Summary This paper describes a computational learning resource on Reproducible Data Science with Python. The resource provides an accessible, hands-on introduction to data science techniques, skills, and workflows necessary to perform open, reproducible, and ethical data analysis. By using research problems of real-world relevance (such as vaccine hesitancy and the impact of COVID-19 lockdown measures on human mobility) and real-world social data (including anonymised mobility data from digital sources and recent COVID-19 survey data), the resource encourages students to use open-source tools and coding to learn from diverse and large social data sources. The learning resource aims to minimise barriers to entry for students from social sciences, public health, and related fields. With no software installation and setup requirements, students can start coding from their web browser using free and open-source software (FOSS), including the Python programming language, Jupyter notebook, and Markdown. Through real-world data applications, students are introduced to the open source Python ecosystem of libraries for data science—including pandas (McKinney, 2010), seaborn (Waskom, 2021), scikit-learn (Pedregosa et al., 2011), statsmodels (Seabold & Perk-told, 2010), and networkX (Hagberg et al., 2008)—and learn about open and reproducible workflow, data wrangling, data exploration and visualization, pattern discovery (e.g., clustering), prediction and machine learning, causal inference, network analysis, and data ethics.
{"title":"Reproducible Data Science with Python: An Open Learning Resource","authors":"V. Danchev","doi":"10.21105/jose.00156","DOIUrl":"https://doi.org/10.21105/jose.00156","url":null,"abstract":"Summary This paper describes a computational learning resource on Reproducible Data Science with Python. The resource provides an accessible, hands-on introduction to data science techniques, skills, and workflows necessary to perform open, reproducible, and ethical data analysis. By using research problems of real-world relevance (such as vaccine hesitancy and the impact of COVID-19 lockdown measures on human mobility) and real-world social data (including anonymised mobility data from digital sources and recent COVID-19 survey data), the resource encourages students to use open-source tools and coding to learn from diverse and large social data sources. The learning resource aims to minimise barriers to entry for students from social sciences, public health, and related fields. With no software installation and setup requirements, students can start coding from their web browser using free and open-source software (FOSS), including the Python programming language, Jupyter notebook, and Markdown. Through real-world data applications, students are introduced to the open source Python ecosystem of libraries for data science—including pandas (McKinney, 2010), seaborn (Waskom, 2021), scikit-learn (Pedregosa et al., 2011), statsmodels (Seabold & Perk-told, 2010), and networkX (Hagberg et al., 2008)—and learn about open and reproducible workflow, data wrangling, data exploration and visualization, pattern discovery (e.g., clustering), prediction and machine learning, causal inference, network analysis, and data ethics.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41456634","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}
A. Moodie, B. Carlson, B. Foreman, J. Kwang, K. Naito, J. Nittrouer
1 Rice University (Houston, TX, USA) 2 University of Texas at Austin (Austin, TX, USA) 3 University of Colorado Boulder (Boulder, CO, USA) 4 Western Washington University (Bellingham, WA, USA) 5 University of Illinois at Urbana-Champaign (Urbana, IL, USA) 6 University of Massachusetts Amherst (Amherst, MA, USA) 7 Universidad de Ingeniería y Tecnología (Lima, Peru) 8 Texas Tech University (Lubbock, TX, USA)
{"title":"SedEdu: software organizing sediment-related educational modules","authors":"A. Moodie, B. Carlson, B. Foreman, J. Kwang, K. Naito, J. Nittrouer","doi":"10.21105/jose.00129","DOIUrl":"https://doi.org/10.21105/jose.00129","url":null,"abstract":"1 Rice University (Houston, TX, USA) 2 University of Texas at Austin (Austin, TX, USA) 3 University of Colorado Boulder (Boulder, CO, USA) 4 Western Washington University (Bellingham, WA, USA) 5 University of Illinois at Urbana-Champaign (Urbana, IL, USA) 6 University of Massachusetts Amherst (Amherst, MA, USA) 7 Universidad de Ingeniería y Tecnología (Lima, Peru) 8 Texas Tech University (Lubbock, TX, USA)","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41390686","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}
Abhijna Parigi, Marisa Lim, Saranya Canchi, Jose Sanchez, Jeremy Walter, Rayna M. Harris, Amanda L Charbonneau, C. Brown
We present our lesson material and resources for introducing the use of Amazon Web Services (AWS, https://aws.amazon.com/) for cloud computation. This lesson was developed for the Common Fund Data Ecosystem (CFDE), an NIH initiative that aims to promote data re-use and cloud computing for biomedical research. The lesson materials, technology set-up instructions, and our instructional experiences can serve as a resource for prospective instructors who wish to re-use and remix our materials for teaching AWS and cloud computing.
{"title":"A virtual training module for introducing the use of Amazon Web Services","authors":"Abhijna Parigi, Marisa Lim, Saranya Canchi, Jose Sanchez, Jeremy Walter, Rayna M. Harris, Amanda L Charbonneau, C. Brown","doi":"10.21105/jose.00167","DOIUrl":"https://doi.org/10.21105/jose.00167","url":null,"abstract":"We present our lesson material and resources for introducing the use of Amazon Web Services (AWS, https://aws.amazon.com/) for cloud computation. This lesson was developed for the Common Fund Data Ecosystem (CFDE), an NIH initiative that aims to promote data re-use and cloud computing for biomedical research. The lesson materials, technology set-up instructions, and our instructional experiences can serve as a resource for prospective instructors who wish to re-use and remix our materials for teaching AWS and cloud computing.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43060772","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}
A. Villano, K. Harris, Judit Bergfalk, Raphael Hatami, Francis Vititoe, Julia Johnston
Dark matter is estimated to make up ~84% of all normal/baryonic matter, but cannot be directly imaged. Despite the fact that dark matter cannot be directly observed yet, its influence on the motion of stars and gas in spiral galaxies have been detected. One way to show motion in galaxies are rotation curves that are plots of velocity measurements of how fast stars and gas move in a galaxy around the center of mass. According to Newton's Law of Gravitation, the rotational velocity is an indication of the amount of visible and non-visible mass in the galaxy. Given that the visible matter is measurable using photometry, dark matter mass can therefore be estimated, offering an insight into the size distribution in galaxies. In order to gain a greater appreciation of the research scientists' findings about dark matter, their method should be easily reproduced by any curious individual. Our interactive workshop is an excellent educational tool to investigate how dark matter impacts the rotation of visible matter by providing a guide to produce galactic rotation curves. The Python-based notebooks are set up to walk you through the whole process of producing rotation curves using an online database (SPARC) and to allow you to learn about each component of the galaxy. The three steps of the rotation curve building process is plotting the measured velocity data, constructing the rotation curves for each component, and fitting the total velocity to the measured values.
{"title":"The Data Behind Dark Matter: Exploring Galactic\u0000Rotation","authors":"A. Villano, K. Harris, Judit Bergfalk, Raphael Hatami, Francis Vititoe, Julia Johnston","doi":"10.21105/jose.00184","DOIUrl":"https://doi.org/10.21105/jose.00184","url":null,"abstract":"Dark matter is estimated to make up ~84% of all normal/baryonic matter, but cannot be directly imaged. Despite the fact that dark matter cannot be directly observed yet, its influence on the motion of stars and gas in spiral galaxies have been detected. One way to show motion in galaxies are rotation curves that are plots of velocity measurements of how fast stars and gas move in a galaxy around the center of mass. According to Newton's Law of Gravitation, the rotational velocity is an indication of the amount of visible and non-visible mass in the galaxy. Given that the visible matter is measurable using photometry, dark matter mass can therefore be estimated, offering an insight into the size distribution in galaxies. In order to gain a greater appreciation of the research scientists' findings about dark matter, their method should be easily reproduced by any curious individual. Our interactive workshop is an excellent educational tool to investigate how dark matter impacts the rotation of visible matter by providing a guide to produce galactic rotation curves. The Python-based notebooks are set up to walk you through the whole process of producing rotation curves using an online database (SPARC) and to allow you to learn about each component of the galaxy. The three steps of the rotation curve building process is plotting the measured velocity data, constructing the rotation curves for each component, and fitting the total velocity to the measured values.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42617307","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":"Course Materials for Data Science in Practice","authors":"Thomas Donoghue, Bradley Voytek, Shannon E. Ellis","doi":"10.21105/jose.00121","DOIUrl":"https://doi.org/10.21105/jose.00121","url":null,"abstract":"","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46294021","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}
Ethan White, Z. Brym, Andrew Marx, Kristina Riemer, S. Marconi, David Harris, Virnaliz Cruz, S. Ernest
demonstrations, lecture for live coding demonstrations, links to openly available reference readings, coding practice exercises, and the output expected from completed exercises. The course is structured in topics that combine sets of learning materials covering a week of college level material on a single subject. The lessons and exercises focus on biological examples with a particular focus on ecological examples. The course material is designed to be used in two ways. First, it can be used in a self-paced online format for individual learners. This is achieved by having all of the necessary material to understand and complete the course present on the website along with instructions for self-guided learning. Second, the course is designed to be modified and remixed to be taught in college and university classrooms. This is achieved by a modular design that allows modifying all aspects of the course and by detailed documentation for course customization. The website is viewed by thousands of users each month and the material and infrastructure has been used in courses at multiple colleges and universities.
{"title":"Data Carpentry for Biologists: A semester long Data Carpentry course using ecological and other biological examples","authors":"Ethan White, Z. Brym, Andrew Marx, Kristina Riemer, S. Marconi, David Harris, Virnaliz Cruz, S. Ernest","doi":"10.21105/jose.00139","DOIUrl":"https://doi.org/10.21105/jose.00139","url":null,"abstract":"demonstrations, lecture for live coding demonstrations, links to openly available reference readings, coding practice exercises, and the output expected from completed exercises. The course is structured in topics that combine sets of learning materials covering a week of college level material on a single subject. The lessons and exercises focus on biological examples with a particular focus on ecological examples. The course material is designed to be used in two ways. First, it can be used in a self-paced online format for individual learners. This is achieved by having all of the necessary material to understand and complete the course present on the website along with instructions for self-guided learning. Second, the course is designed to be modified and remixed to be taught in college and university classrooms. This is achieved by a modular design that allows modifying all aspects of the course and by detailed documentation for course customization. The website is viewed by thousands of users each month and the material and infrastructure has been used in courses at multiple colleges and universities.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44465809","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 content discussed within this paper refers to the module created for the academic year 2020-2021: https://andrewmaclachlan.github.io/CASA0005repo_20202021/. At the conclusion of each year content is copied to a new repository ending with the academic year it was taught, whilst the content applicable to the current academic year remains on the primary repository: https://github.com/andrewmaclachlan/CASA0005repo. This allows the authors and external users to track the development of the content.
{"title":"An Applied Geographic Information Systems and Science Course in R","authors":"Andrew MacLachlan, A. Dennett","doi":"10.21105/jose.00141","DOIUrl":"https://doi.org/10.21105/jose.00141","url":null,"abstract":"The content discussed within this paper refers to the module created for the academic year 2020-2021: https://andrewmaclachlan.github.io/CASA0005repo_20202021/. At the conclusion of each year content is copied to a new repository ending with the academic year it was taught, whilst the content applicable to the current academic year remains on the primary repository: https://github.com/andrewmaclachlan/CASA0005repo. This allows the authors and external users to track the development of the content.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43498062","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}
Interactive Bin Packing provides a self-guided tutorial on combinatorial optimization, the bin packing problem, and constructive heuristics. It also enables users to interact with bin packing instances to explore their own problem solving strategies, or to test their knowledge of the constructive heuristics covered by the tutorial. The application is not a solver for bin packing, but rather it is a tool for learning about the bin packing problem, and for learning about heuristic techniques for solving instances of the problem.
{"title":"Interactive Bin Packing: A Java Application for Learning Constructive Heuristics for Combinatorial Optimization","authors":"V. Cicirello","doi":"10.21105/jose.00140","DOIUrl":"https://doi.org/10.21105/jose.00140","url":null,"abstract":"Interactive Bin Packing provides a self-guided tutorial on combinatorial optimization, the bin packing problem, and constructive heuristics. It also enables users to interact with bin packing instances to explore their own problem solving strategies, or to test their knowledge of the constructive heuristics covered by the tutorial. The application is not a solver for bin packing, but rather it is a tool for learning about the bin packing problem, and for learning about heuristic techniques for solving instances of the problem.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43100103","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}
This work provides open-source content for an active learning curriculum in data science. The scope of the content is sufficient for a full-semester introduction to scientifically reproducible statistical computation, data wrangling, visualization, basic statistical literacy, and data-driven modeling. The content is broken into short exercises that introduce new concepts, and longer challenges that encourage students to develop those skills in an open-ended context.
{"title":"An Open-Source Active Learning Curriculum for Data Science in Engineering","authors":"Z. del Rosario","doi":"10.21105/jose.00117","DOIUrl":"https://doi.org/10.21105/jose.00117","url":null,"abstract":"This work provides open-source content for an active learning curriculum in data science. The scope of the content is sufficient for a full-semester introduction to scientifically reproducible statistical computation, data wrangling, visualization, basic statistical literacy, and data-driven modeling. The content is broken into short exercises that introduce new concepts, and longer challenges that encourage students to develop those skills in an open-ended context.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45508088","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}
We present a semester-long educational course (see 14-week schedule) and Jupyter Book that provides introductory training in specifying, implementing, and interpreting computational models that characterize human social behavior and neuroscience. Through readings, discussions, and labs (e.g., Jupyter Notebook tutorials using Python), students will receive hands-on training in using mathematical models to test specific theories and hypotheses and explain unobservable aspects of complex social cognitive processes and behaviors. These aspects broadly include learning from and for others, learning about others, and social influences on decision-making and mental states.
{"title":"Computational models of human social behavior and neuroscience: An open educational course and Jupyter Book to advance computational training","authors":"Shawn A. Rhoads, Lin Gan","doi":"10.21105/jose.00146","DOIUrl":"https://doi.org/10.21105/jose.00146","url":null,"abstract":"We present a semester-long educational course (see 14-week schedule) and Jupyter Book that provides introductory training in specifying, implementing, and interpreting computational models that characterize human social behavior and neuroscience. Through readings, discussions, and labs (e.g., Jupyter Notebook tutorials using Python), students will receive hands-on training in using mathematical models to test specific theories and hypotheses and explain unobservable aspects of complex social cognitive processes and behaviors. These aspects broadly include learning from and for others, learning about others, and social influences on decision-making and mental states.","PeriodicalId":75094,"journal":{"name":"The Journal of open source education","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42884256","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：