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Reproducible Science Workshop

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Workshop goals - Why are we teaching this - Why is this important - For future and current you - For research as a whole - Lack of reproducibility in research is a real problem Materials and how we'll use them - Workshop landing page, with - links to the Materials - schedule Structure oriented along the Four Facets of Reproducibility: - Documentation - Organization - Automation - Dissemination Will be available after the Workshop How this workshop is run - This is a Carpentries Workshop - that means friendly learning environment - Code of Conduct - active learning - work with the people next to you - ask for help

Material Type: Module

Author: Dan Leehr

Research project initialization and organization following reproducible research guidelines

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Workshop goals - Why are we teaching this - Why is this important - For future and current you - For research as a whole - Lack of reproducibility in research is a real problem Materials and how we'll use them - Workshop landing page, with - links to the Materials - schedule Structure oriented along the Four Facets of Reproducibility: - Documentation - Organization - Automation - Dissemination Will be available after the Workshop How this workshop is run - This is a Carpentries Workshop - that means friendly learning environment - Code of Conduct - active learning - work with the people next to you - ask for help

Material Type: Module

Author: Hilmar Lapp

Statistics of DOOM

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About Stats of DOOM Support Statistics of DOOM! This page and the YouTube channel to help people learn statistics by including step-by-step instructions for SPSS, R, Excel, and other programs. Demonstrations are provided including power, data screening, analysis, write up tips, effect sizes, and graphs. Help guides and course materials are also provided! When I originally started posting my videos on YouTube, I never really thought people would be interested in them - minus a few overachieving students. I am glad that I've been able to help so many folks! I have taught many statistics courses - you can view full classes by using the Learn tab in the top right. I have also taught cognitive and language courses, some with coding (see the NLP and Language Modeling courses), and some without (see Other Courses). I hope this website provides structure to all my materials for you to use for yourself or your classroom. Each page has an example syllabus, video lectures laid out with that syllabus (if I have them!), and links to the appropriate materials. Any broken links can be reported by sending me an email (linked at the bottom). Stats Tools was designed for learning statistics, which morphed into learning coding, open science, statistics, and more! Recommendations, comments, and other questions are welcome with the general suggestion to post on the specific video or page you have a question on. I do my best to answer, but also work a full-time job. These resources wouldn't be possible without the help of many fantastic people over the years including: All the Help Desk TAs: Rachel E. Monroe, Marshall Beauchamp, Louis Oberdiear, Simone Donaldson, Kim Koch, Jessica Willis, Samantha Hunter, Flora Forbes, Tabatha Hopke Research colleagues: K.D. Valentine, John E. Scofield, Jeff Pavlacic And more! Pages with specific content made by others are noted on that page.

Material Type: Lecture

Author: Erin M. Buchanan

Reproducible Research

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Modern scientific research takes advantage of programs such as Python and R that are open source. As such, they can be modified and shared by the wider community. Additionally, there is added functionality through additional programs and packages, such as IPython, Sweave, and Shiny. These packages can be used to not only execute data analyses, but also to present data and results consistently across platforms (e.g., blogs, websites, repositories and traditional publishing venues). The goal of the course is to show how to implement analyses and share them using IPython for Python, Sweave and knitr for RStudio to create documents that are shareable and analyses that are reproducible. Course outline is as follows: 1) Use of IPython notebooks to demonstrate and explain code, visualize data, and display analysis results 2) Applications of Python modules such as SymPy, NumPy, pandas, and SciPy 3) Use of Sweave to demonstrate and explain code, visualize data, display analysis results, and create documents and presentations 4) Integration and execution of IPython and R code and analyses using the IPython notebook

Material Type: Full Course

Author: Christopher Ahern

Undergraduate Statistics with JASP

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Authors: Erin M. Buchanan, Tabetha Hopke, Simon DonaldsonEducational use: Use these materials to teach an undergraduate statistics course with a primary social science focus.Abstract: Want to teach an undergraduate statistics course using open source materials? You have come to the right place! A complete set of how-to guides for JASP, learning objectives, and pre-made course materials for you to use in your class.Audience: Educators, Students who need extra how-to helpLevel: IntroductoryPrerequisites: None

Material Type: Activity/Lab, Full Course, Lecture, Lecture Notes

Author: Erin Buchanan

Open Education in Promotion, Tenure, and Faculty Development

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This resource was developed by a working group from the Iowa Open Education Action Team (Iowa OER). Our team built upon DOERS3's OER in Tenure & Promotion Matrix to help faculty and staff advocate for the inclusion of open educational practices (OEP) in promotion, tenure, and faculty evaluation practices at their institutions. Below, you can find our main document, directions for interacting with the text, and handouts you can use or adapt for your own advocacy work.

Material Type: Reading

Authors: Abbey Elder, Anne Marie Gruber, Iowa Open Education Action Team (Iowa OER), Mahrya Burnett, Teri Koch

Statistics and Quantitative Methods Example Videos for Teaching

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The goal of this repository is to index and host short videos that can be used to supplement the teaching of introductory statistics concepts. The purpose of these videos is to show students examples of statistical concepts being used in real research, to build off of foundational understanding of the concept they were introduced to in class. Let's show students real people, doing real research, and using real baby statistics to solve science! If you are interested to contribute a video to this page, please read the wiki, which explains what is needed in more depth. If you are still interested to contribute at that point, please request access as a contributor for the specific component(s) you would like to contribute to. If you would like to contribute a video for a topic that is not listed as a component, please contact JK Flake.

Material Type: Lesson

Author: Jessica Kay Flake

Easing Into Open Science: A Guide for Graduate Students and Their Advisors

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This article provides a roadmap to assist graduate students and their advisors to engage in open science practices. We suggest eight open science practices that novice graduate students could begin adopting today. The topics we cover include journal clubs, project workflow, preprints, reproducible code, data sharing, transparent writing, preregistration, and registered reports. To address concerns about not knowing how to engage in open science practices, we provide a difficulty rating of each behavior (easy, medium, difficult), present them in order of suggested adoption, and follow the format of what, why, how, and worries. We give graduate students ideas on how to approach conversations with their advisors/collaborators, ideas on how to integrate open science practices within the graduate school framework, and specific resources on how to engage with each behavior. We emphasize that engaging in open science behaviors need not be an all or nothing approach, but rather graduate students can engage with any number of the behaviors outlined.

Material Type: Reading

Authors: Moin Syed, Priya Silverstein, Ummul-Kiram Kathawalla

Seven Easy Steps to Open Science

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The open science movement is rapidly changing the scientific landscape. Because exact definitions are often lacking and reforms are constantly evolving, accessible guides to open science are needed. This paper provides an introduction to open science and related reforms in the form of an annotated reading list of seven peer-reviewed articles, following the format of Etz, Gronau, Dablander, Edelsbrunner, and Baribault (2018). Written for researchers and students – particularly in psychological science – it highlights and introduces seven topics: understanding open science; open access; open data, materials, and code; reproducible analyses; preregistration and registered reports; replication research; and teaching open science. For each topic, we provide a detailed summary of one particularly informative and actionable article and suggest several further resources. Supporting a broader understanding of open science issues, this overview should enable researchers to engage with, improve, and implement current open, transparent, reproducible, replicable, and cumulative scientific practices.

Material Type: Reading

Authors: Alexander Etz, Amy Orben, and Michael Schulte-Mecklenbeck, Hannah Moshontz, Jesse C. Niebaum, Johnny van Doorn, Matthew C. Makel, Sam Parsons, Sophia Crüwell

Open Developmental Science: An Overview and Annotated Reading List

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The increasing adoption of open science practices in the last decade has been changing the scientific landscape across fields. However, developmental science has been relatively slow in adopting open science practices. To address this issue, we followed the format of Crüwell et al., (2019) and created summaries and an annotated list of informative and actionable resources discussing ten topics in developmental science: Open science; Reproducibility and replication; Open data, materials and code; Open access; Preregistration; Registered reports; Replication; Incentives; Collaborative developmental science. This article offers researchers and students in developmental science a starting point for understanding how open science intersects with developmental science. After getting familiarized with this article, the developmental scientist should understand the core tenets of open and reproducible developmental science, and feel motivated to start applying open science practices in their workflow.

Material Type: Reading

Authors: Sara Hart, Tamara Kalandadze