Updating search results...

Search Resources

9 Results

View
Selected filters:
  • David Brown
Carpentries Instructor Training
Unrestricted Use
CC BY
Rating
0.0 stars

A two-day introduction to modern evidence-based teaching practices, built and maintained by the Carpentry community.

Subject:
Computer Science
Information Science
Education
Higher Education
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Aleksandra Nenadic
Alexander Konovalov
Alistair John Walsh
Allison Weber
amoskane
Amy E. Hodge
Andrew B. Collier
Anita Schürch
AnnaWilliford
Ariel Rokem
Brian Ballsun-Stanton
Callin Switzer
Christian Brueffer
Christina Koch
Christopher Erdmann
Colin Morris
Dan Allan
DanielBrett
Danielle Quinn
Darya Vanichkina
davidbenncsiro
David Jennings
Eric Jankowski
Erin Alison Becker
Evan Peter Williamson
François Michonneau
Gerard Capes
Greg Wilson
Ian Lee
Jason M Gates
Jason Williams
Jeffrey Oliver
Joe Atzberger
John Bradley
John Pellman
Jonah Duckles
Jonathan Bradley
Karen Cranston
Karen Word
Kari L Jordan
Katherine Koziar
Katrin Leinweber
Kees den Heijer
Laurence
Lex Nederbragt
Maneesha Sane
Marie-Helene Burle
Mik Black
Mike Henry
Murray Cadzow
naught101
Neal Davis
Neil Kindlon
Nicholas Tierney
Nicolás Palopoli
Noah Spies
Paula Andrea Martinez
Petraea
Rayna Michelle Harris
Rémi Emonet
Rémi Rampin
Sarah Brown
Sarah M Brown
Sarah Stevens
satya-vinay
Sean
Serah Anne Njambi Kiburu
Stefan Helfrich
Stéphane Guillou
Steve Moss
Ted Laderas
Tiago M. D. Pereira
Toby Hodges
Tracy Teal
Yo Yehudi
Date Added:
08/07/2020
Databases and SQL
Unrestricted Use
CC BY
Rating
0.0 stars

Software Carpentry lesson that teaches how to use databases and SQL In the late 1920s and early 1930s, William Dyer, Frank Pabodie, and Valentina Roerich led expeditions to the Pole of Inaccessibility in the South Pacific, and then onward to Antarctica. Two years ago, their expeditions were found in a storage locker at Miskatonic University. We have scanned and OCR the data they contain, and we now want to store that information in a way that will make search and analysis easy. Three common options for storage are text files, spreadsheets, and databases. Text files are easiest to create, and work well with version control, but then we would have to build search and analysis tools ourselves. Spreadsheets are good for doing simple analyses, but they don’t handle large or complex data sets well. Databases, however, include powerful tools for search and analysis, and can handle large, complex data sets. These lessons will show how to use a database to explore the expeditions’ data.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Amy Brown
Andrew Boughton
Andrew Kubiak
Avishek Kumar
Ben Waugh
Bill Mills
Brian Ballsun-Stanton
Chris Tomlinson
Colleen Fallaw
Daniel Suess
Dan Michael Heggø
Dave Welch
David W Wright
Deborah Gertrude Digges
Donny Winston
Doug Latornell
Erin Alison Becker
Ethan Nelson
Ethan P White
François Michonneau
George Graham
Gerard Capes
Gideon Juve
Greg Wilson
Ioan Vancea
Jake Lever
James Mickley
John Blischak
JohnRMoreau@gmail.com
Jonah Duckles
Jonathan Guyer
Joshua Nahum
Kate Hertweck
Kevin Dyke
lorra
Louis Vernon
Luc Small
Luke William Johnston
Maneesha Sane
Mark Stacy
Matthew Collins
Matty Jones
Mike Jackson
Morgan Taschuk
Patrick McCann
Paula Andrea Martinez
Pauline Barmby
Piotr Banaszkiewicz
Raniere Silva
Ray Bell
Rayna Michelle Harris
Rémi Emonet
Rémi Rampin
Seda Arat
Sheldon John McKay
Sheldon McKay
slimlime
Stephen Davison
Thomas Guignard
Trevor Bekolay
Date Added:
03/20/2017
Educational Learning Theories
Unrestricted Use
CC BY
Rating
5.0 stars

This open textbook was the result of a remix of pre-existing open materials collected and reviewed by Molly Zhou and David Brown. Learning theories covered include the theories of Piaget, Bandura, Vygotsky, Kohlberg, Dewey, Bronfenbrenner, Eriksen, Gardner, Bloom, and Maslow.

Subject:
Education
Material Type:
Textbook
Provider:
University System of Georgia
Provider Set:
Galileo Open Learning Materials
Author:
David Brown
Molly Zhou
Date Added:
03/23/2015
Graduate Student Perspectives, Volume 2
Unrestricted Use
CC BY
Rating
0.0 stars

This book is the result of a co-design project in a class in the Masters of Education program at the University of Calgary. The course, and the resulting book, focus primarily on the safe and ethical use of technology in digital learning environments. The course was organized according to four topics based on Farrow’s (2016) Framework for the Ethics of Open Education.

Subject:
Educational Technology
Material Type:
Primary Source
Provider:
Open Education Alberta
Author:
Christie Hurrell
David Luinstra
Dr Barbara Brown Dr Verena Roberts Dr Michele Jacobsen Christie Hurrell Nicole Neutzling Mia Travers-hayward
Dr Michele Jacobsen
Dr Verena Roberts
Lindsay Humphreys
Mia Travers-hayward
Michael Maciach
Nicole Neutzling
Rob Hendrickson
Date Added:
01/12/2022
Missing Data and Multiple Imputation Decision Tree
Rating
0.0 stars

This document is intended to provide practical guidelines for researchers to follow when examining their data for missingness and making decisions about how to handle that missingness. We primarily offer recommendations for multiple imputation, but also indicate where the same decisional guidelines are appropriate for other types of missing data procedures such as full information maximum likelihood (FIML). Streamlining procedures to address missing data and increasing the transparency of those procedures through consensus on reporting standards is inexorably linked to the goals of open scholarship (i.e., the endeavour to improve openness, integrity, social justice, diversity, equity, inclusivity and accessibility in all areas of scholarly activities, and by extension, academic fields beyond the sciences and academic activities; Pownall et al., 2021). Successfully implementing transparent and accessible guidelines for addressing missing data is also important for Diversity, Equity, Inclusion, and Accessibility (DEIA) improvement efforts (Randall et al., 2021). Structural barriers to participation in research can lead to participants from minoritized groups disproportionately dropping out of longitudinal, developmental studies or not completing measures (Randall et al., 2021). This selection effect can bias model estimates and confidence intervals, leading to unsubstantiated claims about equitable outcomes. In addition to often creating artificially small estimates of inequalities between groups, listwise deletion also limits statistical power for minoritized groups who are already underrepresented in many datasets.

Subject:
Education
Measurement and Data
Statistics and Probability
Social Science
Economics
Psychology
Social Work
Sociology
Women's Studies
Material Type:
Diagram/Illustration
Reading
Author:
Adrienne D. Woods
Alex Uzdavines
Ben Van Dusen
Daria Gerasimova
David Moreau
Denver Brown
James M. Clay
Jayson Nissen
Jessica A. R. Logan
Kathleen Schmidt
Keven Joyal-Desmarais
Kevin M. King
Mahmoud M. Elsherif
Martin Vasilev
Max A. Halvorson
Menglin Xu
Pamela E. Davis-Kean
Rick A. Cruz
Sierra Bainter
Date Added:
04/25/2022
Plotting and Programming in Python
Unrestricted Use
CC BY
Rating
0.0 stars

This lesson is part of Software Carpentry workshops and teach an introduction to plotting and programming using python. This lesson is an introduction to programming in Python for people with little or no previous programming experience. It uses plotting as its motivating example, and is designed to be used in both Data Carpentry and Software Carpentry workshops. This lesson references JupyterLab, but can be taught using a regular Python interpreter as well. Please note that this lesson uses Python 3 rather than Python 2.

Subject:
Computer Science
Information Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Steer
Allen Lee
Andreas Hilboll
Ashley Champagne
Benjamin
Benjamin Roberts
CanWood
Carlos Henrique Brandt
Carlos M Ortiz Marrero
Cephalopd
Cian Wilson
Daniel W Kerchner
Dan Mønster
Daria Orlowska
Dave Lampert
David Matten
Erin Alison Becker
Florian Goth
Francisco J. Martínez
Greg Wilson
ian
Jacob Deppen
Jarno Rantaharju
Jeremy Zucker
Jonah Duckles
Kees den Heijer
Keith Gilbertson
Kyle E Niemeyer
Lex Nederbragt
Logan Cox
Louis Vernon
Lucy Dorothy Whalley
Madeleine Bonsma-Fisher
Mark Phillips
Mark Slater
Maxim Belkin
Michael Beyeler
Mike Henry
mzc9
Narayanan Raghupathy
Nigel Bosch
Olav Vahtras
Pablo Hernandez-Cerdan
Paul Anzel
Phil Tooley
Raniere Silva
Robert Woodward
Ryan Avery
Ryan Gregory James
Sarah M Brown
SBolo
Shyam Dwaraknath
Sourav Singh
Stéphane Guillou
Steven Koenig
Taylor Smith
Thor Wikfeldt
Timothy Warren
Tyler Martin
Vasu Venkateshwaran
Vikas Pejaver
Date Added:
08/07/2020
Secondary Data Preregistration
Unrestricted Use
Public Domain
Rating
0.0 stars

Preregistration is the process of specifying project details, such as hypotheses, data collection procedures, and analytical decisions, prior to conducting a study. It is designed to make a clearer distinction between data-driven, exploratory work and a-priori, confirmatory work. Both modes of research are valuable, but are easy to unintentionally conflate. See the Preregistration Revolution for more background and recommendations.

For research that uses existing datasets, there is an increased risk of analysts being biased by preliminary trends in the dataset. However, that risk can be balanced by proper blinding to any summary statistics in the dataset and the use of hold out datasets (where the "training" and "validation" datasets are kept separate from each other). See this page for specific recommendations about "split samples" or "hold out" datasets. Finally, if those procedures are not followed, disclosure of possible biases can inform the researcher and her audience about the proper role any results should have (i.e. the results should be deemed mostly exploratory and ideal for additional confirmation).

This project contains a template for creating your preregistration, designed specifically for research using existing data. In the future, this template will be integrated into the OSF.

Subject:
Applied Science
Material Type:
Reading
Author:
Alexander C. DeHaven
Andrew Hall
Brian Brown
Charles R. Ebersole
Courtney K. Soderberg
David Thomas Mellor
Elliott Kruse
Jerome Olsen
Jessica Kosie
K. D. Valentine
Lorne Campbell
Marjan Bakker
Olmo van den Akker
Pamela Davis-Kean
Rodica I. Damian
Sara J. Weston
Stuart J. Ritchie
Thuy-vy Ngugen
William J. Chopik
Date Added:
08/12/2021
Secondary Data Preregistration
Unrestricted Use
Public Domain
Rating
0.0 stars

Preregistration is the process of specifying project details, such as hypotheses, data collection procedures, and analytical decisions, prior to conducting a study. It is designed to make a clearer distinction between data-driven, exploratory work and a-priori, confirmatory work. Both modes of research are valuable, but are easy to unintentionally conflate. See the Preregistration Revolution for more background and recommendations.

For research that uses existing datasets, there is an increased risk of analysts being biased by preliminary trends in the dataset. However, that risk can be balanced by proper blinding to any summary statistics in the dataset and the use of hold out datasets (where the "training" and "validation" datasets are kept separate from each other). See this page for specific recommendations about "split samples" or "hold out" datasets. Finally, if those procedures are not followed, disclosure of possible biases can inform the researcher and her audience about the proper role any results should have (i.e. the results should be deemed mostly exploratory and ideal for additional confirmation).

This project contains a template for creating your preregistration, designed specifically for research using existing data. In the future, this template will be integrated into the OSF.

Subject:
Life Science
Social Science
Material Type:
Reading
Author:
Alexander C. DeHaven
Andrew Hall
Brian Brown
Charles R. Ebersole
Courtney K. Soderberg
David Thomas Mellor
Elliott Kruse
Jerome Olsen
Jessica Kosie
K.D. Valentine
Lorne Campbell
Marjan Bakker
Olmo van den Akker
Pamela Davis-Kean
Rodica I. Damian
Sara J. Weston
Stuart J Ritchie
Thuy-vy Nguyen
William J. Chopik
Date Added:
08/03/2021
The Unix Shell
Unrestricted Use
CC BY
Rating
0.0 stars

Software Carpentry lesson on how to use the shell to navigate the filesystem and write simple loops and scripts. The Unix shell has been around longer than most of its users have been alive. It has survived so long because it’s a power tool that allows people to do complex things with just a few keystrokes. More importantly, it helps them combine existing programs in new ways and automate repetitive tasks so they aren’t typing the same things over and over again. Use of the shell is fundamental to using a wide range of other powerful tools and computing resources (including “high-performance computing” supercomputers). These lessons will start you on a path towards using these resources effectively.

Subject:
Computer Science
Measurement and Data
Material Type:
Module
Provider:
The Carpentries
Author:
Adam Huffman
Adam James Orr
Adam Richie-Halford
AidaMirsalehi
Alexander Konovalov
Alexander Morley
Alex Kassil
Alex Mac
Alix Keener
Amy Brown
Andrea Bedini
Andrew Boughton
Andrew Reid
Andrew T. T. McRae
Andrew Walker
Ariel Rokem
Armin Sobhani
Ashwin Srinath
Bagus Tris Atmaja
Bartosz Telenczuk
Ben Bolker
Benjamin Gabriel
Bertie Seyffert
Bill Mills
Brian Ballsun-Stanton
BrianBill
Camille Marini
Chris Mentzel
Christina Koch
Colin Morris
Colin Sauze
csqrs
Damien Irving
Dana Brunson
Daniel Baird
Danielle M. Nielsen
Daniel McCloy
Daniel Standage
Dan Jones
Dave Bridges
David Eyers
David McKain
David Vollmer
Dean Attali
Devinsuit
Dmytro Lituiev
Donny Winston
Doug Latornell
Dustin Lang
earkpr
ekaterinailin
Elena Denisenko
Emily Dolson
Emily Jane McTavish
Eric Jankowski
Erin Alison Becker
Ethan P White
Evgenij Belikov
Farah Shamma
Fatma Deniz
Filipe Fernandes
Francis Gacenga
François Michonneau
Gabriel A. Devenyi
Gerard Capes
Giuseppe Profiti
Greg Wilson
Halle Burns
Hannah Burkhardt
Harriet Alexander
Hugues Fontenelle
Ian van der Linde
Inigo Aldazabal Mensa
Jackie Milhans
Jake Cowper Szamosi
James Guelfi
Jan T. Kim
Jarek Bryk
Jarno Rantaharju
Jason Macklin
Jay van Schyndel
Jens vdL
John Blischak
John Pellman
John Simpson
Jonah Duckles
Jonny Williams
Joshua Madin
Kai Blin
Kathy Chung
Katrin Leinweber
Kevin M. Buckley
Kirill Palamartchouk
Klemens Noga
Kristopher Keipert
Kunal Marwaha
Laurence
Lee Zamparo
Lex Nederbragt
Mahdi Sadjadi
Marcel Stimberg
Marc Rajeev Gouw
Maria Doyle
Marie-Helene Burle
Marisa Lim
Mark Mandel
Martha Robinson
Martin Feller
Matthew Gidden
Matthew Peterson
M Carlise
Megan Fritz
Michael Zingale
Mike Henry
Mike Jackson
Morgan Oneka
Murray Hoggett
Nicolas Barral
Nicola Soranzo
Noah D Brenowitz
Noam Ross
Norman Gray
nther
Orion Buske
Owen Kaluza
Patrick McCann
Paul Gardner
Pauline Barmby
Peter R. Hoyt
Peter Steinbach
Philip Lijnzaad
Phillip Doehle
Piotr Banaszkiewicz
Rafi Ullah
Raniere Silva
Rémi Emonet
reshama shaikh
Robert A Beagrie
Ruud Steltenpool
Ry4an Brase
Sarah Mount
Sarah Simpkin
s-boardman
Scott Ritchie
sjnair
Stéphane Guillou
Stephan Schmeing
Stephen Jones
Stephen Turner
Steve Leak
Susan Miller
Thomas Mellan
Tim Keighley
Tobin Magle
Tom Dowrick
Trevor Bekolay
Varda F. Hagh
Victor Koppejan
Vikram Chhatre
Yee Mey
Date Added:
03/20/2017