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1 - Pattern & Inquiry
Conditional Remix & Share Permitted
CC BY-NC-SA
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In Part 1 of this unit, students will learn about data collection, graphing skills (both by hand and computer aided [Desmos]), and the fundamental mathematical patterns of the course: horizontal line, proportional, linear, quadratic, and inverse. Students perform several experiments, each targeting a different pattern and build the mathematical models of physical phenomena. During each experiment, students start with an uninformed wild guess, then through inquiry and making sense through group consensus, can make an accurate data informed prediction.

Subject:
Physics
Material Type:
Unit of Study
Provider:
Portland Metro STEM Partnership
Provider Set:
Patterns Physics
Date Added:
08/01/2018
7 Easy Steps to Open Science: An Annotated Reading List
Unrestricted Use
CC BY
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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 et al. (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.

Subject:
Applied Science
Life Science
Physical Science
Social Science
Material Type:
Reading
Author:
Alexander Etz
Amy Orben
Hannah Moshontz
Jesse Niebaum
Johnny van Doorn
Matthew Makel
Michael Schulte-Mecklenbeck
Sam Parsons
Sophia Crüwell
Date Added:
08/12/2019
ACC Basketball
Conditional Remix & Share Permitted
CC BY-NC-SA
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The students will use ACC basketball statistics to practice the process of converting fractions to decimals then to percents and will learn how to create and edit a spreadsheet. They will then use this spreadsheet to analyze their data. This unit is done during the basketball season which takes approximately 15 weeks from the middle of November to the middle of March. Teachers must have Clarisworks to open the sample spreadsheet in the lesson, but may recreate it in another spreadsheet program.

Subject:
Statistics and Probability
Material Type:
Lesson Plan
Provider:
University of North Carolina at Chapel Hill School of Education
Provider Set:
LEARN NC Lesson Plans
Author:
Susan Dougherty
Date Added:
07/14/2000
Accelerometer: Centripetal Acceleration
Read the Fine Print
Educational Use
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Students work as physicists to understand centripetal acceleration concepts. They also learn about a good robot design and the accelerometer sensor. They also learn about the relationship between centripetal acceleration and centripetal force governed by the radius between the motor and accelerometer and the amount of mass at the end of the robot's arm. Students graph and analyze data collected from an accelerometer, and learn to design robots with proper weight distribution across the robot for their robotic arms. Upon using a data logging program, they view their own data collected during the activity. By activity end , students understand how a change in radius or mass can affect the data obtained from the accelerometer through the plots generated from the data logging program. More specifically, students learn about the accuracy and precision of the accelerometer measurements from numerous trials.

Subject:
Engineering
Physics
Material Type:
Activity/Lab
Provider:
TeachEngineering
Provider Set:
TeachEngineering
Author:
Carlo Yuvienco
Jennifer S. Haghpanah
Date Added:
09/18/2014
An Agenda for Purely Confirmatory Research
Unrestricted Use
CC BY
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The veracity of substantive research claims hinges on the way experimental data are collected and analyzed. In this article, we discuss an uncomfortable fact that threatens the core of psychology’s academic enterprise: almost without exception, psychologists do not commit themselves to a method of data analysis before they see the actual data. It then becomes tempting to fine tune the analysis to the data in order to obtain a desired result—a procedure that invalidates the interpretation of the common statistical tests. The extent of the fine tuning varies widely across experiments and experimenters but is almost impossible for reviewers and readers to gauge. To remedy the situation, we propose that researchers preregister their studies and indicate in advance the analyses they intend to conduct. Only these analyses deserve the label “confirmatory,” and only for these analyses are the common statistical tests valid. Other analyses can be carried out but these should be labeled “exploratory.” We illustrate our proposal with a confirmatory replication attempt of a study on extrasensory perception.

Subject:
Psychology
Material Type:
Reading
Provider:
Perspectives on Psychological Science
Author:
Denny Borsboom
Eric-Jan Wagenmakers
Han L. J. van der Maas
Rogier A. Kievit
Ruud Wetzels
Date Added:
08/07/2020
Analysis of Open Data and Computational Reproducibility in Registered Reports in Psychology
Read the Fine Print
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5.0 stars

Ongoing technological developments have made it easier than ever before for scientists to share their data, materials, and analysis code. Sharing data and analysis code makes it easier for other researchers to re-use or check published research. These benefits will only emerge if researchers can reproduce the analysis reported in published articles, and if data is annotated well enough so that it is clear what all variables mean. Because most researchers have not been trained in computational reproducibility, it is important to evaluate current practices to identify practices that can be improved. We examined data and code sharing, as well as computational reproducibility of the main results, without contacting the original authors, for Registered Reports published in the psychological literature between 2014 and 2018. Of the 62 articles that met our inclusion criteria, data was available for 40 articles, and analysis scripts for 37 articles. For the 35 articles that shared both data and code and performed analyses in SPSS, R, Python, MATLAB, or JASP, we could run the scripts for 31 articles, and reproduce the main results for 20 articles. Although the articles that shared both data and code (35 out of 62, or 56%) and articles that could be computationally reproduced (20 out of 35, or 57%) was relatively high compared to other studies, there is clear room for improvement. We provide practical recommendations based on our observations, and link to examples of good research practices in the papers we reproduced.

Subject:
Psychology
Material Type:
Reading
Author:
Daniel Lakens
Jaroslav Gottfried
Nicholas Alvaro Coles
Pepijn Obels
Seth Ariel Green
Date Added:
08/07/2020
Analyzing Data - Out Teach
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CC BY-NC-SA
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STUDENT ACTIVITY - 1st -- VAThis is a distance-learning lesson students can complete at home.Students will collect data outdoors and record the data in a table. Then, they will compare the numbers collected by writing greater-than, less-than or equal-to statements.This activity was created by Out Teach (out-teach.org), a nonprofit providing outdoor experiential learning to transform Science education for students in under-served communities. .

Subject:
Mathematics
Material Type:
Activity/Lab
Author:
Out Teach
Date Added:
07/22/2021
Analyzing Education Data with Open Science Best Practices, R, and OSF
Unrestricted Use
CC BY
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The webinar features Dr. Joshua Rosenberg from the University of Tennessee, Knoxville and Dr. Cynthia D’Angelo from the University of Illinois at Urbana-Champaign discussing best practices examples for using R. They will present: a) general strategies for using R to analyze educational data and b) accessing and using data on the Open Science Framework (OSF) with R via the osfr package. This session is for those both new to R and those with R experience looking to learn more about strategies and workflows that can help to make it possible to analyze data in a more transparent, reliable, and trustworthy way.

Subject:
Education
Material Type:
Lesson
Author:
Cynthia D'Angelo
Joshua Rosenberg
Date Added:
05/03/2021
Analyzing and Making Mathematical and Historical Claims from (Linear) Data Representations
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CC BY-NC-SA
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A statistics lesson on describing and making claims from data representations, specifically linearly increasing data. Applies ideas of rate-of-change to develop writing a linear equation to fit the data, using the equation to interpolate and extrapolate additional information, and integrating the mathematical interpretation appropriately into a social sciences argument.

Subject:
Mathematics
Material Type:
Lesson Plan
Author:
Sarah Ahmed
Johanna Langill
Date Added:
01/28/2016
Answering questions with data: Introductory Statistics for Psychology Students
Conditional Remix & Share Permitted
CC BY-SA
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This is a free textbook teaching introductory statistics for undergraduates in Psychology. This textbook is part of a larger OER course package for teaching undergraduate statistics in Psychology, including this textbook, a lab manual, and a course website. All of the materials are free and copiable, with source code maintained in Github repositories.

Subject:
Psychology
Material Type:
Textbook
Author:
Matthew J.C. Crump
Date Added:
11/26/2019
Análisis y visualización de datos usando Python
Unrestricted Use
CC BY
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Python es un lenguaje de programación general que es útil para escribir scripts para trabajar con datos de manera efectiva y reproducible. Esta es una introducción a Python diseñada para participantes sin experiencia en programación. Estas lecciones pueden enseñarse en un día (~ 6 horas). Las lecciones empiezan con información básica sobre la sintaxis de Python, la interface de Jupyter Notebook, y continúan con cómo importar archivos CSV, usando el paquete Pandas para trabajar con DataFrames, cómo calcular la información resumen de un DataFrame, y una breve introducción en cómo crear visualizaciones. La última lección demuestra cómo trabajar con bases de datos directamente desde Python. Nota: los datos no han sido traducidos de la versión original en inglés, por lo que los nombres de variables se mantienen en inglés y los números de cada observación usan la sintaxis de habla inglesa (coma separador de miles y punto separador de decimales).

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Alejandra Gonzalez-Beltran
April Wright
chekos
Christopher Erdmann
Enric Escorsa O'Callaghan
Erin Becker
Fernando Garcia
Hely Salgado
Juan Martín Barrios
Juan M. Barrios
Katrin Leinweber
Laura Angelone
Leonardo Ulises Spairani
LUS24
Maxim Belkin
Miguel González
monialo2000
Nicolás Palopoli
Nohemi Huanca Nunez
Paula Andrea Martinez
Raniere Silva
Rayna Harris
rzayas
Sarah Brown
Silvana Pereyra
Spencer Harris
Stephan Druskat
Trevor Keller
Wilson Lozano
Date Added:
08/07/2020
Archiving for the Future: Simple Steps for Archiving Language Documentation Collections
Conditional Remix & Share Permitted
CC BY-SA
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Archiving for the Future is a free training course designed to teach language documenters, activists, and researchers how to organize, arrange, and archive language documentation, revitalization, and maintenance materials and metadata in a digital repository or language archive. Then entire course can be completed in approximately 3-5 hours.

This course was developed by the staff of the Archive of the Indigenous Languages of Latin America at the University of Texas at Austin in consultation with representatives of various DELAMAN (https://www.delaman.org/) archives and other digital data repositories in the United States, the United Kingdom, the European Union, Australia, and Cameroon.

The course material is based upon work supported by the National Science Foundation under Grant No. BCS-1653380 (September 1, 2016 to August 31, 2020). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Subject:
Information Science
Languages
Anthropology
Ethnic Studies
Linguistics
Material Type:
Full Course
Interactive
Author:
Alicia Niwagaba
Elena Pojman
Ryan Sullivant
Susan Smythe Kung
Date Added:
11/05/2020
Artists, Information Literacy & Climate Change
Conditional Remix & Share Permitted
CC BY-NC-SA
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This unit explores the various ways information and ideas about climate change are presented through a variety of media. This includes the evaluation of social media posts, research into climate change issues, and an exploration of contemporary art and artists. This was designed and taught in an honors 9th grade English Language Arts Classroom by Dr. Tavia Quaid in response to student interest in climate change and to reinforce key information literacy skills.

Subject:
Environmental Science
Visual Arts
Environmental Studies
Reading Informational Text
Measurement and Data
Material Type:
Assessment
Diagram/Illustration
Homework/Assignment
Lesson Plan
Reading
Author:
Shana Ferguson
Date Added:
04/21/2021
Assessing data availability and research reproducibility in hydrology and water resources
Unrestricted Use
CC BY
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0.0 stars

There is broad interest to improve the reproducibility of published research. We developed a survey tool to assess the availability of digital research artifacts published alongside peer-reviewed journal articles (e.g. data, models, code, directions for use) and reproducibility of article results. We used the tool to assess 360 of the 1,989 articles published by six hydrology and water resources journals in 2017. Like studies from other fields, we reproduced results for only a small fraction of articles (1.6% of tested articles) using their available artifacts. We estimated, with 95% confidence, that results might be reproduced for only 0.6% to 6.8% of all 1,989 articles. Unlike prior studies, the survey tool identified key bottlenecks to making work more reproducible. Bottlenecks include: only some digital artifacts available (44% of articles), no directions (89%), or all artifacts available but results not reproducible (5%). The tool (or extensions) can help authors, journals, funders, and institutions to self-assess manuscripts, provide feedback to improve reproducibility, and recognize and reward reproducible articles as examples for others.

Subject:
Information Science
Physical Science
Hydrology
Material Type:
Reading
Provider:
Scientific Data
Author:
Adel M. Abdallah
David E. Rosenberg
Hadia Akbar
James H. Stagge
Nour A. Attallah
Ryan James
Date Added:
08/07/2020
At the Doctor's
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Educational Use
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In this simulation of a doctor's office, students play the roles of physician, nurse, patients, and time-keeper, with the objective to improve the patient waiting time. They collect and graph data as part of their analysis. This serves as a hands-on example of using engineering principles and engineering design approaches (such as models and simulations) to research, analyze, test and improve processes.

Subject:
Engineering
Education
Material Type:
Activity/Lab
Provider:
TeachEngineering
Provider Set:
TeachEngineering
Author:
Courtney Feliciani Patricio Rocha
Dayna Martinez
Tapas K. Das
Date Added:
09/18/2014
Automation and Make
Unrestricted Use
CC BY
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0.0 stars

A Software Carpentry lesson to learn how to use Make Make is a tool which can run commands to read files, process these files in some way, and write out the processed files. For example, in software development, Make is used to compile source code into executable programs or libraries, but Make can also be used to: run analysis scripts on raw data files to get data files that summarize the raw data; run visualization scripts on data files to produce plots; and to parse and combine text files and plots to create papers. Make is called a build tool - it builds data files, plots, papers, programs or libraries. It can also update existing files if desired. Make tracks the dependencies between the files it creates and the files used to create these. If one of the original files (e.g. a data file) is changed, then Make knows to recreate, or update, the files that depend upon this file (e.g. a plot). There are now many build tools available, all of which are based on the same concepts as Make.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Richie-Halford
Ana Costa Conrado
Andrew Boughton
Andrew Fraser
Andy Kleinhesselink
Andy Teucher
Anna Krystalli
Bill Mills
Brandon Curtis
David E. Bernholdt
Deborah Gertrude Digges
François Michonneau
Gerard Capes
Greg Wilson
Jake Lever
Jason Sherman
John Blischak
Jonah Duckles
Juan F Fung
Kate Hertweck
Lex Nederbragt
Luiz Irber
Matthew Thomas
Michael Culshaw-Maurer
Mike Jackson
Pete Bachant
Piotr Banaszkiewicz
Radovan Bast
Raniere Silva
Rémi Emonet
Samuel Lelièvre
Satya Mishra
Trevor Bekolay
Date Added:
03/20/2017
Awesome Open Science Resources
Unrestricted Use
CC BY
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0.0 stars

Scientific data and tools should, as much as possible, be free as in beer and free as in freedom. The vast majority of science today is paid for by taxpayer-funded grants; at the same time, the incredible successes of science are strong evidence for the benefit of collaboration in knowledgable pursuits. Within the scientific academy, sharing of expertise, data, tools, etc. is prolific, but only recently with the rise of the Open Access movement has this sharing come to embrace the public. Even though most research data is never shared, both the public and even scientists in their own fields are often unaware of just much data, tools, and other resources are made freely available for analysis! This list is a small attempt at bringing light to data repositories and computational science tools that are often siloed according to each scientific discipline, in the hopes of spurring along both public and professional contributions to science.

Subject:
Applied Science
Life Science
Physical Science
Social Science
Material Type:
Reading
Author:
Austin Soplata
Date Added:
09/23/2018