that allows students to create a metapopulation (a collection of isolated subpopulations) of draggles: fictitious, diploid (meaning they have a mom and a dad and two copies of every gene in their genomes, just like humans), monogamous (only mating with one other individual), and simultaneously-hermaphroditic animals (any individual can mate with any other individual and the sex of the individual is a non-issue). The animals mate at a rate consistent with replacement-level fertility (2 offspring per couple), and the simulator tracks these matings and how they change the composition of alleles (versions of genes) across generations.
{"title":"DraggleSimulator: An Open Source Web Application for Teaching Genetic Drift","authors":"Mark Fisher","doi":"10.21105/jose.00070","DOIUrl":"https://doi.org/10.21105/jose.00070","url":null,"abstract":"The concept of genetic drift in population genetics is a notoriously difficult concept to teach and to learn. DraggleSimulator is an open-source simulation web application written using Google’s Angular framework that allows students to create a metapopulation (a collection of isolated subpopulations) of draggles: fictitious, diploid (meaning they have a mom and a dad and two copies of every gene in their genomes, just like humans), monogamous (only mating with one other individual), and simultaneously-hermaphroditic animals (any individual can mate with any other individual and the sex of the individual is a non-issue). The animals mate at a rate consistent with replacement-level fertility (2 offspring per couple), and the simulator tracks these matings and how they change the composition of alleles (versions of genes) across generations.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44253485","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}
Our civilization’s ability to generate data has far surpassed our current, collective capabilities to efficiently access and utilize it. And while the necessary educational infrastructure is trying to adapt and grow as quickly as possible, many graduate students, postdocs, faculty, and other researchers have found ourselves in situations where self-education with little direction is the only option available. This can make it difficult to get through the initial steep learning curve of beginning to use bioinformatics effectively – particularly when this is expected to happen alongside all of our other research and educational responsibilities.
{"title":"Happy Belly Bioinformatics: an open-source resource dedicated to helping biologists utilize bioinformatics","authors":"Michael Lee","doi":"10.21105/jose.00053","DOIUrl":"https://doi.org/10.21105/jose.00053","url":null,"abstract":"Our civilization’s ability to generate data has far surpassed our current, collective capabilities to efficiently access and utilize it. And while the necessary educational infrastructure is trying to adapt and grow as quickly as possible, many graduate students, postdocs, faculty, and other researchers have found ourselves in situations where self-education with little direction is the only option available. This can make it difficult to get through the initial steep learning curve of beginning to use bioinformatics effectively – particularly when this is expected to happen alongside all of our other research and educational responsibilities.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43302878","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}
• teachers who want to automatically create problem sets with their solutions (easily scriptable, random-problem-generator friendly); • students who want to verify their solutions to introductory problem sets; • students who like to play with example problems and receive immediate visual feedback (i.e. about how simple modifications to imposed loads affect the resulting reaction forces and internal stresses).
{"title":"BeamBending: a teaching aid for 1-D shear force and bending moment diagrams","authors":"A. Carella","doi":"10.21105/jose.00065","DOIUrl":"https://doi.org/10.21105/jose.00065","url":null,"abstract":"• teachers who want to automatically create problem sets with their solutions (easily scriptable, random-problem-generator friendly); • students who want to verify their solutions to introductory problem sets; • students who like to play with example problems and receive immediate visual feedback (i.e. about how simple modifications to imposed loads affect the resulting reaction forces and internal stresses).","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43933686","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}
According to the Ljubljana OER Action Plan 2017 (UNESCO, 2017), “Open Educational Resources (OER) support quality education that is equitable, inclusive, open and participatory.” However, several challenges are known that hinder widespread creation, use, and re-use of OER. The first challenge identified in the Action Plan lies in “the capacity of users to find, re-use, create,and share OER”, and the first action category addressing that challenge, “Building awareness and skills to use OER”, lists ten actions, among which action (c) reads as follows: “Disseminate the findings of research on OER to support models of good practice with a focus on cost-effectiveness, sustainability, exploration of new tools and technologies for the creation and sharing of OER”.
{"title":"Emacs-reveal: A software bundle to create OER presentations","authors":"Jens Lechtenbörger","doi":"10.21105/JOSE.00050","DOIUrl":"https://doi.org/10.21105/JOSE.00050","url":null,"abstract":"According to the Ljubljana OER Action Plan 2017 (UNESCO, 2017), “Open Educational Resources (OER) support quality education that is equitable, inclusive, open and participatory.” However, several challenges are known that hinder widespread creation, use, and re-use of OER. The first challenge identified in the Action Plan lies in “the capacity of users to find, re-use, create,and share OER”, and the first action category addressing that challenge, “Building awareness and skills to use OER”, lists ten actions, among which action (c) reads as follows: “Disseminate the findings of research on OER to support models of good practice with a focus on cost-effectiveness, sustainability, exploration of new tools and technologies for the creation and sharing of OER”.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47038298","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}
Luke Johnston, Madeleine Bonsma-Fisher, Joel Ostblom, Ahmed R. Hasan, James S. Santangelo, L. Coome, Lina Tran, Elliott de Andrade, Sara Mahallati
1 Department of Physics, University of Toronto 2 Department of Nutritional Sciences, University of Toronto 3 Department of Public Health, Aarhus University 4 Institute of Biomaterials and Biomedical Engineering, University of Toronto 5 Department of Psychology, University of Toronto 6 Department of Cell and Systems Biology, University of Toronto 7 Department of Ecology and Evolutionary Biology, University of Toronto 8 Department of Physiology, University of Toronto
{"title":"A graduate student-led participatory live-coding quantitative methods course in R: Experiences on initiating, developing, and teaching","authors":"Luke Johnston, Madeleine Bonsma-Fisher, Joel Ostblom, Ahmed R. Hasan, James S. Santangelo, L. Coome, Lina Tran, Elliott de Andrade, Sara Mahallati","doi":"10.21105/jose.00049","DOIUrl":"https://doi.org/10.21105/jose.00049","url":null,"abstract":"1 Department of Physics, University of Toronto 2 Department of Nutritional Sciences, University of Toronto 3 Department of Public Health, Aarhus University 4 Institute of Biomaterials and Biomedical Engineering, University of Toronto 5 Department of Psychology, University of Toronto 6 Department of Cell and Systems Biology, University of Toronto 7 Department of Ecology and Evolutionary Biology, University of Toronto 8 Department of Physiology, University of Toronto","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45524799","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}
The AeroPython set of lessons present a computational approach to an engineering course in classical aerodynamics. The audience of such a course would be advanced undergraduate students in mechanical or aerospace engineering (or master’s students), with a fluid mechanics pre-requisite. Classical aerodynamics is a broad theoretical subject— this learning module focuses on one topic: the use of potential flow for aerodynamic analysis, via the panel method.
{"title":"Aero Python: classical aerodynamics of potential flow using Python","authors":"L. Barba, O. Mesnard","doi":"10.21105/JOSE.00045","DOIUrl":"https://doi.org/10.21105/JOSE.00045","url":null,"abstract":"The AeroPython set of lessons present a computational approach to an engineering course in classical aerodynamics. The audience of such a course would be advanced undergraduate students in mechanical or aerospace engineering (or master’s students), with a fluid mechanics pre-requisite. Classical aerodynamics is a broad theoretical subject— this learning module focuses on one topic: the use of potential flow for aerodynamic analysis, via the panel method.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47808430","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}
Data Science (Donoho, 2017) has become one of the most demanded skills thanks to an explosion in the availability of data (Kitchin, 2014). Most of these new sources are, directly or indirecly, geographic in that they can be related to a particular location on a map. However, the vast majority of data science resources available currently ignore the spatial dimension of data, particularly when it comes to the more analytic set of methods covered. At the same time, traditional resources for teaching the handling, visualisation, and analysis of geographic data are based on a paradigm that emphasises graphical interfaces and “point-and-click” software packages. This approach, although valid, limits the flexiblity with which the analyst can effectively move from data to insights, and is more difficult to connect with and benefit from modern advances in both data tools and workflows. This paper presents a pedagogical bridge between the “spatially unaware” set of practices emerging from Data Science, and more traditional resources designed to teach spatial analysis within a Geographic Information Systems (GIS) environment.
{"title":"A course on Geographic Data Science","authors":"Daniel Arribas-Bel","doi":"10.21105/JOSE.00042","DOIUrl":"https://doi.org/10.21105/JOSE.00042","url":null,"abstract":"Data Science (Donoho, 2017) has become one of the most demanded skills thanks to an explosion in the availability of data (Kitchin, 2014). Most of these new sources are, directly or indirecly, geographic in that they can be related to a particular location on a map. However, the vast majority of data science resources available currently ignore the spatial dimension of data, particularly when it comes to the more analytic set of methods covered. At the same time, traditional resources for teaching the handling, visualisation, and analysis of geographic data are based on a paradigm that emphasises graphical interfaces and “point-and-click” software packages. This approach, although valid, limits the flexiblity with which the analyst can effectively move from data to insights, and is more difficult to connect with and benefit from modern advances in both data tools and workflows. This paper presents a pedagogical bridge between the “spatially unaware” set of practices emerging from Data Science, and more traditional resources designed to teach spatial analysis within a Geographic Information Systems (GIS) environment.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49072462","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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